From 4049ed6de99bef8ebbe91216fa78d581bee66065 Mon Sep 17 00:00:00 2001
From: SamueleSoraggi <samuelson1990@gmail.com>
Date: Thu, 29 Jun 2023 13:51:22 +0200
Subject: [PATCH] Deployed fa5d6d5 with MkDocs version: 1.3.0

---
 nb/scrna.html            | 355 ++++++++++++++++++---------------------
 nb/vcf.html              |   2 +-
 search/search_index.json |   2 +-
 sitemap.xml              |  18 +-
 sitemap.xml.gz           | Bin 203 -> 203 bytes
 5 files changed, 179 insertions(+), 198 deletions(-)

diff --git a/nb/scrna.html b/nb/scrna.html
index 453cf4d..b19c838 100644
--- a/nb/scrna.html
+++ b/nb/scrna.html
@@ -15313,7 +15313,7 @@ <h1 id="Data-analysis">Data analysis<a class="anchor-link" href="#Data-analysis"
 </div>
 </div>
 
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@@ -15332,6 +15332,7 @@ <h1 id="Data-analysis">Data analysis<a class="anchor-link" href="#Data-analysis"
 <span class="kn">import</span> <span class="nn">gc</span>
 <span class="kn">import</span> <span class="nn">diffxpy.api</span> <span class="k">as</span> <span class="nn">dxpy</span>
 <span class="kn">import</span> <span class="nn">plotly.express</span> <span class="k">as</span> <span class="nn">px</span>
+<span class="kn">import</span> <span class="nn">re</span>
 </pre></div>
 
      </div>
@@ -15339,27 +15340,6 @@ <h1 id="Data-analysis">Data analysis<a class="anchor-link" href="#Data-analysis"
 </div>
 </div>
 
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-<pre>Matplotlib is building the font cache; this may take a moment.
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@@ -15775,7 +15755,7 @@ <h1 id="Data-analysis">Data analysis<a class="anchor-link" href="#Data-analysis"
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [10]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [13]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">adata</span><span class="o">.</span><span class="n">obs</span><span class="p">[</span><span class="s1">'batch'</span><span class="p">]</span> <span class="c1">#sample label - the data contains 15 separate samples</span>
@@ -15794,7 +15774,7 @@ <h1 id="Data-analysis">Data analysis<a class="anchor-link" href="#Data-analysis"
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-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[10]:</div>
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@@ -15839,7 +15819,7 @@ <h1 id="Data-analysis">Data analysis<a class="anchor-link" href="#Data-analysis"
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [11]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [14]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">adata</span><span class="o">.</span><span class="n">var</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
@@ -15858,7 +15838,7 @@ <h1 id="Data-analysis">Data analysis<a class="anchor-link" href="#Data-analysis"
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-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[11]:</div>
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@@ -15960,7 +15940,7 @@ <h1 id="Data-analysis">Data analysis<a class="anchor-link" href="#Data-analysis"
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [12]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [15]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">adata</span><span class="o">.</span><span class="n">var</span><span class="p">[</span><span class="s1">'n_cells_by_counts'</span><span class="p">]</span> <span class="c1">#nr of cells showing transcripts of a gene</span>
@@ -15979,7 +15959,7 @@ <h1 id="Data-analysis">Data analysis<a class="anchor-link" href="#Data-analysis"
 <div class="jp-OutputArea jp-Cell-outputArea">
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-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[12]:</div>
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[15]:</div>
 
 
 
@@ -16035,7 +16015,7 @@ <h3 id="Quality-Filtering">Quality Filtering<a class="anchor-link" href="#Qualit
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [13]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [16]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">MT</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'MT-'</span> <span class="ow">in</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">adata</span><span class="o">.</span><span class="n">var_names</span><span class="p">]</span> <span class="c1">#a vector with True and False to find MT genes</span>
@@ -16065,7 +16045,7 @@ <h3 id="Quality-Filtering">Quality Filtering<a class="anchor-link" href="#Qualit
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [14]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [17]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sc</span><span class="o">.</span><span class="n">pl</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">adata</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s1">'total_counts'</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s1">'n_genes_by_counts'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'perc_mito'</span><span class="p">,</span> 
@@ -16118,7 +16098,7 @@ <h3 id="Quality-Filtering">Quality Filtering<a class="anchor-link" href="#Qualit
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [15]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [18]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sc</span><span class="o">.</span><span class="n">pl</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">adata</span><span class="p">[</span><span class="n">adata</span><span class="o">.</span><span class="n">obs</span><span class="p">[</span><span class="s1">'n_genes_by_counts'</span><span class="p">]</span><span class="o">&lt;</span><span class="mi">3000</span><span class="p">],</span> <span class="n">x</span><span class="o">=</span><span class="s1">'total_counts'</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s1">'n_genes_by_counts'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'perc_mito'</span><span class="p">,</span>
@@ -16171,7 +16151,7 @@ <h3 id="Quality-Filtering">Quality Filtering<a class="anchor-link" href="#Qualit
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [16]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [19]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">px</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">adata</span><span class="p">[</span><span class="n">adata</span><span class="o">.</span><span class="n">obs</span><span class="p">[</span><span class="s1">'total_counts'</span><span class="p">]</span><span class="o">&lt;</span><span class="mi">20000</span><span class="p">]</span><span class="o">.</span><span class="n">obs</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s1">'total_counts'</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
@@ -16228,9 +16208,9 @@ <h3 id="Quality-Filtering">Quality Filtering<a class="anchor-link" href="#Qualit
 
 
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@@ -16354,7 +16334,7 @@ <h3 id="Quality-Filtering">Quality Filtering<a class="anchor-link" href="#Qualit
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [18]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [21]:</div>
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09,5037,3753,2905,5029,3659,5684,3158,3215,1845,1594,945,1923,6227,4302,3169,1899,3108,1567,4745,5327,7614,5583,1958,4891,3766,1146,5387,1121,4424,1459,2361,2248,5691,4040,2369,8118,5167,4455,4444,3199,2423,2437,2015,3234,2978,1799,5511,3157,2292,1394,4445,5843,2167,6063,4630,2001,3980,1905,2585,4555,5096,1339,3363,2572,6034,5959,188,2770,3276,6076,2519,3393,1512,2076,6594,3562,2615,3310,1912,6072,1650,3247,2479,2146,1205,3528,5201,3860,5971,2083,3370,3339,5283,5748,4094,4138,2397,5294,4316,2405,5865,2015,5918,4441,3137,4285,5236,2524,5003,1045,1811,2855,4030,4122,5422,1749,5089,6334,2079,4490,5925,1998,2213,1319,3188,1755,2692,1556,2852,2790,6490,6913,1236,2996,5192,2116,2968,2520,5265,5274,1563,4083,3654,6376,5555,2287,4875,2469,5129,2529,5517,7290,5379,4716,1280,1421,1844,3374,2500,5703,4775,4580,3448,2096,1865,4632,1792,1485,4027,1337,7942,2881,3982,2741,5430,2769,1879,5342,1838,5127,7004,3151,2210,3656,8254,6293,6005,3177,4009,3109,1411,7289,3335,2388,3380,3754,5964,4683,4359,1077,4679,2024,2847,2077,1202,1452,1653,1885,4218,1633,7728,6228,3947,3120,4193,4999,5658,3280,1580,4315,3973,3845,5170,4287,1544,1357,4279,1951,5452,2719,1614,5987,1349,5773,5072,2518,5472,5341,1731,6249,7316,5360,5022,4323,8486,1045,4577,5330,1518,6350,2115,5010,2416,5916,1999,6367,4522,3337,2817,1311,1878,2154,1813,4679,2138,2082,1037,3024,1096,5271,1727,2370,4676,5503,5031,1708,5198,3742,1442,5230,2031,2675,2909,4446,4741,1608,1547,4920,1527,1959,2307,1468,4664,6306,4467,4011,3214,4487,3425,2783,2171,2599,3370,2788,5533,1395,3007,1229,3386,4431,6025,5230,1126,2790,5871,2073,1603,3261,7732,1451,3432,6426,1892,7179,4411,7397,1414,2337,5815,5422,6259,4753,4321,2766,2218,4867,4623,5274,7877,2646,4585,946,3818,3895,1216,1306,2362,5822,2319,2828,5937,1301,2834,4499,6034,8524,8014,1657,4928,4895,5944,5636,4566,5812,2769,1533,2715,7510,3910,4733,3537,1436,8300,3617,1485,5312,3590,4537,5411,2951,6229,2182,3009,4425,1604,5024,4498,4932,1838,1776,1906,1920,4212,7085,8621,2981,3139,2617,1748,5604,2368,3467,3776,2987,6109,2019,4273,2141,1351,2220,2848,2569,4046,1372,5267,1884,3476,1973,9257,2828,2765,4148,1088,2550,2386,3728,4414,2116,6931,3342,5208,1327,5954,1975,3258,1998,3392,3969,2008,7160,2088,5715,4266,2118,5026,3120,2016,1812,1149,1214,6974,3923,4526,4500,5112,268,2329,1547,1918,4299,2849,1348,5422,3585,2321,4679,6096,1991,4990,1126,1663,6764,1422,2400,2614,2612,1605,5776,3700,5789,2002,2181,2479,4822,1068,1997,2851,5073,1434,2627,1107,3337,1979,5261,4310,1349,4710,4373,2590,2258,1607,3247,1775,2242,2139,6236,4134,9396,6561,1898,7121,2237,1159,2050,4311,5832,2643,2914,2811,3094,3330,5423,5114,6457,929,1263,2086,3840,4103,4488,3887,4285,3886,5524,6053,4128,4618,1631,3710,1305,5654,2046,5639,1522,4839,1663,1894,1955,4003,3175,1801,1793,3679,6128,1848,3924,2779,4166,7041,1686,2101,5131,1626,3172,2701,2137,7377,1879,6885,1160,2559,1212,1076,3222,5669,4903,1261,7181,1663,1193,4535,1194,2079,3803,4808,2030,5753,2481,4886,3731,2691,3909,1884,3470,2207,2572,5082,3107,4671,1632,3720,5369,1589,5064,1589,1821,5505,3753,1492,8225,5633,4937,1787,2431,2412,1498,2353,5801,1805,5893,2240,1473,2463,4532,5020,5064,2625,2910,4330,4720,5445,4929,2680,2909,2114,2369,2831,963,5191,1704,3910,2627,1157,1017,7088,4399,2838,5277,5780,1548,4835,4139,4854,5085,1566,4120,2994,3338,5925,5588,6296,5401,4176,1511,2455,4826,1723,4949,2642,5861,2975,1646,3818,5473,1763,3882,4210,5102,2320,5857,1278,1687,4720,2062,5248,2080,4018,3796,3331,3279,3051,1884,1275,6251,3323,7351,3926,5054,2668,4262,2148,1507,2313,1210,1038,7434,3524,4529,2956,2603,4886,4478,2499,1452,2951,5209,1842,3129,1724,1830,1028,5210,1931,3988,5463,3784,4318,4263,3463,1969,5290,4213,2238,5008,3535,4880,3391,2755,1108,1555,3194,4481,5723,2538,1667,8042,2603,8226,4537,1404,2530,7302,7652,3566,5531,1764,1857,6029,5008,5513,4755,2177,1793,1693,6723,2258,4217,4453,4358,5056,6623,2470,2234,4832,3542,4174,4994,1980,8592,1092,3263,2751,7241,2078,1515,1166,1386,2557,3109,4631,10232,1781,1330,1930,4297,5517,4590,7970,2788,1447,1262,1879,2665,3169,2368,5199,2983,1592,3479,2177,4649,4493,2662,2653,2121,2308,3607,3276,2395,3214,2178,3664,941,1342,4659,5739,5858,3436,5441,4871,3969,2239,4839,1550,3700,1510,7462,5977,4755,3690,6071,2080,207,2299,6716,1613,2204,6518,6916,8067,2803,4346,2342,6334,4934,3998,1850,3573,5149,5758,4825,3030,3500,5734,4715,1607,3769,5005,7465,1078,3649,3184,1941,5603,5134,1756,6565,2656,5721,2198,5513,1515,5587,1657,1948,5125,1461,2576,1433,3507,2994,5901,3736,5423,2019,1582,5901,3976,1761,5232,3153,4139,4803,2989,1610,2944,2538,3866,2560,2349,5691,3259,5113,3801,2053,2860,9688,6650,2226,1963,4742,1120,2061,1422,1611,3216,5009,1133,3565,2352,2895,1576,1425,5510,1849,1014,4812,6609,2297,5149,5068,7262,2663,1524,1631,1739,2558,4571,2373,4298,5275,3699,7597,1810,4452,7049,3762,1920,2397,1528,7191,5283,2591,1525,969,3970,4098,1540,4882,8409,1891,2095,3886,1553,5671,5170,2409,2178,4216,4405,1958,4007,8553,2205,3606,2768,5721,2255,212,4786,2079,2649,2783,6362,1941,1799,4103,2746,4706,5828,1056,3513,5046,4099,5507,2311,1414,4855,2469,4685,3244,1592,4147,5081,1977,5417,3259,1597,1141,5091,1404,3608,4494,7373,3650,2549,3934,1984,1839,3322,5766,4327,1771,5382,6603,2917,5523,1391,4157,5514,4712,1546,1393,1409,2096,2172,1039,1811,4252,2551,5986,2897,4617,3551,4899,5016,1111,1113,4837,1763,4981,2500,2327,3249,1438,2550,4116,1638,4851,2753,2988,1681,2761,6042,5451,1087,2924,3668,3545,2676,4419,2232,2723,5335,4243,3034,1186,1267,5826,1418,1646,5846,5422,1352,5607,2476,5869,1975,7044,1921,1690,4638,4136,1450,1941,5427,4894,4049,3589,4622,2437,5452,1769,4493,2288,7103,6137,3061,2153,3457,4325,1923,6221,2217,3107,5711,1535,1624,4195,3005,5373,1278,2655,5182,2115,1484,1341,2282,1693,2623,2102,5663,4888,5022,1776,2842,6312,2707,2100,5091,4714,1580,4185,2969,5014,9168,4385,3025,4797,5377,3289,3589,5447,5978,2250,1942,1755,7661,4683,3031,2714,4823,3079,4539,2982,6183,3363,7382,5157,3643,3939,3055,6751,4099,1645,5579,3681,5819,3682,2036,2975,7218,4908,5291,3498,3581,6307,3601,4002,6362,2973,3256,3852,5492,4476,3425,5309,3093,2969,4338,2941,5221,4891,4353,3926,4349,2674,3995,1828,8286,2670,5068,3913,4730,7877,4656,2866,4243,7312,8510,3961,7895,4300,4781,3479,4355,3399,2622,1608,2737,6748,5144,5920,2818,6547,4476,2444,6121,7591,4644,4154,4317,4345,5757,4377,4916,6996,3507,6961,3555,3803,4231,5990,3210,3984,3902,7321,4594,5281,8888,8692,7841,6418,5992,4216,3550,2093,4159,3643,5545,6205,3958,5221,1818,6201,2571,3125,4495,5944,5269,3828,3532,3458,9280,4132,3897,5210,4416,3590,5727,9792,1645,5878,3990,3434,9396,3778,3793,4938,2357,3346,8265,7450,5269,4209,4669,3337,6956,5470,3726,3376,5190,5165,4815,3388,1947,3642,4296,8588,4021,5007,3985,4455,2120,7892,3287,3852,4086,3270,4232,7335,1999,4722,3521,3099,3546,4711,4041,2233,5639,8179,3350,6296,4300,4928,5542,3511,3743,5001,3524,3370,3950,1569,2937,7374,4618,3423,3968,4839,7359,4768,3283,3311,2274,2723,4030,4563,6409,4111,2193,3779,4689,4978,3248,3293,4787,3918,5719,3743,3801,5182,6261,6277,6759,2422,3870,7584,3177,3218,4856,4504,9581,4544,6212,6073,3961,3293,7776,4361,4718,3437,4292,3491,3777,3921,6962,3933,5243,4781,1786,7436,4210,4186,2000,4395,3161,2610,5141,6111,6232,7725,3847,7491,4507,6415,6660,4841,6584,3728,4270,4836,3515,4082,3759,3461,4218,3874,5686,7462,4883,2411,8579,2994,7631,5073,6798,4008,5254,4427,6779,4180,4034,3330,4987,5911,1763,7325,7255,4123,3586,1855,5139,5198,4166,5619,5997,3960,10033,1915,3202,5726,6479,7473,6220,4334,3443,4469,4153,2142,4950,3950,8541,5131,6724,6313,5367,6092,6414,4880,6249,5473,4961,3323,4819,3758,2923,3546,5167,4579,4545,6222,6361,3237,4190,3225,5417,4596,4347,5224,5872,2592,5005,2956,6740,3026,8552,4455,5181,6778,5555,3153,6966,2141,5636,5152,3995,7120,3049,5108,4741,3520,3638,1746,6920,2571,4409,3634,8743,3504,5462,4599,5040,3270,4674,7258,3028,2827,2700,5272,7612,6129,5088,8558,5817,3894,4694,5155,3634,5171,4777,5000,4354,3728,3583,4420,8758,3741,6322,5340,4778,4029,4125,4788,8514,3229,3929,6280,5266,6650,8802,3423,3268,4883,10869,5073,4163,5926,4548,4640,4884,1942,3349,4760,7736,5893,4237,6369,2996,6276,3871,8738,8800,3012,7578,8900,4354,4892,6257,8828,3231,5890,1576,2991,2491,4896,4289,5009,4947,8377,5721,5304,9118,7865,6537,7365,5269,1466,3458,6759,4334,3584,3077,2537,5152,3636,4212,5221,4617,5224,4810,2462,7247,3494,5828,3919,8442,4749,3002,7036,6667,3601,4699,6389,4777,7078,7818,2927,2507,7398,3593,3576,4238,4085,3180,1448,6562,6007,7019,3200,1853,2017,4197,3684,7508,4940,5988,4341,5415,2255,8940,6752,3462,4332,3629,4576,5268,4182,10132,5164,7031,4837,6761,4813,8325,3541,7496,1887,3843,7072,3301,2695,7061,6095,8543,4222,5386,4077,3389,3737,8432,2423,2769,3433,4557,1829,4819,5103,4526,1840,3283,3722,3209,3990,11602,7366,5389,4190,3563,5832,3137,3365,1643,6916,2999,4223,4615,4882,3575,8423,5077,3994,4110,3671,4535,2678,4027,4060,3873,2763,5600,4104,4924,5651,6101,3781,5935,2971,8050,4311,5979,5689,1986,6362,4015,3972,3340,6185,5957,5319,5127,4856,4529,10938,6241,4897,7634,3270,3428,3435,5207,3111,7616,3233,3555,6574,9831,7446,5472,5821,4359,6621,6960,2731,3171,4785,6365,5797,8817,7335,3552,9692,4560,2664,8028,4577,5826,3178,2751,3645,5035,3918,3425,5641,8416,5844,1691,6996,7416,3577,4391,7014,4010,8949,3823,5654,4436,2853,4733,3543,4991,1970,4893,6024,3638,7795,5713,4801,2668,3290,4888,3033,4274,3852,5180,4878,7550,4282,8084,2190,4896,5435,4049,6675,3787,4770,3310,4703,4431,5024,4256,2928,5698,7796,4108,5829,4463,2311,2865,3744,5474,4993,5854,4968,4878,7402,3362,3348,3779,8110,4937,7363,3967,7062,3852,3674,4321,5312,1988,4151,3852,4770,4264,3710,5061,5010,7002,6972,6643,3428,3832,7090,4520,3186,3849,2240,1941,3734,9365,7725,3079,4534,4349,6249,2174,6768,9032,5679,1960,3520,3464,3075,2713,4895,4957,4213,6147,6459,9177,4375,8934,3458,7104,4990,2869,5669,5505,5236,2303,4412,5586,4046,9333,3207,4118,6864,4504,4664,3028,6187,4306,5260,7165,2622,5213,3963,6158,3832,6465,8151,4879,3167,5030,3100,5095,6585,4704,2182,3893,5026,5209,4743,8047,3696,2998,5309,3600,3973,4179,3135,6874,4174,3106,4760,4946,7847,4904,3474,7715,4249,4472,5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3828,4244,5505,4919,4830,4458,4905,11248,3481,4310,5740,2706,4695,3593,4436,5869,10080,5571,4907,3250,4630,6923,3716,8336,4918,7143,3184,6333,4224,3260,4606,4032,4740,4768,3909,4698,7555,3806,3068,7582,3560,3992,10655,3513,3601,7972,4120,2364,5062,5126,7626,4121,3971,5183,4083,3178,2231,7914,6533,7904,2823,5563,6314,8573,7713,4506,3098,6814,4222,3121,6585,4016,4324,3117,3555,3935,5494,5852,3027,3369,6761,3917,7033,3219,3453,3287,7166,4598,5118,2047,3159,3173,6280,3956,6209,7943,4051,5679,6697,6960,7135,4821,3587,4508,5883,6689,4784,2061,5214,4548,6862,5152,6067,9299,9030,4203,2545,3635,5691,3804,5032,5405,7237,7782,4634,4140,4813,5860,5672,5594,6152,3528,3684,3861,3842,3905,4207,3078,3824,9701,6613,7450,3903,4685,1761,4243,3254,7776,4964,3617,6742,4246,1952,5539,3600,8019,6384,4743,5565,7214,5156,4009,8211,4844,5195,5036,5938,6330,4680,4227,2254,3551,3791,7562,3122,3418,5541,7562,4253,2477,6580,5778,3029,6078,3987,5395,4018,8268,6764,2612,4806,5824,8592,5071,3231,3280,6025,1804,5433,7567,5758,9102,4667,2391,4871,4424,3142,3433,5181,4774,5406,2198,6541,5526,3369,3399,3979,8212,3930,2632,4173,4863,4575,4767,5026,4248,1693,2255,2151,4827,5748,4925,5729,4937,5134,5366,4111,4474,4052,2967,5156,2071,3676,3343,4285,3935,2168,8483,4958,5853,2984,3324,2955,6041,3574,3356,4047,3440,3498,4327,4206,3788,5597,4648,8216,3284,2422,3698,8215,3851,6208,7188,3203,4230,3277,8979,3245,2999,4735,5065,5438,3612,4039,3768,3417,6216,6094,3852,4482,5935,5193,3760,3841,3560,2481,8819,5416,6626,8605,5768,2763,8105,3057,4058,3602,4471,4953,4741,3069,10307,4812,4184,7639,5888,5754,7733,4190,4231,6097,5400,6059,9832,8646,8238,1851,4414,5469,5790,5377,4577,4895,4638,8048,7220,3136,2019,3109,9501,5942,6861,1481,6920,7267,4470,3234,4405,3102,3075,2959,3488,6006,5681,2347,3439,5158,4938,3282,3573,3295,5918,5668,5013,4849,7266,2298,4320,4879,3820,3443,5380,3766,7465,5537,3510,5558,3070,3787,4119,7757,2008,3923,5677,6989,1940,4676,2622,4577,2056,3700,3247,3450,3688,2546,4929,7529,8783,3406,4790,5359,4730,5208,4285,4283,5305,3474,2046,5816,3637,6652,3655,6768,1611,4887,3606,4144,4493,7485,5173,7510,3102,7246,6636,4939,3476,3204,9162,4703,8339,7435,3559,4836,6316,4265,6000,3560,3473,4265,4504,6107,5718,3750,8602,6009,7127,2593,3674,5143,4328,2017,4530,4823,1474,7319,6352,3058,3094,5793,2779,6069,1607,4216,4574,6608,4773,3412,3001,3243,7015,1984,8476,6382,4259,4792,5837,4964,5612,5678,3801,5571,6559,2215,3111,4883,4688,4183,4244,5318,5304,3188,3017,3533,4358,6364,4770,5020,4463,8752,4571,3218,8029,3986,5051,4106,4740,5928,4097,3094,3606,4069,5153,1517,4479,4820,3617,3166,3294,4111,6813,6685,3840,4569,6237,5279,8511,6133,3986,5951,4445,4339,3522,5202,4247,4346,5472,5076,3875,3170,3090,5315,4063,4091,3438,2453,5555,5333,5116,5672,3611,3194,4167,3823,3497,8010,6880,8288,5066,7365,6076,5803,5428,3604,3919,3946,4384,5052,4742,3620,5923,3843,3543,3619,3814,5536,3536,3398,3982,4424,5079,5486,5246,3742,4959,8841,4473,4917,6514,4289,5432,5210,2758,5617,5826,6545,4603,6072,5020,4789,5990,7372,4306,7816,4642,6681,6096,8043,10693,4618,4403,3659,4926,6403,4961,4088,3180,4733,4973,6416,5268,5051,3772,6170,4854,3848,6266,3598,6161,7840,3577,4410,6785,3336,5422,3981,6041,5605,9166,5067,5290,3548,5281,5039,7868,6626,4258,10684,4697,3760,4296,3993,5135,8061,4194,3955,7870,4363,3776,3860,5167,4742,4964,4018,6562,6371,4444,6444,7035,9173,7841,5297,5050,4301,4362,6272,4500,5276,5847,5469,5541,5490,5507,5013,4410,5548,4551,6552,4403,8943,1917,6077,3478,3714,8062,6132,6304,8003,5814,3849,5275,4757,2080,4910,5183,5820,4567,5549,5111,5159,4555,6439,3838,7226,2936,6191,3276,4469,4044,3946,5930,6055,2379,6534,4742,5056,7989,4919,3937,3813,5457,7057,4517,4749,4572,5480,5394,3499,4710,4141,5616,4670,5910,5398,5834,6239,4563,4169,8133,4831,6349,5105,3980,4815,7033,3666,3669,4919,4658,4263,10175,5788,12383,4941,8097,5157,4479,8410,3791,5019,4023,3571,3886,4472,9261,3656,4490,4715,3989,7259,3712,1880,4614,3557,3920,5578,392,3564,3945,9140,3688,8749,4106,5343,4479,8790,5377,6725,5080,5279,5163,3980,5795,7796,3425,5244,4566,6723,6307,7581,3583,3816,3609,5546,7638,5510,3252,5942,4317,5186,7119,4529,7063,7923,6330,4861,3654,3149,4873,6070,7200,3667,3767,5573,10330,4931,3925,6766,4442,3894,2600,3993,6282,5390,6293,4236,3441,3753,4235,7451,8061,9843,5560,9978,8648,3671,3919,9368,5854,3640,4974,4981,4892,6817,4926,5249,4713,3427,4560,3497,5506,7510,4867,3719,4814,3805,7508,6518,7753,7886,3536,4965,6354,6008,4136,4806,4817,2748,4882,5315,4639,4362,6184,3829,4665,9915,5645,3667,3326,5763,3181,8382,4725,4599,3648,3396,3944,5243,3948,8182,4096,7720,5248,4110,5751,5490,3642,4614,3928,5613,4116,8050,4649,4006,7861,4569,4902,7481,7638,3586,3914,5186,7149,4201,6555,8120,4538,2696,4788,4544,5215,4064,4655,5068,4148,3700,5346,3225,8369,5693,6265,6357,5040,4879,4246,6092,4846,5275,4351,4589,5942,4711,6858,8151,5320,2488,7791,8598,8210,8623,4163,4981,1885,2971,4606,3324,5928,5483,6897,5077,6994,4357,4488,4936,6338,6574,4413,5331,3864,5343,6279,4357,3682,4587,4153,5427,4337,7538,5361,5368,4344,46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09,5037,3753,2905,5029,3659,5684,3158,3215,1845,1594,945,1923,6227,4302,3169,1899,3108,1567,4745,5327,7614,5583,1958,4891,3766,1146,5387,1121,4424,1459,2361,2248,5691,4040,2369,8118,5167,4455,4444,3199,2423,2437,2015,3234,2978,1799,5511,3157,2292,1394,4445,5843,2167,6063,4630,2001,3980,1905,2585,4555,5096,1339,3363,2572,6034,5959,188,2770,3276,6076,2519,3393,1512,2076,6594,3562,2615,3310,1912,6072,1650,3247,2479,2146,1205,3528,5201,3860,5971,2083,3370,3339,5283,5748,4094,4138,2397,5294,4316,2405,5865,2015,5918,4441,3137,4285,5236,2524,5003,1045,1811,2855,4030,4122,5422,1749,5089,6334,2079,4490,5925,1998,2213,1319,3188,1755,2692,1556,2852,2790,6490,6913,1236,2996,5192,2116,2968,2520,5265,5274,1563,4083,3654,6376,5555,2287,4875,2469,5129,2529,5517,7290,5379,4716,1280,1421,1844,3374,2500,5703,4775,4580,3448,2096,1865,4632,1792,1485,4027,1337,7942,2881,3982,2741,5430,2769,1879,5342,1838,5127,7004,3151,2210,3656,8254,6293,6005,3177,4009,3109,1411,7289,3335,2388,3380,3754,5964,4683,4359,1077,4679,2024,2847,2077,1202,1452,1653,1885,4218,1633,7728,6228,3947,3120,4193,4999,5658,3280,1580,4315,3973,3845,5170,4287,1544,1357,4279,1951,5452,2719,1614,5987,1349,5773,5072,2518,5472,5341,1731,6249,7316,5360,5022,4323,8486,1045,4577,5330,1518,6350,2115,5010,2416,5916,1999,6367,4522,3337,2817,1311,1878,2154,1813,4679,2138,2082,1037,3024,1096,5271,1727,2370,4676,5503,5031,1708,5198,3742,1442,5230,2031,2675,2909,4446,4741,1608,1547,4920,1527,1959,2307,1468,4664,6306,4467,4011,3214,4487,3425,2783,2171,2599,3370,2788,5533,1395,3007,1229,3386,4431,6025,5230,1126,2790,5871,2073,1603,3261,7732,1451,3432,6426,1892,7179,4411,7397,1414,2337,5815,5422,6259,4753,4321,2766,2218,4867,4623,5274,7877,2646,4585,946,3818,3895,1216,1306,2362,5822,2319,2828,5937,1301,2834,4499,6034,8524,8014,1657,4928,4895,5944,5636,4566,5812,2769,1533,2715,7510,3910,4733,3537,1436,8300,3617,1485,5312,3590,4537,5411,2951,6229,2182,3009,4425,1604,5024,4498,4932,1838,1776,1906,1920,4212,7085,8621,2981,3139,2617,1748,5604,2368,3467,3776,2987,6109,2019,4273,2141,1351,2220,2848,2569,4046,1372,5267,1884,3476,1973,9257,2828,2765,4148,1088,2550,2386,3728,4414,2116,6931,3342,5208,1327,5954,1975,3258,1998,3392,3969,2008,7160,2088,5715,4266,2118,5026,3120,2016,1812,1149,1214,6974,3923,4526,4500,5112,268,2329,1547,1918,4299,2849,1348,5422,3585,2321,4679,6096,1991,4990,1126,1663,6764,1422,2400,2614,2612,1605,5776,3700,5789,2002,2181,2479,4822,1068,1997,2851,5073,1434,2627,1107,3337,1979,5261,4310,1349,4710,4373,2590,2258,1607,3247,1775,2242,2139,6236,4134,9396,6561,1898,7121,2237,1159,2050,4311,5832,2643,2914,2811,3094,3330,5423,5114,6457,929,1263,2086,3840,4103,4488,3887,4285,3886,5524,6053,4128,4618,1631,3710,1305,5654,2046,5639,1522,4839,1663,1894,1955,4003,3175,1801,1793,3679,6128,1848,3924,2779,4166,7041,1686,2101,5131,1626,3172,2701,2137,7377,1879,6885,1160,2559,1212,1076,3222,5669,4903,1261,7181,1663,1193,4535,1194,2079,3803,4808,2030,5753,2481,4886,3731,2691,3909,1884,3470,2207,2572,5082,3107,4671,1632,3720,5369,1589,5064,1589,1821,5505,3753,1492,8225,5633,4937,1787,2431,2412,1498,2353,5801,1805,5893,2240,1473,2463,4532,5020,5064,2625,2910,4330,4720,5445,4929,2680,2909,2114,2369,2831,963,5191,1704,3910,2627,1157,1017,7088,4399,2838,5277,5780,1548,4835,4139,4854,5085,1566,4120,2994,3338,5925,5588,6296,5401,4176,1511,2455,4826,1723,4949,2642,5861,2975,1646,3818,5473,1763,3882,4210,5102,2320,5857,1278,1687,4720,2062,5248,2080,4018,3796,3331,3279,3051,1884,1275,6251,3323,7351,3926,5054,2668,4262,2148,1507,2313,1210,1038,7434,3524,4529,2956,2603,4886,4478,2499,1452,2951,5209,1842,3129,1724,1830,1028,5210,1931,3988,5463,3784,4318,4263,3463,1969,5290,4213,2238,5008,3535,4880,3391,2755,1108,1555,3194,4481,5723,2538,1667,8042,2603,8226,4537,1404,2530,7302,7652,3566,5531,1764,1857,6029,5008,5513,4755,2177,1793,1693,6723,2258,4217,4453,4358,5056,6623,2470,2234,4832,3542,4174,4994,1980,8592,1092,3263,2751,7241,2078,1515,1166,1386,2557,3109,4631,10232,1781,1330,1930,4297,5517,4590,7970,2788,1447,1262,1879,2665,3169,2368,5199,2983,1592,3479,2177,4649,4493,2662,2653,2121,2308,3607,3276,2395,3214,2178,3664,941,1342,4659,5739,5858,3436,5441,4871,3969,2239,4839,1550,3700,1510,7462,5977,4755,3690,6071,2080,207,2299,6716,1613,2204,6518,6916,8067,2803,4346,2342,6334,4934,3998,1850,3573,5149,5758,4825,3030,3500,5734,4715,1607,3769,5005,7465,1078,3649,3184,1941,5603,5134,1756,6565,2656,5721,2198,5513,1515,5587,1657,1948,5125,1461,2576,1433,3507,2994,5901,3736,5423,2019,1582,5901,3976,1761,5232,3153,4139,4803,2989,1610,2944,2538,3866,2560,2349,5691,3259,5113,3801,2053,2860,9688,6650,2226,1963,4742,1120,2061,1422,1611,3216,5009,1133,3565,2352,2895,1576,1425,5510,1849,1014,4812,6609,2297,5149,5068,7262,2663,1524,1631,1739,2558,4571,2373,4298,5275,3699,7597,1810,4452,7049,3762,1920,2397,1528,7191,5283,2591,1525,969,3970,4098,1540,4882,8409,1891,2095,3886,1553,5671,5170,2409,2178,4216,4405,1958,4007,8553,2205,3606,2768,5721,2255,212,4786,2079,2649,2783,6362,1941,1799,4103,2746,4706,5828,1056,3513,5046,4099,5507,2311,1414,4855,2469,4685,3244,1592,4147,5081,1977,5417,3259,1597,1141,5091,1404,3608,4494,7373,3650,2549,3934,1984,1839,3322,5766,4327,1771,5382,6603,2917,5523,1391,4157,5514,4712,1546,1393,1409,2096,2172,1039,1811,4252,2551,5986,2897,4617,3551,4899,5016,1111,1113,4837,1763,4981,2500,2327,3249,1438,2550,4116,1638,4851,2753,2988,1681,2761,6042,5451,1087,2924,3668,3545,2676,4419,2232,2723,5335,4243,3034,1186,1267,5826,1418,1646,5846,5422,1352,5607,2476,5869,1975,7044,1921,1690,4638,4136,1450,1941,5427,4894,4049,3589,4622,2437,5452,1769,4493,2288,7103,6137,3061,2153,3457,4325,1923,6221,2217,3107,5711,1535,1624,4195,3005,5373,1278,2655,5182,2115,1484,1341,2282,1693,2623,2102,5663,4888,5022,1776,2842,6312,2707,2100,5091,4714,1580,4185,2969,5014,9168,4385,3025,4797,5377,3289,3589,5447,5978,2250,1942,1755,7661,4683,3031,2714,4823,3079,4539,2982,6183,3363,7382,5157,3643,3939,3055,6751,4099,1645,5579,3681,5819,3682,2036,2975,7218,4908,5291,3498,3581,6307,3601,4002,6362,2973,3256,3852,5492,4476,3425,5309,3093,2969,4338,2941,5221,4891,4353,3926,4349,2674,3995,1828,8286,2670,5068,3913,4730,7877,4656,2866,4243,7312,8510,3961,7895,4300,4781,3479,4355,3399,2622,1608,2737,6748,5144,5920,2818,6547,4476,2444,6121,7591,4644,4154,4317,4345,5757,4377,4916,6996,3507,6961,3555,3803,4231,5990,3210,3984,3902,7321,4594,5281,8888,8692,7841,6418,5992,4216,3550,2093,4159,3643,5545,6205,3958,5221,1818,6201,2571,3125,4495,5944,5269,3828,3532,3458,9280,4132,3897,5210,4416,3590,5727,9792,1645,5878,3990,3434,9396,3778,3793,4938,2357,3346,8265,7450,5269,4209,4669,3337,6956,5470,3726,3376,5190,5165,4815,3388,1947,3642,4296,8588,4021,5007,3985,4455,2120,7892,3287,3852,4086,3270,4232,7335,1999,4722,3521,3099,3546,4711,4041,2233,5639,8179,3350,6296,4300,4928,5542,3511,3743,5001,3524,3370,3950,1569,2937,7374,4618,3423,3968,4839,7359,4768,3283,3311,2274,2723,4030,4563,6409,4111,2193,3779,4689,4978,3248,3293,4787,3918,5719,3743,3801,5182,6261,6277,6759,2422,3870,7584,3177,3218,4856,4504,9581,4544,6212,6073,3961,3293,7776,4361,4718,3437,4292,3491,3777,3921,6962,3933,5243,4781,1786,7436,4210,4186,2000,4395,3161,2610,5141,6111,6232,7725,3847,7491,4507,6415,6660,4841,6584,3728,4270,4836,3515,4082,3759,3461,4218,3874,5686,7462,4883,2411,8579,2994,7631,5073,6798,4008,5254,4427,6779,4180,4034,3330,4987,5911,1763,7325,7255,4123,3586,1855,5139,5198,4166,5619,5997,3960,10033,1915,3202,5726,6479,7473,6220,4334,3443,4469,4153,2142,4950,3950,8541,5131,6724,6313,5367,6092,6414,4880,6249,5473,4961,3323,4819,3758,2923,3546,5167,4579,4545,6222,6361,3237,4190,3225,5417,4596,4347,5224,5872,2592,5005,2956,6740,3026,8552,4455,5181,6778,5555,3153,6966,2141,5636,5152,3995,7120,3049,5108,4741,3520,3638,1746,6920,2571,4409,3634,8743,3504,5462,4599,5040,3270,4674,7258,3028,2827,2700,5272,7612,6129,5088,8558,5817,3894,4694,5155,3634,5171,4777,5000,4354,3728,3583,4420,8758,3741,6322,5340,4778,4029,4125,4788,8514,3229,3929,6280,5266,6650,8802,3423,3268,4883,10869,5073,4163,5926,4548,4640,4884,1942,3349,4760,7736,5893,4237,6369,2996,6276,3871,8738,8800,3012,7578,8900,4354,4892,6257,8828,3231,5890,1576,2991,2491,4896,4289,5009,4947,8377,5721,5304,9118,7865,6537,7365,5269,1466,3458,6759,4334,3584,3077,2537,5152,3636,4212,5221,4617,5224,4810,2462,7247,3494,5828,3919,8442,4749,3002,7036,6667,3601,4699,6389,4777,7078,7818,2927,2507,7398,3593,3576,4238,4085,3180,1448,6562,6007,7019,3200,1853,2017,4197,3684,7508,4940,5988,4341,5415,2255,8940,6752,3462,4332,3629,4576,5268,4182,10132,5164,7031,4837,6761,4813,8325,3541,7496,1887,3843,7072,3301,2695,7061,6095,8543,4222,5386,4077,3389,3737,8432,2423,2769,3433,4557,1829,4819,5103,4526,1840,3283,3722,3209,3990,11602,7366,5389,4190,3563,5832,3137,3365,1643,6916,2999,4223,4615,4882,3575,8423,5077,3994,4110,3671,4535,2678,4027,4060,3873,2763,5600,4104,4924,5651,6101,3781,5935,2971,8050,4311,5979,5689,1986,6362,4015,3972,3340,6185,5957,5319,5127,4856,4529,10938,6241,4897,7634,3270,3428,3435,5207,3111,7616,3233,3555,6574,9831,7446,5472,5821,4359,6621,6960,2731,3171,4785,6365,5797,8817,7335,3552,9692,4560,2664,8028,4577,5826,3178,2751,3645,5035,3918,3425,5641,8416,5844,1691,6996,7416,3577,4391,7014,4010,8949,3823,5654,4436,2853,4733,3543,4991,1970,4893,6024,3638,7795,5713,4801,2668,3290,4888,3033,4274,3852,5180,4878,7550,4282,8084,2190,4896,5435,4049,6675,3787,4770,3310,4703,4431,5024,4256,2928,5698,7796,4108,5829,4463,2311,2865,3744,5474,4993,5854,4968,4878,7402,3362,3348,3779,8110,4937,7363,3967,7062,3852,3674,4321,5312,1988,4151,3852,4770,4264,3710,5061,5010,7002,6972,6643,3428,3832,7090,4520,3186,3849,2240,1941,3734,9365,7725,3079,4534,4349,6249,2174,6768,9032,5679,1960,3520,3464,3075,2713,4895,4957,4213,6147,6459,9177,4375,8934,3458,7104,4990,2869,5669,5505,5236,2303,4412,5586,4046,9333,3207,4118,6864,4504,4664,3028,6187,4306,5260,7165,2622,5213,3963,6158,3832,6465,8151,4879,3167,5030,3100,5095,6585,4704,2182,3893,5026,5209,4743,8047,3696,2998,5309,3600,3973,4179,3135,6874,4174,3106,4760,4946,7847,4904,3474,7715,4249,4472,5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3828,4244,5505,4919,4830,4458,4905,11248,3481,4310,5740,2706,4695,3593,4436,5869,10080,5571,4907,3250,4630,6923,3716,8336,4918,7143,3184,6333,4224,3260,4606,4032,4740,4768,3909,4698,7555,3806,3068,7582,3560,3992,10655,3513,3601,7972,4120,2364,5062,5126,7626,4121,3971,5183,4083,3178,2231,7914,6533,7904,2823,5563,6314,8573,7713,4506,3098,6814,4222,3121,6585,4016,4324,3117,3555,3935,5494,5852,3027,3369,6761,3917,7033,3219,3453,3287,7166,4598,5118,2047,3159,3173,6280,3956,6209,7943,4051,5679,6697,6960,7135,4821,3587,4508,5883,6689,4784,2061,5214,4548,6862,5152,6067,9299,9030,4203,2545,3635,5691,3804,5032,5405,7237,7782,4634,4140,4813,5860,5672,5594,6152,3528,3684,3861,3842,3905,4207,3078,3824,9701,6613,7450,3903,4685,1761,4243,3254,7776,4964,3617,6742,4246,1952,5539,3600,8019,6384,4743,5565,7214,5156,4009,8211,4844,5195,5036,5938,6330,4680,4227,2254,3551,3791,7562,3122,3418,5541,7562,4253,2477,6580,5778,3029,6078,3987,5395,4018,8268,6764,2612,4806,5824,8592,5071,3231,3280,6025,1804,5433,7567,5758,9102,4667,2391,4871,4424,3142,3433,5181,4774,5406,2198,6541,5526,3369,3399,3979,8212,3930,2632,4173,4863,4575,4767,5026,4248,1693,2255,2151,4827,5748,4925,5729,4937,5134,5366,4111,4474,4052,2967,5156,2071,3676,3343,4285,3935,2168,8483,4958,5853,2984,3324,2955,6041,3574,3356,4047,3440,3498,4327,4206,3788,5597,4648,8216,3284,2422,3698,8215,3851,6208,7188,3203,4230,3277,8979,3245,2999,4735,5065,5438,3612,4039,3768,3417,6216,6094,3852,4482,5935,5193,3760,3841,3560,2481,8819,5416,6626,8605,5768,2763,8105,3057,4058,3602,4471,4953,4741,3069,10307,4812,4184,7639,5888,5754,7733,4190,4231,6097,5400,6059,9832,8646,8238,1851,4414,5469,5790,5377,4577,4895,4638,8048,7220,3136,2019,3109,9501,5942,6861,1481,6920,7267,4470,3234,4405,3102,3075,2959,3488,6006,5681,2347,3439,5158,4938,3282,3573,3295,5918,5668,5013,4849,7266,2298,4320,4879,3820,3443,5380,3766,7465,5537,3510,5558,3070,3787,4119,7757,2008,3923,5677,6989,1940,4676,2622,4577,2056,3700,3247,3450,3688,2546,4929,7529,8783,3406,4790,5359,4730,5208,4285,4283,5305,3474,2046,5816,3637,6652,3655,6768,1611,4887,3606,4144,4493,7485,5173,7510,3102,7246,6636,4939,3476,3204,9162,4703,8339,7435,3559,4836,6316,4265,6000,3560,3473,4265,4504,6107,5718,3750,8602,6009,7127,2593,3674,5143,4328,2017,4530,4823,1474,7319,6352,3058,3094,5793,2779,6069,1607,4216,4574,6608,4773,3412,3001,3243,7015,1984,8476,6382,4259,4792,5837,4964,5612,5678,3801,5571,6559,2215,3111,4883,4688,4183,4244,5318,5304,3188,3017,3533,4358,6364,4770,5020,4463,8752,4571,3218,8029,3986,5051,4106,4740,5928,4097,3094,3606,4069,5153,1517,4479,4820,3617,3166,3294,4111,6813,6685,3840,4569,6237,5279,8511,6133,3986,5951,4445,4339,3522,5202,4247,4346,5472,5076,3875,3170,3090,5315,4063,4091,3438,2453,5555,5333,5116,5672,3611,3194,4167,3823,3497,8010,6880,8288,5066,7365,6076,5803,5428,3604,3919,3946,4384,5052,4742,3620,5923,3843,3543,3619,3814,5536,3536,3398,3982,4424,5079,5486,5246,3742,4959,8841,4473,4917,6514,4289,5432,5210,2758,5617,5826,6545,4603,6072,5020,4789,5990,7372,4306,7816,4642,6681,6096,8043,10693,4618,4403,3659,4926,6403,4961,4088,3180,4733,4973,6416,5268,5051,3772,6170,4854,3848,6266,3598,6161,7840,3577,4410,6785,3336,5422,3981,6041,5605,9166,5067,5290,3548,5281,5039,7868,6626,4258,10684,4697,3760,4296,3993,5135,8061,4194,3955,7870,4363,3776,3860,5167,4742,4964,4018,6562,6371,4444,6444,7035,9173,7841,5297,5050,4301,4362,6272,4500,5276,5847,5469,5541,5490,5507,5013,4410,5548,4551,6552,4403,8943,1917,6077,3478,3714,8062,6132,6304,8003,5814,3849,5275,4757,2080,4910,5183,5820,4567,5549,5111,5159,4555,6439,3838,7226,2936,6191,3276,4469,4044,3946,5930,6055,2379,6534,4742,5056,7989,4919,3937,3813,5457,7057,4517,4749,4572,5480,5394,3499,4710,4141,5616,4670,5910,5398,5834,6239,4563,4169,8133,4831,6349,5105,3980,4815,7033,3666,3669,4919,4658,4263,10175,5788,12383,4941,8097,5157,4479,8410,3791,5019,4023,3571,3886,4472,9261,3656,4490,4715,3989,7259,3712,1880,4614,3557,3920,5578,392,3564,3945,9140,3688,8749,4106,5343,4479,8790,5377,6725,5080,5279,5163,3980,5795,7796,3425,5244,4566,6723,6307,7581,3583,3816,3609,5546,7638,5510,3252,5942,4317,5186,7119,4529,7063,7923,6330,4861,3654,3149,4873,6070,7200,3667,3767,5573,10330,4931,3925,6766,4442,3894,2600,3993,6282,5390,6293,4236,3441,3753,4235,7451,8061,9843,5560,9978,8648,3671,3919,9368,5854,3640,4974,4981,4892,6817,4926,5249,4713,3427,4560,3497,5506,7510,4867,3719,4814,3805,7508,6518,7753,7886,3536,4965,6354,6008,4136,4806,4817,2748,4882,5315,4639,4362,6184,3829,4665,9915,5645,3667,3326,5763,3181,8382,4725,4599,3648,3396,3944,5243,3948,8182,4096,7720,5248,4110,5751,5490,3642,4614,3928,5613,4116,8050,4649,4006,7861,4569,4902,7481,7638,3586,3914,5186,7149,4201,6555,8120,4538,2696,4788,4544,5215,4064,4655,5068,4148,3700,5346,3225,8369,5693,6265,6357,5040,4879,4246,6092,4846,5275,4351,4589,5942,4711,6858,8151,5320,2488,7791,8598,8210,8623,4163,4981,1885,2971,4606,3324,5928,5483,6897,5077,6994,4357,4488,4936,6338,6574,4413,5331,3864,5343,6279,4357,3682,4587,4153,5427,4337,7538,5361,5368,4344,46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@@ -16429,7 +16409,7 @@ <h3 id="Quality-Filtering">Quality Filtering<a class="anchor-link" href="#Qualit
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [19]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [22]:</div>
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@@ -16504,7 +16484,7 @@ <h3 id="Quality-Filtering">Quality Filtering<a class="anchor-link" href="#Qualit
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [20]:</div>
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
@@ -16558,7 +16538,7 @@ <h3 id="Quality-Filtering">Quality Filtering<a class="anchor-link" href="#Qualit
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+<div class="jp-InputPrompt jp-InputArea-prompt">In [24]:</div>
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">perc_malat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span> <span class="n">adata</span><span class="p">[:,</span><span class="s1">'MALAT1'</span><span class="p">]</span><span class="o">.</span><span class="n">X</span><span class="p">,</span> <span class="mi">1</span> <span class="p">)</span><span class="o">.</span><span class="n">A1</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span> <span class="n">adata</span><span class="o">.</span><span class="n">X</span><span class="p">,</span> <span class="mi">1</span> <span class="p">)</span><span class="o">.</span><span class="n">A1</span>
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@@ -16725,7 +16705,7 @@ <h3 id="Quality-Filtering">Quality Filtering<a class="anchor-link" href="#Qualit
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [24]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [27]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sc</span><span class="o">.</span><span class="n">preprocessing</span><span class="o">.</span><span class="n">filter_cells</span><span class="p">(</span><span class="n">adata</span><span class="p">,</span> <span class="n">max_genes</span><span class="o">=</span><span class="mi">8000</span><span class="p">)</span>
@@ -16741,7 +16721,7 @@ <h3 id="Quality-Filtering">Quality Filtering<a class="anchor-link" href="#Qualit
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [25]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [28]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sc</span><span class="o">.</span><span class="n">preprocessing</span><span class="o">.</span><span class="n">filter_cells</span><span class="p">(</span><span class="n">adata</span><span class="p">,</span> <span class="n">min_genes</span><span class="o">=</span><span class="mi">800</span><span class="p">)</span>
@@ -16757,7 +16737,7 @@ <h3 id="Quality-Filtering">Quality Filtering<a class="anchor-link" href="#Qualit
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [26]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [29]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sc</span><span class="o">.</span><span class="n">preprocessing</span><span class="o">.</span><span class="n">filter_cells</span><span class="p">(</span><span class="n">adata</span><span class="p">,</span> <span class="n">max_counts</span><span class="o">=</span><span class="mi">40000</span><span class="p">)</span>
@@ -16773,7 +16753,7 @@ <h3 id="Quality-Filtering">Quality Filtering<a class="anchor-link" href="#Qualit
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [27]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [30]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">adata</span> <span class="o">=</span> <span class="n">adata</span><span class="p">[</span><span class="n">adata</span><span class="o">.</span><span class="n">obs</span><span class="p">[</span><span class="s1">'perc_mito'</span><span class="p">]</span><span class="o">&lt;</span><span class="mf">0.2</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
@@ -16789,7 +16769,7 @@ <h3 id="Quality-Filtering">Quality Filtering<a class="anchor-link" href="#Qualit
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [28]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [31]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">adata</span> <span class="o">=</span> <span class="n">adata</span><span class="p">[</span><span class="n">adata</span><span class="o">.</span><span class="n">obs</span><span class="p">[</span><span class="s1">'perc_MALAT1'</span><span class="p">]</span><span class="o">&lt;</span><span class="mf">0.1</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
@@ -16817,7 +16797,7 @@ <h3 id="Quality-Filtering">Quality Filtering<a class="anchor-link" href="#Qualit
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [29]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [32]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sc</span><span class="o">.</span><span class="n">preprocessing</span><span class="o">.</span><span class="n">filter_genes</span><span class="p">(</span><span class="n">adata</span><span class="p">,</span> <span class="n">min_cells</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
@@ -16833,7 +16813,7 @@ <h3 id="Quality-Filtering">Quality Filtering<a class="anchor-link" href="#Qualit
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [30]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [33]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">'There are now'</span><span class="p">,</span> <span class="n">adata</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s1">'cells and'</span><span class="p">,</span> <span class="n">adata</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="s1">'genes after filtering'</span><span class="p">)</span>
@@ -16892,7 +16872,7 @@ <h3 id="Doublets-removal">Doublets removal<a class="anchor-link" href="#Doublets
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [31]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [34]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sc</span><span class="o">.</span><span class="n">external</span><span class="o">.</span><span class="n">pp</span><span class="o">.</span><span class="n">scrublet</span><span class="p">(</span><span class="n">adata</span><span class="p">,</span> <span class="c1">#dataset</span>
@@ -16949,7 +16929,7 @@ <h3 id="Doublets-removal">Doublets removal<a class="anchor-link" href="#Doublets
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [32]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [35]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">px</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">adata</span><span class="o">.</span><span class="n">obs</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s1">'doublet_score'</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s1">'Distribution of doublet scores per cell'</span><span class="p">)</span>
@@ -16974,9 +16954,9 @@ <h3 id="Doublets-removal">Doublets removal<a class="anchor-link" href="#Doublets
 
 
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@@ -17012,7 +16992,7 @@ <h3 id="Doublets-removal">Doublets removal<a class="anchor-link" href="#Doublets
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [33]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [36]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">adata</span> <span class="o">=</span> <span class="n">adata</span><span class="p">[</span> <span class="n">adata</span><span class="o">.</span><span class="n">obs</span><span class="p">[</span><span class="s1">'doublet_score'</span><span class="p">]</span><span class="o">&lt;</span><span class="mf">.1</span> <span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
@@ -17066,7 +17046,7 @@ <h3 id="Effect-of-normalization-on-technical-features">Effect of normalization o
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [34]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [37]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sc</span><span class="o">.</span><span class="n">preprocessing</span><span class="o">.</span><span class="n">pca</span><span class="p">(</span><span class="n">adata</span><span class="p">,</span> <span class="n">svd_solver</span><span class="o">=</span><span class="s1">'arpack'</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">12345</span><span class="p">)</span>
@@ -17092,7 +17072,7 @@ <h3 id="Effect-of-normalization-on-technical-features">Effect of normalization o
 
 
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" 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" class="
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 </div>
 
@@ -17218,7 +17198,7 @@ <h3 id="---Optional-task---"><div class="alert-success"> <font size="+2"> <b> Op
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 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [35]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [38]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">variance</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log1p</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">adata</span><span class="o">.</span><span class="n">X</span><span class="o">.</span><span class="n">todense</span><span class="p">(),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)))</span>
@@ -17243,9 +17223,9 @@ <h3 id="---Optional-task---"><div class="alert-success"> <font size="+2"> <b> Op
 
 
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@@ -17293,7 +17273,7 @@ <h3 id="---Optional-task---"><div class="alert-success"> <font size="+2"> <b> Op
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [36]:</div>
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">px</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">variance</span><span class="p">,</span> <span class="n">range_x</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">),</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s1">'Log-variance of each gene (zoom up to 1)'</span><span class="p">)</span>
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@@ -17355,7 +17335,7 @@ <h3 id="---Optional-task---"><div class="alert-success"> <font size="+2"> <b> Op
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [37]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [40]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="nb">sum</span><span class="p">(</span><span class="n">variance</span><span class="o">&gt;</span><span class="mf">0.05</span><span class="p">)</span><span class="si">}</span><span class="s1"> genes have a variance above 0.05'</span><span class="p">)</span>
@@ -17404,7 +17384,7 @@ <h3 id="---Optional-task---"><div class="alert-success"> <font size="+2"> <b> Op
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [38]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [41]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># save raw data matrix</span>
@@ -17442,7 +17422,7 @@ <h3 id="---Optional-task---"><div class="alert-success"> <font size="+2"> <b> Op
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [39]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [42]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sc</span><span class="o">.</span><span class="n">preprocessing</span><span class="o">.</span><span class="n">pca</span><span class="p">(</span><span class="n">adata</span><span class="p">,</span> <span class="n">svd_solver</span><span class="o">=</span><span class="s1">'arpack'</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">123</span><span class="p">,</span> <span class="n">use_highly_variable</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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" 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" class="
 ">
 </div>
 
@@ -17521,7 +17501,7 @@ <h3 id="PCA">PCA<a class="anchor-link" href="#PCA">¶</a></h3><p>PCA is one of t
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [40]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [43]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sc</span><span class="o">.</span><span class="n">plotting</span><span class="o">.</span><span class="n">pca_variance_ratio</span><span class="p">(</span><span class="n">adata</span><span class="p">)</span>
@@ -17574,7 +17554,7 @@ <h3 id="PCA">PCA<a class="anchor-link" href="#PCA">¶</a></h3><p>PCA is one of t
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [41]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [44]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'figure.figsize'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">6</span><span class="p">)</span> <span class="c1">#reduce figure size</span>
@@ -17640,7 +17620,7 @@ <h3 id="UMAP-projection">UMAP projection<a class="anchor-link" href="#UMAP-proje
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [42]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [45]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">bbknn</span><span class="o">.</span><span class="n">bbknn</span><span class="p">(</span><span class="n">adata</span><span class="p">,</span> <span class="n">n_pcs</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
@@ -17668,7 +17648,7 @@ <h3 id="UMAP-projection">UMAP projection<a class="anchor-link" href="#UMAP-proje
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [43]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [46]:</div>
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sc</span><span class="o">.</span><span class="n">tools</span><span class="o">.</span><span class="n">umap</span><span class="p">(</span><span class="n">adata</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">123</span><span class="p">,</span> <span class="n">n_components</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
@@ -17696,7 +17676,7 @@ <h3 id="UMAP-projection">UMAP projection<a class="anchor-link" href="#UMAP-proje
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [44]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [47]:</div>
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sc</span><span class="o">.</span><span class="n">plotting</span><span class="o">.</span><span class="n">umap</span><span class="p">(</span><span class="n">adata</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">'TNP2'</span><span class="p">],</span> <span class="n">components</span><span class="o">=</span><span class="p">[</span><span class="s1">'1,2'</span><span class="p">,</span><span class="s1">'1,3'</span><span class="p">,</span><span class="s1">'2,3'</span><span class="p">])</span>
@@ -17748,7 +17728,7 @@ <h3 id="UMAP-projection">UMAP projection<a class="anchor-link" href="#UMAP-proje
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 </div>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [45]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [48]:</div>
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">X</span> <span class="o">=</span> <span class="n">adata</span><span class="o">.</span><span class="n">obsm</span><span class="p">[</span><span class="s1">'X_umap'</span><span class="p">]</span>
@@ -17774,9 +17754,9 @@ <h3 id="UMAP-projection">UMAP projection<a class="anchor-link" href="#UMAP-proje
 
 
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+<div class="jp-InputPrompt jp-InputArea-prompt">In [49]:</div>
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@@ -17900,7 +17880,7 @@ <h3 id="UMAP-projection">UMAP projection<a class="anchor-link" href="#UMAP-proje
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [47]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [50]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sc</span><span class="o">.</span><span class="n">plotting</span><span class="o">.</span><span class="n">umap</span><span class="p">(</span><span class="n">adata</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">'TNP2'</span><span class="p">],</span> <span class="n">projection</span><span class="o">=</span><span class="s1">'3d'</span><span class="p">,</span> <span class="n">components</span><span class="o">=</span><span class="p">[</span><span class="s1">'1,2,3'</span><span class="p">]</span> <span class="p">)</span>
@@ -18091,7 +18071,7 @@ <h3 id="Print-markers'-scores">Print markers' scores<a class="anchor-link" href=
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [48]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [51]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">markers</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span> <span class="c1">#make an empty dictionary</span>
@@ -18134,7 +18114,7 @@ <h3 id="Print-markers'-scores">Print markers' scores<a class="anchor-link" href=
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [49]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [52]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">markers_scores</span><span class="p">,</span> <span class="n">adata</span> <span class="o">=</span> <span class="n">marker_score</span><span class="p">(</span><span class="n">markers</span><span class="p">,</span> <span class="n">adata</span><span class="p">)</span>
@@ -18162,7 +18142,7 @@ <h3 id="Print-markers'-scores">Print markers' scores<a class="anchor-link" href=
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [50]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [53]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sc</span><span class="o">.</span><span class="n">plotting</span><span class="o">.</span><span class="n">umap</span><span class="p">(</span><span class="n">adata</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">markers_scores</span><span class="p">,</span> <span class="n">components</span><span class="o">=</span><span class="p">[</span><span class="s1">'1,2'</span><span class="p">],</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">'Blues'</span><span class="p">)</span>
@@ -18219,7 +18199,7 @@ <h3 id="Leiden-clustering-algorithm">Leiden clustering algorithm<a class="anchor
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [51]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [54]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#leiden clustering at various resolutions</span>
@@ -18251,7 +18231,7 @@ <h3 id="Leiden-clustering-algorithm">Leiden clustering algorithm<a class="anchor
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [52]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [55]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sc</span><span class="o">.</span><span class="n">plotting</span><span class="o">.</span><span class="n">umap</span><span class="p">(</span><span class="n">adata</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">'leiden_R1'</span><span class="p">,</span><span class="s1">'leiden_R.5'</span><span class="p">,</span><span class="s1">'leiden_R.25'</span><span class="p">,</span> <span class="s1">'leiden_R.1'</span><span class="p">],</span> <span class="n">legend_loc</span><span class="o">=</span><span class="s1">'on data'</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
@@ -18316,7 +18296,7 @@ <h3 id="Differential-Gene-expression">Differential Gene expression<a class="anch
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [53]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [56]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Perform the test on logarithmized data</span>
@@ -18371,7 +18351,7 @@ <h3 id="Differential-Gene-expression">Differential Gene expression<a class="anch
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [54]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [57]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">result</span> <span class="o">=</span> <span class="n">adata</span><span class="o">.</span><span class="n">uns</span><span class="p">[</span><span class="s1">'rank_genes_groups'</span><span class="p">]</span>
@@ -18395,7 +18375,7 @@ <h3 id="Differential-Gene-expression">Differential Gene expression<a class="anch
 <div class="jp-OutputArea jp-Cell-outputArea">
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
     
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[54]:</div>
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[57]:</div>
 
 
 
@@ -18591,7 +18571,7 @@ <h3 id="Differential-Gene-expression">Differential Gene expression<a class="anch
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [55]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [58]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">X</span><span class="p">[</span> <span class="p">[</span><span class="s1">'5_N'</span><span class="p">]</span> <span class="p">]</span>
@@ -18610,7 +18590,7 @@ <h3 id="Differential-Gene-expression">Differential Gene expression<a class="anch
 <div class="jp-OutputArea jp-Cell-outputArea">
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
     
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[55]:</div>
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[58]:</div>
 
 
 
@@ -18865,7 +18845,7 @@ <h3 id="Differential-Gene-expression">Differential Gene expression<a class="anch
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [56]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [59]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>mkdir<span class="w"> </span>-p<span class="w"> </span>results_scrna
@@ -18881,7 +18861,7 @@ <h3 id="Differential-Gene-expression">Differential Gene expression<a class="anch
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [57]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [60]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">X</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s1">'./results_scrna/DiffExpression_NoAnnotation.csv'</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">','</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
@@ -18910,7 +18890,7 @@ <h3 id="Cluster-assignment">Cluster assignment<a class="anchor-link" href="#Clus
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [58]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [61]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">new_names</span> <span class="o">=</span> <span class="p">{</span>
@@ -18952,7 +18932,7 @@ <h3 id="Cluster-assignment">Cluster assignment<a class="anchor-link" href="#Clus
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [59]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [62]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">adata</span><span class="o">.</span><span class="n">obs</span><span class="p">[</span><span class="s1">'spermatogenesis_types'</span><span class="p">]</span> <span class="o">=</span> <span class="n">rename_clusters</span><span class="p">(</span><span class="n">new_names</span><span class="p">,</span> <span class="n">adata</span><span class="o">.</span><span class="n">obs</span><span class="p">[</span><span class="s1">'leiden_R.25'</span><span class="p">])</span>
@@ -18968,7 +18948,7 @@ <h3 id="Cluster-assignment">Cluster assignment<a class="anchor-link" href="#Clus
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [60]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [63]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'figure.figsize'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">12</span><span class="p">)</span>
@@ -19210,7 +19190,7 @@ <h2 id="Data-dynamics">Data dynamics<a class="anchor-link" href="#Data-dynamics"
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [61]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [64]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">subdata</span> <span class="o">=</span> <span class="n">adata</span><span class="p">[</span> <span class="p">[</span><span class="n">i</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'Endothelial'</span><span class="p">,</span><span class="s1">'Macroph'</span><span class="p">,</span><span class="s1">'Peritubular_Myoid'</span><span class="p">,</span><span class="s1">'Smooth_Muscle'</span><span class="p">,</span><span class="s1">'Leydig'</span><span class="p">]</span> 
@@ -19227,7 +19207,7 @@ <h2 id="Data-dynamics">Data dynamics<a class="anchor-link" href="#Data-dynamics"
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [62]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [65]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">subdata</span><span class="o">.</span><span class="n">uns</span><span class="p">[</span><span class="s1">'iroot'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">flatnonzero</span><span class="p">(</span><span class="n">subdata</span><span class="o">.</span><span class="n">obs</span><span class="p">[</span><span class="s1">'spermatogenesis_types'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'SpermatogoniaA'</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
@@ -19243,7 +19223,7 @@ <h2 id="Data-dynamics">Data dynamics<a class="anchor-link" href="#Data-dynamics"
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [63]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [66]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sc</span><span class="o">.</span><span class="n">tl</span><span class="o">.</span><span class="n">dpt</span><span class="p">(</span><span class="n">subdata</span><span class="p">,</span> <span class="n">n_dcs</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
@@ -19280,7 +19260,7 @@ <h2 id="Data-dynamics">Data dynamics<a class="anchor-link" href="#Data-dynamics"
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [64]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [67]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'figure.figsize'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">8</span><span class="p">)</span>
@@ -19329,7 +19309,7 @@ <h2 id="Data-dynamics">Data dynamics<a class="anchor-link" href="#Data-dynamics"
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [65]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [68]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'figure.figsize'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">8</span><span class="p">)</span>
@@ -19357,9 +19337,9 @@ <h2 id="Data-dynamics">Data dynamics<a class="anchor-link" href="#Data-dynamics"
 
 
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@@ -19407,7 +19387,7 @@ <h2 id="Data-dynamics">Data dynamics<a class="anchor-link" href="#Data-dynamics"
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [66]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [69]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sc</span><span class="o">.</span><span class="n">pl</span><span class="o">.</span><span class="n">violin</span><span class="p">(</span><span class="n">subdata</span><span class="p">,</span> <span class="n">keys</span><span class="o">=</span><span class="s1">'dpt_pseudotime'</span><span class="p">,</span> <span class="n">groupby</span><span class="o">=</span><span class="s1">'spermatogenesis_types'</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">90</span><span class="p">,</span>
@@ -19460,7 +19440,7 @@ <h2 id="Data-dynamics">Data dynamics<a class="anchor-link" href="#Data-dynamics"
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [67]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [70]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">adata</span><span class="o">.</span><span class="n">obs</span><span class="p">[</span><span class="s1">'dpt_pseudotime'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">adata</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
@@ -19489,7 +19469,7 @@ <h2 id="Data-dynamics">Data dynamics<a class="anchor-link" href="#Data-dynamics"
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [68]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [71]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#remove subdata as it is no longer used</span>
@@ -19510,13 +19490,13 @@ <h2 id="Data-dynamics">Data dynamics<a class="anchor-link" href="#Data-dynamics"
 <div class="jp-OutputArea jp-Cell-outputArea">
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
     
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[68]:</div>
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[71]:</div>
 
 
 
 
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
-<pre>116747</pre>
+<pre>144124</pre>
 </div>
 
 </div>
@@ -19554,7 +19534,7 @@ <h3 id="Reference-based-annotation">Reference-based annotation<a class="anchor-l
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [69]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [72]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">crypto</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">read_h5ad</span><span class="p">(</span><span class="s1">'../Data/scrna_data/crypto_azoospermia.h5ad'</span><span class="p">)</span>
@@ -19570,7 +19550,7 @@ <h3 id="Reference-based-annotation">Reference-based annotation<a class="anchor-l
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [70]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [73]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#use only genes present in both datasets</span>
@@ -19601,7 +19581,7 @@ <h3 id="Reference-based-annotation">Reference-based annotation<a class="anchor-l
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [71]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [74]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sc</span><span class="o">.</span><span class="n">tl</span><span class="o">.</span><span class="n">ingest</span><span class="p">(</span><span class="n">crypto</span><span class="p">,</span> <span class="n">adata</span><span class="p">,</span> <span class="n">obs</span><span class="o">=</span><span class="p">[</span><span class="s1">'spermatogenesis_types'</span><span class="p">,</span><span class="s1">'dpt_pseudotime'</span><span class="p">])</span>
@@ -19629,7 +19609,7 @@ <h3 id="Reference-based-annotation">Reference-based annotation<a class="anchor-l
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [72]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [75]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">crypto</span><span class="o">.</span><span class="n">uns</span><span class="p">[</span><span class="s1">'spermatogenesis_types_colors'</span><span class="p">]</span> <span class="o">=</span> <span class="n">adata</span><span class="o">.</span><span class="n">uns</span><span class="p">[</span><span class="s1">'spermatogenesis_types_colors'</span><span class="p">]</span>  <span class="c1"># fix colors</span>
@@ -19657,7 +19637,7 @@ <h3 id="Reference-based-annotation">Reference-based annotation<a class="anchor-l
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [73]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [76]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">crypto</span><span class="o">.</span><span class="n">obs</span><span class="p">[</span><span class="s1">'dpt_pseudotime'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">crypto</span><span class="o">.</span><span class="n">obs</span><span class="p">[</span><span class="s1">'dpt_pseudotime'</span><span class="p">])</span>
@@ -19685,7 +19665,7 @@ <h3 id="Reference-based-annotation">Reference-based annotation<a class="anchor-l
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [74]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [77]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sc</span><span class="o">.</span><span class="n">pl</span><span class="o">.</span><span class="n">umap</span><span class="p">(</span><span class="n">crypto</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">'spermatogenesis_types'</span><span class="p">,</span> <span class="s1">'dpt_pseudotime'</span><span class="p">],</span> <span class="n">wspace</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
@@ -19711,7 +19691,7 @@ <h3 id="Reference-based-annotation">Reference-based annotation<a class="anchor-l
 
 
 <div class="jp-RenderedImage jp-OutputArea-output ">
-<img 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@@ -19738,7 +19718,7 @@ <h3 id="Reference-based-annotation">Reference-based annotation<a class="anchor-l
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [75]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [78]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">merged</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">AnnData</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">adata</span><span class="p">,</span> <span class="n">crypto</span><span class="p">,</span> <span class="n">batch_key</span><span class="o">=</span><span class="s1">'condition'</span><span class="p">,</span> <span class="n">batch_categories</span><span class="o">=</span><span class="p">[</span><span class="s1">'Healthy'</span><span class="p">,</span><span class="s1">'Crypto'</span><span class="p">])</span>
@@ -19754,7 +19734,7 @@ <h3 id="Reference-based-annotation">Reference-based annotation<a class="anchor-l
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [76]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [79]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">merged</span><span class="o">.</span><span class="n">uns</span><span class="p">[</span><span class="s1">'spermatogenesis_types_colors'</span><span class="p">]</span> <span class="o">=</span> <span class="n">adata</span><span class="o">.</span><span class="n">uns</span><span class="p">[</span><span class="s1">'spermatogenesis_types_colors'</span><span class="p">]</span>  <span class="c1"># fix category colors</span>
@@ -19782,7 +19762,7 @@ <h3 id="Reference-based-annotation">Reference-based annotation<a class="anchor-l
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [77]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [80]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sc</span><span class="o">.</span><span class="n">pl</span><span class="o">.</span><span class="n">umap</span><span class="p">(</span><span class="n">merged</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">'condition'</span><span class="p">,</span><span class="s1">'spermatogenesis_types'</span><span class="p">,</span><span class="s1">'dpt_pseudotime'</span><span class="p">],</span> <span class="n">wspace</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
@@ -19807,7 +19787,7 @@ <h3 id="Reference-based-annotation">Reference-based annotation<a class="anchor-l
 
 
 <div class="jp-RenderedImage jp-OutputArea-output ">
-<img 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" 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" class="
 ">
 </div>
 
@@ -19834,7 +19814,7 @@ <h3 id="Reference-based-annotation">Reference-based annotation<a class="anchor-l
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [78]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [81]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">8</span><span class="p">),</span> <span class="n">gridspec_kw</span><span class="o">=</span><span class="p">{</span><span class="s1">'wspace'</span><span class="p">:</span><span class="mf">0.5</span><span class="p">})</span>
@@ -19862,7 +19842,7 @@ <h3 id="Reference-based-annotation">Reference-based annotation<a class="anchor-l
 
 
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" 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" class="
 ">
 </div>
 
@@ -19889,7 +19869,7 @@ <h3 id="Reference-based-annotation">Reference-based annotation<a class="anchor-l
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [79]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [82]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="k">del</span> <span class="n">adata</span><span class="p">,</span> <span class="n">crypto</span>
@@ -19909,13 +19889,13 @@ <h3 id="Reference-based-annotation">Reference-based annotation<a class="anchor-l
 <div class="jp-OutputArea jp-Cell-outputArea">
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
     
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[79]:</div>
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[82]:</div>
 
 
 
 
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
-<pre>28105</pre>
+<pre>28125</pre>
 </div>
 
 </div>
@@ -19941,7 +19921,7 @@ <h3 id="Reference-based-annotation">Reference-based annotation<a class="anchor-l
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [80]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [83]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">merged</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">'merged.h5ad'</span><span class="p">)</span>
@@ -19977,7 +19957,7 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [81]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [84]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">merged</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="s1">'merged.h5ad'</span><span class="p">)</span> <span class="c1">#read the data</span>
@@ -20005,7 +19985,7 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [92]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [85]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">MT_RP</span> <span class="o">=</span> <span class="p">[(</span><span class="s1">'MT-'</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">i</span><span class="p">)</span><span class="ow">and</span><span class="p">(</span><span class="n">re</span><span class="o">.</span><span class="n">match</span><span class="p">(</span><span class="s1">'^RP[A-Za-z]'</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">merged</span><span class="o">.</span><span class="n">var_names</span><span class="p">]</span> <span class="c1">#a vector with True and False to find mitocondrial/ribosomal genes</span>
@@ -20034,7 +20014,7 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [93]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [86]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">merged</span><span class="o">.</span><span class="n">X</span> <span class="o">=</span> <span class="n">merged</span><span class="o">.</span><span class="n">layers</span><span class="p">[</span><span class="s1">'raw_counts'</span><span class="p">]</span><span class="o">.</span><span class="n">todense</span><span class="p">()</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
@@ -20050,7 +20030,7 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [94]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [87]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">merged</span><span class="p">[</span><span class="n">merged</span><span class="o">.</span><span class="n">obs</span><span class="p">[</span><span class="s1">'condition'</span><span class="p">]</span><span class="o">==</span><span class="s1">'Healthy'</span><span class="p">]</span><span class="o">.</span><span class="n">obs</span><span class="p">[</span><span class="s1">'spermatogenesis_types'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span> <span class="c1">#healthy sample</span>
@@ -20069,7 +20049,7 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 <div class="jp-OutputArea jp-Cell-outputArea">
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
     
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[94]:</div>
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[87]:</div>
 
 
 
@@ -20112,7 +20092,7 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [95]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [88]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">merged</span><span class="p">[</span><span class="n">merged</span><span class="o">.</span><span class="n">obs</span><span class="p">[</span><span class="s1">'condition'</span><span class="p">]</span><span class="o">==</span><span class="s1">'Crypto'</span><span class="p">]</span><span class="o">.</span><span class="n">obs</span><span class="p">[</span><span class="s1">'spermatogenesis_types'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
@@ -20131,7 +20111,7 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 <div class="jp-OutputArea jp-Cell-outputArea">
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
     
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[95]:</div>
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[88]:</div>
 
 
 
@@ -20140,13 +20120,14 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 <pre>SpermatogoniaA       652
 Round_Spermatids     256
 Endothelial          251
-Peritubular_Myoid    228
+Peritubular_Myoid    229
 Leydig               214
-SpermatocitesII      178
+SpermatocitesII      176
 SpermatogoniaB       168
 Elong_Spermatids     102
 SpermatocitesI        61
 Smooth_Muscle         37
+Macroph                1
 Name: spermatogenesis_types, dtype: int64</pre>
 </div>
 
@@ -20173,7 +20154,7 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [96]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [89]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sc</span><span class="o">.</span><span class="n">pp</span><span class="o">.</span><span class="n">log1p</span><span class="p">(</span><span class="n">merged</span><span class="p">)</span>
@@ -20203,7 +20184,7 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [97]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [90]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">matrix</span> <span class="o">=</span> <span class="n">merged</span><span class="p">[:,</span><span class="n">merged</span><span class="o">.</span><span class="n">var</span><span class="p">[</span><span class="s1">'highly_variable'</span><span class="p">]]</span><span class="o">.</span><span class="n">X</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
@@ -20219,7 +20200,7 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [98]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [91]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">matrix_bulk</span><span class="p">,</span> <span class="n">clusters</span><span class="p">,</span> <span class="n">conditions</span> <span class="o">=</span> <span class="n">pseudobulk_matrix</span><span class="p">(</span><span class="n">adata</span><span class="o">=</span><span class="n">merged</span><span class="p">,</span> 
@@ -20779,7 +20760,7 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 --------Endothelial
 --------Leydig
 --------Macroph
-Only 0 cells: skipping
+Only 1 cells: skipping
 --------Peritubular_Myoid
 --------Round_Spermatids
 --------Smooth_Muscle
@@ -20961,7 +20942,7 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [99]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [92]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">matrix_bulk</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
@@ -20980,7 +20961,7 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 <div class="jp-OutputArea jp-Cell-outputArea">
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
     
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[99]:</div>
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[92]:</div>
 
 
 
@@ -21176,7 +21157,7 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [100]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [93]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">matrix_bulk</span> <span class="o">=</span> <span class="n">matrix_bulk</span><span class="o">.</span><span class="n">T</span>
@@ -21196,7 +21177,7 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 <div class="jp-OutputArea jp-Cell-outputArea">
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
     
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[100]:</div>
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[93]:</div>
 
 
 
@@ -21392,7 +21373,7 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [101]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [94]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">anndata</span> <span class="k">as</span> <span class="nn">ad</span>
@@ -21408,7 +21389,7 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [102]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [95]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">pbulk</span> <span class="o">=</span> <span class="n">ad</span><span class="o">.</span><span class="n">AnnData</span><span class="p">(</span><span class="n">matrix_bulk</span><span class="p">)</span>
@@ -21424,7 +21405,7 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [103]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [96]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">pbulk</span>
@@ -21443,7 +21424,7 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 <div class="jp-OutputArea jp-Cell-outputArea">
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
     
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[103]:</div>
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[96]:</div>
 
 
 
@@ -21475,7 +21456,7 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [104]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [97]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sc</span><span class="o">.</span><span class="n">pp</span><span class="o">.</span><span class="n">filter_genes</span><span class="p">(</span><span class="n">pbulk</span><span class="p">,</span> <span class="n">min_cells</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
@@ -21503,7 +21484,7 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [105]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [98]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">pbulk</span><span class="o">.</span><span class="n">obs</span><span class="p">[</span><span class="s1">'condition'</span><span class="p">]</span> <span class="o">=</span> <span class="n">conditions</span>
@@ -21532,7 +21513,7 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [106]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [99]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sc</span><span class="o">.</span><span class="n">pp</span><span class="o">.</span><span class="n">normalize_per_cell</span><span class="p">(</span><span class="n">pbulk</span><span class="p">)</span>
@@ -21561,7 +21542,7 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [107]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [100]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sc</span><span class="o">.</span><span class="n">pp</span><span class="o">.</span><span class="n">pca</span><span class="p">(</span><span class="n">pbulk</span><span class="p">)</span>
@@ -21577,7 +21558,7 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [108]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [101]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sc</span><span class="o">.</span><span class="n">pl</span><span class="o">.</span><span class="n">pca</span><span class="p">(</span><span class="n">pbulk</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">'condition'</span><span class="p">,</span><span class="s1">'spermatogenesis_types'</span><span class="p">],</span> <span class="n">s</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
@@ -21602,7 +21583,7 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 
 
 <div class="jp-RenderedImage jp-OutputArea-output ">
-<img 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" class="
 ">
 </div>
 
@@ -21629,7 +21610,7 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [113]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [102]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">sc</span><span class="o">.</span><span class="n">tl</span><span class="o">.</span><span class="n">rank_genes_groups</span><span class="p">(</span><span class="n">pbulk</span><span class="p">,</span> <span class="n">groupby</span><span class="o">=</span><span class="s2">"condition"</span><span class="p">,</span> <span class="n">reference</span><span class="o">=</span><span class="s1">'Healthy'</span><span class="p">,</span> <span class="n">n_genes</span><span class="o">=</span><span class="n">pbulk</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
@@ -21678,7 +21659,7 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [114]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [103]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">DEG_matrix</span> <span class="o">=</span> <span class="n">pseudobulk_extract_DEG</span><span class="p">(</span><span class="n">pbulk</span><span class="o">=</span><span class="n">pbulk</span><span class="p">,</span> <span class="n">adata</span><span class="o">=</span><span class="n">merged</span><span class="p">)</span>
@@ -21729,7 +21710,7 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [115]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [104]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">DEG_matrix</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
@@ -21748,7 +21729,7 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 <div class="jp-OutputArea jp-Cell-outputArea">
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
     
-    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[115]:</div>
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[104]:</div>
 
 
 
@@ -21786,60 +21767,60 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
     <tr>
       <th>0</th>
       <td>EEF1G</td>
-      <td>8.083860e-264</td>
-      <td>3.280161e-260</td>
-      <td>8.069817</td>
+      <td>6.363668e-264</td>
+      <td>2.582164e-260</td>
+      <td>8.071482</td>
       <td>3.659107</td>
       <td>97.345133</td>
-      <td>268.693298</td>
+      <td>269.003601</td>
       <td>50.0</td>
       <td>50.0</td>
     </tr>
     <tr>
       <th>1</th>
       <td>MRPS24</td>
-      <td>6.104508e-246</td>
-      <td>1.857754e-242</td>
-      <td>6.899997</td>
+      <td>1.586329e-244</td>
+      <td>4.827596e-241</td>
+      <td>6.896244</td>
       <td>2.184827</td>
       <td>80.763857</td>
-      <td>119.427994</td>
+      <td>119.117706</td>
       <td>50.0</td>
       <td>50.0</td>
     </tr>
     <tr>
       <th>2</th>
       <td>GABARAP</td>
-      <td>2.604783e-268</td>
-      <td>1.585401e-264</td>
-      <td>5.501691</td>
+      <td>2.589673e-269</td>
+      <td>1.576204e-265</td>
+      <td>5.500985</td>
       <td>14.155640</td>
       <td>97.438286</td>
-      <td>45.307903</td>
+      <td>45.285748</td>
       <td>50.0</td>
       <td>50.0</td>
     </tr>
     <tr>
       <th>3</th>
       <td>BSCL2</td>
-      <td>7.177415e-160</td>
-      <td>7.942789e-157</td>
-      <td>4.967065</td>
+      <td>2.402479e-161</td>
+      <td>2.658670e-158</td>
+      <td>4.969571</td>
       <td>8.456367</td>
       <td>85.700978</td>
-      <td>31.277761</td>
+      <td>31.332123</td>
       <td>50.0</td>
       <td>50.0</td>
     </tr>
     <tr>
       <th>4</th>
       <td>RNASEK</td>
-      <td>3.207617e-166</td>
-      <td>3.904632e-163</td>
-      <td>4.682927</td>
+      <td>1.200985e-166</td>
+      <td>1.624398e-163</td>
+      <td>4.696043</td>
       <td>3.708037</td>
       <td>72.100605</td>
-      <td>25.686298</td>
+      <td>25.920876</td>
       <td>50.0</td>
       <td>50.0</td>
     </tr>
@@ -21872,7 +21853,7 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [117]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [105]:</div>
 <div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
      <div class="CodeMirror cm-s-jupyter">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">pseudobulk_volcano</span><span class="p">(</span><span class="n">DEG_matrix</span><span class="p">,</span> <span class="n">logfold_threshold</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">logpval_threshold</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">plot_size</span><span class="o">=</span><span class="p">(</span><span class="mi">800</span><span class="p">,</span><span class="mi">800</span><span class="p">))</span>
@@ -21896,9 +21877,9 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
 
 
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@@ -21946,10 +21927,10 @@ <h3 id="Cross-dataset-differential-expression">Cross-dataset differential expres
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [118]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [106]:</div>
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      <div class="CodeMirror cm-s-jupyter">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">X</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s1">'./pseudobulk_markers.csv'</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">'</span><span class="se">\t</span><span class="s1">'</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
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diff --git a/nb/vcf.html b/nb/vcf.html
index e7c2465..31a7810 100644
--- a/nb/vcf.html
+++ b/nb/vcf.html
@@ -3940,7 +3940,7 @@ <h1 id="comparison-of-vcf-files">Comparison of VCF files<a class="anchor-link" h
 <p>In order to count the genotypes for the matching positions we can use once again <code>bcftools filter</code>  command. Only that this time we will filter based on the genotype columns for the two samples (GT[0] and GT[1]). GT[0] repesents the genotype for the first sample in the merge command and GT[1] is the genotype for the second sample in the merge command.</p>
 <ul>
 <li>AR - heterozygous</li>
-<li>AA - heterozygous alternate</li>
+<li>AA - homozygous alternate</li>
 </ul>
 <p>For example, the filter <code>GT[0]="AR" &amp;&amp; GT[1]="AR"</code> will keep only position in which both samples have a heterozygous genotype.
 A quick way to count how many positions are in the VCF file is by suing the simple bash command <code>grep</code>. This will count all the lines in the VCF files that do not start with a <code>#</code>, thus all the data lines.</p>
diff --git a/search/search_index.json b/search/search_index.json
index 3dbcfeb..eb8dc7a 100644
--- a/search/search_index.json
+++ b/search/search_index.json
@@ -1 +1 @@
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Apply bioinformatics tools within the selected application areas and reflect on the results, formulating your own conclusion in the proposed tasks \ud83d\udd70 Total Time Estimation: 20 hours \ud83d\udcc1 Supporting Materials: jupyter notebooks for interactive coding lecture slides from the instructor You can find the links to the material in the table at the bottom of this page. \ud83d\udd8d Course authors and instructors: Mikkel H Schierup Stig U Andersen Samuele Soraggi Peter S Porsborg Adri\u00e1n G Repoll\u00e9s \ud83d\udccb License: Tutorial Content is licensed under Creative Commons Attribution 4.0 International License \ud83d\udcdd Citation: If you use any of this material for your research, please cite this course with the DOI below, and acknowledge the Health Data Science Sandbox project of the Novo Nordisk Foundation (grant number NNF20OC0063268). It is of great help to support the project. \ud83d\udce7 Contact: Samuele Soraggi (samuele at birc.au.dk). 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Biological introduction \u00b6 White clover ( Trifolium repens ) is an allotetraploid . This means that it contains genomes originating from two different species within the same nucleus. Normally, white clover is an outbreeding species, but a self-compatible line was used for sequencing the white clover genome . This line is designated S10 in your data, indicating that this is the 10 th self-fertilized generation. In addition, you have data from a wild clover accession (ecotype) called Tienshan ( Ti ), which is collected from Chinese mountains and is adapted to alpine conditions. Figure: Characterisation of the white clover population. T.Repens is a hybrid of T.Occidentale and T.Pallescens Exercise guide \u00b6 Prepare the tools and data \u00b6 Install IGV on your computer from here . This is a genome browser you will use to look at some files. Create an account at usegalaxy.org and log into galaxy. Find the course data by going to this web address and by clicking on Import this history (top left corner of the page). Note: If usegalaxy.org has availability problems, you can use the other server https://usegalaxy.eu/ and get the data at this link . You might need to create an account there as well. Data \u00b6 You will be working with two types of sequencing data. The first is PacBio Hifi reads , which are long and accurate. You can find them under Hifi_reads_white_clover.fastq. The second type is Illumina RNA-seq reads , which are short and accurate and should be aligned using a spliced aligner, such as STAR . There are 24 of these files, 12 for each of the two genotypes mentioned before. The files are named [genotype]_[treatment]_[replicate].fastq . Treatment 1 is before and treatment 2 is after exposure to frost, respectively. In addition to the sequencing data, there are also three reference files: one for homologous contig 1 (referencing T. occidentale-derived subgenome), one for contig 2 (T. pallescens-derived subgenome) and one for both Contigs 1 and 2. The reference files are in fasta format. The file white_clover_genes.gtf contains the gene annotations for the two contigs. Workflow illustration \u00b6 Through Galaxy , we build a workflow applying tools to the data. We will look at the quality of the raw reads for both PacBio HiFi and Illumina RNA-seq reads. Afterwards, we align to references, using two different tools for the two types of data. Finally, we will look at the alignments on a genome browser. We will work then on a computing cluster through uCloud to analyze the aligned data in some of the upcoming lessons of the course. Galaxy Workflow \u00b6 When you import the files, what you actually import is a History - a sequence of files and softwares applied on the data. You can see the history on the right side of your usegalaxy.org webpage with green panels. Here, we only have the starting data, and you will build the rest of your history through various tools. On the left side of the screen, you have a menu with various available tools organized by category. All those softwares are also available on a classical computing command line (we will try those as well). Quality control \u00b6 1 Run FastQC on the PacBio Hifi reads and on two of the Illumina RNA-seq libraries. FastQC does quality control of the raw sequence data, providing an overview of the data which can help identify if there are any problems that should be addressed before further analysis. In the tool menu, click on FASTQ quality control --> FASTQC read quality reports . You will see a window with tool parameters : for the first option (raw read data from history), choose multiple files and select Hifi_reads_white_clover.fastq plus other fastq files you want to see the quality of (example in figure below). Then click on the button Run Tool . You will notice that some new elements are added to your history. Part of them are FastQC producing a text file, while others are FastQC producing a webpage report. The reports are ready when coloured in green: click on the eye symbol of a history item to read a report. 2 FastQC provides a report for each sample. To have a better comparison between the Hifi and Illumina data, we would combine the three FastQC reports into one using MultiQC . Choose the MultiQC tool from FASTQ quality control --> MultiQC aggregate results from ... . In the options, select FastQC as the used tool for the logsselect FastQC as the tool used to generate the output, and then select the items of FastQC of your history producing RawData (Figure below). In this way, you build a pipeline from the previous reports to the new tool you are using. Now click on Run Tool . The tool will be now running in your history. When it is done, click on the eye symbol to see the report. select the three \u201cRawData\u201d outputs generated by FastQC. Visualize the Webpage generated by MultiQC. Hint: You can find a \u201cHelp\u201d button that offers additional information about the plots for each panel. Questions: Focus on the following panels: \u201cPer base sequence quality\u201d, \u201cPer sequence quality scores\u201d\u2026. (\u201cPer base sequence content\u201d always gives a FAIL for RNA-seq data). What do you notice with respect to the sequence quality scores? And are there any other quality issues worth noting? Hifi Data Alignment \u00b6 3 Map the PacBio Hifi reads ( Hifi_reads_white_clover.fastq ) to the white clover reference sequence (Contigs 1 and 2) using minimap2 (Map with minimap2). Find Genomics Analysis --> Mapping --> Map with minimap2 . In the options, do not leave Use a built-in genome index , but select the option for having a genome from history. Choose then DNA_Contig1_2.fasta as the reference sequence. Under the profile with preset options, choose PacBio/Oxford Nanopore read to reference mapping (map-pb) . Then click on Run Tool . 4 Run the same alignment, but choose as preset options Long assembly to reference mapping. Divergence is far below 20% (asm20) . Rename then the two alignments using the edit function ( pen symbol in the history). Use for example names Contig1_2_mappb and Contig1_2_asm20 , to distinguish alignment options and reference genome. 5 The aligned genomes are not sorted by coordinates. Sort the alignments using Samtools sort (Find the tool under Genomic file manipulation --> SAM/BAM --> Samtools sort ... ). In the options, choose the two aligned files with multiple selection. Then click on Run Tool . 6 Download the two alignments to your computer. To do so, click on the disk symbol of each file in your history, and for each download both the Dataset (alignments in bam format) and their index files (in bai format). Download as well the reference genome in fasta format ( DNA_Contig1_2.fasta from the history). 7 Open IGV on your computer. Load the reference first: go on Genome --> Load genome from file and select the fasta file you downloaded. Then load the two alignments: go on File --> Load from file and select the bam and bai files you downloaded, together. You can now visualize the alignments. Questions: Look at the alignments in IGV. What do you notice about the alignments? What is the difference between the two alignments? Do you think one of them is better than the other? Choose on of the two alignments for the next steps. 8 Repeat the alignment with Minimap2 (using the chosen alignment option from the question above) and the sorting, but using the reference genomes for Contig 1 and for Contig 2 searaately. Note: you can run all at once by choosing multiple reference genomes in the options! Questions: Download the two references for Contig 1 and 2, and the two sorted alignments. Load the references from the menu `Genomes` in IGV, and then open the two alignments using the menu `File` in IGV. - Why do you see fluctuations in coverage and large regions without any apparent subgenome SNPs? - What are the major differences between the stats for the reads mapped to Contigs1&2 versus contig1 and contig2? What is your interpretation of the differences? RNA Data Alignment \u00b6 9 First, group the 24 RNA-seq libraries into two dataset lists, one list of pairs for S10 libraries and another for Tienshan libraries. so we can work with multiple samples simultaneously. You can do this by selecting the libraries for each genotype and choosing Build Lists of Dataset Pairs . The grouping suggested by Galaxy is wrong, because the paired reads are paired according to _1 and _2 in their names. Change those two with R1 and R2 , click on Unpair all , and then click on the suggested corrected pairs with the buttons Pair these datasets . Your sequences will be substituted by two elements in your history. Here we chose for example to name them S10 and TI . 10 Do alignment of the RNA-seq lists of raw files to the reference DNA_Contig1_2.fasta using STAR . Go to Genomics analysis --> RNA-seq --> RNA STAR Gapped-read mapper for RNA-seq data . In the options use: as data, the parameter Paired-end (as collection) , and then choose one of the two collections (you cannot run them all at once) as reference, DNA_Contig1_2.fasta , with Length of SA pre-indexing string equal to 9 as index with gene-model, use white_clover_annotations.gtf as output, Per gene read counts (GeneCounts) . 11 Use MultiQC to see the quality of the output. The alignment of STAR produces log files which can be used for quality reports. Go on Genomic File Manipulation --> MultiQC . In the options select the tool STAR . Then Insert STAR output , as type of output the Log , and choose the two logs listing collections of STAR alignments. Then click on Run Tool . View the report to see the alignment statistics. Final note: `Galaxy can also be used to create an automatic workflow that will map the data. This workflow can be useful when running multiple samples. You can generate a workflow from the analysis already completed in a history, by going to Settings \u2192 Extract workflow. You can also create a workflow from scratch using the Workflow editor.","title":"1.Galaxy Exercise"},{"location":"galaxy.html#quality-control-and-alignment-of-raw-reads","text":"This first exercise will be executed on Galaxy , an interactive platform to run bioinformatics workflows. We will replicate this lesson with computer code later in this course. Galaxy has the possibility of working with a free account.","title":"Quality Control and Alignment of raw reads"},{"location":"galaxy.html#biological-introduction","text":"White clover ( Trifolium repens ) is an allotetraploid . This means that it contains genomes originating from two different species within the same nucleus. Normally, white clover is an outbreeding species, but a self-compatible line was used for sequencing the white clover genome . This line is designated S10 in your data, indicating that this is the 10 th self-fertilized generation. In addition, you have data from a wild clover accession (ecotype) called Tienshan ( Ti ), which is collected from Chinese mountains and is adapted to alpine conditions. Figure: Characterisation of the white clover population. T.Repens is a hybrid of T.Occidentale and T.Pallescens","title":"Biological introduction"},{"location":"galaxy.html#exercise-guide","text":"","title":"Exercise guide"},{"location":"galaxy.html#prepare-the-tools-and-data","text":"Install IGV on your computer from here . This is a genome browser you will use to look at some files. Create an account at usegalaxy.org and log into galaxy. Find the course data by going to this web address and by clicking on Import this history (top left corner of the page). Note: If usegalaxy.org has availability problems, you can use the other server https://usegalaxy.eu/ and get the data at this link . You might need to create an account there as well.","title":"Prepare the tools and data"},{"location":"galaxy.html#data","text":"You will be working with two types of sequencing data. The first is PacBio Hifi reads , which are long and accurate. You can find them under Hifi_reads_white_clover.fastq. The second type is Illumina RNA-seq reads , which are short and accurate and should be aligned using a spliced aligner, such as STAR . There are 24 of these files, 12 for each of the two genotypes mentioned before. The files are named [genotype]_[treatment]_[replicate].fastq . Treatment 1 is before and treatment 2 is after exposure to frost, respectively. In addition to the sequencing data, there are also three reference files: one for homologous contig 1 (referencing T. occidentale-derived subgenome), one for contig 2 (T. pallescens-derived subgenome) and one for both Contigs 1 and 2. The reference files are in fasta format. The file white_clover_genes.gtf contains the gene annotations for the two contigs.","title":"Data"},{"location":"galaxy.html#workflow-illustration","text":"Through Galaxy , we build a workflow applying tools to the data. We will look at the quality of the raw reads for both PacBio HiFi and Illumina RNA-seq reads. Afterwards, we align to references, using two different tools for the two types of data. Finally, we will look at the alignments on a genome browser. We will work then on a computing cluster through uCloud to analyze the aligned data in some of the upcoming lessons of the course.","title":"Workflow illustration"},{"location":"galaxy.html#galaxy-workflow","text":"When you import the files, what you actually import is a History - a sequence of files and softwares applied on the data. You can see the history on the right side of your usegalaxy.org webpage with green panels. Here, we only have the starting data, and you will build the rest of your history through various tools. On the left side of the screen, you have a menu with various available tools organized by category. All those softwares are also available on a classical computing command line (we will try those as well).","title":"Galaxy Workflow"},{"location":"galaxy.html#quality-control","text":"1 Run FastQC on the PacBio Hifi reads and on two of the Illumina RNA-seq libraries. FastQC does quality control of the raw sequence data, providing an overview of the data which can help identify if there are any problems that should be addressed before further analysis. In the tool menu, click on FASTQ quality control --> FASTQC read quality reports . You will see a window with tool parameters : for the first option (raw read data from history), choose multiple files and select Hifi_reads_white_clover.fastq plus other fastq files you want to see the quality of (example in figure below). Then click on the button Run Tool . You will notice that some new elements are added to your history. Part of them are FastQC producing a text file, while others are FastQC producing a webpage report. The reports are ready when coloured in green: click on the eye symbol of a history item to read a report. 2 FastQC provides a report for each sample. To have a better comparison between the Hifi and Illumina data, we would combine the three FastQC reports into one using MultiQC . Choose the MultiQC tool from FASTQ quality control --> MultiQC aggregate results from ... . In the options, select FastQC as the used tool for the logsselect FastQC as the tool used to generate the output, and then select the items of FastQC of your history producing RawData (Figure below). In this way, you build a pipeline from the previous reports to the new tool you are using. Now click on Run Tool . The tool will be now running in your history. When it is done, click on the eye symbol to see the report. select the three \u201cRawData\u201d outputs generated by FastQC. Visualize the Webpage generated by MultiQC. Hint: You can find a \u201cHelp\u201d button that offers additional information about the plots for each panel. Questions: Focus on the following panels: \u201cPer base sequence quality\u201d, \u201cPer sequence quality scores\u201d\u2026. (\u201cPer base sequence content\u201d always gives a FAIL for RNA-seq data). What do you notice with respect to the sequence quality scores? And are there any other quality issues worth noting?","title":"Quality control"},{"location":"galaxy.html#hifi-data-alignment","text":"3 Map the PacBio Hifi reads ( Hifi_reads_white_clover.fastq ) to the white clover reference sequence (Contigs 1 and 2) using minimap2 (Map with minimap2). Find Genomics Analysis --> Mapping --> Map with minimap2 . In the options, do not leave Use a built-in genome index , but select the option for having a genome from history. Choose then DNA_Contig1_2.fasta as the reference sequence. Under the profile with preset options, choose PacBio/Oxford Nanopore read to reference mapping (map-pb) . Then click on Run Tool . 4 Run the same alignment, but choose as preset options Long assembly to reference mapping. Divergence is far below 20% (asm20) . Rename then the two alignments using the edit function ( pen symbol in the history). Use for example names Contig1_2_mappb and Contig1_2_asm20 , to distinguish alignment options and reference genome. 5 The aligned genomes are not sorted by coordinates. Sort the alignments using Samtools sort (Find the tool under Genomic file manipulation --> SAM/BAM --> Samtools sort ... ). In the options, choose the two aligned files with multiple selection. Then click on Run Tool . 6 Download the two alignments to your computer. To do so, click on the disk symbol of each file in your history, and for each download both the Dataset (alignments in bam format) and their index files (in bai format). Download as well the reference genome in fasta format ( DNA_Contig1_2.fasta from the history). 7 Open IGV on your computer. Load the reference first: go on Genome --> Load genome from file and select the fasta file you downloaded. Then load the two alignments: go on File --> Load from file and select the bam and bai files you downloaded, together. You can now visualize the alignments. Questions: Look at the alignments in IGV. What do you notice about the alignments? What is the difference between the two alignments? Do you think one of them is better than the other? Choose on of the two alignments for the next steps. 8 Repeat the alignment with Minimap2 (using the chosen alignment option from the question above) and the sorting, but using the reference genomes for Contig 1 and for Contig 2 searaately. Note: you can run all at once by choosing multiple reference genomes in the options! Questions: Download the two references for Contig 1 and 2, and the two sorted alignments. Load the references from the menu `Genomes` in IGV, and then open the two alignments using the menu `File` in IGV. - Why do you see fluctuations in coverage and large regions without any apparent subgenome SNPs? - What are the major differences between the stats for the reads mapped to Contigs1&2 versus contig1 and contig2? What is your interpretation of the differences?","title":"Hifi Data Alignment"},{"location":"galaxy.html#rna-data-alignment","text":"9 First, group the 24 RNA-seq libraries into two dataset lists, one list of pairs for S10 libraries and another for Tienshan libraries. so we can work with multiple samples simultaneously. You can do this by selecting the libraries for each genotype and choosing Build Lists of Dataset Pairs . The grouping suggested by Galaxy is wrong, because the paired reads are paired according to _1 and _2 in their names. Change those two with R1 and R2 , click on Unpair all , and then click on the suggested corrected pairs with the buttons Pair these datasets . Your sequences will be substituted by two elements in your history. Here we chose for example to name them S10 and TI . 10 Do alignment of the RNA-seq lists of raw files to the reference DNA_Contig1_2.fasta using STAR . Go to Genomics analysis --> RNA-seq --> RNA STAR Gapped-read mapper for RNA-seq data . In the options use: as data, the parameter Paired-end (as collection) , and then choose one of the two collections (you cannot run them all at once) as reference, DNA_Contig1_2.fasta , with Length of SA pre-indexing string equal to 9 as index with gene-model, use white_clover_annotations.gtf as output, Per gene read counts (GeneCounts) . 11 Use MultiQC to see the quality of the output. The alignment of STAR produces log files which can be used for quality reports. Go on Genomic File Manipulation --> MultiQC . In the options select the tool STAR . Then Insert STAR output , as type of output the Log , and choose the two logs listing collections of STAR alignments. Then click on Run Tool . View the report to see the alignment statistics. Final note: `Galaxy can also be used to create an automatic workflow that will map the data. This workflow can be useful when running multiple samples. You can generate a workflow from the analysis already completed in a history, by going to Settings \u2192 Extract workflow. You can also create a workflow from scratch using the Workflow editor.","title":"RNA Data Alignment"},{"location":"instructions.html","text":"Instructions \u00b6 You have various options to run the course material. You can execute it on a supercomputing system or on your own computer. Access the material on uCloud \u00b6 Ucloud is an interactive online platform from the University of Southern Danmark eScience center that allows users to execute softwares and computer code directly from their browser. If you are enrolled in a danish university or hospital, you can access the platform for free and try the course. Please follow these instructions to access uCloud and get access to the course: To access uCloud for the first time, go to the ucloud webpage and log in by pressing on the green button, choosing your institution, and writing your institutional credentials. If you have a mail address from a danish institution or university, you should be able to login with your institutional credentials. If you are participating in the NGS Summer Course 2023 in Aarhus, click AFTER logging in on this link . This will add you to a project on uCloud, where we have data and extra computing credit for the course. You should see a message on your browser where you have to accept the invitation to the project. Click on the uCloud symbol on the top-left corner of the uCloud webpage. You should see a dashboard window similar to the one below. The dashboard is a home page you will always see when opening uCloud. You can use the menu on the left to navigate to different sections of uCloud (File explorer, applications, etc.) On top of the uCloud interface you have My Workspace . That is your private space where you have some free hundreds of CPU hours and GBs of memory to run different applications (that you can see by using the menu Apps on the left). Click on Apps and find the application called Genomics Courses . Here choose the course Intro to NGS data , and use the other options to choose, for example, how many resources you want to use (we suggest at least 8 vCPUs) Run the docker container \u00b6 With Docker , you can create containers that will work in the same way on all the machines. The results of the course are exactly reproducible if you use such a Docker container. We created a container for the course that already contains packages and course material installed. You need to download Docker Desktop at this link . We suggest you have at least 8GB of RAM and 20GB of hard disk space. When Docker Desktop is installed, you can use the command line to retrieve the container from the web: docker pull samuelesoraggi/ngs-summer-aarhus:2022.08.01 You can now execute the container. You will see a web address in the output of the command line: you need to copy that into your web browser to see jupyterlab . docker run --rm -it -p 8888:8888 samuelesoraggi/ngs-summer-aarhus:2022.08.01","title":"Instructions"},{"location":"instructions.html#instructions","text":"You have various options to run the course material. You can execute it on a supercomputing system or on your own computer.","title":"Instructions"},{"location":"instructions.html#access-the-material-on-ucloud","text":"Ucloud is an interactive online platform from the University of Southern Danmark eScience center that allows users to execute softwares and computer code directly from their browser. If you are enrolled in a danish university or hospital, you can access the platform for free and try the course. Please follow these instructions to access uCloud and get access to the course: To access uCloud for the first time, go to the ucloud webpage and log in by pressing on the green button, choosing your institution, and writing your institutional credentials. If you have a mail address from a danish institution or university, you should be able to login with your institutional credentials. If you are participating in the NGS Summer Course 2023 in Aarhus, click AFTER logging in on this link . This will add you to a project on uCloud, where we have data and extra computing credit for the course. You should see a message on your browser where you have to accept the invitation to the project. Click on the uCloud symbol on the top-left corner of the uCloud webpage. You should see a dashboard window similar to the one below. The dashboard is a home page you will always see when opening uCloud. You can use the menu on the left to navigate to different sections of uCloud (File explorer, applications, etc.) On top of the uCloud interface you have My Workspace . That is your private space where you have some free hundreds of CPU hours and GBs of memory to run different applications (that you can see by using the menu Apps on the left). Click on Apps and find the application called Genomics Courses . Here choose the course Intro to NGS data , and use the other options to choose, for example, how many resources you want to use (we suggest at least 8 vCPUs)","title":"Access the material on uCloud"},{"location":"instructions.html#run-the-docker-container","text":"With Docker , you can create containers that will work in the same way on all the machines. The results of the course are exactly reproducible if you use such a Docker container. We created a container for the course that already contains packages and course material installed. You need to download Docker Desktop at this link . We suggest you have at least 8GB of RAM and 20GB of hard disk space. When Docker Desktop is installed, you can use the command line to retrieve the container from the web: docker pull samuelesoraggi/ngs-summer-aarhus:2022.08.01 You can now execute the container. You will see a web address in the output of the command line: you need to copy that into your web browser to see jupyterlab . docker run --rm -it -p 8888:8888 samuelesoraggi/ngs-summer-aarhus:2022.08.01","title":"Run the docker container"},{"location":"ucloud.html","text":"Access through uCloud \u00b6 Ucloud is an interactive online platform from the University of Southern Danmark eScience center that allows users to execute softwares and computer code directly from their browser. If you have an account from a danish university, hospital, and other institutions, you can access the platform for free and try the course. Starting a session of the Introduction to NGS data analysis on uCloud \u00b6 Log onto ucloud at the address http://cloud.sdu.dk using the university/institution credentials you possess. If you are participating in the NGS Summer Course 2023 in Aarhus, click AFTER logging in on this link . You should see a message on your browser where you have to accept the invitation to the project. This will add you to a project on uCloud, where we have data and extra computing credit for the course. Be sure to be in the right project. If you are participating in the NGS Summer Course 2023 in Aarhus, choose NGS Summer Course from the project menu (red circle). Afterwards, click on the App menu (green circle) Note: The project My Workspace is instead your private space where you have some free hundreds of CPU hours and GBs of memory. Find the app Genomics Sandbox (red circle), which is under the title Featured . Click on it. You will get into the settings window. Choose any Job Name (Nr 1 in the figure below), how many hours you want to use for the job (Nr 2, choose at least 2 hours, you can increase this later), and how many CPUs (Nr 3, choose at least 4 CPUs for the first three exercises, but use at least 8 CPUs to run the single cell analysis). Select the Introduction to NGS Data Analysis as course (Nr 4). Then click on Submit (Nr 5). You will now enter a waiting queue. When the session starts, the timer begins to count down (red circle). In a couple of minutes you should be able to open the interface through the button (green circle). Note the buttons to add time to your session (blue circle) and the button to stop the session when you are done (pink circle) You will be able to see jupyterlab in your browser. On the left side of the screen you can see various folders. You can find the exercises inside the folder Notebooks . You can download any file by right-clicking on it and choosing Download . You can also upload files by clicking on the Upload button on the top left corner of the screen, if you want to experiment with your own data! Reuse your notebooks and data \u00b6 The first time you run the Genomics Sandbox, you download data and notebooks. You can reuse them! Open the Genomics Sandbox App and use the settings to run it. You can click on your previous job name to recycle the settings (red circle). Now click on Add Folder (red circle) and on the appearing browsing bar (green circle). You need to find a drive with your user name (red circle), written as Member Files: NameSurname#Number . Then, go into Jobs/Genomics Sandbox/JobID/Notebooks and choose the folder (green circle). JobID corresponds to the Job Name you used in the previous session, followed by some random number in parenthesis. Do the same procedure adding a folder again and finding Jobs/Genomics Sandbox/JobID/Data . At the end you should have two added folders like this: Click on Submit. Now you will be able to reuse data, results and exercises on which you worked before. Note: remember to recycle the settings of this session the next time, so you can actually reuse the data and notebooks without having to add folders.","title":"2.1.Ucloud Access"},{"location":"ucloud.html#access-through-ucloud","text":"Ucloud is an interactive online platform from the University of Southern Danmark eScience center that allows users to execute softwares and computer code directly from their browser. If you have an account from a danish university, hospital, and other institutions, you can access the platform for free and try the course.","title":"Access through uCloud"},{"location":"ucloud.html#starting-a-session-of-the-introduction-to-ngs-data-analysis-on-ucloud","text":"Log onto ucloud at the address http://cloud.sdu.dk using the university/institution credentials you possess. If you are participating in the NGS Summer Course 2023 in Aarhus, click AFTER logging in on this link . You should see a message on your browser where you have to accept the invitation to the project. This will add you to a project on uCloud, where we have data and extra computing credit for the course. Be sure to be in the right project. If you are participating in the NGS Summer Course 2023 in Aarhus, choose NGS Summer Course from the project menu (red circle). Afterwards, click on the App menu (green circle) Note: The project My Workspace is instead your private space where you have some free hundreds of CPU hours and GBs of memory. Find the app Genomics Sandbox (red circle), which is under the title Featured . Click on it. You will get into the settings window. Choose any Job Name (Nr 1 in the figure below), how many hours you want to use for the job (Nr 2, choose at least 2 hours, you can increase this later), and how many CPUs (Nr 3, choose at least 4 CPUs for the first three exercises, but use at least 8 CPUs to run the single cell analysis). Select the Introduction to NGS Data Analysis as course (Nr 4). Then click on Submit (Nr 5). You will now enter a waiting queue. When the session starts, the timer begins to count down (red circle). In a couple of minutes you should be able to open the interface through the button (green circle). Note the buttons to add time to your session (blue circle) and the button to stop the session when you are done (pink circle) You will be able to see jupyterlab in your browser. On the left side of the screen you can see various folders. You can find the exercises inside the folder Notebooks . You can download any file by right-clicking on it and choosing Download . You can also upload files by clicking on the Upload button on the top left corner of the screen, if you want to experiment with your own data!","title":"Starting a session of the Introduction to NGS data analysis on uCloud"},{"location":"ucloud.html#reuse-your-notebooks-and-data","text":"The first time you run the Genomics Sandbox, you download data and notebooks. You can reuse them! Open the Genomics Sandbox App and use the settings to run it. You can click on your previous job name to recycle the settings (red circle). Now click on Add Folder (red circle) and on the appearing browsing bar (green circle). You need to find a drive with your user name (red circle), written as Member Files: NameSurname#Number . Then, go into Jobs/Genomics Sandbox/JobID/Notebooks and choose the folder (green circle). JobID corresponds to the Job Name you used in the previous session, followed by some random number in parenthesis. Do the same procedure adding a folder again and finding Jobs/Genomics Sandbox/JobID/Data . At the end you should have two added folders like this: Click on Submit. Now you will be able to reuse data, results and exercises on which you worked before. Note: remember to recycle the settings of this session the next time, so you can actually reuse the data and notebooks without having to add folders.","title":"Reuse your notebooks and data"},{"location":"nb/align.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); 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In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Raw data alignment This tutorial will cover the steps for performing the alignment of raw RNA- and HiFi-sequencing data. You will need to use the software IGV on your computer to visualize some of the output files, which can be easily downloaded once they are produced. At the end of this tutorial you will be able to: perform and discuss quality control on raw data in fastq format using FastQC and MultiQC align HiFi and RNA sequencing data with dedicated tools such as MiniMap2 and STAR analyze the quality the alignment with qualimap </div> The output of this notebook will be used for the Variant calling analysis and the bulk RNA-sequencing analysis. If you do not want to run this notebook, you can alternatively use the free interactive tool Galaxy to perform the alignment steps. We have uploaded the data on Galaxy, and the manual to perform the exercise is found at the course webpage . The present tutorial, like the rest of the course material, is available at our open-source github repository . A few introductory points to run this notebook (click to show): To use this notebook, use the NGS (python) kernel that contains the packages. Choose it by selecting Kernel -> Change Kernel in the menu on top of the window. In this notebook you will use only bash commands as you would do in the command line (this is why you read %%bash at the beginning of each piece of code). Those commands can be replicated in the command line, but we thought of integrating them in a notebook to make the tutorial understandable. The bash commands can also be marked with an ! sign at the beginning of the line On some computers, you might see the result of the commands once they are done running. This means you will wait some time while the computer is crunching, and only afterwards you will see the result of the command you have executed You can run the code in each cell by clicking on the run cell button, or by pressing Shift + Enter . When the code is done running, a small green check sign will appear on the left side You need to run the cells in sequential order, please do not run a cell until the one above finished running and do not skip any cells Each cell contains a short description of the code and the output you should get. Please try not to focus on understanding the code for each command in too much detail, but rather try to focus on the output You can create new code cells by pressing + in the Menu bar above. </details> Biological background \u00b6 White clover (Trifolium repens) is an allotetraploid. It is a relatively young, outcrossing species, which originated during the most recent glaciation around 20,000 years ago by hybridisation of two diploid species, T. occidentale and T. pallescens (see figure below). This means that it contains genomes originating from two different species within the same nucleus. Normally, white clover is an outbreeding species, but a self-compatible line was used for sequencing the white clover genome (Griffiths et al, 2019) . This line will be designated as S10 in the data, indicating that this is the 10th self-fertilized generation. In addition, we have data from a wild clover accession (ecotype) called Tienshan ( Ti ), which is collected from the Chinese mountains and is adapted to alpine conditions. We will perform alignment of the data to the white clover's reference genome containing both T. occidentale and T. pallescens (called contig 1 and contig 2 in the data). We will also perform alignment to each subgenome, and see which are the differences with the quality control tools. Quality control and mapping \u00b6 Quality Control \u00b6 We run FastQC on the PacBio Hifi reads and on two of the Illumina RNA-seq libraries. FastQC does quality control of the raw sequence data, providing an overview of the data which can help identify if there are any problems that should be addressed before further analysis. You can find the report for each file into the folder results/fastqc_output/ . The output is in HTML format and can be opened in any browser or in jupyterlab . It is however not easy to compare the various libraries by opening separate reports. To aggregate all the results, we apply the MultiQC software to the reports' folder. The output of MultiQC is in the directory results/multiqc_output/fastqc_data . In [1]: Copied! %% bash #run fastqc mkdir - p results / fastqc_output fastqc - q - o results / fastqc_output ../ Data / Clover_Data /*. fastq > / dev / null 2 >& 1 %%bash #run fastqc mkdir -p results/fastqc_output fastqc -q -o results/fastqc_output ../Data/Clover_Data/*.fastq > /dev/null 2>&1 Note: fastqc prints a lot of output conisting of a simple confirmation of execution without error, even when using the option -q , which means quiet . Therefore we added > /dev/null 2>&1 to the command to mute the printing of that output. In [2]: Copied! %% bash #run multiqc multiqc -- outdir results / multiqc_output / fastqc_data results / fastqc_output %%bash #run multiqc multiqc --outdir results/multiqc_output/fastqc_data results/fastqc_output / / / \u001b]8;id=903972;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/fastqc_output | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 50/50 .html | fastqc | Found 25 reports | multiqc | Compressing plot data | multiqc | Report : results/multiqc_output/fastqc_data/multiqc_report.html | multiqc | Data : results/multiqc_output/fastqc_data/multiqc_data | multiqc | MultiQC complete Questions Visualize the Webpage generated by MultiQC. Hint: You can find a Help button that offers additional information about the plots for each panel. Focus on the following panels: \u201cPer base sequence quality\u201d, \u201cPer sequence quality scores\u201d.... (\u201cPer base sequence content\u201d always gives a FAIL for RNA-seq data). What do you notice with respect to the sequence quality scores? Are there any other quality issues worth noting? Hifi data mapping \u00b6 We map the PacBio Hifi reads ( Hifi_reads_white_clover.fastq ) to the white clover reference sequence (Contig1&2) using minimap2 . We run two mapping rounds, using two different preset options ( -x in the command) for the technology: PacBio/Oxford Nanopore read to reference mapping: map-pb Long assembly to reference mapping. Divergence is below 20%\u201d settings asm20 . Next, we create reports of the mapping results by running QualiMap on the two obtained SAM files. We first need to index the reference fasta files using samtools faidx . This produces files in .fai format containing informations about length of the reference sequence, offset for the quality scores, name of the reference sequence. Click here for a detailed overview. In [3]: Copied! %% bash #copy the reference data in the folder reference_data, so that you can write the indexing files mkdir - p reference_data cp ../ Data / Clover_Data / DNA_Contig1_2 . fasta ../ Data / Clover_Data / DNA_Contig1 . fasta ../ Data / Clover_Data / DNA_Contig2 . fasta reference_data %%bash #copy the reference data in the folder reference_data, so that you can write the indexing files mkdir -p reference_data cp ../Data/Clover_Data/DNA_Contig1_2.fasta ../Data/Clover_Data/DNA_Contig1.fasta ../Data/Clover_Data/DNA_Contig2.fasta reference_data In [4]: Copied! %% bash samtools faidx reference_data / DNA_Contig1_2 . fasta samtools faidx reference_data / DNA_Contig1 . fasta samtools faidx reference_data / DNA_Contig2 . fasta %%bash samtools faidx reference_data/DNA_Contig1_2.fasta samtools faidx reference_data/DNA_Contig1.fasta samtools faidx reference_data/DNA_Contig2.fasta we create an output folder for the HIFI alignment, and run minimap2 with the settings explained before. In [5]: Copied! %% bash mkdir - p results / HIFI_alignment / minimap2 - a - x map - pb - o results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sam \\ reference_data / DNA_Contig1_2 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq minimap2 - a - x asm20 - o results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sam \\ reference_data / DNA_Contig1_2 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq %%bash mkdir -p results/HIFI_alignment/ minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sam \\ reference_data/DNA_Contig1_2.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq minimap2 -a -x asm20 -o results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sam \\ reference_data/DNA_Contig1_2.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::mm_idx_gen::0.075*1.04] collected minimizers [M::mm_idx_gen::0.099*1.52] sorted minimizers [M::main::0.099*1.52] loaded/built the index for 2 target sequence(s) [M::mm_mapopt_update::0.114*1.45] mid_occ = 11 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 1; #seq: 2 [M::mm_idx_stat::0.118*1.43] distinct minimizers: 203943 (79.05% are singletons); average occurrences: 1.273; average spacing: 8.047; total length: 2089554 [M::worker_pipeline::11.859*2.89] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sam reference_data/DNA_Contig1_2.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 11.873 sec; CPU: 34.246 sec; Peak RSS: 1.186 GB [M::mm_idx_gen::0.084*1.04] collected minimizers [M::mm_idx_gen::0.121*1.62] sorted minimizers [M::main::0.121*1.62] loaded/built the index for 2 target sequence(s) [M::mm_mapopt_update::0.138*1.54] mid_occ = 50 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 0; #seq: 2 [M::mm_idx_stat::0.143*1.52] distinct minimizers: 298340 (78.21% are singletons); average occurrences: 1.277; average spacing: 5.484; total length: 2089554 [M::worker_pipeline::18.366*2.88] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x asm20 -o results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sam reference_data/DNA_Contig1_2.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 18.394 sec; CPU: 52.932 sec; Peak RSS: 1.692 GB samtools sort is used to sort the alignment with left-to-right coordinates. The output is in .bam format, with .sam files in input (Note that you could have gotten .bam files from minimap2 with a specific option). In [6]: Copied! %% bash samtools sort results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sam \\ - o results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sort . bam samtools sort results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sam \\ - o results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam %%bash samtools sort results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sam \\ -o results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sort.bam samtools sort results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sam \\ -o results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam samtools index creates the index for the bam file, stored in .bai format. The index file lets programs access any position into the aligned data without reading the whole file, which would take too much time. In [7]: Copied! %% bash samtools index results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sort . bam samtools index results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam %%bash samtools index results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sort.bam samtools index results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam Run quality control on both files In [8]: Copied! %% bash qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sort . bam \\ - outdir results / qualimap_output / PacBio_clover_alignment_1_2_mappb qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam \\ - outdir results / qualimap_output / PacBio_clover_alignment_1_2_asm20 %%bash qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sort.bam \\ -outdir results/qualimap_output/PacBio_clover_alignment_1_2_mappb qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam \\ -outdir results/qualimap_output/PacBio_clover_alignment_1_2_asm20 Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 401 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 401 windows... Processed 100 out of 401 windows... Processed 150 out of 401 windows... Processed 200 out of 401 windows... Processed 250 out of 401 windows... Processed 300 out of 401 windows... Processed 350 out of 401 windows... Processed 400 out of 401 windows... Total processed windows:401 Number of reads: 4395 Number of valid reads: 4696 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4395 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 12 Computing descriptors... numberOfMappedBases: 70274383 referenceSize: 2089554 numberOfSequencedBases: 70039397 numberOfAs: 23621784 Computing per chromosome statistics... Computing histograms... Overall analysis time: 12 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 401 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 401 windows... Processed 100 out of 401 windows... Processed 150 out of 401 windows... Processed 200 out of 401 windows... Processed 250 out of 401 windows... Processed 300 out of 401 windows... Processed 350 out of 401 windows... Processed 400 out of 401 windows... Total processed windows:401 Number of reads: 4395 Number of valid reads: 4747 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4395 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 12 Computing descriptors... numberOfMappedBases: 69947563 referenceSize: 2089554 numberOfSequencedBases: 69812688 numberOfAs: 23541478 Computing per chromosome statistics... Computing histograms... Overall analysis time: 12 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished For easier comparison, we can again collapse the two reports into a single one using MultiQC , in the same way we did for putting together the other reports from fastQC . In [9]: Copied! %% bash #run multiqc multiqc -- outdir results / qualimap_output results / qualimap_output %%bash #run multiqc multiqc --outdir results/qualimap_output results/qualimap_output / / / \u001b]8;id=941002;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/qualimap_output | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 93/93 nt.t\u2026 | qualimap | Found 2 BamQC reports | multiqc | Compressing plot data | multiqc | Report : results/qualimap_output/multiqc_report.html | multiqc | Data : results/qualimap_output/multiqc_data | multiqc | MultiQC complete Now you can visualize the report generated, which is in results/qualimap_output/multiqc_report.html . Next, we map the white clover PacBio Hifi reads to contig1 and contig2 separately, using the setting you selected at the previous step (let's say map-pb was chosen, but you are free to change this setting in the commands). As the two contigs represent the two white clover subgenomes, this mapping will allow you to see the two subgenome haplotypes and call subgenome SNPs. In [10]: Copied! %% bash minimap2 - a - x map - pb - o results / HIFI_alignment / PacBio_clover_alignment_1 . sam \\ reference_data / DNA_Contig1 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq %%bash minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1.sam \\ reference_data/DNA_Contig1.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::mm_idx_gen::0.050*0.96] collected minimizers [M::mm_idx_gen::0.071*1.55] sorted minimizers [M::main::0.071*1.55] loaded/built the index for 1 target sequence(s) [M::mm_mapopt_update::0.079*1.50] mid_occ = 10 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 1; #seq: 1 [M::mm_idx_stat::0.082*1.48] distinct minimizers: 112547 (91.45% are singletons); average occurrences: 1.141; average spacing: 8.031; total length: 1031631 [M::worker_pipeline::27.024*2.90] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1.sam reference_data/DNA_Contig1.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 27.035 sec; CPU: 78.431 sec; Peak RSS: 2.258 GB In [11]: Copied! %% bash minimap2 - a - x map - pb - o results / HIFI_alignment / PacBio_clover_alignment_2 . sam \\ reference_data / DNA_Contig2 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq %%bash minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_2.sam \\ reference_data/DNA_Contig2.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::mm_idx_gen::0.048*1.04] collected minimizers [M::mm_idx_gen::0.069*1.61] sorted minimizers [M::main::0.069*1.61] loaded/built the index for 1 target sequence(s) [M::mm_mapopt_update::0.076*1.55] mid_occ = 10 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 1; #seq: 1 [M::mm_idx_stat::0.080*1.53] distinct minimizers: 120722 (93.18% are singletons); average occurrences: 1.087; average spacing: 8.062; total length: 1057923 [M::worker_pipeline::27.150*2.94] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_2.sam reference_data/DNA_Contig2.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 27.162 sec; CPU: 79.819 sec; Peak RSS: 2.058 GB Sort the bam files and create their index using samtools In [12]: Copied! %% bash samtools sort results / HIFI_alignment / PacBio_clover_alignment_1 . sam - o results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam samtools sort results / HIFI_alignment / PacBio_clover_alignment_2 . sam - o results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam %%bash samtools sort results/HIFI_alignment/PacBio_clover_alignment_1.sam -o results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam samtools sort results/HIFI_alignment/PacBio_clover_alignment_2.sam -o results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam In [13]: Copied! %% bash samtools index results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam samtools index results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam %%bash samtools index results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam samtools index results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam Perform quality control In [14]: Copied! %% bash mkdir - p results / qualimap_output qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam - outdir results / qualimap_output / PacBio_clover_alignment_1 %%bash mkdir -p results/qualimap_output qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam -outdir results/qualimap_output/PacBio_clover_alignment_1 Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 400 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 400 windows... Processed 100 out of 400 windows... Processed 150 out of 400 windows... Processed 200 out of 400 windows... Processed 250 out of 400 windows... Processed 300 out of 400 windows... Processed 350 out of 400 windows... Processed 400 out of 400 windows... Total processed windows:400 Number of reads: 4395 Number of valid reads: 9070 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4356 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 10 Computing descriptors... numberOfMappedBases: 59058058 referenceSize: 1031631 numberOfSequencedBases: 54917933 numberOfAs: 18508479 Computing per chromosome statistics... Computing histograms... Overall analysis time: 11 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished In [15]: Copied! %% bash qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam - outdir results / qualimap_output / PacBio_clover_alignment_2 %%bash qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam -outdir results/qualimap_output/PacBio_clover_alignment_2 Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 400 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 400 windows... Processed 100 out of 400 windows... Processed 150 out of 400 windows... Processed 200 out of 400 windows... Processed 250 out of 400 windows... Processed 300 out of 400 windows... Processed 350 out of 400 windows... Processed 400 out of 400 windows... Total processed windows:400 Number of reads: 4395 Number of valid reads: 9462 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4394 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 13 Computing descriptors... numberOfMappedBases: 59388453 referenceSize: 1057923 numberOfSequencedBases: 55867300 numberOfAs: 18822184 Computing per chromosome statistics... Computing histograms... Overall analysis time: 13 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished In [16]: Copied! %% bash #run multiqc multiqc -- outdir results / qualimap_output results / qualimap_output %%bash #run multiqc multiqc --outdir results/qualimap_output results/qualimap_output / / / \u001b]8;id=999624;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/qualimap_output | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 187/187 .t\u2026 | qualimap | Found 4 BamQC reports | multiqc | Compressing plot data | multiqc | Previous MultiQC output found! Adjusting filenames.. | multiqc | Use -f or --force to overwrite existing reports instead | multiqc | Report : results/qualimap_output/multiqc_report_1.html | multiqc | Data : results/qualimap_output/multiqc_data_1 | multiqc | MultiQC complete Task: IGV visualization and Questions Now you can inspect the alignment files in IGV . First, you will need to download the reference fasta sequence in ../Data/Clover_Data/DNA_Contig1_2.fasta and import it into IGV. You can do the same for the files DNA_Contig1.fasta and DNA_Contig2.fasta that you might need later. In IGV, this is done with the menu Genomes --> Load Genome from file menu and by selecting the relevant fasta file. Then, choose the reference you need from the drop-down menu (see figure below). You will not yet see much, but you can choose one of the two subgenomes (contig 1 or 2) and double click on a chromosome position to inspect the reference sequence. The next step will visualize the mapped files on IGV. Each mapped genome can be seen in IGV against the reference file of choice. To load an aligned file, first download it together with the index file in .bai format. For example, you need to download both results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam and results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam.bai to see this alignment (you need to open only the .bam file with IGV). If you open more files, their alignments will be distributed in the IGV interface, and you can change the size of each visualization yourself (below shown with only one opened alignment). Now compare in IGV the two bam files PacBio_clover_alignment_1.sort.bam and PacBio_clover_alignment_2.sort.bam . What do you observe when comparing the two BAM files? Have a look at the polymorphic regions in IGV. Are they true polymorphisms? Add to the visualization the third alignment PacBio_clover_alignment_1_2_mappb.sort.bam in IGV. Why do you see fluctuations in coverage and large regions without any apparent subgenome SNPs? What are the major differences between the stats for the reads mapped to Contigs1&2 versus contig1 and contig2? What is your interpretation of the differences? RNA-seq mapping \u00b6 In the ../Data folder you will find 24 RNA-seq libraries, 12 S10 libraries and 12 Tienshan libraries. Each library is paired-end, which is denoted by R1 and R2 at the end of two files having the same name, such as S10_1_1.R1.fastq and S10_1_1.R2.fastq . We will align each library separately and then merge the alignments to create two final samples for S10 and Tienshan . First, we need to create a genome file for the reference fasta file of contigs 1 and 2 . This is done with STAR , using the option --runMode genomeGenerate . We also need to convert the gene annotation from gff to gtf format with gffread to allow counting gene transcripts. STAR is a very complex tool with many options, so it is always useful to have a reference manual . In [17]: Copied! %% bash gffread - T - o reference_data / white_clover_genes . gtf ../ Data / Clover_Data / white_clover_genes . gff %%bash gffread -T -o reference_data/white_clover_genes.gtf ../Data/Clover_Data/white_clover_genes.gff In [18]: Copied! %% bash STAR -- runThreadN 8 \\ -- runMode genomeGenerate \\ -- genomeDir results / STAR_output / indexing_contigs_1_2 \\ -- genomeFastaFiles reference_data / DNA_Contig1_2 . fasta \\ -- sjdbGTFfile reference_data / white_clover_genes . gtf %%bash STAR --runThreadN 8 \\ --runMode genomeGenerate \\ --genomeDir results/STAR_output/indexing_contigs_1_2 \\ --genomeFastaFiles reference_data/DNA_Contig1_2.fasta \\ --sjdbGTFfile reference_data/white_clover_genes.gtf STAR --runThreadN 8 --runMode genomeGenerate --genomeDir results/STAR_output/indexing_contigs_1_2 --genomeFastaFiles reference_data/DNA_Contig1_2.fasta --sjdbGTFfile reference_data/white_clover_genes.gtf STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:03 ..... started STAR run Jun 19 10:23:03 ... starting to generate Genome files Jun 19 10:23:03 ..... processing annotations GTF !!!!! WARNING: --genomeSAindexNbases 14 is too large for the genome size=2089554, which may cause seg-fault at the mapping step. Re-run genome generation with recommended --genomeSAindexNbases 9 Jun 19 10:23:03 ... starting to sort Suffix Array. This may take a long time... Jun 19 10:23:03 ... sorting Suffix Array chunks and saving them to disk... Jun 19 10:23:04 ... loading chunks from disk, packing SA... Jun 19 10:23:04 ... finished generating suffix array Jun 19 10:23:04 ... generating Suffix Array index Jun 19 10:23:08 ... completed Suffix Array index Jun 19 10:23:08 ..... inserting junctions into the genome indices Jun 19 10:23:16 ... writing Genome to disk ... Jun 19 10:23:16 ... writing Suffix Array to disk ... Jun 19 10:23:17 ... writing SAindex to disk Jun 19 10:23:18 ..... finished successfully We got a warning saying !!!!! WARNING: --genomeSAindexNbases 14 is too large for the genome size=2089554, which may cause seg-fault at the mapping step. Re-run genome generation with recommended --genomeSAindexNbases 9 meaning we need shorter strings of bases (9 bases instead of 14) to be indexed, as our reference genome is very short, and too long strings would cause many alignment errors. So we rerun the command with the suggested option. In [19]: Copied! %% bash STAR -- runThreadN 8 \\ -- runMode genomeGenerate \\ -- genomeDir results / STAR_output / indexing_contigs_1_2 \\ -- genomeFastaFiles reference_data / DNA_Contig1_2 . fasta \\ -- sjdbGTFfile reference_data / white_clover_genes . gtf \\ -- genomeSAindexNbases 9 %%bash STAR --runThreadN 8 \\ --runMode genomeGenerate \\ --genomeDir results/STAR_output/indexing_contigs_1_2 \\ --genomeFastaFiles reference_data/DNA_Contig1_2.fasta \\ --sjdbGTFfile reference_data/white_clover_genes.gtf \\ --genomeSAindexNbases 9 STAR --runThreadN 8 --runMode genomeGenerate --genomeDir results/STAR_output/indexing_contigs_1_2 --genomeFastaFiles reference_data/DNA_Contig1_2.fasta --sjdbGTFfile reference_data/white_clover_genes.gtf --genomeSAindexNbases 9 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:18 ..... started STAR run Jun 19 10:23:18 ... starting to generate Genome files Jun 19 10:23:18 ..... processing annotations GTF Jun 19 10:23:20 ... starting to sort Suffix Array. This may take a long time... Jun 19 10:23:20 ... sorting Suffix Array chunks and saving them to disk... Jun 19 10:23:21 ... loading chunks from disk, packing SA... Jun 19 10:23:21 ... finished generating suffix array Jun 19 10:23:21 ... generating Suffix Array index Jun 19 10:23:21 ... completed Suffix Array index Jun 19 10:23:22 ..... inserting junctions into the genome indices Jun 19 10:23:23 ... writing Genome to disk ... Jun 19 10:23:23 ... writing Suffix Array to disk ... Jun 19 10:23:23 ... writing SAindex to disk Jun 19 10:23:24 ..... finished successfully We use again STAR to align every single library for S10 . We extract the library name of each file and run STAR through each pair of files. Note that plant introns are very rarely more than 5000 bp and that you are mapping to two homoeologous contigs that show high similarity, especially in genic regions. We set the maximum size to 5000 using --alignIntronMax 5000 . In [20]: Copied! %% bash for i in ` ls ../ Data / Clover_Data / S10 *. R1 . fastq ` do PREFIXNAME = ` basename $ i . R1 . fastq ` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \" $ PREFIXNAME echo \"###############################################\" STAR -- genomeDir results / STAR_output / indexing_contigs_1_2 / \\ -- runThreadN 8 \\ -- runMode alignReads \\ -- readFilesIn ../ Data / Clover_Data / $ PREFIXNAME . R1 . fastq ../ Data / Clover_Data / $ PREFIXNAME . R2 . fastq \\ -- outFileNamePrefix results / STAR_output / S10_align_contigs_1_2 / $ PREFIXNAME \\ -- outSAMtype BAM SortedByCoordinate \\ -- outSAMattributes Standard \\ -- quantMode GeneCounts \\ -- alignIntronMax 5000 done %%bash for i in `ls ../Data/Clover_Data/S10*.R1.fastq` do PREFIXNAME=`basename $i .R1.fastq` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \"$PREFIXNAME echo \"###############################################\" STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ \\ --runThreadN 8 \\ --runMode alignReads \\ --readFilesIn ../Data/Clover_Data/$PREFIXNAME.R1.fastq ../Data/Clover_Data/$PREFIXNAME.R2.fastq \\ --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/$PREFIXNAME \\ --outSAMtype BAM SortedByCoordinate \\ --outSAMattributes Standard \\ --quantMode GeneCounts \\ --alignIntronMax 5000 done ############################################### ##### ALIGNING PAIRED-END READS S10_1_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_1_1.R1.fastq ../Data/Clover_Data/S10_1_1.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_1_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:24 ..... started STAR run Jun 19 10:23:24 ..... loading genome Jun 19 10:23:24 ..... started mapping Jun 19 10:23:35 ..... finished mapping Jun 19 10:23:35 ..... started sorting BAM Jun 19 10:23:37 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_1_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_1_2.R1.fastq ../Data/Clover_Data/S10_1_2.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_1_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:38 ..... started STAR run Jun 19 10:23:38 ..... loading genome Jun 19 10:23:38 ..... started mapping Jun 19 10:23:48 ..... finished mapping Jun 19 10:23:48 ..... started sorting BAM Jun 19 10:23:51 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_1_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_1_3.R1.fastq ../Data/Clover_Data/S10_1_3.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_1_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:52 ..... started STAR run Jun 19 10:23:52 ..... loading genome Jun 19 10:23:52 ..... started mapping Jun 19 10:24:03 ..... finished mapping Jun 19 10:24:03 ..... started sorting BAM Jun 19 10:24:06 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_2_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_2_1.R1.fastq ../Data/Clover_Data/S10_2_1.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_2_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:06 ..... started STAR run Jun 19 10:24:06 ..... loading genome Jun 19 10:24:06 ..... started mapping Jun 19 10:24:16 ..... finished mapping Jun 19 10:24:16 ..... started sorting BAM Jun 19 10:24:18 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_2_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_2_2.R1.fastq ../Data/Clover_Data/S10_2_2.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_2_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:20 ..... started STAR run Jun 19 10:24:20 ..... loading genome Jun 19 10:24:20 ..... started mapping Jun 19 10:24:31 ..... finished mapping Jun 19 10:24:31 ..... started sorting BAM Jun 19 10:24:33 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_2_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_2_3.R1.fastq ../Data/Clover_Data/S10_2_3.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_2_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:33 ..... started STAR run Jun 19 10:24:33 ..... loading genome Jun 19 10:24:33 ..... started mapping Jun 19 10:24:45 ..... finished mapping Jun 19 10:24:45 ..... started sorting BAM Jun 19 10:24:48 ..... finished successfully Do the same alignment for Tienshan libraries In [21]: Copied! %% bash for i in ` ls ../ Data / Clover_Data / TI *. R1 . fastq ` do PREFIXNAME = ` basename $ i . R1 . fastq ` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \" $ PREFIXNAME echo \"###############################################\" STAR -- genomeDir results / STAR_output / indexing_contigs_1_2 / \\ -- runThreadN 8 \\ -- readFilesIn ../ Data / Clover_Data / $ PREFIXNAME . R1 . fastq ../ Data / Clover_Data / $ PREFIXNAME . R2 . fastq \\ -- outFileNamePrefix results / STAR_output / TI_align_contigs_1_2 / $ PREFIXNAME \\ -- outSAMtype BAM SortedByCoordinate \\ -- outSAMattributes Standard \\ -- quantMode GeneCounts \\ -- alignIntronMax 5000 done %%bash for i in `ls ../Data/Clover_Data/TI*.R1.fastq` do PREFIXNAME=`basename $i .R1.fastq` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \"$PREFIXNAME echo \"###############################################\" STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ \\ --runThreadN 8 \\ --readFilesIn ../Data/Clover_Data/$PREFIXNAME.R1.fastq ../Data/Clover_Data/$PREFIXNAME.R2.fastq \\ --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/$PREFIXNAME \\ --outSAMtype BAM SortedByCoordinate \\ --outSAMattributes Standard \\ --quantMode GeneCounts \\ --alignIntronMax 5000 done ############################################### ##### ALIGNING PAIRED-END READS TI_1_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_1_1.R1.fastq ../Data/Clover_Data/TI_1_1.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_1_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:50 ..... started STAR run Jun 19 10:24:50 ..... loading genome Jun 19 10:24:50 ..... started mapping Jun 19 10:25:03 ..... finished mapping Jun 19 10:25:04 ..... started sorting BAM Jun 19 10:25:18 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_1_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_1_2.R1.fastq ../Data/Clover_Data/TI_1_2.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_1_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:25:18 ..... started STAR run Jun 19 10:25:18 ..... loading genome Jun 19 10:25:18 ..... started mapping Jun 19 10:25:30 ..... finished mapping Jun 19 10:25:30 ..... started sorting BAM Jun 19 10:25:33 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_1_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_1_3.R1.fastq ../Data/Clover_Data/TI_1_3.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_1_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:25:36 ..... started STAR run Jun 19 10:25:36 ..... loading genome Jun 19 10:25:36 ..... started mapping Jun 19 10:25:50 ..... finished mapping Jun 19 10:25:50 ..... started sorting BAM Jun 19 10:25:51 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_2_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_2_1.R1.fastq ../Data/Clover_Data/TI_2_1.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_2_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:25:51 ..... started STAR run Jun 19 10:25:51 ..... loading genome Jun 19 10:25:52 ..... started mapping Jun 19 10:26:06 ..... finished mapping Jun 19 10:26:06 ..... started sorting BAM Jun 19 10:26:09 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_2_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_2_2.R1.fastq ../Data/Clover_Data/TI_2_2.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_2_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:26:09 ..... started STAR run Jun 19 10:26:09 ..... loading genome Jun 19 10:26:09 ..... started mapping Jun 19 10:26:23 ..... finished mapping Jun 19 10:26:23 ..... started sorting BAM Jun 19 10:26:31 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_2_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_2_3.R1.fastq ../Data/Clover_Data/TI_2_3.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_2_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:26:31 ..... started STAR run Jun 19 10:26:31 ..... loading genome Jun 19 10:26:31 ..... started mapping Jun 19 10:26:46 ..... finished mapping Jun 19 10:26:46 ..... started sorting BAM Jun 19 10:26:49 ..... finished successfully Run quality control on each aligned library with MultiQC . In this way there will be a whole report to compare S10 files and Tienshan files. In [22]: Copied! %% bash multiqc -- outdir results / multiqc_output / TI_STAR_align_1_2 \\ results / STAR_output / TI_align_contigs_1_2 / %%bash multiqc --outdir results/multiqc_output/TI_STAR_align_1_2 \\ results/STAR_output/TI_align_contigs_1_2/ / / / \u001b]8;id=121575;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/STAR_output/TI_align_contigs_1_2 | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 36/36 og.o\u2026 | star | Found 6 reports and 6 gene count files | multiqc | Compressing plot data | multiqc | Report : results/multiqc_output/TI_STAR_align_1_2/multiqc_report.html | multiqc | Data : results/multiqc_output/TI_STAR_align_1_2/multiqc_data | multiqc | MultiQC complete In [23]: Copied! %% bash multiqc -- outdir results / multiqc_output / S10_STAR_align_1_2 \\ results / STAR_output / S10_align_contigs_1_2 / %%bash multiqc --outdir results/multiqc_output/S10_STAR_align_1_2 \\ results/STAR_output/S10_align_contigs_1_2/ / / / \u001b]8;id=596946;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/STAR_output/S10_align_contigs_1_2 | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 36/36 ut.t\u2026 | star | Found 6 reports and 6 gene count files | multiqc | Compressing plot data | multiqc | Report : results/multiqc_output/S10_STAR_align_1_2/multiqc_report.html | multiqc | Data : results/multiqc_output/S10_STAR_align_1_2/multiqc_data | multiqc | MultiQC complete We merge the outputs of each group of aligned libraries. Here is how the files look like for the Tienshan . In [24]: Copied! ! ls - lh results / STAR_output / TI_align_contigs_1_2 / TI_ *. sortedByCoord . out . bam !ls -lh results/STAR_output/TI_align_contigs_1_2/TI_*.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 6.6M Jun 19 10:25 results/STAR_output/TI_align_contigs_1_2/TI_1_1Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 6.0M Jun 19 10:25 results/STAR_output/TI_align_contigs_1_2/TI_1_2Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 7.1M Jun 19 10:25 results/STAR_output/TI_align_contigs_1_2/TI_1_3Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 9.6M Jun 19 10:26 results/STAR_output/TI_align_contigs_1_2/TI_2_1Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 7.2M Jun 19 10:26 results/STAR_output/TI_align_contigs_1_2/TI_2_2Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 8.2M Jun 19 10:26 results/STAR_output/TI_align_contigs_1_2/TI_2_3Aligned.sortedByCoord.out.bam Apply samtools merge In [25]: Copied! %% bash mkdir - p results / STAR_output / TI_align_contigs_1_2_merge / samtools merge - f results / STAR_output / TI_align_contigs_1_2_merge / TI . sorted . bam results / STAR_output / TI_align_contigs_1_2 / TI_ *. sortedByCoord . out . bam %%bash mkdir -p results/STAR_output/TI_align_contigs_1_2_merge/ samtools merge -f results/STAR_output/TI_align_contigs_1_2_merge/TI.sorted.bam results/STAR_output/TI_align_contigs_1_2/TI_*.sortedByCoord.out.bam In [26]: Copied! %% bash mkdir - p results / STAR_output / S10_align_contigs_1_2_merge / samtools merge - f results / STAR_output / S10_align_contigs_1_2_merge / S10 . sorted . bam results / STAR_output / S10_align_contigs_1_2 / S10_ *. sortedByCoord . out . bam %%bash mkdir -p results/STAR_output/S10_align_contigs_1_2_merge/ samtools merge -f results/STAR_output/S10_align_contigs_1_2_merge/S10.sorted.bam results/STAR_output/S10_align_contigs_1_2/S10_*.sortedByCoord.out.bam Index both merging outputs. A file in format bam.bai will appear in their respective folders. In [27]: Copied! %% bash samtools index results / STAR_output / S10_align_contigs_1_2_merge / S10 . sorted . bam %%bash samtools index results/STAR_output/S10_align_contigs_1_2_merge/S10.sorted.bam In [28]: Copied! %% bash samtools index results / STAR_output / TI_align_contigs_1_2_merge / TI . sorted . bam %%bash samtools index results/STAR_output/TI_align_contigs_1_2_merge/TI.sorted.bam Wrapping up \ud83c\udf89 \ud83c\udf89 \ud83c\udf89 \u00b6 In this exercise, you learnt to align various types of data after performing quality control for raw data. We looked at some of the options for the aligners and at how to use some of the basic samtools manipulation programs. The outputs from the RNA alignments will be used for the VCF file analysis in the next notebook, and the RNA alignments will be use for the bulk RNA data analysis.","title":"3.1.Alignment of raw data"},{"location":"nb/align.html#biological-background","text":"White clover (Trifolium repens) is an allotetraploid. It is a relatively young, outcrossing species, which originated during the most recent glaciation around 20,000 years ago by hybridisation of two diploid species, T. occidentale and T. pallescens (see figure below). This means that it contains genomes originating from two different species within the same nucleus. Normally, white clover is an outbreeding species, but a self-compatible line was used for sequencing the white clover genome (Griffiths et al, 2019) . This line will be designated as S10 in the data, indicating that this is the 10th self-fertilized generation. In addition, we have data from a wild clover accession (ecotype) called Tienshan ( Ti ), which is collected from the Chinese mountains and is adapted to alpine conditions. We will perform alignment of the data to the white clover's reference genome containing both T. occidentale and T. pallescens (called contig 1 and contig 2 in the data). We will also perform alignment to each subgenome, and see which are the differences with the quality control tools.","title":"Biological background"},{"location":"nb/align.html#quality-control-and-mapping","text":"","title":"Quality control and mapping"},{"location":"nb/align.html#quality-control","text":"We run FastQC on the PacBio Hifi reads and on two of the Illumina RNA-seq libraries. FastQC does quality control of the raw sequence data, providing an overview of the data which can help identify if there are any problems that should be addressed before further analysis. You can find the report for each file into the folder results/fastqc_output/ . The output is in HTML format and can be opened in any browser or in jupyterlab . It is however not easy to compare the various libraries by opening separate reports. To aggregate all the results, we apply the MultiQC software to the reports' folder. The output of MultiQC is in the directory results/multiqc_output/fastqc_data . In [1]: Copied! %% bash #run fastqc mkdir - p results / fastqc_output fastqc - q - o results / fastqc_output ../ Data / Clover_Data /*. fastq > / dev / null 2 >& 1 %%bash #run fastqc mkdir -p results/fastqc_output fastqc -q -o results/fastqc_output ../Data/Clover_Data/*.fastq > /dev/null 2>&1 Note: fastqc prints a lot of output conisting of a simple confirmation of execution without error, even when using the option -q , which means quiet . Therefore we added > /dev/null 2>&1 to the command to mute the printing of that output. In [2]: Copied! %% bash #run multiqc multiqc -- outdir results / multiqc_output / fastqc_data results / fastqc_output %%bash #run multiqc multiqc --outdir results/multiqc_output/fastqc_data results/fastqc_output / / / \u001b]8;id=903972;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/fastqc_output | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 50/50 .html | fastqc | Found 25 reports | multiqc | Compressing plot data | multiqc | Report : results/multiqc_output/fastqc_data/multiqc_report.html | multiqc | Data : results/multiqc_output/fastqc_data/multiqc_data | multiqc | MultiQC complete Questions Visualize the Webpage generated by MultiQC. Hint: You can find a Help button that offers additional information about the plots for each panel. Focus on the following panels: \u201cPer base sequence quality\u201d, \u201cPer sequence quality scores\u201d.... (\u201cPer base sequence content\u201d always gives a FAIL for RNA-seq data). What do you notice with respect to the sequence quality scores? Are there any other quality issues worth noting?","title":"Quality Control"},{"location":"nb/align.html#hifi-data-mapping","text":"We map the PacBio Hifi reads ( Hifi_reads_white_clover.fastq ) to the white clover reference sequence (Contig1&2) using minimap2 . We run two mapping rounds, using two different preset options ( -x in the command) for the technology: PacBio/Oxford Nanopore read to reference mapping: map-pb Long assembly to reference mapping. Divergence is below 20%\u201d settings asm20 . Next, we create reports of the mapping results by running QualiMap on the two obtained SAM files. We first need to index the reference fasta files using samtools faidx . This produces files in .fai format containing informations about length of the reference sequence, offset for the quality scores, name of the reference sequence. Click here for a detailed overview. In [3]: Copied! %% bash #copy the reference data in the folder reference_data, so that you can write the indexing files mkdir - p reference_data cp ../ Data / Clover_Data / DNA_Contig1_2 . fasta ../ Data / Clover_Data / DNA_Contig1 . fasta ../ Data / Clover_Data / DNA_Contig2 . fasta reference_data %%bash #copy the reference data in the folder reference_data, so that you can write the indexing files mkdir -p reference_data cp ../Data/Clover_Data/DNA_Contig1_2.fasta ../Data/Clover_Data/DNA_Contig1.fasta ../Data/Clover_Data/DNA_Contig2.fasta reference_data In [4]: Copied! %% bash samtools faidx reference_data / DNA_Contig1_2 . fasta samtools faidx reference_data / DNA_Contig1 . fasta samtools faidx reference_data / DNA_Contig2 . fasta %%bash samtools faidx reference_data/DNA_Contig1_2.fasta samtools faidx reference_data/DNA_Contig1.fasta samtools faidx reference_data/DNA_Contig2.fasta we create an output folder for the HIFI alignment, and run minimap2 with the settings explained before. In [5]: Copied! %% bash mkdir - p results / HIFI_alignment / minimap2 - a - x map - pb - o results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sam \\ reference_data / DNA_Contig1_2 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq minimap2 - a - x asm20 - o results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sam \\ reference_data / DNA_Contig1_2 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq %%bash mkdir -p results/HIFI_alignment/ minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sam \\ reference_data/DNA_Contig1_2.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq minimap2 -a -x asm20 -o results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sam \\ reference_data/DNA_Contig1_2.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::mm_idx_gen::0.075*1.04] collected minimizers [M::mm_idx_gen::0.099*1.52] sorted minimizers [M::main::0.099*1.52] loaded/built the index for 2 target sequence(s) [M::mm_mapopt_update::0.114*1.45] mid_occ = 11 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 1; #seq: 2 [M::mm_idx_stat::0.118*1.43] distinct minimizers: 203943 (79.05% are singletons); average occurrences: 1.273; average spacing: 8.047; total length: 2089554 [M::worker_pipeline::11.859*2.89] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sam reference_data/DNA_Contig1_2.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 11.873 sec; CPU: 34.246 sec; Peak RSS: 1.186 GB [M::mm_idx_gen::0.084*1.04] collected minimizers [M::mm_idx_gen::0.121*1.62] sorted minimizers [M::main::0.121*1.62] loaded/built the index for 2 target sequence(s) [M::mm_mapopt_update::0.138*1.54] mid_occ = 50 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 0; #seq: 2 [M::mm_idx_stat::0.143*1.52] distinct minimizers: 298340 (78.21% are singletons); average occurrences: 1.277; average spacing: 5.484; total length: 2089554 [M::worker_pipeline::18.366*2.88] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x asm20 -o results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sam reference_data/DNA_Contig1_2.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 18.394 sec; CPU: 52.932 sec; Peak RSS: 1.692 GB samtools sort is used to sort the alignment with left-to-right coordinates. The output is in .bam format, with .sam files in input (Note that you could have gotten .bam files from minimap2 with a specific option). In [6]: Copied! %% bash samtools sort results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sam \\ - o results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sort . bam samtools sort results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sam \\ - o results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam %%bash samtools sort results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sam \\ -o results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sort.bam samtools sort results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sam \\ -o results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam samtools index creates the index for the bam file, stored in .bai format. The index file lets programs access any position into the aligned data without reading the whole file, which would take too much time. In [7]: Copied! %% bash samtools index results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sort . bam samtools index results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam %%bash samtools index results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sort.bam samtools index results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam Run quality control on both files In [8]: Copied! %% bash qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sort . bam \\ - outdir results / qualimap_output / PacBio_clover_alignment_1_2_mappb qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam \\ - outdir results / qualimap_output / PacBio_clover_alignment_1_2_asm20 %%bash qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sort.bam \\ -outdir results/qualimap_output/PacBio_clover_alignment_1_2_mappb qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam \\ -outdir results/qualimap_output/PacBio_clover_alignment_1_2_asm20 Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 401 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 401 windows... Processed 100 out of 401 windows... Processed 150 out of 401 windows... Processed 200 out of 401 windows... Processed 250 out of 401 windows... Processed 300 out of 401 windows... Processed 350 out of 401 windows... Processed 400 out of 401 windows... Total processed windows:401 Number of reads: 4395 Number of valid reads: 4696 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4395 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 12 Computing descriptors... numberOfMappedBases: 70274383 referenceSize: 2089554 numberOfSequencedBases: 70039397 numberOfAs: 23621784 Computing per chromosome statistics... Computing histograms... Overall analysis time: 12 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 401 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 401 windows... Processed 100 out of 401 windows... Processed 150 out of 401 windows... Processed 200 out of 401 windows... Processed 250 out of 401 windows... Processed 300 out of 401 windows... Processed 350 out of 401 windows... Processed 400 out of 401 windows... Total processed windows:401 Number of reads: 4395 Number of valid reads: 4747 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4395 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 12 Computing descriptors... numberOfMappedBases: 69947563 referenceSize: 2089554 numberOfSequencedBases: 69812688 numberOfAs: 23541478 Computing per chromosome statistics... Computing histograms... Overall analysis time: 12 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished For easier comparison, we can again collapse the two reports into a single one using MultiQC , in the same way we did for putting together the other reports from fastQC . In [9]: Copied! %% bash #run multiqc multiqc -- outdir results / qualimap_output results / qualimap_output %%bash #run multiqc multiqc --outdir results/qualimap_output results/qualimap_output / / / \u001b]8;id=941002;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/qualimap_output | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 93/93 nt.t\u2026 | qualimap | Found 2 BamQC reports | multiqc | Compressing plot data | multiqc | Report : results/qualimap_output/multiqc_report.html | multiqc | Data : results/qualimap_output/multiqc_data | multiqc | MultiQC complete Now you can visualize the report generated, which is in results/qualimap_output/multiqc_report.html . Next, we map the white clover PacBio Hifi reads to contig1 and contig2 separately, using the setting you selected at the previous step (let's say map-pb was chosen, but you are free to change this setting in the commands). As the two contigs represent the two white clover subgenomes, this mapping will allow you to see the two subgenome haplotypes and call subgenome SNPs. In [10]: Copied! %% bash minimap2 - a - x map - pb - o results / HIFI_alignment / PacBio_clover_alignment_1 . sam \\ reference_data / DNA_Contig1 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq %%bash minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1.sam \\ reference_data/DNA_Contig1.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::mm_idx_gen::0.050*0.96] collected minimizers [M::mm_idx_gen::0.071*1.55] sorted minimizers [M::main::0.071*1.55] loaded/built the index for 1 target sequence(s) [M::mm_mapopt_update::0.079*1.50] mid_occ = 10 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 1; #seq: 1 [M::mm_idx_stat::0.082*1.48] distinct minimizers: 112547 (91.45% are singletons); average occurrences: 1.141; average spacing: 8.031; total length: 1031631 [M::worker_pipeline::27.024*2.90] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1.sam reference_data/DNA_Contig1.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 27.035 sec; CPU: 78.431 sec; Peak RSS: 2.258 GB In [11]: Copied! %% bash minimap2 - a - x map - pb - o results / HIFI_alignment / PacBio_clover_alignment_2 . sam \\ reference_data / DNA_Contig2 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq %%bash minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_2.sam \\ reference_data/DNA_Contig2.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::mm_idx_gen::0.048*1.04] collected minimizers [M::mm_idx_gen::0.069*1.61] sorted minimizers [M::main::0.069*1.61] loaded/built the index for 1 target sequence(s) [M::mm_mapopt_update::0.076*1.55] mid_occ = 10 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 1; #seq: 1 [M::mm_idx_stat::0.080*1.53] distinct minimizers: 120722 (93.18% are singletons); average occurrences: 1.087; average spacing: 8.062; total length: 1057923 [M::worker_pipeline::27.150*2.94] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_2.sam reference_data/DNA_Contig2.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 27.162 sec; CPU: 79.819 sec; Peak RSS: 2.058 GB Sort the bam files and create their index using samtools In [12]: Copied! %% bash samtools sort results / HIFI_alignment / PacBio_clover_alignment_1 . sam - o results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam samtools sort results / HIFI_alignment / PacBio_clover_alignment_2 . sam - o results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam %%bash samtools sort results/HIFI_alignment/PacBio_clover_alignment_1.sam -o results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam samtools sort results/HIFI_alignment/PacBio_clover_alignment_2.sam -o results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam In [13]: Copied! %% bash samtools index results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam samtools index results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam %%bash samtools index results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam samtools index results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam Perform quality control In [14]: Copied! %% bash mkdir - p results / qualimap_output qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam - outdir results / qualimap_output / PacBio_clover_alignment_1 %%bash mkdir -p results/qualimap_output qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam -outdir results/qualimap_output/PacBio_clover_alignment_1 Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 400 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 400 windows... Processed 100 out of 400 windows... Processed 150 out of 400 windows... Processed 200 out of 400 windows... Processed 250 out of 400 windows... Processed 300 out of 400 windows... Processed 350 out of 400 windows... Processed 400 out of 400 windows... Total processed windows:400 Number of reads: 4395 Number of valid reads: 9070 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4356 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 10 Computing descriptors... numberOfMappedBases: 59058058 referenceSize: 1031631 numberOfSequencedBases: 54917933 numberOfAs: 18508479 Computing per chromosome statistics... Computing histograms... Overall analysis time: 11 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished In [15]: Copied! %% bash qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam - outdir results / qualimap_output / PacBio_clover_alignment_2 %%bash qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam -outdir results/qualimap_output/PacBio_clover_alignment_2 Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 400 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 400 windows... Processed 100 out of 400 windows... Processed 150 out of 400 windows... Processed 200 out of 400 windows... Processed 250 out of 400 windows... Processed 300 out of 400 windows... Processed 350 out of 400 windows... Processed 400 out of 400 windows... Total processed windows:400 Number of reads: 4395 Number of valid reads: 9462 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4394 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 13 Computing descriptors... numberOfMappedBases: 59388453 referenceSize: 1057923 numberOfSequencedBases: 55867300 numberOfAs: 18822184 Computing per chromosome statistics... Computing histograms... Overall analysis time: 13 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished In [16]: Copied! %% bash #run multiqc multiqc -- outdir results / qualimap_output results / qualimap_output %%bash #run multiqc multiqc --outdir results/qualimap_output results/qualimap_output / / / \u001b]8;id=999624;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/qualimap_output | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 187/187 .t\u2026 | qualimap | Found 4 BamQC reports | multiqc | Compressing plot data | multiqc | Previous MultiQC output found! Adjusting filenames.. | multiqc | Use -f or --force to overwrite existing reports instead | multiqc | Report : results/qualimap_output/multiqc_report_1.html | multiqc | Data : results/qualimap_output/multiqc_data_1 | multiqc | MultiQC complete Task: IGV visualization and Questions Now you can inspect the alignment files in IGV . First, you will need to download the reference fasta sequence in ../Data/Clover_Data/DNA_Contig1_2.fasta and import it into IGV. You can do the same for the files DNA_Contig1.fasta and DNA_Contig2.fasta that you might need later. In IGV, this is done with the menu Genomes --> Load Genome from file menu and by selecting the relevant fasta file. Then, choose the reference you need from the drop-down menu (see figure below). You will not yet see much, but you can choose one of the two subgenomes (contig 1 or 2) and double click on a chromosome position to inspect the reference sequence. The next step will visualize the mapped files on IGV. Each mapped genome can be seen in IGV against the reference file of choice. To load an aligned file, first download it together with the index file in .bai format. For example, you need to download both results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam and results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam.bai to see this alignment (you need to open only the .bam file with IGV). If you open more files, their alignments will be distributed in the IGV interface, and you can change the size of each visualization yourself (below shown with only one opened alignment). Now compare in IGV the two bam files PacBio_clover_alignment_1.sort.bam and PacBio_clover_alignment_2.sort.bam . What do you observe when comparing the two BAM files? Have a look at the polymorphic regions in IGV. Are they true polymorphisms? Add to the visualization the third alignment PacBio_clover_alignment_1_2_mappb.sort.bam in IGV. Why do you see fluctuations in coverage and large regions without any apparent subgenome SNPs? What are the major differences between the stats for the reads mapped to Contigs1&2 versus contig1 and contig2? What is your interpretation of the differences?","title":"Hifi data mapping"},{"location":"nb/align.html#rna-seq-mapping","text":"In the ../Data folder you will find 24 RNA-seq libraries, 12 S10 libraries and 12 Tienshan libraries. Each library is paired-end, which is denoted by R1 and R2 at the end of two files having the same name, such as S10_1_1.R1.fastq and S10_1_1.R2.fastq . We will align each library separately and then merge the alignments to create two final samples for S10 and Tienshan . First, we need to create a genome file for the reference fasta file of contigs 1 and 2 . This is done with STAR , using the option --runMode genomeGenerate . We also need to convert the gene annotation from gff to gtf format with gffread to allow counting gene transcripts. STAR is a very complex tool with many options, so it is always useful to have a reference manual . In [17]: Copied! %% bash gffread - T - o reference_data / white_clover_genes . gtf ../ Data / Clover_Data / white_clover_genes . gff %%bash gffread -T -o reference_data/white_clover_genes.gtf ../Data/Clover_Data/white_clover_genes.gff In [18]: Copied! %% bash STAR -- runThreadN 8 \\ -- runMode genomeGenerate \\ -- genomeDir results / STAR_output / indexing_contigs_1_2 \\ -- genomeFastaFiles reference_data / DNA_Contig1_2 . fasta \\ -- sjdbGTFfile reference_data / white_clover_genes . gtf %%bash STAR --runThreadN 8 \\ --runMode genomeGenerate \\ --genomeDir results/STAR_output/indexing_contigs_1_2 \\ --genomeFastaFiles reference_data/DNA_Contig1_2.fasta \\ --sjdbGTFfile reference_data/white_clover_genes.gtf STAR --runThreadN 8 --runMode genomeGenerate --genomeDir results/STAR_output/indexing_contigs_1_2 --genomeFastaFiles reference_data/DNA_Contig1_2.fasta --sjdbGTFfile reference_data/white_clover_genes.gtf STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:03 ..... started STAR run Jun 19 10:23:03 ... starting to generate Genome files Jun 19 10:23:03 ..... processing annotations GTF !!!!! WARNING: --genomeSAindexNbases 14 is too large for the genome size=2089554, which may cause seg-fault at the mapping step. Re-run genome generation with recommended --genomeSAindexNbases 9 Jun 19 10:23:03 ... starting to sort Suffix Array. This may take a long time... Jun 19 10:23:03 ... sorting Suffix Array chunks and saving them to disk... Jun 19 10:23:04 ... loading chunks from disk, packing SA... Jun 19 10:23:04 ... finished generating suffix array Jun 19 10:23:04 ... generating Suffix Array index Jun 19 10:23:08 ... completed Suffix Array index Jun 19 10:23:08 ..... inserting junctions into the genome indices Jun 19 10:23:16 ... writing Genome to disk ... Jun 19 10:23:16 ... writing Suffix Array to disk ... Jun 19 10:23:17 ... writing SAindex to disk Jun 19 10:23:18 ..... finished successfully We got a warning saying !!!!! WARNING: --genomeSAindexNbases 14 is too large for the genome size=2089554, which may cause seg-fault at the mapping step. Re-run genome generation with recommended --genomeSAindexNbases 9 meaning we need shorter strings of bases (9 bases instead of 14) to be indexed, as our reference genome is very short, and too long strings would cause many alignment errors. So we rerun the command with the suggested option. In [19]: Copied! %% bash STAR -- runThreadN 8 \\ -- runMode genomeGenerate \\ -- genomeDir results / STAR_output / indexing_contigs_1_2 \\ -- genomeFastaFiles reference_data / DNA_Contig1_2 . fasta \\ -- sjdbGTFfile reference_data / white_clover_genes . gtf \\ -- genomeSAindexNbases 9 %%bash STAR --runThreadN 8 \\ --runMode genomeGenerate \\ --genomeDir results/STAR_output/indexing_contigs_1_2 \\ --genomeFastaFiles reference_data/DNA_Contig1_2.fasta \\ --sjdbGTFfile reference_data/white_clover_genes.gtf \\ --genomeSAindexNbases 9 STAR --runThreadN 8 --runMode genomeGenerate --genomeDir results/STAR_output/indexing_contigs_1_2 --genomeFastaFiles reference_data/DNA_Contig1_2.fasta --sjdbGTFfile reference_data/white_clover_genes.gtf --genomeSAindexNbases 9 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:18 ..... started STAR run Jun 19 10:23:18 ... starting to generate Genome files Jun 19 10:23:18 ..... processing annotations GTF Jun 19 10:23:20 ... starting to sort Suffix Array. This may take a long time... Jun 19 10:23:20 ... sorting Suffix Array chunks and saving them to disk... Jun 19 10:23:21 ... loading chunks from disk, packing SA... Jun 19 10:23:21 ... finished generating suffix array Jun 19 10:23:21 ... generating Suffix Array index Jun 19 10:23:21 ... completed Suffix Array index Jun 19 10:23:22 ..... inserting junctions into the genome indices Jun 19 10:23:23 ... writing Genome to disk ... Jun 19 10:23:23 ... writing Suffix Array to disk ... Jun 19 10:23:23 ... writing SAindex to disk Jun 19 10:23:24 ..... finished successfully We use again STAR to align every single library for S10 . We extract the library name of each file and run STAR through each pair of files. Note that plant introns are very rarely more than 5000 bp and that you are mapping to two homoeologous contigs that show high similarity, especially in genic regions. We set the maximum size to 5000 using --alignIntronMax 5000 . In [20]: Copied! %% bash for i in ` ls ../ Data / Clover_Data / S10 *. R1 . fastq ` do PREFIXNAME = ` basename $ i . R1 . fastq ` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \" $ PREFIXNAME echo \"###############################################\" STAR -- genomeDir results / STAR_output / indexing_contigs_1_2 / \\ -- runThreadN 8 \\ -- runMode alignReads \\ -- readFilesIn ../ Data / Clover_Data / $ PREFIXNAME . R1 . fastq ../ Data / Clover_Data / $ PREFIXNAME . R2 . fastq \\ -- outFileNamePrefix results / STAR_output / S10_align_contigs_1_2 / $ PREFIXNAME \\ -- outSAMtype BAM SortedByCoordinate \\ -- outSAMattributes Standard \\ -- quantMode GeneCounts \\ -- alignIntronMax 5000 done %%bash for i in `ls ../Data/Clover_Data/S10*.R1.fastq` do PREFIXNAME=`basename $i .R1.fastq` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \"$PREFIXNAME echo \"###############################################\" STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ \\ --runThreadN 8 \\ --runMode alignReads \\ --readFilesIn ../Data/Clover_Data/$PREFIXNAME.R1.fastq ../Data/Clover_Data/$PREFIXNAME.R2.fastq \\ --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/$PREFIXNAME \\ --outSAMtype BAM SortedByCoordinate \\ --outSAMattributes Standard \\ --quantMode GeneCounts \\ --alignIntronMax 5000 done ############################################### ##### ALIGNING PAIRED-END READS S10_1_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_1_1.R1.fastq ../Data/Clover_Data/S10_1_1.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_1_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:24 ..... started STAR run Jun 19 10:23:24 ..... loading genome Jun 19 10:23:24 ..... started mapping Jun 19 10:23:35 ..... finished mapping Jun 19 10:23:35 ..... started sorting BAM Jun 19 10:23:37 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_1_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_1_2.R1.fastq ../Data/Clover_Data/S10_1_2.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_1_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:38 ..... started STAR run Jun 19 10:23:38 ..... loading genome Jun 19 10:23:38 ..... started mapping Jun 19 10:23:48 ..... finished mapping Jun 19 10:23:48 ..... started sorting BAM Jun 19 10:23:51 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_1_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_1_3.R1.fastq ../Data/Clover_Data/S10_1_3.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_1_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:52 ..... started STAR run Jun 19 10:23:52 ..... loading genome Jun 19 10:23:52 ..... started mapping Jun 19 10:24:03 ..... finished mapping Jun 19 10:24:03 ..... started sorting BAM Jun 19 10:24:06 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_2_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_2_1.R1.fastq ../Data/Clover_Data/S10_2_1.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_2_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:06 ..... started STAR run Jun 19 10:24:06 ..... loading genome Jun 19 10:24:06 ..... started mapping Jun 19 10:24:16 ..... finished mapping Jun 19 10:24:16 ..... started sorting BAM Jun 19 10:24:18 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_2_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_2_2.R1.fastq ../Data/Clover_Data/S10_2_2.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_2_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:20 ..... started STAR run Jun 19 10:24:20 ..... loading genome Jun 19 10:24:20 ..... started mapping Jun 19 10:24:31 ..... finished mapping Jun 19 10:24:31 ..... started sorting BAM Jun 19 10:24:33 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_2_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_2_3.R1.fastq ../Data/Clover_Data/S10_2_3.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_2_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:33 ..... started STAR run Jun 19 10:24:33 ..... loading genome Jun 19 10:24:33 ..... started mapping Jun 19 10:24:45 ..... finished mapping Jun 19 10:24:45 ..... started sorting BAM Jun 19 10:24:48 ..... finished successfully Do the same alignment for Tienshan libraries In [21]: Copied! %% bash for i in ` ls ../ Data / Clover_Data / TI *. R1 . fastq ` do PREFIXNAME = ` basename $ i . R1 . fastq ` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \" $ PREFIXNAME echo \"###############################################\" STAR -- genomeDir results / STAR_output / indexing_contigs_1_2 / \\ -- runThreadN 8 \\ -- readFilesIn ../ Data / Clover_Data / $ PREFIXNAME . R1 . fastq ../ Data / Clover_Data / $ PREFIXNAME . R2 . fastq \\ -- outFileNamePrefix results / STAR_output / TI_align_contigs_1_2 / $ PREFIXNAME \\ -- outSAMtype BAM SortedByCoordinate \\ -- outSAMattributes Standard \\ -- quantMode GeneCounts \\ -- alignIntronMax 5000 done %%bash for i in `ls ../Data/Clover_Data/TI*.R1.fastq` do PREFIXNAME=`basename $i .R1.fastq` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \"$PREFIXNAME echo \"###############################################\" STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ \\ --runThreadN 8 \\ --readFilesIn ../Data/Clover_Data/$PREFIXNAME.R1.fastq ../Data/Clover_Data/$PREFIXNAME.R2.fastq \\ --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/$PREFIXNAME \\ --outSAMtype BAM SortedByCoordinate \\ --outSAMattributes Standard \\ --quantMode GeneCounts \\ --alignIntronMax 5000 done ############################################### ##### ALIGNING PAIRED-END READS TI_1_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_1_1.R1.fastq ../Data/Clover_Data/TI_1_1.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_1_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:50 ..... started STAR run Jun 19 10:24:50 ..... loading genome Jun 19 10:24:50 ..... started mapping Jun 19 10:25:03 ..... finished mapping Jun 19 10:25:04 ..... started sorting BAM Jun 19 10:25:18 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_1_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_1_2.R1.fastq ../Data/Clover_Data/TI_1_2.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_1_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:25:18 ..... started STAR run Jun 19 10:25:18 ..... loading genome Jun 19 10:25:18 ..... started mapping Jun 19 10:25:30 ..... finished mapping Jun 19 10:25:30 ..... started sorting BAM Jun 19 10:25:33 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_1_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_1_3.R1.fastq ../Data/Clover_Data/TI_1_3.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_1_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:25:36 ..... started STAR run Jun 19 10:25:36 ..... loading genome Jun 19 10:25:36 ..... started mapping Jun 19 10:25:50 ..... finished mapping Jun 19 10:25:50 ..... started sorting BAM Jun 19 10:25:51 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_2_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_2_1.R1.fastq ../Data/Clover_Data/TI_2_1.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_2_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:25:51 ..... started STAR run Jun 19 10:25:51 ..... loading genome Jun 19 10:25:52 ..... started mapping Jun 19 10:26:06 ..... finished mapping Jun 19 10:26:06 ..... started sorting BAM Jun 19 10:26:09 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_2_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_2_2.R1.fastq ../Data/Clover_Data/TI_2_2.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_2_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:26:09 ..... started STAR run Jun 19 10:26:09 ..... loading genome Jun 19 10:26:09 ..... started mapping Jun 19 10:26:23 ..... finished mapping Jun 19 10:26:23 ..... started sorting BAM Jun 19 10:26:31 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_2_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_2_3.R1.fastq ../Data/Clover_Data/TI_2_3.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_2_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:26:31 ..... started STAR run Jun 19 10:26:31 ..... loading genome Jun 19 10:26:31 ..... started mapping Jun 19 10:26:46 ..... finished mapping Jun 19 10:26:46 ..... started sorting BAM Jun 19 10:26:49 ..... finished successfully Run quality control on each aligned library with MultiQC . In this way there will be a whole report to compare S10 files and Tienshan files. In [22]: Copied! %% bash multiqc -- outdir results / multiqc_output / TI_STAR_align_1_2 \\ results / STAR_output / TI_align_contigs_1_2 / %%bash multiqc --outdir results/multiqc_output/TI_STAR_align_1_2 \\ results/STAR_output/TI_align_contigs_1_2/ / / / \u001b]8;id=121575;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/STAR_output/TI_align_contigs_1_2 | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 36/36 og.o\u2026 | star | Found 6 reports and 6 gene count files | multiqc | Compressing plot data | multiqc | Report : results/multiqc_output/TI_STAR_align_1_2/multiqc_report.html | multiqc | Data : results/multiqc_output/TI_STAR_align_1_2/multiqc_data | multiqc | MultiQC complete In [23]: Copied! %% bash multiqc -- outdir results / multiqc_output / S10_STAR_align_1_2 \\ results / STAR_output / S10_align_contigs_1_2 / %%bash multiqc --outdir results/multiqc_output/S10_STAR_align_1_2 \\ results/STAR_output/S10_align_contigs_1_2/ / / / \u001b]8;id=596946;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/STAR_output/S10_align_contigs_1_2 | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 36/36 ut.t\u2026 | star | Found 6 reports and 6 gene count files | multiqc | Compressing plot data | multiqc | Report : results/multiqc_output/S10_STAR_align_1_2/multiqc_report.html | multiqc | Data : results/multiqc_output/S10_STAR_align_1_2/multiqc_data | multiqc | MultiQC complete We merge the outputs of each group of aligned libraries. Here is how the files look like for the Tienshan . In [24]: Copied! ! ls - lh results / STAR_output / TI_align_contigs_1_2 / TI_ *. sortedByCoord . out . bam !ls -lh results/STAR_output/TI_align_contigs_1_2/TI_*.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 6.6M Jun 19 10:25 results/STAR_output/TI_align_contigs_1_2/TI_1_1Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 6.0M Jun 19 10:25 results/STAR_output/TI_align_contigs_1_2/TI_1_2Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 7.1M Jun 19 10:25 results/STAR_output/TI_align_contigs_1_2/TI_1_3Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 9.6M Jun 19 10:26 results/STAR_output/TI_align_contigs_1_2/TI_2_1Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 7.2M Jun 19 10:26 results/STAR_output/TI_align_contigs_1_2/TI_2_2Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 8.2M Jun 19 10:26 results/STAR_output/TI_align_contigs_1_2/TI_2_3Aligned.sortedByCoord.out.bam Apply samtools merge In [25]: Copied! %% bash mkdir - p results / STAR_output / TI_align_contigs_1_2_merge / samtools merge - f results / STAR_output / TI_align_contigs_1_2_merge / TI . sorted . bam results / STAR_output / TI_align_contigs_1_2 / TI_ *. sortedByCoord . out . bam %%bash mkdir -p results/STAR_output/TI_align_contigs_1_2_merge/ samtools merge -f results/STAR_output/TI_align_contigs_1_2_merge/TI.sorted.bam results/STAR_output/TI_align_contigs_1_2/TI_*.sortedByCoord.out.bam In [26]: Copied! %% bash mkdir - p results / STAR_output / S10_align_contigs_1_2_merge / samtools merge - f results / STAR_output / S10_align_contigs_1_2_merge / S10 . sorted . bam results / STAR_output / S10_align_contigs_1_2 / S10_ *. sortedByCoord . out . bam %%bash mkdir -p results/STAR_output/S10_align_contigs_1_2_merge/ samtools merge -f results/STAR_output/S10_align_contigs_1_2_merge/S10.sorted.bam results/STAR_output/S10_align_contigs_1_2/S10_*.sortedByCoord.out.bam Index both merging outputs. A file in format bam.bai will appear in their respective folders. In [27]: Copied! %% bash samtools index results / STAR_output / S10_align_contigs_1_2_merge / S10 . sorted . bam %%bash samtools index results/STAR_output/S10_align_contigs_1_2_merge/S10.sorted.bam In [28]: Copied! %% bash samtools index results / STAR_output / TI_align_contigs_1_2_merge / TI . sorted . bam %%bash samtools index results/STAR_output/TI_align_contigs_1_2_merge/TI.sorted.bam","title":"RNA-seq mapping"},{"location":"nb/align.html#wrapping-up","text":"In this exercise, you learnt to align various types of data after performing quality control for raw data. We looked at some of the options for the aligners and at how to use some of the basic samtools manipulation programs. 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padding-left: 5px; padding-right: 5px; } td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); RNA-seq differential gene expression This tutorial will cover the steps for performing Differential Gene Expression on the RNA-seq data obtained from Galaxy. At the end of this tutorial you will be able to use R to Perform preliminary analyses of the RNA-seq results Find differentially expressed genes between two conditions using edgeR </div> The present tutorial, like the rest of the course material, is available at our open-source github repository . To use this notebook, use the NGS (R) kernel that contains the packages. Choose it by selecting Kernel -> Change Kernel in the menu on top of the window. A few introductory points to run this notebook (click to show): To use this notebook, use the NGS (R) kernel that contains the packages. Choose it by selecting Kernel -> Change Kernel in the menu on top of the window. In this notebook you will use only R commands On some computers, you might see the result of the commands once they are done running. This means you will wait some time while the computer is crunching, and only afterwards you will see the result of the command you have executed You can run the code in each cell by clicking on the run cell button, or by pressing Shift + Enter . When the code is done running, a small green check sign will appear on the left side You need to run the cells in sequential order, please do not run a cell until the one above finished running and do not skip any cells Each cell contains a short description of the code and the output you should get. Please try not to focus on understanding the code for each command in too much detail, but rather try to focus on the output You can create new code cells by pressing + in the Menu bar above. </details> Load the necessary R libraries In [1]: Copied! library ( VennDiagram ) library ( dplyr ) library ( tibble ) library ( formattable ) library ( mixOmics ) library ( pheatmap ) library ( edgeR ) library(VennDiagram) library(dplyr) library(tibble) library(formattable) library(mixOmics) library(pheatmap) library(edgeR) Warning message: \u201cpackage \u2018VennDiagram\u2019 was built under R version 4.1.3\u201d Loading required package: grid Loading required package: futile.logger Warning message: \u201cpackage \u2018futile.logger\u2019 was built under R version 4.1.3\u201d Warning message: \u201cpackage \u2018dplyr\u2019 was built under R version 4.1.3\u201d Attaching package: \u2018dplyr\u2019 The following objects are masked from \u2018package:stats\u2019: filter, lag The following objects are masked from \u2018package:base\u2019: intersect, setdiff, setequal, union Warning message: \u201cpackage \u2018tibble\u2019 was built under R version 4.1.3\u201d Warning message: \u201cpackage \u2018formattable\u2019 was built under R version 4.1.3\u201d Loading required package: MASS Warning message: \u201cpackage \u2018MASS\u2019 was built under R version 4.1.3\u201d Attaching package: \u2018MASS\u2019 The following object is masked from \u2018package:formattable\u2019: area The following object is masked from \u2018package:dplyr\u2019: select Loading required package: lattice Warning message: \u201cpackage \u2018lattice\u2019 was built under R version 4.1.3\u201d Loading required package: ggplot2 Warning message: \u201cpackage \u2018ggplot2\u2019 was built under R version 4.1.3\u201d Loaded mixOmics 6.17.26 Thank you for using mixOmics! Tutorials: http://mixomics.org Bookdown vignette: https://mixomicsteam.github.io/Bookdown Questions, issues: Follow the prompts at http://mixomics.org/contact-us Cite us: citation('mixOmics') Warning message: \u201cpackage \u2018pheatmap\u2019 was built under R version 4.1.3\u201d Warning message: \u201cpackage \u2018edgeR\u2019 was built under R version 4.1.3\u201d Loading required package: limma Warning message: \u201cpackage \u2018limma\u2019 was built under R version 4.1.3\u201d EdgeR - Data filtering and Normalization \u00b6 We will use the edgeR package to test for differentially expressed genes between our Control and Treatment Samples. You can read more about the edgeR package and its functionalities here . This exercise largely follows their user manual. File processing \u00b6 The data for this exercise comes from the 12 tabular files with Reads per Gene counts generated by STAR Mapping in the raw-data alignment part of this course. We want to create a table where each column is a sample, and the content of the table are the read counts from STAR. We must merge the 12 files with Reads per Gene information into a single file. If you aligned datasets in the first notebook with jupyterlab, then you will find the files using the following command: In [2]: Copied! samples <- sort ( system ( \"find results/STAR_output/*_align_contigs_1_2 -name \\\"*ReadsPerGene.out.tab\\\"\" , intern = TRUE )) print ( samples ) Read_counts <- do.call ( cbind , lapply ( samples , function ( x ) read.delim ( file = x , header = FALSE ))) samples < - sort(system(\"find results/STAR_output/*_align_contigs_1_2 -name \\\"*ReadsPerGene.out.tab\\\"\", intern=TRUE)) print(samples) Read_counts < - do.call(cbind, lapply(samples, function(x) read.delim(file=x, header = FALSE))) [1] \"results/STAR_output/S10_align_contigs_1_2/S10_1_1ReadsPerGene.out.tab\" [2] \"results/STAR_output/S10_align_contigs_1_2/S10_1_2ReadsPerGene.out.tab\" [3] \"results/STAR_output/S10_align_contigs_1_2/S10_1_3ReadsPerGene.out.tab\" [4] \"results/STAR_output/S10_align_contigs_1_2/S10_2_1ReadsPerGene.out.tab\" [5] \"results/STAR_output/S10_align_contigs_1_2/S10_2_2ReadsPerGene.out.tab\" [6] \"results/STAR_output/S10_align_contigs_1_2/S10_2_3ReadsPerGene.out.tab\" [7] \"results/STAR_output/TI_align_contigs_1_2/TI_1_1ReadsPerGene.out.tab\" [8] \"results/STAR_output/TI_align_contigs_1_2/TI_1_2ReadsPerGene.out.tab\" [9] \"results/STAR_output/TI_align_contigs_1_2/TI_1_3ReadsPerGene.out.tab\" [10] \"results/STAR_output/TI_align_contigs_1_2/TI_2_1ReadsPerGene.out.tab\" [11] \"results/STAR_output/TI_align_contigs_1_2/TI_2_2ReadsPerGene.out.tab\" [12] \"results/STAR_output/TI_align_contigs_1_2/TI_2_3ReadsPerGene.out.tab\" If you aligned datasets interactively with Galaxy , then you will need to create the folder tabular_files into the results folder copy the tabular files from STAR into the created folder. Each file must have the sample name and end by ReadsPerGene.out.tab . For example S10_1_1ReadsPerGene.out.tab run the following commands removing the # symbol In [ ]: Copied! #samples <- sort(system(\"find results/tabular_files/ -name \\\"*ReadsPerGene.out.tab\\\"\", intern=TRUE)) #print(samples) #Read_counts <- do.call(cbind, lapply(samples, function(x) read.delim(file=x, header = FALSE))) #samples < - sort(system(\"find results/tabular_files/ -name \\\"*ReadsPerGene.out.tab\\\"\", intern=TRUE)) #print(samples) #Read_counts < - do.call(cbind, lapply(samples, function(x) read.delim(file=x, header = FALSE))) The data frame has genes as rows and samples as columns and stores the gene expression counts (value representing the total number of sequence reads that originated from a particular gene in a sample) for each of the 12 samples. This data frame should have 12 columns and 366 rows. In [3]: Copied! rownames ( Read_counts ) <- Read_counts [, 1 ] Read_counts <- Read_counts [ c ( 5 : nrow ( Read_counts )), c ( seq ( 2 , 46 , by = 4 ))] colnames ( Read_counts ) <- c ( \"S10_1_1\" , \"S10_1_2\" , \"S10_1_3\" , \"S10_2_1\" , \"S10_2_2\" , \"S10_2_3\" , \"TI_1_1\" , \"TI_1_2\" , \"TI_1_3\" , \"TI_2_1\" , \"TI_2_2\" , \"TI_2_3\" ) head ( Read_counts , n = 10 ) dim ( Read_counts ) # dimensions of the data frame rownames(Read_counts) < - Read_counts[,1] Read_counts < - Read_counts[c(5:nrow(Read_counts)), c(seq(2, 46, by=4))] colnames(Read_counts) < - c(\"S10_1_1\", \"S10_1_2\", \"S10_1_3\", \"S10_2_1\", \"S10_2_2\", \"S10_2_3\", \"TI_1_1\", \"TI_1_2\", \"TI_1_3\", \"TI_2_1\", \"TI_2_2\", \"TI_2_3\") head(Read_counts, n=10) dim(Read_counts) # dimensions of the data frame A data.frame: 10 \u00d7 12 S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> g175 143 226 227 217 206 193 234 205 198 221 206 165 g176 17 12 15 8 14 18 29 19 30 6 11 23 g177 0 0 0 0 0 0 0 0 0 0 0 0 g178 0 0 0 0 0 0 0 0 2 2 1 2 g179 0 0 0 0 0 0 0 0 0 0 0 0 g180 2773 5223 4350 4139 3209 4644 1015 1313 889 2675 1023 2565 g181 473 679 583 437 390 450 642 700 697 650 459 410 g182 19 15 20 9 8 4 13 7 8 6 5 4 g183 0 0 0 0 0 0 0 0 0 0 0 0 g184 30 22 50 57 103 83 36 29 18 107 69 89 .list-inline {list-style: none; margin:0; padding: 0} .list-inline>li {display: inline-block} .list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex} 366 12 Import the Metadata table. This file, as its name suggests, contains information about each of the 12 RNA-seq samples, such as the treatment (Condition), genotype and replicate. Note that the order of the rows in the Metadata table should be the same as the columns in the Read_counts file generated above. In [4]: Copied! metadata <- read.csv ( \"../Data/Clover_Data/metadata.csv\" , sep = \";\" , row.names = 1 , stringsAsFactors = TRUE ) metadata metadata < - read.csv(\"../Data/Clover_Data/metadata.csv\", sep =\";\", row.names=1, stringsAsFactors=TRUE) metadata A data.frame: 12 \u00d7 3 Condition Genotype Replicate <fct> <fct> <int> S10_1_1 Control S10 1 S10_1_2 Control S10 2 S10_1_3 Control S10 3 S10_2_1 Treatment S10 1 S10_2_2 Treatment S10 2 S10_2_3 Treatment S10 3 TI_1_1 Control Tienshan 1 TI_1_2 Control Tienshan 2 TI_1_3 Control Tienshan 3 TI_2_1 Treatment Tienshan 1 TI_2_2 Treatment Tienshan 2 TI_2_3 Treatment Tienshan 3 In order to aid the following steps, we will create a Group for each sample (a new column in the metadata) based on the Genotype&Condition of each sample and assign the three replicates to this group. In [5]: Copied! Group <- factor ( paste ( metadata $ Genotype , metadata $ Condition , sep = \"_\" )) metadata <- cbind ( metadata , Group = Group ) metadata Group < - factor(paste(metadata$Genotype, metadata$Condition, sep=\"_\")) metadata < - cbind(metadata,Group=Group) metadata A data.frame: 12 \u00d7 4 Condition Genotype Replicate Group <fct> <fct> <int> <fct> S10_1_1 Control S10 1 S10_Control S10_1_2 Control S10 2 S10_Control S10_1_3 Control S10 3 S10_Control S10_2_1 Treatment S10 1 S10_Treatment S10_2_2 Treatment S10 2 S10_Treatment S10_2_3 Treatment S10 3 S10_Treatment TI_1_1 Control Tienshan 1 Tienshan_Control TI_1_2 Control Tienshan 2 Tienshan_Control TI_1_3 Control Tienshan 3 Tienshan_Control TI_2_1 Treatment Tienshan 1 Tienshan_Treatment TI_2_2 Treatment Tienshan 2 Tienshan_Treatment TI_2_3 Treatment Tienshan 3 Tienshan_Treatment Create the DGEList object \u00b6 We will merge the read counts and the metadata into a list-based data object named DGEList, which can be manipulated as any list object in R. The main components of the DGEList object are the matrix \"counts\" containing our read per gene counts and a data.frame \"samples\" containing the metadata. Note that all the genes with zero counts across all samples were eliminated. In [6]: Copied! DGEList <- DGEList ( Read_counts , remove.zeros = TRUE ) DGEList $ samples $ Condition <- relevel ( metadata $ Condition , ref = \"Control\" ) DGEList $ samples $ Genotype <- metadata $ Genotype DGEList $ samples $ group <- metadata $ Group DGEList DGEList < - DGEList(Read_counts, remove.zeros = TRUE) DGEList$samples$Condition < - relevel(metadata$Condition, ref = \"Control\") DGEList$samples$Genotype < - metadata$Genotype DGEList$samples$group < - metadata$Group DGEList Removing 99 rows with all zero counts $counts A matrix: 267 \u00d7 12 of type int S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g175 143 226 227 217 206 193 234 205 198 221 206 165 g176 17 12 15 8 14 18 29 19 30 6 11 23 g178 0 0 0 0 0 0 0 0 2 2 1 2 g180 2773 5223 4350 4139 3209 4644 1015 1313 889 2675 1023 2565 g181 473 679 583 437 390 450 642 700 697 650 459 410 g182 19 15 20 9 8 4 13 7 8 6 5 4 g184 30 22 50 57 103 83 36 29 18 107 69 89 g185 0 2 0 0 0 0 0 1 0 0 0 1 g186 1 0 0 0 0 0 0 0 0 0 0 0 g187 1 1 2 0 1 1 58 49 57 28 32 29 g188 316 285 417 200 184 322 1276 855 1341 402 809 547 g190 14 18 29 25 17 31 7 6 12 20 7 6 g191 2 2 1 1 0 0 0 0 0 0 0 0 g192 1 1 1 1 1 0 6 10 2 3 8 2 g193 208 231 301 289 161 279 70 61 60 128 160 91 g194 40 41 37 44 17 43 8 10 7 8 105 25 g195 54 85 85 71 54 43 56 54 42 67 68 56 g196 77 120 118 186 152 177 5 9 5 42 20 34 g197 1 6 5 18 14 21 15 44 12 32 16 17 g198 14 27 10 11 2 2 6 6 1 2 2 0 g199 0 0 0 0 0 0 0 0 0 0 1 0 g200 57 80 94 11 8 23 89 47 67 5 47 5 g203 815 1188 1412 1139 1034 1127 911 852 662 892 781 784 g204 250 312 348 277 275 264 125 104 133 119 91 130 g205 109 132 181 140 114 151 89 73 66 78 46 50 g206 0 1 2 7 4 9 4 0 7 0 1 2 g207 0 1 0 0 4 3 1 3 4 4 3 3 g208 0 1 0 0 0 0 0 1 0 1 0 0 g209 0 0 0 0 0 0 0 0 1 0 0 0 g210 52 76 60 12 12 13 61 73 94 54 45 21 \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee g135 0 0 0 1 0 0 5 1 0 2 0 0 g139 0 1 1 0 0 0 12 13 11 4 3 3 g140 0 5 1 0 0 2 5 4 0 0 2 0 g141 177 217 224 131 117 166 117 85 67 80 79 77 g142 488 713 557 241 154 228 983 1057 1023 1075 794 933 g144 3319 3987 4763 2249 1996 2260 4405 3880 4585 5072 3618 3590 g146 293 330 350 913 775 731 108 92 115 431 151 226 g147 6 3 3 0 8 10 1 1 1 0 1 0 g148 19 30 24 15 4 12 23 19 25 10 19 8 g149 35 62 65 57 58 53 114 103 91 92 79 93 g150 263 411 278 752 1378 1300 106 112 70 516 200 508 g151 44 44 50 92 92 84 23 26 25 56 43 31 g152 242 258 294 425 381 521 15 8 8 16 21 15 g153 142 144 181 233 177 233 31 49 50 50 45 57 g155 0 4 2 1 0 0 1 0 3 0 0 1 g156 0 0 0 0 0 0 0 3 2 0 0 1 g157 6 1 3 223 52 152 4 7 4 153 47 155 g158 0 0 0 7 1 3 0 0 1 1 0 0 g161 25 38 18 1 9 2 63 23 43 0 24 6 g162 31 43 38 43 25 28 0 0 0 0 0 0 g163 1 0 12 37 8 18 9 4 8 16 13 15 g164 69 58 97 294 123 168 56 58 60 146 101 135 g165 203 376 594 1343 2062 1610 265 229 214 488 270 490 g167 2 0 0 2 0 0 0 0 0 0 0 0 g168 2 14 6 0 0 5 31 26 38 3 27 10 g169 0 0 1 0 1 1 0 0 0 0 0 0 g170 0 0 0 0 0 0 0 1 0 0 0 0 g171 0 5 0 0 0 5 0 0 0 1 0 0 g172 1 2 2 6 4 3 3 1 2 3 3 0 g173 80 107 120 78 138 121 67 86 69 96 105 105 $samples A data.frame: 12 \u00d7 5 group lib.size norm.factors Condition Genotype <fct> <dbl> <dbl> <fct> <fct> S10_1_1 S10_Control 43614 1 Control S10 S10_1_2 S10_Control 58803 1 Control S10 S10_1_3 S10_Control 62654 1 Control S10 S10_2_1 S10_Treatment 59909 1 Treatment S10 S10_2_2 S10_Treatment 56761 1 Treatment S10 S10_2_3 S10_Treatment 63152 1 Treatment S10 TI_1_1 Tienshan_Control 43387 1 Control Tienshan TI_1_2 Tienshan_Control 38355 1 Control Tienshan TI_1_3 Tienshan_Control 40304 1 Control Tienshan TI_2_1 Tienshan_Treatment 55509 1 Treatment Tienshan TI_2_2 Tienshan_Treatment 39909 1 Treatment Tienshan TI_2_3 Tienshan_Treatment 45008 1 Treatment Tienshan Preliminary data analysis \u00b6 First, we will calculate the \"pseudoCounts\" as log2 of the reads per gene counts. This is not part of the actual differential gene expression analysis but is helpful for data exploration and quality assessment. We will look at a histogram of one of the samples and a boxplot representation of the log2 counts for all the 12 samples. Note that there are many genes with a low number of mapped reads and that there are differences between the average read counts for each library. In [7]: Copied! pseudoCounts <- log2 ( DGEList $ counts + 1 ) head ( pseudoCounts ) hist ( pseudoCounts [ , \"S10_1_1\" ], main = \"\" , xlab = \"Read counts\" ) boxplot ( pseudoCounts , col = \"gray\" , las = 3 , cex.names = 1 ) pseudoCounts < - log2(DGEList$counts + 1) head(pseudoCounts) hist(pseudoCounts[ ,\"S10_1_1\"], main = \"\", xlab = \"Read counts\") boxplot(pseudoCounts, col = \"gray\", las = 3, cex.names = 1) A matrix: 6 \u00d7 12 of type dbl S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g175 7.169925 7.826548 7.832890 7.768184 7.693487 7.599913 7.876517 7.686501 7.636625 7.794416 7.693487 7.375039 g176 4.169925 3.700440 4.000000 3.169925 3.906891 4.247928 4.906891 4.321928 4.954196 2.807355 3.584963 4.584963 g178 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.584963 1.584963 1.000000 1.584963 g180 11.437752 12.350939 12.087131 12.015415 11.648358 12.181463 9.988685 10.359750 9.797662 11.385862 10.000000 11.325305 g181 8.888743 9.409391 9.189825 8.774787 8.611025 8.816984 9.328675 9.453271 9.447083 9.346514 8.845490 8.682995 g182 4.321928 4.000000 4.392317 3.321928 3.169925 2.321928 3.807355 3.000000 3.169925 2.807355 2.584963 2.321928 We can also create a PCA plot of the samples in order to assess the differences between the Genotypes and Conditions, but also between the replicates. In this plot the samples that are similar cluster together, while samples that are different are further apart. In this type of plot, we would expect samples from the same group (the three replicates for each sample) to exhibit a similar gene expression profile thus clustering together while being separated from the other samples. In [8]: Copied! resPCA <- pca ( t ( pseudoCounts ), ncomp = 6 ) plotIndiv ( resPCA , group = metadata $ Genotype , pch = metadata $ Condition , legend = T , legend.title = 'Genotype' , legend.title.pch = 'Condition' , title = 'PCA plot raw counts' , style = 'ggplot2' , size.xlabel = 10 , size.ylabel = 10 ) resPCA < - pca(t(pseudoCounts), ncomp = 6) plotIndiv(resPCA, group = metadata$Genotype, pch=metadata$Condition, legend = T, legend.title = 'Genotype', legend.title.pch = 'Condition', title = 'PCA plot raw counts', style = 'ggplot2', size.xlabel = 10, size.ylabel = 10) Filtering the lowly expressed genes \u00b6 As seen previously, many genes have a low number of read counts in our samples. The genes with very low counts across all libraries provide little evidence for differential expression, thus we should eliminate these genes before the analysis. One of the filtering methods we can use is the \"filterByExpr\" function provided by the edgeR package. By default, this function keeps only the genes that have at least 10 reads per group, but other cutoffs can also be applied. In [9]: Copied! keep <- filterByExpr ( DGEList , group = metadata $ Group ) #create the filter DGEList <- DGEList [ keep , , keep.lib.sizes = FALSE ] #apply the filter to on the DGEList object table ( keep ) #Check the number of genes that passed the filter keep < - filterByExpr(DGEList, group=metadata$Group) #create the filter DGEList < - DGEList[keep, , keep.lib.sizes=FALSE] #apply the filter to on the DGEList object table(keep) #Check the number of genes that passed the filter keep FALSE TRUE 104 163 Normalization \u00b6 As we are working with multiple samples we need to normalize the read counts per gene in order to account for compositional and technical differences between the 12 RNA-seq libraries. For this, we will calculate normalization factors using the trimmed mean of M-values (TMM) method. You can read more about different normalization methods in the user manual. Note that running \"calcNormFactors\" does not change the actual reads per gene counts, it just fills the \"norm.factors\" column in DGEList$samples . These factors will be used to scale the read counts for each library. From the user guide: \"A normalization factor below one indicates that a small number of high count genes are monopolizing the sequencing, causing the counts for other genes to be lower than would be usual given the library size.\" In [10]: Copied! DGEList <- calcNormFactors ( DGEList , method = \"RLE\" ) DGEList $ samples DGEList < - calcNormFactors(DGEList, method=\"RLE\") DGEList$samples A data.frame: 12 \u00d7 5 group lib.size norm.factors Condition Genotype <fct> <dbl> <dbl> <fct> <fct> S10_1_1 S10_Control 43506 1.0897129 Control S10 S10_1_2 S10_Control 58660 1.0385093 Control S10 S10_1_3 S10_Control 62512 1.0362962 Control S10 S10_2_1 S10_Treatment 59741 0.9802860 Treatment S10 S10_2_2 S10_Treatment 56653 0.8418644 Treatment S10 S10_2_3 S10_Treatment 63008 0.8949144 Treatment S10 TI_1_1 Tienshan_Control 43174 1.0618440 Control Tienshan TI_1_2 Tienshan_Control 38203 1.0431678 Control Tienshan TI_1_3 Tienshan_Control 40178 1.0676351 Control Tienshan TI_2_1 Tienshan_Treatment 55391 0.9048196 Treatment Tienshan TI_2_2 Tienshan_Treatment 39810 1.1235077 Treatment Tienshan TI_2_3 Tienshan_Treatment 44910 0.9603761 Treatment Tienshan Normalized counts - Exploratory Data analysis \u00b6 For data analysis purposes normalized log2 counts can be extracted from the DGEList object using the function CPM (counts per million). We will generate the same plots as for the raw counts in order to compare the data before and after normalization. Do the plots for normalized counts look different compared with the plots computed before data filtering and normalization? In [11]: Copied! pseudoNormCounts <- cpm ( DGEList , log = TRUE , prior.count = 1 ) head ( pseudoNormCounts ) hist ( pseudoNormCounts [ , \"S10_1_1\" ], main = \"\" , xlab = \"counts\" ) boxplot ( pseudoNormCounts , col = \"gray\" , las = 3 , cex.names = 1 ) resPCA <- pca ( t ( pseudoNormCounts ), ncomp = 6 ) plotIndiv ( resPCA , group = metadata $ Genotype , pch = metadata $ Condition , legend = T , legend.title = 'Genotype' , legend.title.pch = 'Condition' , title = 'PCA plot normalized counts' , style = 'ggplot2' , size.xlabel = 10 , size.ylabel = 10 ) pseudoNormCounts < - cpm(DGEList, log = TRUE, prior.count = 1) head(pseudoNormCounts) hist(pseudoNormCounts[ ,\"S10_1_1\"], main = \"\", xlab = \"counts\") boxplot(pseudoNormCounts, col = \"gray\", las = 3, cex.names = 1) resPCA < - pca(t(pseudoNormCounts), ncomp = 6) plotIndiv(resPCA, group = metadata$Genotype, pch=metadata$Condition, legend = T, legend.title = 'Genotype', legend.title.pch = 'Condition', title = 'PCA plot normalized counts', style = 'ggplot2', size.xlabel = 10, size.ylabel = 10) A matrix: 6 \u00d7 12 of type dbl S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g175 11.568005 11.864813 11.782957 11.863090 12.083127 11.749272 12.323049 12.334198 12.178544 12.112851 12.175386 11.908906 g176 8.564108 7.760868 7.974199 7.290213 8.292080 8.405708 9.349782 8.956131 9.490388 7.125494 8.054467 9.111569 g180 15.836359 16.387906 16.035457 16.109276 16.038322 16.329943 14.435626 15.008665 14.340400 15.704307 14.482504 15.860317 g181 13.287211 13.446752 13.138771 12.869139 13.000828 12.965820 13.775545 14.101988 13.989757 13.664955 13.327806 13.217591 g182 8.716557 8.056048 8.360376 7.439590 7.551908 6.505541 8.245462 7.611500 7.689402 7.125494 7.041192 6.815835 g184 9.350249 8.573811 9.628852 9.955925 11.089735 10.542877 9.653135 9.546090 8.779819 11.073313 10.609695 11.024686 EdgeR - Testing for Differentially expressed genes (DEGs) \u00b6 We use edgeR for Differential Gene Expression using the GLM method (Generalized linear model) with treatment and genotype effects After we concluded the exploratory data analysis and the filtering and normalization steps we can now start testing for differentially expressed genes between our samples. For this, we first need to define a \"design matrix\" which describes our experimental design. We construct a matrix containing samples as rows and our previously defined groups (S10_Control, S10_Treatment, Tienshan_Control, Tienshan_Treatment) as columns. The samples belonging to each group are assigned a \"1\" in this matrix. In [12]: Copied! design.matrix <- model.matrix ( ~ 0 + Group ) rownames ( design.matrix ) <- colnames ( DGEList ) colnames ( design.matrix ) <- levels ( metadata $ Group ) design.matrix design.matrix < - model.matrix(~0+Group) rownames(design.matrix) < - colnames(DGEList) colnames(design.matrix) < - levels(metadata$Group) design.matrix A matrix: 12 \u00d7 4 of type dbl S10_Control S10_Treatment Tienshan_Control Tienshan_Treatment S10_1_1 1 0 0 0 S10_1_2 1 0 0 0 S10_1_3 1 0 0 0 S10_2_1 0 1 0 0 S10_2_2 0 1 0 0 S10_2_3 0 1 0 0 TI_1_1 0 0 1 0 TI_1_2 0 0 1 0 TI_1_3 0 0 1 0 TI_2_1 0 0 0 1 TI_2_2 0 0 0 1 TI_2_3 0 0 0 1 For easier pairwise comparisons between the groups we can create some \"contrasts\" using the \"makeContrasts\" function. This will tell the program which samples we will like to consider as our \"Control\" and which as our \"Treatment\" groups. The Control groups will be assigned a \"-1\" and the Treatment groups a \"1\". When using multiple Control and Treatment samples in the same contrast we need to devide by the number of samples used as for example in the contrast S10_Tienshan, where we use both S10 and Ti samples. For example, the contrast S10 = S10_Treatment - S10_Control will test for differentially expressed genes between the S10 treatment samples vs the S10 Control samples. In [13]: Copied! contrasts <- makeContrasts ( S10 = S10_Treatment - S10_Control , Tienshan = Tienshan_Treatment - Tienshan_Control , S10_Tienshan = ( S10_Treatment + Tienshan_Treatment ) / 2 - ( S10_Control + Tienshan_Control ) / 2 , levels = design.matrix ) contrasts contrasts < - makeContrasts( S10 = S10_Treatment-S10_Control, Tienshan = Tienshan_Treatment-Tienshan_Control, S10_Tienshan= (S10_Treatment+Tienshan_Treatment)/2 - (S10_Control+Tienshan_Control)/2, levels=design.matrix) contrasts A matrix: 4 \u00d7 3 of type dbl S10 Tienshan S10_Tienshan S10_Control -1 0 -0.5 S10_Treatment 1 0 0.5 Tienshan_Control 0 -1 -0.5 Tienshan_Treatment 0 1 0.5 Any ideas for other contrasts that migth be interesting to explore? The following cell will run two mandatory steps in order to fit a model in edgeR. First, the dispersion needs to be estimated, this is a measure of the degree of inter-library variation for each gene. Afterwards, a QL ( quasi-likelihood) model representing the sample design is fitted using the \"glmQLFit\" function. You can read more about these steps in the edgeR user guide. In [14]: Copied! DGEList <- estimateDisp ( DGEList , design.matrix ) fit <- glmQLFit ( DGEList , design.matrix ) DGEList < - estimateDisp(DGEList, design.matrix) fit < - glmQLFit(DGEList, design.matrix) DEGs for White clover S10 plants Treatment vs Control \u00b6 Find DEGs for White clover S10 plants in Treatment condition compared with the Control condition We will test for differentially expressed genes using the quasi-likelihood F-tests method. This is easily done just by running the \"glmQFTest\" function on the fit model and selecting one of the contrasts we created above (in this case the \"S10\" contrast). Next, we will use the topTags function to extract the information about all the genes. In [15]: Copied! glmqlf_S10 <- glmQLFTest ( fit , contrast = contrasts [, \"S10\" ]) DEG_S10 <- topTags ( glmqlf_S10 , n = nrow ( DGEList $ counts )) DEG_S10 glmqlf_S10 < - glmQLFTest(fit, contrast=contrasts[,\"S10\"]) DEG_S10 < - topTags(glmqlf_S10, n = nrow(DGEList$counts)) DEG_S10 $table A data.frame: 163 \u00d7 5 logFC logCPM F PValue FDR <dbl> <dbl> <dbl> <dbl> <dbl> g39 3.5861702 14.124630 336.66158 5.227331e-10 8.520550e-08 g232 4.0494125 11.950441 157.50997 3.685988e-08 2.591779e-06 g6 2.3225347 12.999541 150.27854 4.770146e-08 2.591779e-06 g350 2.0530029 13.324566 97.56453 4.890245e-07 1.992775e-05 g99 -1.5387874 11.681004 85.17478 9.976659e-07 3.195090e-05 g142 -1.4235276 13.830692 82.52308 1.176107e-06 3.195090e-05 g240 1.3603712 14.708794 79.98114 1.383201e-06 3.220882e-05 g269 -1.4191681 11.834217 75.66563 1.841279e-06 3.751606e-05 g26 2.1254241 11.366827 68.55898 3.047273e-06 5.518949e-05 g129 -5.3324794 8.679594 60.45459 5.738115e-06 9.353127e-05 g272 3.1405978 13.860652 59.08187 6.432977e-06 9.532503e-05 g157 5.3813801 10.414972 57.03712 7.659515e-06 1.040417e-04 g42 -1.9856547 9.962762 53.97164 1.005146e-05 1.260298e-04 g165 2.2666761 13.690016 51.66777 1.243606e-05 1.447913e-04 g101 3.1488142 13.181625 48.21727 1.736838e-05 1.887363e-04 g15 2.7410696 10.684840 47.29143 1.906062e-05 1.941800e-04 g210 -2.2430064 9.993002 46.52627 2.060621e-05 1.975772e-04 g299 -2.3509276 10.984491 45.75893 2.230566e-05 2.019901e-04 g103 2.6810410 10.579777 44.26963 2.609599e-05 2.238762e-04 g144 -0.7963477 16.186987 43.17707 2.936057e-05 2.392886e-04 g77 1.0984806 14.118231 41.46740 3.548119e-05 2.754016e-04 g125 -1.3099534 10.195970 39.85537 4.266319e-05 3.160955e-04 g298 -2.0471647 9.837072 33.49832 9.395967e-05 6.658881e-04 g104 2.4081173 9.219160 38.54240 1.121281e-04 7.615370e-04 g122 -0.6485441 12.457152 30.90261 1.341148e-04 8.744287e-04 g164 1.4415064 11.156457 30.50383 1.419308e-04 8.897970e-04 g146 1.3983889 12.824184 29.92911 1.541544e-04 9.306356e-04 g313 2.2492040 13.614815 28.41728 1.926822e-04 1.121686e-03 g150 1.9650641 13.229706 27.90020 2.083770e-04 1.171222e-03 g13 -0.6522187 13.101579 26.45593 2.608176e-04 1.417109e-03 \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee g96 -1.873792302 8.939397 0.4844512655 0.4999546 0.6041584 g10 0.233134670 14.382992 0.4834790913 0.5003766 0.6041584 g253 0.052426914 13.741219 0.3075042687 0.5896191 0.7066759 g289 -0.212573283 9.390864 0.2887857862 0.6010328 0.7150974 g312 0.093543041 10.849110 0.2666731965 0.6151559 0.7265972 g301 -0.102000599 10.135184 0.2572520652 0.6214023 0.7274503 g221 -0.277095720 8.469909 0.2522121463 0.6248039 0.7274503 g162 -0.145155616 8.517263 0.2278112475 0.6463283 0.7413085 g50 -0.170907352 8.749274 0.2158314533 0.6507284 0.7413085 g203 0.062836598 14.227601 0.2124612164 0.6532634 0.7413085 g88 0.045949497 14.235850 0.2103049178 0.6548983 0.7413085 g355 -0.287897714 8.890754 0.1803234432 0.6787703 0.7630314 g204 -0.055975779 11.947887 0.1408988543 0.7140799 0.7972262 g306 0.054269738 11.294732 0.0923937800 0.7664733 0.8498990 g193 0.049946343 11.682386 0.0642557849 0.8042743 0.8857886 g205 0.037877227 10.986961 0.0473457745 0.8314821 0.9029713 g194 -0.142990391 9.403497 0.0447552162 0.8360811 0.9029713 g362 0.025629967 11.336529 0.0415198735 0.8420254 0.9029713 g315 0.027958374 11.878331 0.0409766489 0.8430470 0.9029713 g180 0.059680256 15.711409 0.0386140448 0.8475743 0.9029713 g282 -0.026492114 11.756078 0.0247991469 0.8775434 0.9288284 g176 -0.061055115 8.587203 0.0144510429 0.9063467 0.9531258 g126 0.061707217 8.311212 0.0024786868 0.9648334 1.0000000 g128 0.063825269 7.671043 0.0019414812 0.9655950 1.0000000 g227 -0.025740630 7.939955 0.0011908008 0.9730516 1.0000000 g121 0.002660852 12.896620 0.0005233398 0.9821327 1.0000000 g284 0.000000000 8.538720 0.0000000000 1.0000000 1.0000000 g116 0.000000000 7.180297 0.0000000000 1.0000000 1.0000000 g117 0.000000000 7.033307 0.0000000000 1.0000000 1.0000000 g118 0.000000000 7.515646 0.0000000000 1.0000000 1.0000000 $adjust.method 'BH' $comparison '-1*S10_Control 1*S10_Treatment' $test 'glm' We have now created a table with certain parameters calculated for each of the genes analysed. logFC represents the base 2 logarithm of the fold change and it shows us how much the expression of the gene has changed between the two conditions. A logFC of 1 means a doubling in the read per gene count between the control and treatment samples. The genes with a logFC higher than 0 are upregulated while the genes with a logFC lower than 0 are downregulated between the control and treatment samples. logCPM represents the average log2-counts-per-million, the abundance of the gene. F - F-statistic. PValue is the raw p-value. FDR (The false discovery rate) represents the adjusted p-value and is calculated using Benjamini and Hochberg's algorithm. It controls the rate of false positive values under multiple testing. Usually, a threshold of under 5% is set for the FDR. The important information for us in this table is stored in the \"logFC\" and the \"FDR\" columns. The top DE genes have small FDR values and large fold changes Many of the genes in the samples are uninteresting for us, as they have a high FDR and/or low logFC values so we cannot consider them as differentially expressed. We will apply a filtering step in order to keep only the statistically significant genes. We will filter out the genes with an FDR higher than 0.05 and an absolute logFC lower than 1. In [16]: Copied! DEG_S10_filtered <- DEG_S10 $ table [ DEG_S10 $ table $ FDR < 0.05 & abs ( DEG_S10 $ table $ logFC ) > 1 ,] #Filtering DEG_S10_filtered <- rownames_to_column ( DEG_S10_filtered ) %>% rename ( gene_ID = rowname ) #Adding the gene_ID column head ( DEG_S10_filtered ) nrow ( DEG_S10_filtered ) # finding the number of genes that passed the filter DEG_S10_filtered < - DEG_S10$table[DEG_S10$table$FDR < 0.05 & abs(DEG_S10$table$logFC) > 1,] #Filtering DEG_S10_filtered < - rownames_to_column(DEG_S10_filtered) %>% rename(gene_ID = rowname) #Adding the gene_ID column head(DEG_S10_filtered) nrow(DEG_S10_filtered) # finding the number of genes that passed the filter A data.frame: 6 \u00d7 6 gene_ID logFC logCPM F PValue FDR <chr> <dbl> <dbl> <dbl> <dbl> <dbl> 1 g39 3.586170 14.12463 336.66158 5.227331e-10 8.520550e-08 2 g232 4.049413 11.95044 157.50997 3.685988e-08 2.591779e-06 3 g6 2.322535 12.99954 150.27854 4.770146e-08 2.591779e-06 4 g350 2.053003 13.32457 97.56453 4.890245e-07 1.992775e-05 5 g99 -1.538787 11.68100 85.17478 9.976659e-07 3.195090e-05 6 g142 -1.423528 13.83069 82.52308 1.176107e-06 3.195090e-05 52 We can also visualize the selected genes by plotting a Smear plot or a Volcano plot. The Genes that passed the filter are coloured in red, and the top 10 genes with the lowest FDR value are labelled with their gene ID. We can see that the majority of the genes analysed are either not statistically significant or have a very small logFC. In [17]: Copied! plotSmear ( glmqlf_S10 , de.tags = rownames ( DEG_S10 $ table )[ which ( DEG_S10 $ table $ FDR < 0.05 & abs ( DEG_S10 $ table $ logFC ) > 1 )]) text ( x = DEG_S10_filtered $ logCPM [ 1 : 10 ], y = DEG_S10_filtered $ logFC [ 1 : 10 ], labels = DEG_S10_filtered $ gene_ID [ 1 : 10 ], cex = 0.7 , pos = 1 ) abline ( h = c ( -1 , 1 ), col = \"blue\" ) plotSmear(glmqlf_S10, de.tags = rownames(DEG_S10$table)[which(DEG_S10$table$FDR < 0.05 & abs(DEG_S10$table$logFC) > 1)]) text(x=DEG_S10_filtered$logCPM[1:10], y=DEG_S10_filtered$logFC[1:10], labels=DEG_S10_filtered$gene_ID[1:10], cex=0.7, pos=1) abline(h = c(-1, 1), col = \"blue\") We can also create a heatmap with the log2 read counts of the selected differentially expressed genes so that we can visualise the differences in normalized counts between the Control and the Treatment samples. The genes are ordered by the FDR value. In [18]: Copied! annot_col <- data.frame ( row.names = colnames ( pseudoNormCounts )[ 1 : 6 ], Condition = c ( rep ( \"Control\" , 3 ), rep ( \"Treatment\" , 3 ))) pheatmap ( as.matrix ( pseudoNormCounts [ DEG_S10_filtered $ gene_ID , c ( 1 : 6 )]), cluster_rows = F , cluster_col = F , annotation_col = annot_col ) annot_col < - data.frame(row.names = colnames(pseudoNormCounts)[1:6], Condition = c(rep(\"Control\", 3),rep( \"Treatment\", 3))) pheatmap(as.matrix(pseudoNormCounts[DEG_S10_filtered$gene_ID,c(1:6)]), cluster_rows = F, cluster_col = F, annotation_col = annot_col) DEGs for White clover Tienshan Treatment vs Control \u00b6 We can do this using the same functions as above and changing the contrast. This time we will directly filter the differentially expressed genes using the same parameters as for the S10 samples. In [19]: Copied! glmqlf_Ti <- glmQLFTest ( fit , contrast = contrasts [, \"Tienshan\" ]) DEG_Ti <- topTags ( glmqlf_Ti , n = nrow ( DGEList $ counts )) DEG_Ti_filtered <- DEG_Ti $ table [ DEG_Ti $ table $ FDR < 0.05 & abs ( DEG_Ti $ table $ logFC ) > 1 ,] DEG_Ti_filtered <- rownames_to_column ( DEG_Ti_filtered ) %>% rename ( gene_ID = rowname ) print ( head ( DEG_Ti_filtered )) print ( \"Nr of differentially expressed genes:\" ) print ( nrow ( DEG_Ti_filtered )) glmqlf_Ti < - glmQLFTest(fit, contrast=contrasts[,\"Tienshan\"]) DEG_Ti < - topTags(glmqlf_Ti, n = nrow(DGEList$counts)) DEG_Ti_filtered < - DEG_Ti$table[DEG_Ti$table$FDR < 0.05 & abs(DEG_Ti$table$logFC) > 1,] DEG_Ti_filtered < - rownames_to_column(DEG_Ti_filtered) %>% rename(gene_ID = rowname) print(head(DEG_Ti_filtered)) print(\"Nr of differentially expressed genes:\") print(nrow(DEG_Ti_filtered)) gene_ID logFC logCPM F PValue FDR 1 g234 -3.348667 13.00417 278.01679 1.547626e-09 2.522630e-07 2 g269 -1.885843 11.83422 127.39949 1.172628e-07 9.556922e-06 3 g26 2.423725 11.36683 74.33983 2.016212e-06 8.910761e-05 4 g101 4.303290 13.18162 73.17140 2.186690e-06 8.910761e-05 5 g272 3.391225 13.86065 66.80131 3.475790e-06 9.840357e-05 6 g99 -1.430395 11.68100 66.25814 3.622217e-06 9.840357e-05 [1] \"Nr of differentially expressed genes:\" [1] 42 We can now plot a Venn diagram with the DEGs for the two genotypes in order to observe the number of common and specific differentially expressed genes between the two genotypes as response to the cold exposure. You can see that a high percentage of the identified genes are common for the two genotypes, while each genotype has also specific genes. In [20]: Copied! vd <- venn.diagram ( x = list ( DEG_S10_filtered $ gene_ID , DEG_Ti_filtered $ gene_ID ), category.names = c ( \"S10\" , \"Tienshan\" ), lwd = 4 , fill = c ( \"cornflowerblue\" , \"yellowgreen\" ), filename = NULL , cat.cex = 1 , cat.fontface = \"bold\" , output = TRUE ) grid.draw ( vd ) vd < - venn.diagram( x = list(DEG_S10_filtered$gene_ID, DEG_Ti_filtered$gene_ID), category.names = c(\"S10\" , \"Tienshan\"), lwd = 4, fill = c(\"cornflowerblue\", \"yellowgreen\"), filename = NULL, cat.cex = 1, cat.fontface = \"bold\", output=TRUE ) grid.draw(vd) DEGs for S10+Tienshan Treatment vs Control w/o genotype effects \u00b6 Until now we tested for DEGs specific for each of the two genotypes under cold treatment. We can also run a test where we ignore the genotype and just test for the differences in the cold response. Consider the counts for both genotypes as a single dataset using the previously created contrast \"S10_Tienshan\". Do the results look different compared with the previous tests? In [21]: Copied! glmqlf_S10_Ti <- glmQLFTest ( fit , contrast = contrasts [, \"S10_Tienshan\" ]) DEG_S10_Ti <- topTags ( glmqlf_S10_Ti , n = nrow ( DGEList $ counts )) DEG_S10_Ti_filtered <- DEG_S10_Ti $ table [ DEG_S10_Ti $ table $ FDR < 0.05 & abs ( DEG_S10_Ti $ table $ logFC ) > 1 ,] DEG_S10_Ti_filtered <- rownames_to_column ( DEG_S10_Ti_filtered ) %>% rename ( gene_ID = rowname ) print ( head ( DEG_S10_Ti_filtered )) print ( \"Nr of differentially expressed genes:\" ) print ( nrow ( DEG_S10_Ti_filtered )) glmqlf_S10_Ti < - glmQLFTest(fit, contrast=contrasts[,\"S10_Tienshan\"]) DEG_S10_Ti < - topTags(glmqlf_S10_Ti, n = nrow(DGEList$counts)) DEG_S10_Ti_filtered < - DEG_S10_Ti$table[DEG_S10_Ti$table$FDR < 0.05 & abs(DEG_S10_Ti$table$logFC) > 1,] DEG_S10_Ti_filtered < - rownames_to_column(DEG_S10_Ti_filtered) %>% rename(gene_ID = rowname) print(head(DEG_S10_Ti_filtered)) print(\"Nr of differentially expressed genes:\") print(nrow(DEG_S10_Ti_filtered)) gene_ID logFC logCPM F PValue FDR 1 g39 2.057128 14.12463 246.6519 3.039491e-09 4.954370e-07 2 g232 3.261008 11.95044 203.4580 8.934889e-09 5.288546e-07 3 g269 -1.652505 11.83422 200.3564 9.733520e-09 5.288546e-07 4 g6 1.714849 12.99954 166.2297 2.740066e-08 1.116577e-06 5 g99 -1.484591 11.68100 150.3246 4.762148e-08 1.552460e-06 6 g26 2.274575 11.36683 142.8625 6.290957e-08 1.623160e-06 [1] \"Nr of differentially expressed genes:\" [1] 52 Plot the results from the 3 tests in a Venn diagram to visualize the number of common and unique genes In [22]: Copied! vd <- venn.diagram ( x = list ( DEG_S10_filtered $ gene_ID , DEG_Ti_filtered $ gene_ID , DEG_S10_Ti_filtered $ gene_ID ), category.names = c ( \"S10\" , \"Tienshan\" , \"S10_Tienshan\" ), lwd = 3 , fill = c ( \"cornflowerblue\" , \"yellowgreen\" , \"thistle3\" ), filename = NULL , cat.cex = 1 , cat.fontface = \"bold\" , output = TRUE ) grid.draw ( vd ) vd < - venn.diagram( x = list(DEG_S10_filtered$gene_ID, DEG_Ti_filtered$gene_ID, DEG_S10_Ti_filtered$gene_ID), category.names = c(\"S10\" , \"Tienshan\", \"S10_Tienshan\"), lwd = 3, fill = c(\"cornflowerblue\", \"yellowgreen\", \"thistle3\"), filename = NULL, cat.cex = 1, cat.fontface = \"bold\", output=TRUE ) grid.draw(vd) Explore DGE results \u00b6 Select the genes which appear only in the analysis using both genotypes for further examination. \u00b6 We can use the \"anti_join\" function from the dplyr package to keep only the unique genes that appear only when using both genotypes. In [23]: Copied! S10_Ti_unique <- anti_join ( DEG_S10_Ti_filtered , DEG_S10_filtered , by = \"gene_ID\" ) %>% anti_join ( DEG_Ti_filtered , by = \"gene_ID\" ) S10_Ti_unique S10_Ti_unique < - anti_join(DEG_S10_Ti_filtered, DEG_S10_filtered, by=\"gene_ID\") %>% anti_join(DEG_Ti_filtered, by=\"gene_ID\") S10_Ti_unique A data.frame: 2 \u00d7 6 gene_ID logFC logCPM F PValue FDR <chr> <dbl> <dbl> <dbl> <dbl> <dbl> g247 -1.412652 8.424523 10.877350 0.006535994 0.01500517 g360 1.180849 7.900076 9.804067 0.013809376 0.02813660 Linking the genes selected as differentially expressed back to the raw read counts \u00b6 How do the counts look for these genes, does it make sense that they are differentially expressed only when using the two genotypes? In [24]: Copied! Read_counts <- rownames_to_column ( Read_counts ) %>% rename ( gene_ID = rowname ) S10_Ti_unique_counts <- inner_join ( S10_Ti_unique [, c ( 1 , 2 , 6 )], Read_counts , by = \"gene_ID\" ) S10_Ti_unique_counts Read_counts < - rownames_to_column(Read_counts) %>% rename(gene_ID = rowname) S10_Ti_unique_counts < - inner_join(S10_Ti_unique[,c(1,2,6)], Read_counts, by=\"gene_ID\") S10_Ti_unique_counts A data.frame: 2 \u00d7 15 gene_ID logFC FDR S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 <chr> <dbl> <dbl> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> g247 -1.412652 0.01500517 4 9 3 1 2 1 42 27 32 8 22 22 g360 1.180849 0.02813660 1 0 1 3 2 3 16 10 16 22 19 22 Adding functional annotation to DEGs \u00b6 Until now we looked only at gene IDs, but we can also add functional annotations to the DEGs. The functional annotations were generated using protein sequences and the EggNOG software. Identifying the molecular function of the differentially expressed genes can help us do a literature survey in order to check if any of the genes discovered have been previously identified as being involved in the cold response. In [25]: Copied! Functional_annotations <- read.delim ( \"../Data/Clover_Data/Functional_Annotations.txt\" ) S10_Ti_unique_FA <- inner_join ( S10_Ti_unique [, c ( 1 , 2 , 6 )], Functional_annotations , by = \"gene_ID\" ) S10_Ti_unique_FA Functional_annotations < - read.delim(\"../Data/Clover_Data/Functional_Annotations.txt\") S10_Ti_unique_FA < - inner_join(S10_Ti_unique[,c(1,2,6)], Functional_annotations, by=\"gene_ID\") S10_Ti_unique_FA A data.frame: 2 \u00d7 4 gene_ID logFC FDR Human.Readable.Description <chr> <dbl> <dbl> <chr> g247 -1.412652 0.01500517 Nitrogen regulatory protein P-II homolog g360 1.180849 0.02813660 Unknown protein Tasks and Questions Based on the results obtained in the analysis so far, would you change the cut-off for the FDR and logFC to be more strict or more permissive? Look back at the raw counts for different FDR and logFC values and set the thresholds as you find appropriate. You can also plot histograms with the FDR and logFC values. In [ ]: Copied! hist ( DEG_S10 $ table $ FDR , main = \"\" , xlab = \"FDR\" , breaks = 200 , xlim = range ( c ( 0 , 0.1 ))) hist(DEG_S10$table$FDR , main = \"\", xlab = \"FDR\", breaks= 200, xlim = range(c(0, 0.1))) In [ ]: Copied! hist ( DEG_S10 $ table $ logFC , main = \"\" , xlab = \"logFC\" , breaks = 50 , xlim = range ( c ( -6 , 6 ))) hist(DEG_S10$table$logFC , main = \"\", xlab = \"logFC\", breaks= 50, xlim = range(c(-6, 6))) Separate the upregulated and downregulated genes for each genotype and append functional annotations to them. Identify the genes that are commonly upregulated in S10 and Tienshan samples and the uniquely upregulated genes for each genotype. Why do you think some of the proteins appear in duplicates? To answer these questions, it may be convenient to save summary tables from R and open them in excel. See the code below for examples of how to do this. Files can be downloaded by right-clicking on the file name. If you are familiar with R functions, you are welcome to use those for counting. In [ ]: Copied! dir.create ( \"DEG_Output_tables\" , showWarnings = FALSE ) dir.create(\"DEG_Output_tables\", showWarnings = FALSE) In [ ]: Copied! #Create the table with the DEGs, Raw counts(just for the Ti samples in this case, change to columns (2:7) for the S10 samples) and Functional annotations DEG_Ti_counts_FA <- inner_join ( DEG_Ti_filtered [, c ( 1 , 2 , 6 )], Read_counts [, c ( 1 , 8 : 13 )], by = \"gene_ID\" ) %>% inner_join ( Functional_annotations , by = \"gene_ID\" ) #Write the table to file write.table ( DEG_Ti_counts_FA , file = \"DEG_Output_tables/Ti_Treatment_Control_DGE.txt\" , quote = FALSE , row.names = FALSE , sep = \"\\t\" ) #Display the first 10 rows of the table head ( DEG_Ti_counts_FA , n = 10 ) #Create the table with the DEGs, Raw counts(just for the Ti samples in this case, change to columns (2:7) for the S10 samples) and Functional annotations DEG_Ti_counts_FA < - inner_join(DEG_Ti_filtered[,c(1, 2, 6)], Read_counts[, c(1, 8:13)], by=\"gene_ID\") %>% inner_join(Functional_annotations, by=\"gene_ID\") #Write the table to file write.table(DEG_Ti_counts_FA, file = \"DEG_Output_tables/Ti_Treatment_Control_DGE.txt\", quote = FALSE, row.names = FALSE, sep = \"\\t\") #Display the first 10 rows of the table head(DEG_Ti_counts_FA, n=10) Example for filtering and counting the Up/Down genes. You can easily filter using the \"filter()\" function just by specifying the dataframe and a logical argument. In [ ]: Copied! DEG_Ti_up <- filter ( DEG_Ti_counts_FA , logFC > 0 ) print ( \"Nr of upregulated genes:\" ) print ( nrow ( DEG_Ti_up )) DEG_Ti_down <- filter ( DEG_Ti_counts_FA , logFC < 0 ) print ( \"Nr of downregulated genes:\" ) print ( nrow ( DEG_Ti_down )) DEG_Ti_up < - filter(DEG_Ti_counts_FA, logFC > 0) print(\"Nr of upregulated genes:\") print(nrow(DEG_Ti_up)) DEG_Ti_down < - filter(DEG_Ti_counts_FA, logFC < 0) print(\"Nr of downregulated genes:\") print(nrow(DEG_Ti_down)) You can use the \"inner_join\" and the \"anti_join\" functions from the dplyr package to select the common and unique genes for each genotype: example_file_joined <- inner_join(file1, file2, by=\"gene_ID\") Wrapping up \ud83c\udf89 \ud83c\udf89 \ud83c\udf89 \u00b6 In this notebook, you have worked with RNA-seq results for two white clover genotypes exposed to one night of cold treatment, aiming to identify genes that change their expression in response to the cold treatment. You have learned to perform exploratory data analysis of raw and normalized RNA-seq read counts. You have also performed differential gene expression using edgeR .","title":"3.3.Bulk RNA data analysis"},{"location":"nb/bulk.html#edger-data-filtering-and-normalization","text":"We will use the edgeR package to test for differentially expressed genes between our Control and Treatment Samples. You can read more about the edgeR package and its functionalities here . This exercise largely follows their user manual.","title":"EdgeR - Data filtering and Normalization"},{"location":"nb/bulk.html#file-processing","text":"The data for this exercise comes from the 12 tabular files with Reads per Gene counts generated by STAR Mapping in the raw-data alignment part of this course. We want to create a table where each column is a sample, and the content of the table are the read counts from STAR. We must merge the 12 files with Reads per Gene information into a single file. If you aligned datasets in the first notebook with jupyterlab, then you will find the files using the following command: In [2]: Copied! samples <- sort ( system ( \"find results/STAR_output/*_align_contigs_1_2 -name \\\"*ReadsPerGene.out.tab\\\"\" , intern = TRUE )) print ( samples ) Read_counts <- do.call ( cbind , lapply ( samples , function ( x ) read.delim ( file = x , header = FALSE ))) samples < - sort(system(\"find results/STAR_output/*_align_contigs_1_2 -name \\\"*ReadsPerGene.out.tab\\\"\", intern=TRUE)) print(samples) Read_counts < - do.call(cbind, lapply(samples, function(x) read.delim(file=x, header = FALSE))) [1] \"results/STAR_output/S10_align_contigs_1_2/S10_1_1ReadsPerGene.out.tab\" [2] \"results/STAR_output/S10_align_contigs_1_2/S10_1_2ReadsPerGene.out.tab\" [3] \"results/STAR_output/S10_align_contigs_1_2/S10_1_3ReadsPerGene.out.tab\" [4] \"results/STAR_output/S10_align_contigs_1_2/S10_2_1ReadsPerGene.out.tab\" [5] \"results/STAR_output/S10_align_contigs_1_2/S10_2_2ReadsPerGene.out.tab\" [6] \"results/STAR_output/S10_align_contigs_1_2/S10_2_3ReadsPerGene.out.tab\" [7] \"results/STAR_output/TI_align_contigs_1_2/TI_1_1ReadsPerGene.out.tab\" [8] \"results/STAR_output/TI_align_contigs_1_2/TI_1_2ReadsPerGene.out.tab\" [9] \"results/STAR_output/TI_align_contigs_1_2/TI_1_3ReadsPerGene.out.tab\" [10] \"results/STAR_output/TI_align_contigs_1_2/TI_2_1ReadsPerGene.out.tab\" [11] \"results/STAR_output/TI_align_contigs_1_2/TI_2_2ReadsPerGene.out.tab\" [12] \"results/STAR_output/TI_align_contigs_1_2/TI_2_3ReadsPerGene.out.tab\" If you aligned datasets interactively with Galaxy , then you will need to create the folder tabular_files into the results folder copy the tabular files from STAR into the created folder. Each file must have the sample name and end by ReadsPerGene.out.tab . For example S10_1_1ReadsPerGene.out.tab run the following commands removing the # symbol In [ ]: Copied! #samples <- sort(system(\"find results/tabular_files/ -name \\\"*ReadsPerGene.out.tab\\\"\", intern=TRUE)) #print(samples) #Read_counts <- do.call(cbind, lapply(samples, function(x) read.delim(file=x, header = FALSE))) #samples < - sort(system(\"find results/tabular_files/ -name \\\"*ReadsPerGene.out.tab\\\"\", intern=TRUE)) #print(samples) #Read_counts < - do.call(cbind, lapply(samples, function(x) read.delim(file=x, header = FALSE))) The data frame has genes as rows and samples as columns and stores the gene expression counts (value representing the total number of sequence reads that originated from a particular gene in a sample) for each of the 12 samples. This data frame should have 12 columns and 366 rows. In [3]: Copied! rownames ( Read_counts ) <- Read_counts [, 1 ] Read_counts <- Read_counts [ c ( 5 : nrow ( Read_counts )), c ( seq ( 2 , 46 , by = 4 ))] colnames ( Read_counts ) <- c ( \"S10_1_1\" , \"S10_1_2\" , \"S10_1_3\" , \"S10_2_1\" , \"S10_2_2\" , \"S10_2_3\" , \"TI_1_1\" , \"TI_1_2\" , \"TI_1_3\" , \"TI_2_1\" , \"TI_2_2\" , \"TI_2_3\" ) head ( Read_counts , n = 10 ) dim ( Read_counts ) # dimensions of the data frame rownames(Read_counts) < - Read_counts[,1] Read_counts < - Read_counts[c(5:nrow(Read_counts)), c(seq(2, 46, by=4))] colnames(Read_counts) < - c(\"S10_1_1\", \"S10_1_2\", \"S10_1_3\", \"S10_2_1\", \"S10_2_2\", \"S10_2_3\", \"TI_1_1\", \"TI_1_2\", \"TI_1_3\", \"TI_2_1\", \"TI_2_2\", \"TI_2_3\") head(Read_counts, n=10) dim(Read_counts) # dimensions of the data frame A data.frame: 10 \u00d7 12 S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> g175 143 226 227 217 206 193 234 205 198 221 206 165 g176 17 12 15 8 14 18 29 19 30 6 11 23 g177 0 0 0 0 0 0 0 0 0 0 0 0 g178 0 0 0 0 0 0 0 0 2 2 1 2 g179 0 0 0 0 0 0 0 0 0 0 0 0 g180 2773 5223 4350 4139 3209 4644 1015 1313 889 2675 1023 2565 g181 473 679 583 437 390 450 642 700 697 650 459 410 g182 19 15 20 9 8 4 13 7 8 6 5 4 g183 0 0 0 0 0 0 0 0 0 0 0 0 g184 30 22 50 57 103 83 36 29 18 107 69 89 .list-inline {list-style: none; margin:0; padding: 0} .list-inline>li {display: inline-block} .list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex} 366 12 Import the Metadata table. This file, as its name suggests, contains information about each of the 12 RNA-seq samples, such as the treatment (Condition), genotype and replicate. Note that the order of the rows in the Metadata table should be the same as the columns in the Read_counts file generated above. In [4]: Copied! metadata <- read.csv ( \"../Data/Clover_Data/metadata.csv\" , sep = \";\" , row.names = 1 , stringsAsFactors = TRUE ) metadata metadata < - read.csv(\"../Data/Clover_Data/metadata.csv\", sep =\";\", row.names=1, stringsAsFactors=TRUE) metadata A data.frame: 12 \u00d7 3 Condition Genotype Replicate <fct> <fct> <int> S10_1_1 Control S10 1 S10_1_2 Control S10 2 S10_1_3 Control S10 3 S10_2_1 Treatment S10 1 S10_2_2 Treatment S10 2 S10_2_3 Treatment S10 3 TI_1_1 Control Tienshan 1 TI_1_2 Control Tienshan 2 TI_1_3 Control Tienshan 3 TI_2_1 Treatment Tienshan 1 TI_2_2 Treatment Tienshan 2 TI_2_3 Treatment Tienshan 3 In order to aid the following steps, we will create a Group for each sample (a new column in the metadata) based on the Genotype&Condition of each sample and assign the three replicates to this group. In [5]: Copied! Group <- factor ( paste ( metadata $ Genotype , metadata $ Condition , sep = \"_\" )) metadata <- cbind ( metadata , Group = Group ) metadata Group < - factor(paste(metadata$Genotype, metadata$Condition, sep=\"_\")) metadata < - cbind(metadata,Group=Group) metadata A data.frame: 12 \u00d7 4 Condition Genotype Replicate Group <fct> <fct> <int> <fct> S10_1_1 Control S10 1 S10_Control S10_1_2 Control S10 2 S10_Control S10_1_3 Control S10 3 S10_Control S10_2_1 Treatment S10 1 S10_Treatment S10_2_2 Treatment S10 2 S10_Treatment S10_2_3 Treatment S10 3 S10_Treatment TI_1_1 Control Tienshan 1 Tienshan_Control TI_1_2 Control Tienshan 2 Tienshan_Control TI_1_3 Control Tienshan 3 Tienshan_Control TI_2_1 Treatment Tienshan 1 Tienshan_Treatment TI_2_2 Treatment Tienshan 2 Tienshan_Treatment TI_2_3 Treatment Tienshan 3 Tienshan_Treatment","title":"File processing"},{"location":"nb/bulk.html#create-the-dgelist-object","text":"We will merge the read counts and the metadata into a list-based data object named DGEList, which can be manipulated as any list object in R. The main components of the DGEList object are the matrix \"counts\" containing our read per gene counts and a data.frame \"samples\" containing the metadata. Note that all the genes with zero counts across all samples were eliminated. In [6]: Copied! DGEList <- DGEList ( Read_counts , remove.zeros = TRUE ) DGEList $ samples $ Condition <- relevel ( metadata $ Condition , ref = \"Control\" ) DGEList $ samples $ Genotype <- metadata $ Genotype DGEList $ samples $ group <- metadata $ Group DGEList DGEList < - DGEList(Read_counts, remove.zeros = TRUE) DGEList$samples$Condition < - relevel(metadata$Condition, ref = \"Control\") DGEList$samples$Genotype < - metadata$Genotype DGEList$samples$group < - metadata$Group DGEList Removing 99 rows with all zero counts $counts A matrix: 267 \u00d7 12 of type int S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g175 143 226 227 217 206 193 234 205 198 221 206 165 g176 17 12 15 8 14 18 29 19 30 6 11 23 g178 0 0 0 0 0 0 0 0 2 2 1 2 g180 2773 5223 4350 4139 3209 4644 1015 1313 889 2675 1023 2565 g181 473 679 583 437 390 450 642 700 697 650 459 410 g182 19 15 20 9 8 4 13 7 8 6 5 4 g184 30 22 50 57 103 83 36 29 18 107 69 89 g185 0 2 0 0 0 0 0 1 0 0 0 1 g186 1 0 0 0 0 0 0 0 0 0 0 0 g187 1 1 2 0 1 1 58 49 57 28 32 29 g188 316 285 417 200 184 322 1276 855 1341 402 809 547 g190 14 18 29 25 17 31 7 6 12 20 7 6 g191 2 2 1 1 0 0 0 0 0 0 0 0 g192 1 1 1 1 1 0 6 10 2 3 8 2 g193 208 231 301 289 161 279 70 61 60 128 160 91 g194 40 41 37 44 17 43 8 10 7 8 105 25 g195 54 85 85 71 54 43 56 54 42 67 68 56 g196 77 120 118 186 152 177 5 9 5 42 20 34 g197 1 6 5 18 14 21 15 44 12 32 16 17 g198 14 27 10 11 2 2 6 6 1 2 2 0 g199 0 0 0 0 0 0 0 0 0 0 1 0 g200 57 80 94 11 8 23 89 47 67 5 47 5 g203 815 1188 1412 1139 1034 1127 911 852 662 892 781 784 g204 250 312 348 277 275 264 125 104 133 119 91 130 g205 109 132 181 140 114 151 89 73 66 78 46 50 g206 0 1 2 7 4 9 4 0 7 0 1 2 g207 0 1 0 0 4 3 1 3 4 4 3 3 g208 0 1 0 0 0 0 0 1 0 1 0 0 g209 0 0 0 0 0 0 0 0 1 0 0 0 g210 52 76 60 12 12 13 61 73 94 54 45 21 \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee g135 0 0 0 1 0 0 5 1 0 2 0 0 g139 0 1 1 0 0 0 12 13 11 4 3 3 g140 0 5 1 0 0 2 5 4 0 0 2 0 g141 177 217 224 131 117 166 117 85 67 80 79 77 g142 488 713 557 241 154 228 983 1057 1023 1075 794 933 g144 3319 3987 4763 2249 1996 2260 4405 3880 4585 5072 3618 3590 g146 293 330 350 913 775 731 108 92 115 431 151 226 g147 6 3 3 0 8 10 1 1 1 0 1 0 g148 19 30 24 15 4 12 23 19 25 10 19 8 g149 35 62 65 57 58 53 114 103 91 92 79 93 g150 263 411 278 752 1378 1300 106 112 70 516 200 508 g151 44 44 50 92 92 84 23 26 25 56 43 31 g152 242 258 294 425 381 521 15 8 8 16 21 15 g153 142 144 181 233 177 233 31 49 50 50 45 57 g155 0 4 2 1 0 0 1 0 3 0 0 1 g156 0 0 0 0 0 0 0 3 2 0 0 1 g157 6 1 3 223 52 152 4 7 4 153 47 155 g158 0 0 0 7 1 3 0 0 1 1 0 0 g161 25 38 18 1 9 2 63 23 43 0 24 6 g162 31 43 38 43 25 28 0 0 0 0 0 0 g163 1 0 12 37 8 18 9 4 8 16 13 15 g164 69 58 97 294 123 168 56 58 60 146 101 135 g165 203 376 594 1343 2062 1610 265 229 214 488 270 490 g167 2 0 0 2 0 0 0 0 0 0 0 0 g168 2 14 6 0 0 5 31 26 38 3 27 10 g169 0 0 1 0 1 1 0 0 0 0 0 0 g170 0 0 0 0 0 0 0 1 0 0 0 0 g171 0 5 0 0 0 5 0 0 0 1 0 0 g172 1 2 2 6 4 3 3 1 2 3 3 0 g173 80 107 120 78 138 121 67 86 69 96 105 105 $samples A data.frame: 12 \u00d7 5 group lib.size norm.factors Condition Genotype <fct> <dbl> <dbl> <fct> <fct> S10_1_1 S10_Control 43614 1 Control S10 S10_1_2 S10_Control 58803 1 Control S10 S10_1_3 S10_Control 62654 1 Control S10 S10_2_1 S10_Treatment 59909 1 Treatment S10 S10_2_2 S10_Treatment 56761 1 Treatment S10 S10_2_3 S10_Treatment 63152 1 Treatment S10 TI_1_1 Tienshan_Control 43387 1 Control Tienshan TI_1_2 Tienshan_Control 38355 1 Control Tienshan TI_1_3 Tienshan_Control 40304 1 Control Tienshan TI_2_1 Tienshan_Treatment 55509 1 Treatment Tienshan TI_2_2 Tienshan_Treatment 39909 1 Treatment Tienshan TI_2_3 Tienshan_Treatment 45008 1 Treatment Tienshan","title":"Create the DGEList object"},{"location":"nb/bulk.html#preliminary-data-analysis","text":"First, we will calculate the \"pseudoCounts\" as log2 of the reads per gene counts. This is not part of the actual differential gene expression analysis but is helpful for data exploration and quality assessment. We will look at a histogram of one of the samples and a boxplot representation of the log2 counts for all the 12 samples. Note that there are many genes with a low number of mapped reads and that there are differences between the average read counts for each library. In [7]: Copied! pseudoCounts <- log2 ( DGEList $ counts + 1 ) head ( pseudoCounts ) hist ( pseudoCounts [ , \"S10_1_1\" ], main = \"\" , xlab = \"Read counts\" ) boxplot ( pseudoCounts , col = \"gray\" , las = 3 , cex.names = 1 ) pseudoCounts < - log2(DGEList$counts + 1) head(pseudoCounts) hist(pseudoCounts[ ,\"S10_1_1\"], main = \"\", xlab = \"Read counts\") boxplot(pseudoCounts, col = \"gray\", las = 3, cex.names = 1) A matrix: 6 \u00d7 12 of type dbl S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g175 7.169925 7.826548 7.832890 7.768184 7.693487 7.599913 7.876517 7.686501 7.636625 7.794416 7.693487 7.375039 g176 4.169925 3.700440 4.000000 3.169925 3.906891 4.247928 4.906891 4.321928 4.954196 2.807355 3.584963 4.584963 g178 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.584963 1.584963 1.000000 1.584963 g180 11.437752 12.350939 12.087131 12.015415 11.648358 12.181463 9.988685 10.359750 9.797662 11.385862 10.000000 11.325305 g181 8.888743 9.409391 9.189825 8.774787 8.611025 8.816984 9.328675 9.453271 9.447083 9.346514 8.845490 8.682995 g182 4.321928 4.000000 4.392317 3.321928 3.169925 2.321928 3.807355 3.000000 3.169925 2.807355 2.584963 2.321928 We can also create a PCA plot of the samples in order to assess the differences between the Genotypes and Conditions, but also between the replicates. In this plot the samples that are similar cluster together, while samples that are different are further apart. In this type of plot, we would expect samples from the same group (the three replicates for each sample) to exhibit a similar gene expression profile thus clustering together while being separated from the other samples. In [8]: Copied! resPCA <- pca ( t ( pseudoCounts ), ncomp = 6 ) plotIndiv ( resPCA , group = metadata $ Genotype , pch = metadata $ Condition , legend = T , legend.title = 'Genotype' , legend.title.pch = 'Condition' , title = 'PCA plot raw counts' , style = 'ggplot2' , size.xlabel = 10 , size.ylabel = 10 ) resPCA < - pca(t(pseudoCounts), ncomp = 6) plotIndiv(resPCA, group = metadata$Genotype, pch=metadata$Condition, legend = T, legend.title = 'Genotype', legend.title.pch = 'Condition', title = 'PCA plot raw counts', style = 'ggplot2', size.xlabel = 10, size.ylabel = 10)","title":"Preliminary data analysis"},{"location":"nb/bulk.html#filtering-the-lowly-expressed-genes","text":"As seen previously, many genes have a low number of read counts in our samples. The genes with very low counts across all libraries provide little evidence for differential expression, thus we should eliminate these genes before the analysis. One of the filtering methods we can use is the \"filterByExpr\" function provided by the edgeR package. By default, this function keeps only the genes that have at least 10 reads per group, but other cutoffs can also be applied. In [9]: Copied! keep <- filterByExpr ( DGEList , group = metadata $ Group ) #create the filter DGEList <- DGEList [ keep , , keep.lib.sizes = FALSE ] #apply the filter to on the DGEList object table ( keep ) #Check the number of genes that passed the filter keep < - filterByExpr(DGEList, group=metadata$Group) #create the filter DGEList < - DGEList[keep, , keep.lib.sizes=FALSE] #apply the filter to on the DGEList object table(keep) #Check the number of genes that passed the filter keep FALSE TRUE 104 163","title":"Filtering the lowly expressed genes"},{"location":"nb/bulk.html#normalization","text":"As we are working with multiple samples we need to normalize the read counts per gene in order to account for compositional and technical differences between the 12 RNA-seq libraries. For this, we will calculate normalization factors using the trimmed mean of M-values (TMM) method. You can read more about different normalization methods in the user manual. Note that running \"calcNormFactors\" does not change the actual reads per gene counts, it just fills the \"norm.factors\" column in DGEList$samples . These factors will be used to scale the read counts for each library. From the user guide: \"A normalization factor below one indicates that a small number of high count genes are monopolizing the sequencing, causing the counts for other genes to be lower than would be usual given the library size.\" In [10]: Copied! DGEList <- calcNormFactors ( DGEList , method = \"RLE\" ) DGEList $ samples DGEList < - calcNormFactors(DGEList, method=\"RLE\") DGEList$samples A data.frame: 12 \u00d7 5 group lib.size norm.factors Condition Genotype <fct> <dbl> <dbl> <fct> <fct> S10_1_1 S10_Control 43506 1.0897129 Control S10 S10_1_2 S10_Control 58660 1.0385093 Control S10 S10_1_3 S10_Control 62512 1.0362962 Control S10 S10_2_1 S10_Treatment 59741 0.9802860 Treatment S10 S10_2_2 S10_Treatment 56653 0.8418644 Treatment S10 S10_2_3 S10_Treatment 63008 0.8949144 Treatment S10 TI_1_1 Tienshan_Control 43174 1.0618440 Control Tienshan TI_1_2 Tienshan_Control 38203 1.0431678 Control Tienshan TI_1_3 Tienshan_Control 40178 1.0676351 Control Tienshan TI_2_1 Tienshan_Treatment 55391 0.9048196 Treatment Tienshan TI_2_2 Tienshan_Treatment 39810 1.1235077 Treatment Tienshan TI_2_3 Tienshan_Treatment 44910 0.9603761 Treatment Tienshan","title":"Normalization"},{"location":"nb/bulk.html#normalized-counts-exploratory-data-analysis","text":"For data analysis purposes normalized log2 counts can be extracted from the DGEList object using the function CPM (counts per million). We will generate the same plots as for the raw counts in order to compare the data before and after normalization. Do the plots for normalized counts look different compared with the plots computed before data filtering and normalization? In [11]: Copied! pseudoNormCounts <- cpm ( DGEList , log = TRUE , prior.count = 1 ) head ( pseudoNormCounts ) hist ( pseudoNormCounts [ , \"S10_1_1\" ], main = \"\" , xlab = \"counts\" ) boxplot ( pseudoNormCounts , col = \"gray\" , las = 3 , cex.names = 1 ) resPCA <- pca ( t ( pseudoNormCounts ), ncomp = 6 ) plotIndiv ( resPCA , group = metadata $ Genotype , pch = metadata $ Condition , legend = T , legend.title = 'Genotype' , legend.title.pch = 'Condition' , title = 'PCA plot normalized counts' , style = 'ggplot2' , size.xlabel = 10 , size.ylabel = 10 ) pseudoNormCounts < - cpm(DGEList, log = TRUE, prior.count = 1) head(pseudoNormCounts) hist(pseudoNormCounts[ ,\"S10_1_1\"], main = \"\", xlab = \"counts\") boxplot(pseudoNormCounts, col = \"gray\", las = 3, cex.names = 1) resPCA < - pca(t(pseudoNormCounts), ncomp = 6) plotIndiv(resPCA, group = metadata$Genotype, pch=metadata$Condition, legend = T, legend.title = 'Genotype', legend.title.pch = 'Condition', title = 'PCA plot normalized counts', style = 'ggplot2', size.xlabel = 10, size.ylabel = 10) A matrix: 6 \u00d7 12 of type dbl S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g175 11.568005 11.864813 11.782957 11.863090 12.083127 11.749272 12.323049 12.334198 12.178544 12.112851 12.175386 11.908906 g176 8.564108 7.760868 7.974199 7.290213 8.292080 8.405708 9.349782 8.956131 9.490388 7.125494 8.054467 9.111569 g180 15.836359 16.387906 16.035457 16.109276 16.038322 16.329943 14.435626 15.008665 14.340400 15.704307 14.482504 15.860317 g181 13.287211 13.446752 13.138771 12.869139 13.000828 12.965820 13.775545 14.101988 13.989757 13.664955 13.327806 13.217591 g182 8.716557 8.056048 8.360376 7.439590 7.551908 6.505541 8.245462 7.611500 7.689402 7.125494 7.041192 6.815835 g184 9.350249 8.573811 9.628852 9.955925 11.089735 10.542877 9.653135 9.546090 8.779819 11.073313 10.609695 11.024686","title":"Normalized counts - Exploratory Data analysis"},{"location":"nb/bulk.html#edger-testing-for-differentially-expressed-genes-degs","text":"We use edgeR for Differential Gene Expression using the GLM method (Generalized linear model) with treatment and genotype effects After we concluded the exploratory data analysis and the filtering and normalization steps we can now start testing for differentially expressed genes between our samples. For this, we first need to define a \"design matrix\" which describes our experimental design. We construct a matrix containing samples as rows and our previously defined groups (S10_Control, S10_Treatment, Tienshan_Control, Tienshan_Treatment) as columns. The samples belonging to each group are assigned a \"1\" in this matrix. In [12]: Copied! design.matrix <- model.matrix ( ~ 0 + Group ) rownames ( design.matrix ) <- colnames ( DGEList ) colnames ( design.matrix ) <- levels ( metadata $ Group ) design.matrix design.matrix < - model.matrix(~0+Group) rownames(design.matrix) < - colnames(DGEList) colnames(design.matrix) < - levels(metadata$Group) design.matrix A matrix: 12 \u00d7 4 of type dbl S10_Control S10_Treatment Tienshan_Control Tienshan_Treatment S10_1_1 1 0 0 0 S10_1_2 1 0 0 0 S10_1_3 1 0 0 0 S10_2_1 0 1 0 0 S10_2_2 0 1 0 0 S10_2_3 0 1 0 0 TI_1_1 0 0 1 0 TI_1_2 0 0 1 0 TI_1_3 0 0 1 0 TI_2_1 0 0 0 1 TI_2_2 0 0 0 1 TI_2_3 0 0 0 1 For easier pairwise comparisons between the groups we can create some \"contrasts\" using the \"makeContrasts\" function. This will tell the program which samples we will like to consider as our \"Control\" and which as our \"Treatment\" groups. The Control groups will be assigned a \"-1\" and the Treatment groups a \"1\". When using multiple Control and Treatment samples in the same contrast we need to devide by the number of samples used as for example in the contrast S10_Tienshan, where we use both S10 and Ti samples. For example, the contrast S10 = S10_Treatment - S10_Control will test for differentially expressed genes between the S10 treatment samples vs the S10 Control samples. In [13]: Copied! contrasts <- makeContrasts ( S10 = S10_Treatment - S10_Control , Tienshan = Tienshan_Treatment - Tienshan_Control , S10_Tienshan = ( S10_Treatment + Tienshan_Treatment ) / 2 - ( S10_Control + Tienshan_Control ) / 2 , levels = design.matrix ) contrasts contrasts < - makeContrasts( S10 = S10_Treatment-S10_Control, Tienshan = Tienshan_Treatment-Tienshan_Control, S10_Tienshan= (S10_Treatment+Tienshan_Treatment)/2 - (S10_Control+Tienshan_Control)/2, levels=design.matrix) contrasts A matrix: 4 \u00d7 3 of type dbl S10 Tienshan S10_Tienshan S10_Control -1 0 -0.5 S10_Treatment 1 0 0.5 Tienshan_Control 0 -1 -0.5 Tienshan_Treatment 0 1 0.5 Any ideas for other contrasts that migth be interesting to explore? The following cell will run two mandatory steps in order to fit a model in edgeR. First, the dispersion needs to be estimated, this is a measure of the degree of inter-library variation for each gene. Afterwards, a QL ( quasi-likelihood) model representing the sample design is fitted using the \"glmQLFit\" function. You can read more about these steps in the edgeR user guide. In [14]: Copied! DGEList <- estimateDisp ( DGEList , design.matrix ) fit <- glmQLFit ( DGEList , design.matrix ) DGEList < - estimateDisp(DGEList, design.matrix) fit < - glmQLFit(DGEList, design.matrix)","title":"EdgeR - Testing for Differentially expressed genes (DEGs)"},{"location":"nb/bulk.html#degs-for-white-clover-s10-plants-treatment-vs-control","text":"Find DEGs for White clover S10 plants in Treatment condition compared with the Control condition We will test for differentially expressed genes using the quasi-likelihood F-tests method. This is easily done just by running the \"glmQFTest\" function on the fit model and selecting one of the contrasts we created above (in this case the \"S10\" contrast). Next, we will use the topTags function to extract the information about all the genes. In [15]: Copied! glmqlf_S10 <- glmQLFTest ( fit , contrast = contrasts [, \"S10\" ]) DEG_S10 <- topTags ( glmqlf_S10 , n = nrow ( DGEList $ counts )) DEG_S10 glmqlf_S10 < - glmQLFTest(fit, contrast=contrasts[,\"S10\"]) DEG_S10 < - topTags(glmqlf_S10, n = nrow(DGEList$counts)) DEG_S10 $table A data.frame: 163 \u00d7 5 logFC logCPM F PValue FDR <dbl> <dbl> <dbl> <dbl> <dbl> g39 3.5861702 14.124630 336.66158 5.227331e-10 8.520550e-08 g232 4.0494125 11.950441 157.50997 3.685988e-08 2.591779e-06 g6 2.3225347 12.999541 150.27854 4.770146e-08 2.591779e-06 g350 2.0530029 13.324566 97.56453 4.890245e-07 1.992775e-05 g99 -1.5387874 11.681004 85.17478 9.976659e-07 3.195090e-05 g142 -1.4235276 13.830692 82.52308 1.176107e-06 3.195090e-05 g240 1.3603712 14.708794 79.98114 1.383201e-06 3.220882e-05 g269 -1.4191681 11.834217 75.66563 1.841279e-06 3.751606e-05 g26 2.1254241 11.366827 68.55898 3.047273e-06 5.518949e-05 g129 -5.3324794 8.679594 60.45459 5.738115e-06 9.353127e-05 g272 3.1405978 13.860652 59.08187 6.432977e-06 9.532503e-05 g157 5.3813801 10.414972 57.03712 7.659515e-06 1.040417e-04 g42 -1.9856547 9.962762 53.97164 1.005146e-05 1.260298e-04 g165 2.2666761 13.690016 51.66777 1.243606e-05 1.447913e-04 g101 3.1488142 13.181625 48.21727 1.736838e-05 1.887363e-04 g15 2.7410696 10.684840 47.29143 1.906062e-05 1.941800e-04 g210 -2.2430064 9.993002 46.52627 2.060621e-05 1.975772e-04 g299 -2.3509276 10.984491 45.75893 2.230566e-05 2.019901e-04 g103 2.6810410 10.579777 44.26963 2.609599e-05 2.238762e-04 g144 -0.7963477 16.186987 43.17707 2.936057e-05 2.392886e-04 g77 1.0984806 14.118231 41.46740 3.548119e-05 2.754016e-04 g125 -1.3099534 10.195970 39.85537 4.266319e-05 3.160955e-04 g298 -2.0471647 9.837072 33.49832 9.395967e-05 6.658881e-04 g104 2.4081173 9.219160 38.54240 1.121281e-04 7.615370e-04 g122 -0.6485441 12.457152 30.90261 1.341148e-04 8.744287e-04 g164 1.4415064 11.156457 30.50383 1.419308e-04 8.897970e-04 g146 1.3983889 12.824184 29.92911 1.541544e-04 9.306356e-04 g313 2.2492040 13.614815 28.41728 1.926822e-04 1.121686e-03 g150 1.9650641 13.229706 27.90020 2.083770e-04 1.171222e-03 g13 -0.6522187 13.101579 26.45593 2.608176e-04 1.417109e-03 \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee g96 -1.873792302 8.939397 0.4844512655 0.4999546 0.6041584 g10 0.233134670 14.382992 0.4834790913 0.5003766 0.6041584 g253 0.052426914 13.741219 0.3075042687 0.5896191 0.7066759 g289 -0.212573283 9.390864 0.2887857862 0.6010328 0.7150974 g312 0.093543041 10.849110 0.2666731965 0.6151559 0.7265972 g301 -0.102000599 10.135184 0.2572520652 0.6214023 0.7274503 g221 -0.277095720 8.469909 0.2522121463 0.6248039 0.7274503 g162 -0.145155616 8.517263 0.2278112475 0.6463283 0.7413085 g50 -0.170907352 8.749274 0.2158314533 0.6507284 0.7413085 g203 0.062836598 14.227601 0.2124612164 0.6532634 0.7413085 g88 0.045949497 14.235850 0.2103049178 0.6548983 0.7413085 g355 -0.287897714 8.890754 0.1803234432 0.6787703 0.7630314 g204 -0.055975779 11.947887 0.1408988543 0.7140799 0.7972262 g306 0.054269738 11.294732 0.0923937800 0.7664733 0.8498990 g193 0.049946343 11.682386 0.0642557849 0.8042743 0.8857886 g205 0.037877227 10.986961 0.0473457745 0.8314821 0.9029713 g194 -0.142990391 9.403497 0.0447552162 0.8360811 0.9029713 g362 0.025629967 11.336529 0.0415198735 0.8420254 0.9029713 g315 0.027958374 11.878331 0.0409766489 0.8430470 0.9029713 g180 0.059680256 15.711409 0.0386140448 0.8475743 0.9029713 g282 -0.026492114 11.756078 0.0247991469 0.8775434 0.9288284 g176 -0.061055115 8.587203 0.0144510429 0.9063467 0.9531258 g126 0.061707217 8.311212 0.0024786868 0.9648334 1.0000000 g128 0.063825269 7.671043 0.0019414812 0.9655950 1.0000000 g227 -0.025740630 7.939955 0.0011908008 0.9730516 1.0000000 g121 0.002660852 12.896620 0.0005233398 0.9821327 1.0000000 g284 0.000000000 8.538720 0.0000000000 1.0000000 1.0000000 g116 0.000000000 7.180297 0.0000000000 1.0000000 1.0000000 g117 0.000000000 7.033307 0.0000000000 1.0000000 1.0000000 g118 0.000000000 7.515646 0.0000000000 1.0000000 1.0000000 $adjust.method 'BH' $comparison '-1*S10_Control 1*S10_Treatment' $test 'glm' We have now created a table with certain parameters calculated for each of the genes analysed. logFC represents the base 2 logarithm of the fold change and it shows us how much the expression of the gene has changed between the two conditions. A logFC of 1 means a doubling in the read per gene count between the control and treatment samples. The genes with a logFC higher than 0 are upregulated while the genes with a logFC lower than 0 are downregulated between the control and treatment samples. logCPM represents the average log2-counts-per-million, the abundance of the gene. F - F-statistic. PValue is the raw p-value. FDR (The false discovery rate) represents the adjusted p-value and is calculated using Benjamini and Hochberg's algorithm. It controls the rate of false positive values under multiple testing. Usually, a threshold of under 5% is set for the FDR. The important information for us in this table is stored in the \"logFC\" and the \"FDR\" columns. The top DE genes have small FDR values and large fold changes Many of the genes in the samples are uninteresting for us, as they have a high FDR and/or low logFC values so we cannot consider them as differentially expressed. We will apply a filtering step in order to keep only the statistically significant genes. We will filter out the genes with an FDR higher than 0.05 and an absolute logFC lower than 1. In [16]: Copied! DEG_S10_filtered <- DEG_S10 $ table [ DEG_S10 $ table $ FDR < 0.05 & abs ( DEG_S10 $ table $ logFC ) > 1 ,] #Filtering DEG_S10_filtered <- rownames_to_column ( DEG_S10_filtered ) %>% rename ( gene_ID = rowname ) #Adding the gene_ID column head ( DEG_S10_filtered ) nrow ( DEG_S10_filtered ) # finding the number of genes that passed the filter DEG_S10_filtered < - DEG_S10$table[DEG_S10$table$FDR < 0.05 & abs(DEG_S10$table$logFC) > 1,] #Filtering DEG_S10_filtered < - rownames_to_column(DEG_S10_filtered) %>% rename(gene_ID = rowname) #Adding the gene_ID column head(DEG_S10_filtered) nrow(DEG_S10_filtered) # finding the number of genes that passed the filter A data.frame: 6 \u00d7 6 gene_ID logFC logCPM F PValue FDR <chr> <dbl> <dbl> <dbl> <dbl> <dbl> 1 g39 3.586170 14.12463 336.66158 5.227331e-10 8.520550e-08 2 g232 4.049413 11.95044 157.50997 3.685988e-08 2.591779e-06 3 g6 2.322535 12.99954 150.27854 4.770146e-08 2.591779e-06 4 g350 2.053003 13.32457 97.56453 4.890245e-07 1.992775e-05 5 g99 -1.538787 11.68100 85.17478 9.976659e-07 3.195090e-05 6 g142 -1.423528 13.83069 82.52308 1.176107e-06 3.195090e-05 52 We can also visualize the selected genes by plotting a Smear plot or a Volcano plot. The Genes that passed the filter are coloured in red, and the top 10 genes with the lowest FDR value are labelled with their gene ID. We can see that the majority of the genes analysed are either not statistically significant or have a very small logFC. In [17]: Copied! plotSmear ( glmqlf_S10 , de.tags = rownames ( DEG_S10 $ table )[ which ( DEG_S10 $ table $ FDR < 0.05 & abs ( DEG_S10 $ table $ logFC ) > 1 )]) text ( x = DEG_S10_filtered $ logCPM [ 1 : 10 ], y = DEG_S10_filtered $ logFC [ 1 : 10 ], labels = DEG_S10_filtered $ gene_ID [ 1 : 10 ], cex = 0.7 , pos = 1 ) abline ( h = c ( -1 , 1 ), col = \"blue\" ) plotSmear(glmqlf_S10, de.tags = rownames(DEG_S10$table)[which(DEG_S10$table$FDR < 0.05 & abs(DEG_S10$table$logFC) > 1)]) text(x=DEG_S10_filtered$logCPM[1:10], y=DEG_S10_filtered$logFC[1:10], labels=DEG_S10_filtered$gene_ID[1:10], cex=0.7, pos=1) abline(h = c(-1, 1), col = \"blue\") We can also create a heatmap with the log2 read counts of the selected differentially expressed genes so that we can visualise the differences in normalized counts between the Control and the Treatment samples. The genes are ordered by the FDR value. In [18]: Copied! annot_col <- data.frame ( row.names = colnames ( pseudoNormCounts )[ 1 : 6 ], Condition = c ( rep ( \"Control\" , 3 ), rep ( \"Treatment\" , 3 ))) pheatmap ( as.matrix ( pseudoNormCounts [ DEG_S10_filtered $ gene_ID , c ( 1 : 6 )]), cluster_rows = F , cluster_col = F , annotation_col = annot_col ) annot_col < - data.frame(row.names = colnames(pseudoNormCounts)[1:6], Condition = c(rep(\"Control\", 3),rep( \"Treatment\", 3))) pheatmap(as.matrix(pseudoNormCounts[DEG_S10_filtered$gene_ID,c(1:6)]), cluster_rows = F, cluster_col = F, annotation_col = annot_col)","title":"DEGs for White clover S10 plants Treatment vs Control"},{"location":"nb/bulk.html#degs-for-white-clover-tienshan-treatment-vs-control","text":"We can do this using the same functions as above and changing the contrast. This time we will directly filter the differentially expressed genes using the same parameters as for the S10 samples. In [19]: Copied! glmqlf_Ti <- glmQLFTest ( fit , contrast = contrasts [, \"Tienshan\" ]) DEG_Ti <- topTags ( glmqlf_Ti , n = nrow ( DGEList $ counts )) DEG_Ti_filtered <- DEG_Ti $ table [ DEG_Ti $ table $ FDR < 0.05 & abs ( DEG_Ti $ table $ logFC ) > 1 ,] DEG_Ti_filtered <- rownames_to_column ( DEG_Ti_filtered ) %>% rename ( gene_ID = rowname ) print ( head ( DEG_Ti_filtered )) print ( \"Nr of differentially expressed genes:\" ) print ( nrow ( DEG_Ti_filtered )) glmqlf_Ti < - glmQLFTest(fit, contrast=contrasts[,\"Tienshan\"]) DEG_Ti < - topTags(glmqlf_Ti, n = nrow(DGEList$counts)) DEG_Ti_filtered < - DEG_Ti$table[DEG_Ti$table$FDR < 0.05 & abs(DEG_Ti$table$logFC) > 1,] DEG_Ti_filtered < - rownames_to_column(DEG_Ti_filtered) %>% rename(gene_ID = rowname) print(head(DEG_Ti_filtered)) print(\"Nr of differentially expressed genes:\") print(nrow(DEG_Ti_filtered)) gene_ID logFC logCPM F PValue FDR 1 g234 -3.348667 13.00417 278.01679 1.547626e-09 2.522630e-07 2 g269 -1.885843 11.83422 127.39949 1.172628e-07 9.556922e-06 3 g26 2.423725 11.36683 74.33983 2.016212e-06 8.910761e-05 4 g101 4.303290 13.18162 73.17140 2.186690e-06 8.910761e-05 5 g272 3.391225 13.86065 66.80131 3.475790e-06 9.840357e-05 6 g99 -1.430395 11.68100 66.25814 3.622217e-06 9.840357e-05 [1] \"Nr of differentially expressed genes:\" [1] 42 We can now plot a Venn diagram with the DEGs for the two genotypes in order to observe the number of common and specific differentially expressed genes between the two genotypes as response to the cold exposure. You can see that a high percentage of the identified genes are common for the two genotypes, while each genotype has also specific genes. In [20]: Copied! vd <- venn.diagram ( x = list ( DEG_S10_filtered $ gene_ID , DEG_Ti_filtered $ gene_ID ), category.names = c ( \"S10\" , \"Tienshan\" ), lwd = 4 , fill = c ( \"cornflowerblue\" , \"yellowgreen\" ), filename = NULL , cat.cex = 1 , cat.fontface = \"bold\" , output = TRUE ) grid.draw ( vd ) vd < - venn.diagram( x = list(DEG_S10_filtered$gene_ID, DEG_Ti_filtered$gene_ID), category.names = c(\"S10\" , \"Tienshan\"), lwd = 4, fill = c(\"cornflowerblue\", \"yellowgreen\"), filename = NULL, cat.cex = 1, cat.fontface = \"bold\", output=TRUE ) grid.draw(vd)","title":"DEGs for White clover Tienshan Treatment vs Control"},{"location":"nb/bulk.html#degs-for-s10tienshan-treatment-vs-control-wo-genotype-effects","text":"Until now we tested for DEGs specific for each of the two genotypes under cold treatment. We can also run a test where we ignore the genotype and just test for the differences in the cold response. Consider the counts for both genotypes as a single dataset using the previously created contrast \"S10_Tienshan\". Do the results look different compared with the previous tests? In [21]: Copied! glmqlf_S10_Ti <- glmQLFTest ( fit , contrast = contrasts [, \"S10_Tienshan\" ]) DEG_S10_Ti <- topTags ( glmqlf_S10_Ti , n = nrow ( DGEList $ counts )) DEG_S10_Ti_filtered <- DEG_S10_Ti $ table [ DEG_S10_Ti $ table $ FDR < 0.05 & abs ( DEG_S10_Ti $ table $ logFC ) > 1 ,] DEG_S10_Ti_filtered <- rownames_to_column ( DEG_S10_Ti_filtered ) %>% rename ( gene_ID = rowname ) print ( head ( DEG_S10_Ti_filtered )) print ( \"Nr of differentially expressed genes:\" ) print ( nrow ( DEG_S10_Ti_filtered )) glmqlf_S10_Ti < - glmQLFTest(fit, contrast=contrasts[,\"S10_Tienshan\"]) DEG_S10_Ti < - topTags(glmqlf_S10_Ti, n = nrow(DGEList$counts)) DEG_S10_Ti_filtered < - DEG_S10_Ti$table[DEG_S10_Ti$table$FDR < 0.05 & abs(DEG_S10_Ti$table$logFC) > 1,] DEG_S10_Ti_filtered < - rownames_to_column(DEG_S10_Ti_filtered) %>% rename(gene_ID = rowname) print(head(DEG_S10_Ti_filtered)) print(\"Nr of differentially expressed genes:\") print(nrow(DEG_S10_Ti_filtered)) gene_ID logFC logCPM F PValue FDR 1 g39 2.057128 14.12463 246.6519 3.039491e-09 4.954370e-07 2 g232 3.261008 11.95044 203.4580 8.934889e-09 5.288546e-07 3 g269 -1.652505 11.83422 200.3564 9.733520e-09 5.288546e-07 4 g6 1.714849 12.99954 166.2297 2.740066e-08 1.116577e-06 5 g99 -1.484591 11.68100 150.3246 4.762148e-08 1.552460e-06 6 g26 2.274575 11.36683 142.8625 6.290957e-08 1.623160e-06 [1] \"Nr of differentially expressed genes:\" [1] 52 Plot the results from the 3 tests in a Venn diagram to visualize the number of common and unique genes In [22]: Copied! vd <- venn.diagram ( x = list ( DEG_S10_filtered $ gene_ID , DEG_Ti_filtered $ gene_ID , DEG_S10_Ti_filtered $ gene_ID ), category.names = c ( \"S10\" , \"Tienshan\" , \"S10_Tienshan\" ), lwd = 3 , fill = c ( \"cornflowerblue\" , \"yellowgreen\" , \"thistle3\" ), filename = NULL , cat.cex = 1 , cat.fontface = \"bold\" , output = TRUE ) grid.draw ( vd ) vd < - venn.diagram( x = list(DEG_S10_filtered$gene_ID, DEG_Ti_filtered$gene_ID, DEG_S10_Ti_filtered$gene_ID), category.names = c(\"S10\" , \"Tienshan\", \"S10_Tienshan\"), lwd = 3, fill = c(\"cornflowerblue\", \"yellowgreen\", \"thistle3\"), filename = NULL, cat.cex = 1, cat.fontface = \"bold\", output=TRUE ) grid.draw(vd)","title":"DEGs for S10+Tienshan Treatment vs Control w/o genotype effects"},{"location":"nb/bulk.html#explore-dge-results","text":"","title":"Explore DGE results"},{"location":"nb/bulk.html#select-the-genes-which-appear-only-in-the-analysis-using-both-genotypes-for-further-examination","text":"We can use the \"anti_join\" function from the dplyr package to keep only the unique genes that appear only when using both genotypes. In [23]: Copied! S10_Ti_unique <- anti_join ( DEG_S10_Ti_filtered , DEG_S10_filtered , by = \"gene_ID\" ) %>% anti_join ( DEG_Ti_filtered , by = \"gene_ID\" ) S10_Ti_unique S10_Ti_unique < - anti_join(DEG_S10_Ti_filtered, DEG_S10_filtered, by=\"gene_ID\") %>% anti_join(DEG_Ti_filtered, by=\"gene_ID\") S10_Ti_unique A data.frame: 2 \u00d7 6 gene_ID logFC logCPM F PValue FDR <chr> <dbl> <dbl> <dbl> <dbl> <dbl> g247 -1.412652 8.424523 10.877350 0.006535994 0.01500517 g360 1.180849 7.900076 9.804067 0.013809376 0.02813660","title":"Select the genes which appear only in the analysis using both genotypes for further examination."},{"location":"nb/bulk.html#linking-the-genes-selected-as-differentially-expressed-back-to-the-raw-read-counts","text":"How do the counts look for these genes, does it make sense that they are differentially expressed only when using the two genotypes? In [24]: Copied! Read_counts <- rownames_to_column ( Read_counts ) %>% rename ( gene_ID = rowname ) S10_Ti_unique_counts <- inner_join ( S10_Ti_unique [, c ( 1 , 2 , 6 )], Read_counts , by = \"gene_ID\" ) S10_Ti_unique_counts Read_counts < - rownames_to_column(Read_counts) %>% rename(gene_ID = rowname) S10_Ti_unique_counts < - inner_join(S10_Ti_unique[,c(1,2,6)], Read_counts, by=\"gene_ID\") S10_Ti_unique_counts A data.frame: 2 \u00d7 15 gene_ID logFC FDR S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 <chr> <dbl> <dbl> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> g247 -1.412652 0.01500517 4 9 3 1 2 1 42 27 32 8 22 22 g360 1.180849 0.02813660 1 0 1 3 2 3 16 10 16 22 19 22","title":"Linking the genes selected as differentially expressed back to the raw read counts"},{"location":"nb/bulk.html#adding-functional-annotation-to-degs","text":"Until now we looked only at gene IDs, but we can also add functional annotations to the DEGs. The functional annotations were generated using protein sequences and the EggNOG software. Identifying the molecular function of the differentially expressed genes can help us do a literature survey in order to check if any of the genes discovered have been previously identified as being involved in the cold response. In [25]: Copied! Functional_annotations <- read.delim ( \"../Data/Clover_Data/Functional_Annotations.txt\" ) S10_Ti_unique_FA <- inner_join ( S10_Ti_unique [, c ( 1 , 2 , 6 )], Functional_annotations , by = \"gene_ID\" ) S10_Ti_unique_FA Functional_annotations < - read.delim(\"../Data/Clover_Data/Functional_Annotations.txt\") S10_Ti_unique_FA < - inner_join(S10_Ti_unique[,c(1,2,6)], Functional_annotations, by=\"gene_ID\") S10_Ti_unique_FA A data.frame: 2 \u00d7 4 gene_ID logFC FDR Human.Readable.Description <chr> <dbl> <dbl> <chr> g247 -1.412652 0.01500517 Nitrogen regulatory protein P-II homolog g360 1.180849 0.02813660 Unknown protein Tasks and Questions Based on the results obtained in the analysis so far, would you change the cut-off for the FDR and logFC to be more strict or more permissive? Look back at the raw counts for different FDR and logFC values and set the thresholds as you find appropriate. You can also plot histograms with the FDR and logFC values. In [ ]: Copied! hist ( DEG_S10 $ table $ FDR , main = \"\" , xlab = \"FDR\" , breaks = 200 , xlim = range ( c ( 0 , 0.1 ))) hist(DEG_S10$table$FDR , main = \"\", xlab = \"FDR\", breaks= 200, xlim = range(c(0, 0.1))) In [ ]: Copied! hist ( DEG_S10 $ table $ logFC , main = \"\" , xlab = \"logFC\" , breaks = 50 , xlim = range ( c ( -6 , 6 ))) hist(DEG_S10$table$logFC , main = \"\", xlab = \"logFC\", breaks= 50, xlim = range(c(-6, 6))) Separate the upregulated and downregulated genes for each genotype and append functional annotations to them. Identify the genes that are commonly upregulated in S10 and Tienshan samples and the uniquely upregulated genes for each genotype. Why do you think some of the proteins appear in duplicates? To answer these questions, it may be convenient to save summary tables from R and open them in excel. See the code below for examples of how to do this. Files can be downloaded by right-clicking on the file name. If you are familiar with R functions, you are welcome to use those for counting. In [ ]: Copied! dir.create ( \"DEG_Output_tables\" , showWarnings = FALSE ) dir.create(\"DEG_Output_tables\", showWarnings = FALSE) In [ ]: Copied! #Create the table with the DEGs, Raw counts(just for the Ti samples in this case, change to columns (2:7) for the S10 samples) and Functional annotations DEG_Ti_counts_FA <- inner_join ( DEG_Ti_filtered [, c ( 1 , 2 , 6 )], Read_counts [, c ( 1 , 8 : 13 )], by = \"gene_ID\" ) %>% inner_join ( Functional_annotations , by = \"gene_ID\" ) #Write the table to file write.table ( DEG_Ti_counts_FA , file = \"DEG_Output_tables/Ti_Treatment_Control_DGE.txt\" , quote = FALSE , row.names = FALSE , sep = \"\\t\" ) #Display the first 10 rows of the table head ( DEG_Ti_counts_FA , n = 10 ) #Create the table with the DEGs, Raw counts(just for the Ti samples in this case, change to columns (2:7) for the S10 samples) and Functional annotations DEG_Ti_counts_FA < - inner_join(DEG_Ti_filtered[,c(1, 2, 6)], Read_counts[, c(1, 8:13)], by=\"gene_ID\") %>% inner_join(Functional_annotations, by=\"gene_ID\") #Write the table to file write.table(DEG_Ti_counts_FA, file = \"DEG_Output_tables/Ti_Treatment_Control_DGE.txt\", quote = FALSE, row.names = FALSE, sep = \"\\t\") #Display the first 10 rows of the table head(DEG_Ti_counts_FA, n=10) Example for filtering and counting the Up/Down genes. You can easily filter using the \"filter()\" function just by specifying the dataframe and a logical argument. In [ ]: Copied! DEG_Ti_up <- filter ( DEG_Ti_counts_FA , logFC > 0 ) print ( \"Nr of upregulated genes:\" ) print ( nrow ( DEG_Ti_up )) DEG_Ti_down <- filter ( DEG_Ti_counts_FA , logFC < 0 ) print ( \"Nr of downregulated genes:\" ) print ( nrow ( DEG_Ti_down )) DEG_Ti_up < - filter(DEG_Ti_counts_FA, logFC > 0) print(\"Nr of upregulated genes:\") print(nrow(DEG_Ti_up)) DEG_Ti_down < - filter(DEG_Ti_counts_FA, logFC < 0) print(\"Nr of downregulated genes:\") print(nrow(DEG_Ti_down)) You can use the \"inner_join\" and the \"anti_join\" functions from the dplyr package to select the common and unique genes for each genotype: example_file_joined <- inner_join(file1, file2, by=\"gene_ID\")","title":"Adding functional annotation to DEGs"},{"location":"nb/bulk.html#wrapping-up","text":"In this notebook, you have worked with RNA-seq results for two white clover genotypes exposed to one night of cold treatment, aiming to identify genes that change their expression in response to the cold treatment. 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We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Variant calling and VCF processing This tutorial will cover the steps for performing Variant calling and working on the resulting VCF file format. We will be using the Hifi and RNA-seq mapping data obtained from Galaxy. At the end of this tutorial you will be able to: call variants using alignment files extract data from VCF files using Python filter and count variants </div> The present tutorial, like the rest of the course material, is available at our open-source github repository . To use this notebook, use the NGS (python) kernel that contains the packages. Choose it by selecting Kernel -> Change Kernel in the menu on top of the window. A few introductory points to run this notebook (click to show): To use this notebook, use the NGS (python) kernel that contains the packages. Choose it by selecting Kernel -> Change Kernel in the menu on top of the window. In this notebook you will use both bash and python commands On some computers, you might see the result of the commands once they are done running. This means you will wait some time while the computer is crunching, and only afterwards you will see the result of the command you have executed You can run the code in each cell by clicking on the run cell button, or by pressing Shift + Enter . When the code is done running, a small green check sign will appear on the left side You need to run the cells in sequential order, please do not run a cell until the one above finished running and do not skip any cells Each cell contains a short description of the code and the output you should get. Please try not to focus on understanding the code for each command in too much detail, but rather try to focus on the output You can create new code cells by pressing + in the Menu bar above. </details> Import the necessary Python libraries: In [1]: Copied! import pandas import allel import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt import seaborn as sns import pandas import allel import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt import seaborn as sns Some of the commands used in the course are functions we implement to simplify reading the code of this course. Mostly, those are commands requiring lines of code that would not add anything to your learning curve (management of plots, trivial calculations, few management of the notebook layout). However, you are free to look at the code into the file Scripts/pythonScripts.py and to reuse our code in your own work (citing our course). In [2]: Copied! % run ../ Scripts / pythonScripts . py %run ../Scripts/pythonScripts.py Create a folder for the data necessary to VCF data analysis In [3]: Copied! %% bash mkdir - p Data_for_VCF_analysis %%bash mkdir -p Data_for_VCF_analysis Use the bam files generated from the alignment of raw reads \u00b6 We will use the data created during the alignment session of the course. You had two possibilities: using the interactive Galaxy platform as explained in the course webpage . In this case you will need to upload the some of the created data into the folder Data_for_VCF_analysis , renaming the files as follows: Hifi reads mapped to Contig1: HIFI_contig_1.bam Hifi reads mapped to Contig2: HIFI_contig_2.bam Hifi reads mapped to Contig 1 and 2: HIFI_contig_1_2_asm20.bam (you can of course choose the one with the map-pb alignment option from the alignment notebook) S10 RNAseq reads mapped to Contig 1 and 2 (After merging the bam files): RNA_S10_merged.bam Ti RNAseq reads mapped to Contig 1 and 2 (After merging the bam files): RNA_TI_merged.bam using the notebook 01_align.ipynb . In that case, you just need to run the command below, linking the data in the results folder into the folder Data_for_VCF_analysis . In [4]: Copied! %% bash ln - fs ` realpath results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam ` Data_for_VCF_analysis / HIFI_contig_1 . bam ln - fs ` realpath results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam ` Data_for_VCF_analysis / HIFI_contig_2 . bam ln - fs ` realpath results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam ` Data_for_VCF_analysis / HIFI_contig_1_2 . bam ln - fs ` realpath results / STAR_output / S10_align_contigs_1_2_merge / S10 . sorted . bam ` Data_for_VCF_analysis / RNA_S10_merged . bam ln - fs ` realpath results / STAR_output / TI_align_contigs_1_2_merge / TI . sorted . bam ` Data_for_VCF_analysis / RNA_TI_merged . bam %%bash ln -fs `realpath results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam` Data_for_VCF_analysis/HIFI_contig_1.bam ln -fs `realpath results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam` Data_for_VCF_analysis/HIFI_contig_2.bam ln -fs `realpath results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam` Data_for_VCF_analysis/HIFI_contig_1_2.bam ln -fs `realpath results/STAR_output/S10_align_contigs_1_2_merge/S10.sorted.bam` Data_for_VCF_analysis/RNA_S10_merged.bam ln -fs `realpath results/STAR_output/TI_align_contigs_1_2_merge/TI.sorted.bam` Data_for_VCF_analysis/RNA_TI_merged.bam Variant calling \u00b6 We will use a tool named bcftools to find variants in the alignment files ( .bam ) relative to the reference genome, in this case, the file DNA_Contig1_2.fasta . In this way we can identify positions in the genome where the sequence of the aligned reads differ from the reference genome they were mapped to. There are multiple types of genetic variation , e.g. SNPs (single nucleotide polymorphism), indels (insertions or deletions) and structural variations (such as chromosomal rearrangements). Here we will only be working with SNPs, thus we will use the terms variants and SNPs interchangeably. The called variants will be stored in a specific format file, the Variant Call Format ( VCF ). When looking at an alignment files in IGV you should easily recognize the SNP positions. You can see in the figure below that at the marked positions all the mapped reads have a mismatch compared to the reference. You will be calling two types of SNPs in this notebook. First, we will find subgenome SNPs using the Hifi reads mapped to white clover Contig1 and Contig2. These are positions in the contigs that are different between the two subgenomes of white clover. Secondly, we will find SNPs that differentiate between the sequenced white clover S10 (the reference genome) and Ti genotype, using RNA-seq read mapping. Create a folder that will store all the files generated in this notebook. In [5]: Copied! ! mkdir - p results / VCF_Files !mkdir -p results/VCF_Files Call subgenome SNPs using the Hifi alignments and the reference genome \u00b6 We will use the bcftools software to call SNPs from alignment files. bcftools is a toolkit for variant calling and manipulating VCF files. If you are interested, you can find all the functionalities here http://samtools.github.io/bcftools/bcftools.html#call . We mainly need two commands for this step, first bcftools mpileup which takes as input the alignment and the genome reference files, followed by bcftools call to produce VCF files. The cell below will generate 3 VCF files stored in the folder results/VCF_Files using the Hifi alignment files uploaded from Galaxy. In [6]: Copied! ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f ../ Data / Clover_Data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / HIFI_contig_1 . bam | bcftools call - mv - Ov - o results / VCF_Files / HIFI_Contig_1 . vcf ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f ../ Data / Clover_Data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / HIFI_contig_2 . bam | bcftools call - mv - Ov - o results / VCF_Files / HIFI_Contig_2 . vcf ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f ../ Data / Clover_Data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / HIFI_contig_1_2 . bam | bcftools call - mv - Ov - o results / VCF_Files / HIFI_Contig_1_2 . vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f ../Data/Clover_Data/DNA_Contig1_2.fasta Data_for_VCF_analysis/HIFI_contig_1.bam | bcftools call -mv -Ov -o results/VCF_Files/HIFI_Contig_1.vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f ../Data/Clover_Data/DNA_Contig1_2.fasta Data_for_VCF_analysis/HIFI_contig_2.bam | bcftools call -mv -Ov -o results/VCF_Files/HIFI_Contig_2.vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f ../Data/Clover_Data/DNA_Contig1_2.fasta Data_for_VCF_analysis/HIFI_contig_1_2.bam | bcftools call -mv -Ov -o results/VCF_Files/HIFI_Contig_1_2.vcf Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 TASK Inspect the VCF files using IGV, comparing them to the specific alignment BAM files. (You can download the VCF files to your computer from the results/VCF_Files folder) Are there any problematic positions that may not represent true SNPs? How can you use the HiFi reads aligned to Contigs1+2 identify potential problems? Hint: Try scrolling to the ends of the contigs. Call SNPs using the two RNA-seq genotypes, S10 and Ti \u00b6 We will repeat the same step as above this time using the RNA-seq alignment files for the two white clover genotypes (S10 and Ti). These commands will produce another two VCF files, stored in the same folder. In [7]: Copied! ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f reference_data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / RNA_S10_merged . bam | bcftools call - mv - Ov - o results / VCF_Files / RNA_S10_merged . vcf ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f reference_data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / RNA_TI_merged . bam | bcftools call - mv - Ov - o results / VCF_Files / RNA_TI_merged . vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f reference_data/DNA_Contig1_2.fasta Data_for_VCF_analysis/RNA_S10_merged.bam | bcftools call -mv -Ov -o results/VCF_Files/RNA_S10_merged.vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f reference_data/DNA_Contig1_2.fasta Data_for_VCF_analysis/RNA_TI_merged.bam | bcftools call -mv -Ov -o results/VCF_Files/RNA_TI_merged.vcf [mpileup] 1 samples in 1 input files Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] maximum number of reads per input file set to -d 250 Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 TASK Inspect one of the VCFs and the corresponding alignment files in IGV. Is it relevant to filter for false positives in this case, and what parameters would you look further into? VCF files processing and filtering \u00b6 Take a look at what one of the previously generated VCF files contains by running the cell below. A VCF file begins with the meta-information lines starting with ## followed by one header line, starting with # . Next, there are the data lines, each describing a genetic variant for a certain position in the genome. Each data line contains multiple tab-delimited columns with information about the variant. There are 9 fixed columns, labelled \u201cCHROM\u201d, \u201cPOS\u201d, \u201cID\u201d, \u201cREF\u201d, \u201cALT\u201d, \u201cQUAL\u201d, \u201cFILTER\u201d, \u201cINFO\u201d and \u201cFORMAT\u201d. Find more about the content of those columns in this wikipedia page . In the sample column you can find the most likely genotype of the sample. For diploid organisms, there will be a 0 value for reference allele and 1 for the alternate allele. Figure: possible genotypes in the data. For example, here we read the first 50 rows from the RNAseq_S10_merged.vcf file. You can see that the file format follows the same pattern as explained above. In [8]: Copied! ! cat results / VCF_Files / RNA_S10_merged . vcf | head - 50 !cat results/VCF_Files/RNA_S10_merged.vcf | head -50 ##fileformat=VCFv4.2 ##FILTER=<ID=PASS,Description=\"All filters passed\"> ##bcftoolsVersion=1.16+htslib-1.17 ##bcftoolsCommand=mpileup --threads 4 -Ou --skip-indels -f reference_data/DNA_Contig1_2.fasta Data_for_VCF_analysis/RNA_S10_merged.bam ##reference=file://reference_data/DNA_Contig1_2.fasta ##contig=<ID=1,length=1031631> ##contig=<ID=2,length=1057923> ##ALT=<ID=*,Description=\"Represents allele(s) other than observed.\"> ##INFO=<ID=INDEL,Number=0,Type=Flag,Description=\"Indicates that the variant is an INDEL.\"> ##INFO=<ID=IDV,Number=1,Type=Integer,Description=\"Maximum number of raw reads supporting an indel\"> ##INFO=<ID=IMF,Number=1,Type=Float,Description=\"Maximum fraction of raw reads supporting an indel\"> ##INFO=<ID=DP,Number=1,Type=Integer,Description=\"Raw read depth\"> ##INFO=<ID=VDB,Number=1,Type=Float,Description=\"Variant Distance Bias for filtering splice-site artefacts in RNA-seq data (bigger is better)\",Version=\"3\"> ##INFO=<ID=RPBZ,Number=1,Type=Float,Description=\"Mann-Whitney U-z test of Read Position Bias (closer to 0 is better)\"> ##INFO=<ID=MQBZ,Number=1,Type=Float,Description=\"Mann-Whitney U-z test of Mapping Quality Bias (closer to 0 is better)\"> ##INFO=<ID=BQBZ,Number=1,Type=Float,Description=\"Mann-Whitney U-z test of Base Quality Bias (closer to 0 is better)\"> ##INFO=<ID=MQSBZ,Number=1,Type=Float,Description=\"Mann-Whitney U-z test of Mapping Quality vs Strand Bias (closer to 0 is better)\"> ##INFO=<ID=NMBZ,Number=1,Type=Float,Description=\"Mann-Whitney U-z test of Number of Mismatches within supporting reads (closer to 0 is better)\"> ##INFO=<ID=SCBZ,Number=1,Type=Float,Description=\"Mann-Whitney U-z test of Soft-Clip Length Bias (closer to 0 is better)\"> ##INFO=<ID=FS,Number=1,Type=Float,Description=\"Phred-scaled p-value using Fisher's exact test to detect strand bias\"> ##INFO=<ID=SGB,Number=1,Type=Float,Description=\"Segregation based metric.\"> ##INFO=<ID=MQ0F,Number=1,Type=Float,Description=\"Fraction of MQ0 reads (smaller is better)\"> ##FORMAT=<ID=PL,Number=G,Type=Integer,Description=\"List of Phred-scaled genotype likelihoods\"> ##FORMAT=<ID=GT,Number=1,Type=String,Description=\"Genotype\"> ##INFO=<ID=AC,Number=A,Type=Integer,Description=\"Allele count in genotypes for each ALT allele, in the same order as listed\"> ##INFO=<ID=AN,Number=1,Type=Integer,Description=\"Total number of alleles in called genotypes\"> ##INFO=<ID=DP4,Number=4,Type=Integer,Description=\"Number of high-quality ref-forward , ref-reverse, alt-forward and alt-reverse bases\"> ##INFO=<ID=MQ,Number=1,Type=Integer,Description=\"Average mapping quality\"> ##bcftools_callVersion=1.16+htslib-1.17 ##bcftools_callCommand=call -mv -Ov -o results/VCF_Files/RNA_S10_merged.vcf; Date=Mon Jun 19 10:48:17 2023 #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT Data_for_VCF_analysis/RNA_S10_merged.bam 1 33218 . G A 65.3262 . DP=88;VDB=0.0923504;SGB=-0.692914;RPBZ=3.99376;MQBZ=-1.86933;MQSBZ=-0.901828;BQBZ=-1.8368;SCBZ=2.6559;FS=0;MQ0F=0;AC=1;AN=2;DP4=15,13,16,9;MQ=19 GT:PL 0/1:98,0,138 1 33222 . C G 69.1967 . DP=56;VDB=4.89382e-06;SGB=-0.692352;RPBZ=3.47069;MQBZ=-1.73535;MQSBZ=-0.222384;BQBZ=-1.36705;SCBZ=2.16882;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,13,12,9;MQ=19 GT:PL 0/1:102,0,138 1 33223 . T G 61.1405 . DP=56;VDB=2.99077e-05;SGB=-0.692067;RPBZ=3.40644;MQBZ=-1.80665;MQSBZ=-0.222384;BQBZ=0.830878;SCBZ=2.63256;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,14,12,8;MQ=19 GT:PL 0/1:94,0,132 1 33224 . T G 65.1016 . DP=58;VDB=4.85907e-06;SGB=-0.692352;RPBZ=3.67489;MQBZ=-1.78978;MQSBZ=-0.159627;BQBZ=-1.43699;SCBZ=2.60016;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,15,12,9;MQ=19 GT:PL 0/1:98,0,144 1 33225 . G T 64.0746 . DP=58;VDB=4.36523e-06;SGB=-0.692352;RPBZ=3.67489;MQBZ=-1.78978;MQSBZ=-0.159627;BQBZ=-2.60059;SCBZ=2.60016;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,15,12,9;MQ=19 GT:PL 0/1:97,0,144 1 33227 . T A 64.067 . DP=59;VDB=4.09235e-06;SGB=-0.692352;RPBZ=4.13393;MQBZ=-1.81637;MQSBZ=-0.18317;BQBZ=-1.48941;SCBZ=2.64866;FS=0;MQ0F=0;AC=1;AN=2;DP4=19,15,12,9;MQ=19 GT:PL 0/1:97,0,144 1 49149 . C T 30.4183 . DP=4;VDB=0.0058656;SGB=-0.556411;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,0,4;MQ=20 GT:PL 1/1:60,12,0 1 83968 . C G 78.0347 . DP=10;VDB=5.52906e-06;SGB=-0.662043;RPBZ=-3;MQBZ=0;MQSBZ=0;BQBZ=-0.333333;SCBZ=1.77187;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 83970 . T C 78.0347 . DP=10;VDB=8.25997e-06;SGB=-0.662043;RPBZ=-1.79284;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=1.77187;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 83972 . G T 78.0347 . DP=10;VDB=8.25997e-06;SGB=-0.662043;RPBZ=-1.79284;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=1.57628;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 83975 . G A 78.0347 . DP=10;VDB=1.22485e-05;SGB=-0.662043;RPBZ=-1.79284;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=1.57628;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 174956 . C T 10.7923 . DP=2;VDB=0.14;SGB=-0.453602;MQSBZ=0;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,1,1;MQ=20 GT:PL 1/1:40,6,0 1 175456 . A G 7.30814 . DP=2;VDB=0.02;SGB=-0.453602;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,2,0;MQ=20 GT:PL 1/1:36,6,0 1 211292 . A T 16.7427 . DP=5;VDB=0.0058656;SGB=-0.556411;RPBZ=-2;MQBZ=0;MQSBZ=0;BQBZ=1.22474;SCBZ=2;FS=0;MQ0F=0;AC=1;AN=2;DP4=0,1,4,0;MQ=20 GT:PL 0/1:47,0,5 1 321659 . G A 13.5525 . DP=7;VDB=0.107235;SGB=-0.511536;RPBZ=1.34164;MQBZ=0;MQSBZ=0;BQBZ=1.73205;SCBZ=0.942809;FS=0;MQ0F=0;AC=1;AN=2;DP4=1,0,1,2;MQ=20 GT:PL 0/1:45,0,8 1 321661 . A C 13.5525 . DP=7;VDB=0.107235;SGB=-0.511536;RPBZ=1.34164;MQBZ=0;MQSBZ=0;BQBZ=1.73205;SCBZ=0.942809;FS=0;MQ0F=0;AC=1;AN=2;DP4=1,0,1,2;MQ=20 GT:PL 0/1:45,0,8 1 323166 . G A 7.30814 . DP=4;VDB=0.1;SGB=-0.453602;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,0,2;MQ=20 GT:PL 1/1:36,6,0 1 331785 . A T 7.30814 . DP=2;VDB=0.02;SGB=-0.453602;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,2,0;MQ=20 GT:PL 1/1:36,6,0 1 331890 . C T 39.4149 . DP=6;VDB=0.100421;SGB=-0.556411;MQSBZ=0;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,3,1;MQ=20 GT:PL 1/1:69,12,0 Next, in order to generate plots of the metrics stored in the VCF files, we will first import the VCF files in Python using the scikit-allel package. This package contains a series of function helpful for processing variant calling files. For now, we will import only the VCF files generated using the Hifi data alignments. You can ignore the warning. In [9]: Copied! #Import the VCF files Hifi_Contig_1_vcf = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_1.vcf' , fields = '*' ) Hifi_Contig_2_vcf = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_2.vcf' , fields = '*' ) Hifi_Contig_1_2_vcf = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_1_2.vcf' , fields = '*' ) #Import the VCF files Hifi_Contig_1_vcf = allel.read_vcf('results/VCF_Files/HIFI_Contig_1.vcf', fields='*') Hifi_Contig_2_vcf = allel.read_vcf('results/VCF_Files/HIFI_Contig_2.vcf', fields='*') Hifi_Contig_1_2_vcf = allel.read_vcf('results/VCF_Files/HIFI_Contig_1_2.vcf', fields='*') /work/Course_Env/lib/python3.10/site-packages/allel/io/vcf_read.py:1732: UserWarning: invalid INFO header: '##INFO=<ID=VDB,Number=1,Type=Float,Description=\"Variant Distance Bias for filtering splice-site artefacts in RNA-seq data (bigger is better)\",Version=\"3\">\\n' warnings.warn('invalid INFO header: %r' % header) Let's check the number of SNPs in each of the three VCF files. The first two VCF files contain subgenome SNPs. You can see that we identified a have a high number of polymorphisms between the two subgenomes. On the other hand the Hifi_Contig1_2_vcf file contains what we would call false positives, as we called SNPs using the same Hifi reads that were used for assembling the reference genome. As expected the number of SNPs in this file is much smaller. In [10]: Copied! print ( \"Number of SNPs in Hifi Contig 1 vcf file:\" , Hifi_Contig_1_vcf [ 'variants/POS' ] . size ) print ( \"Number of SNPs in Hifi Contig 2 vcf file:\" , Hifi_Contig_2_vcf [ 'variants/POS' ] . size ) print ( \"Number of SNPs in Hifi Contig 1 and 2 vcf file:\" , Hifi_Contig_1_2_vcf [ 'variants/POS' ] . size ) print(\"Number of SNPs in Hifi Contig 1 vcf file:\", Hifi_Contig_1_vcf['variants/POS'].size) print(\"Number of SNPs in Hifi Contig 2 vcf file:\", Hifi_Contig_2_vcf['variants/POS'].size) print(\"Number of SNPs in Hifi Contig 1 and 2 vcf file:\", Hifi_Contig_1_2_vcf['variants/POS'].size) Number of SNPs in Hifi Contig 1 vcf file: 31981 Number of SNPs in Hifi Contig 2 vcf file: 30823 Number of SNPs in Hifi Contig 1 and 2 vcf file: 1309 As an overview of the distribution of SNPs, we can plot the density of SNPs across the contigs. We have defined a function called plot_variant_density in the file ../Scripts/pythonScripts.sh that does exactly that. We use that to plot the SNP density across the contigs in the three VCF files in bins of 5000 bp. We can approximate that a SNP density of 0.1 SNPs/bp repesents one polymorphic site at every ten nucleotides. You can see that for the two contigs there are regions more conserved between the two subgenomes, which show a low density of SNPs and more variable regions, which have a higher SNP density. In the third plot we can mainly observe SNPs at the beging of the contigs, as you have probably also oberved when inspecting the files in IGV. Taking a look at the scale of the y-axis you can oberve that the SNP density for Hifi_Contig1_2_vcf is much smaller compared to the other two files. Note: For easier plotting of the files that contain SNPs from both Contig 1 and 2, the SNP density is plotted as an overlay of both contigs. In [11]: Copied! #Plot SNP density plot_variant_density ( Hifi_Contig_1_vcf , window_size = 5000 , title = 'Contig 1 Raw SNP density' ) plot_variant_density ( Hifi_Contig_2_vcf , window_size = 5000 , title = 'Contig 2 Raw SNP density' ) plot_variant_density ( Hifi_Contig_1_2_vcf , window_size = 5000 , title = 'Contig 1 and 2 Raw SNP density' ) #Plot SNP density plot_variant_density(Hifi_Contig_1_vcf, window_size=5000, title='Contig 1 Raw SNP density') plot_variant_density(Hifi_Contig_2_vcf, window_size=5000, title='Contig 2 Raw SNP density') plot_variant_density(Hifi_Contig_1_2_vcf, window_size=5000, title='Contig 1 and 2 Raw SNP density') We can also plot some of the most relevant parameters from the VCF files, such as the read depth and the quality, to have an overview of the quality of the SNPs in these samples. We defined for that the function plot_variant_hist in the file ../Scripts/pythonScripts.sh . We can plot the metrics \"DP\" (Read depth), \"QUAL\" (Quality score), and \"MQ\" (Mapping quality for Hifi_Contig1_vcf . The x-axis represents the chosen metric, while the y-axis represents the number of SNPs. You can observe that the majority of the SNPs from this file have a high quality. In [12]: Copied! plot_variant_hist ( Hifi_Contig_1_vcf , 'DP' , \"mediumblue\" ) plot_variant_hist ( Hifi_Contig_1_vcf , 'QUAL' , \"forestgreen\" ) plot_variant_hist ( Hifi_Contig_1_vcf , 'MQ' , \"darkred\" ) plot_variant_hist(Hifi_Contig_1_vcf,'DP', \"mediumblue\") plot_variant_hist(Hifi_Contig_1_vcf, 'QUAL', \"forestgreen\") plot_variant_hist(Hifi_Contig_1_vcf, 'MQ', \"darkred\") TASK In the two cells below generate the same three plots as above using the VCF files for Contig 2 and Contig 1+2. You will just need to copy the three command lines from the cell above and replace the name of the VCF files. In [ ]: Copied! #Plot the metrics for Contig 2 #Plot the metrics for Contig 2 In [ ]: Copied! #Plot the metrics for Contig 1 + 2 #Plot the metrics for Contig 1 + 2 As you can observe in the plots above, there are some positions with very low read depth and low quality. Those might represent false-positive variants. Thus, we will filter the VCF files in order to eliminate those positions. For this purpose, we will use another bcftools function bcftools filter . We can pass thresholds for all the info metrics to this function using the \"-i\" flag. Below is just an example of the thresholds, you can change the values to suit your files. Note that this command will generate a new filtered VCF file in the \"VCF_Files\" Folder. In [ ]: Copied! ! bcftools filter - i '(QUAL>150) & (DP > 20) & (MQ > 40)' results / VCF_Files / HIFI_Contig_1 . vcf - O v > results / VCF_Files / HIFI_Contig_1_filtered . vcf !bcftools filter -i '(QUAL>150) & (DP > 20) & (MQ > 40)' results/VCF_Files/HIFI_Contig_1.vcf -O v > results/VCF_Files/HIFI_Contig_1_filtered.vcf In [ ]: Copied! ! bcftools filter - i '(QUAL>150) & (DP > 20) & (MQ > 40)' results / VCF_Files / HIFI_Contig_2 . vcf - O v > results / VCF_Files / HIFI_Contig_2_filtered . vcf !bcftools filter -i '(QUAL>150) & (DP > 20) & (MQ > 40)' results/VCF_Files/HIFI_Contig_2.vcf -O v > results/VCF_Files/HIFI_Contig_2_filtered.vcf In [ ]: Copied! ! bcftools filter - i '(QUAL>150) & (DP > 20) & (MQ > 40)' results / VCF_Files / HIFI_Contig_1_2 . vcf - O v > results / VCF_Files / HIFI_Contig_1_2_filtered . vcf !bcftools filter -i '(QUAL>150) & (DP > 20) & (MQ > 40)' results/VCF_Files/HIFI_Contig_1_2.vcf -O v > results/VCF_Files/HIFI_Contig_1_2_filtered.vcf We read the filtered VCF file for Contig1 in python and generate the same plots as before. We can read the VCF file and generate all the plots in one cell this time. We check how many positions have been eliminated by the filter and compare the plots with the ones for the unfiltered VCFs. If you consider that there are still positions with with low quality you can change the filter and repeat the process. In [ ]: Copied! Hifi_Contig1_filtered = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_1_filtered.vcf' , fields = '*' ) print ( \"Number of SNPs in Contig 1 VCF filtered file:\" , Hifi_Contig1_filtered [ 'variants/POS' ] . size ) #Plot SNP density plot_variant_density ( Hifi_Contig1_filtered , window_size = 5000 , title = 'Contig 1 Filtered variant density' ) #Plot metrics plot_variant_hist ( Hifi_Contig1_filtered , 'DP' , \"mediumblue\" ) plot_variant_hist ( Hifi_Contig1_filtered , 'QUAL' , \"forestgreen\" ) plot_variant_hist ( Hifi_Contig1_filtered , 'MQ' , \"darkred\" ) Hifi_Contig1_filtered = allel.read_vcf('results/VCF_Files/HIFI_Contig_1_filtered.vcf', fields='*') print(\"Number of SNPs in Contig 1 VCF filtered file:\", Hifi_Contig1_filtered['variants/POS'].size) #Plot SNP density plot_variant_density(Hifi_Contig1_filtered, window_size=5000, title='Contig 1 Filtered variant density') #Plot metrics plot_variant_hist(Hifi_Contig1_filtered,'DP', \"mediumblue\") plot_variant_hist(Hifi_Contig1_filtered, 'QUAL', \"forestgreen\") plot_variant_hist(Hifi_Contig1_filtered, 'MQ', \"darkred\") TASK This exercise covers the rest of the notebook and contains explanations as well . The code will work only if you complete correctly the empty cells. Some cells are filled in to help. Compare the VCF files before and after filtering in IGV. In the two cells below generate the same plots as above using the filtered VCF files for Contig 2 and Contig 1 + 2. You will just need to copy the three command lines from the cell above and replace the name of the VCF files. In [ ]: Copied! #Figures for Contig 2 Filtered VCF #Figures for Contig 2 Filtered VCF In [ ]: Copied! #Figures for Contig 1 + 2 Filtered VCF #Figures for Contig 1 + 2 Filtered VCF Repeat the quality check and filtering as described above for the two RNA-seq VCF files: Read the VCF files in Python: In [ ]: Copied! vcf_S10_RNA = allel . read_vcf ( 'results/VCF_Files/RNA_S10_merged.vcf' , fields = '*' ) vcf_Ti_RNA = allel . read_vcf ( 'results/VCF_Files/RNA_TI_merged.vcf' , fields = '*' ) vcf_S10_RNA = allel.read_vcf('results/VCF_Files/RNA_S10_merged.vcf', fields='*') vcf_Ti_RNA = allel.read_vcf('results/VCF_Files/RNA_TI_merged.vcf', fields='*') Print the number of SNPs in the two RNA-seq VCF files. Note that the number of SNPs is much smaller than for the Hifi VCF files. Also, there is a difference in the number of SNPs for the two genotypes. As S10 RNA-seq data originates from the same genotype as the referece genome, one would expect a small number of SNPs to be identified in this case. In [ ]: Copied! print ( \"Number of SNPs in S10 RNA-seq VCF file:\" , vcf_S10_RNA [ 'variants/POS' ] . size ) print ( \"Number of SNPs in Ti RNA-seq VCF file:\" , vcf_Ti_RNA [ 'variants/POS' ] . size ) print(\"Number of SNPs in S10 RNA-seq VCF file:\", vcf_S10_RNA['variants/POS'].size) print(\"Number of SNPs in Ti RNA-seq VCF file:\", vcf_Ti_RNA['variants/POS'].size) Generate SNP density plots using the plot_variant_density() function: In [ ]: Copied! In [ ]: Copied! Generate info histograms using the plot_variant_hist() function for the same fields. You should observe that the metrics for the RNA-seq vcf files are quite different from the ones using Hifi reads. In [ ]: Copied! In [ ]: Copied! Filter the VCF files using bcftools and your selected tresholds after inspecting the VCF files. Add the filter thresholds before running the command. In [ ]: Copied! ! bcftools filter - i 'add filter thresholds here' results / VCF_Files / RNA_S10_merged . vcf - O v > results / VCF_Files / RNA_S10_merged_filtered . vcf ! bcftools filter - i 'add filter thresholds here' results / VCF_Files / RNA_TI_merged . vcf - O v > results / VCF_Files / RNA_TI_merged_filtered . vcf !bcftools filter -i 'add filter thresholds here' results/VCF_Files/RNA_S10_merged.vcf -O v > results/VCF_Files/RNA_S10_merged_filtered.vcf !bcftools filter -i 'add filter thresholds here' results/VCF_Files/RNA_TI_merged.vcf -O v > results/VCF_Files/RNA_TI_merged_filtered.vcf Read the filtered VCFs in python: In [ ]: Copied! vcf_S10_RNA_filtered = allel . read_vcf ( 'results/VCF_Files/RNA_S10_merged_filtered.vcf' , fields = '*' ) vcf_Ti_RNA_filtered = allel . read_vcf ( 'results/VCF_Files/RNA_TI_merged_filtered.vcf' , fields = '*' ) vcf_S10_RNA_filtered = allel.read_vcf('results/VCF_Files/RNA_S10_merged_filtered.vcf', fields='*') vcf_Ti_RNA_filtered = allel.read_vcf('results/VCF_Files/RNA_TI_merged_filtered.vcf', fields='*') Check the number of SNPs in the filtered VCF files: In [ ]: Copied! print ( \"Number of SNPs in the filtered S10 RNA-seq VCF file:\" , vcf_S10_RNA_filtered [ 'variants/POS' ] . size ) print ( \"Number of SNPs in the filtered Ti RNA-seq VCF file:\" , vcf_Ti_RNA_filtered [ 'variants/POS' ] . size ) print(\"Number of SNPs in the filtered S10 RNA-seq VCF file:\", vcf_S10_RNA_filtered['variants/POS'].size) print(\"Number of SNPs in the filtered Ti RNA-seq VCF file:\", vcf_Ti_RNA_filtered['variants/POS'].size) Plot same histograms for the filtered VCF files. In [ ]: Copied! In [ ]: Copied! Question: Is it relevant to filter for false positives in this case, and what parameters would you look further into? Genotypes counting \u00b6 We want to count the different genotypes in each of the five filtered VCF files generated previously. To make this task easier we can take advantage of the counting functions in the scikit-allel package. We have used those to implement a little function called position_count in the file ../Scripts/pythonScripts.sh . You can then use the function with each of the 5 filtered VCF files to count the number of positions. You need to fill the names of the VCF files imported in Python. In [ ]: Copied! #Contig 1 filtered VCF position_count ( Hifi_Contig1_filtered ) #Contig 1 filtered VCF position_count(Hifi_Contig1_filtered) In [ ]: Copied! #Contig 2 filtered VCF position_count () #Contig 2 filtered VCF position_count() In [ ]: Copied! #Contig 1 and 2 filtered VCF position_count () #Contig 1 and 2 filtered VCF position_count() In [ ]: Copied! #RNAseq S10 filtered VCF position_count () #RNAseq S10 filtered VCF position_count() In [ ]: Copied! #RNAseq Ti filtered VCF position_count () #RNAseq Ti filtered VCF position_count() Comparison of VCF files \u00b6 Compare the VCF files with subgenome variants and the VCF files with S10/Tienshan genotype specific variants obtained from RNA-seq mapping We want now to find overlapping position between the Contig 1 and Contig 2 Hifi VCF files and the Ti RNAseq VCF file. To ease this task we will first create two merged VCF files containing: Hifi Contig 1 VCF + Ti RNAseq VCF Hifi Contig 2 VCF + Ti RNAseq VCF Once again, we will use bcftools for this task, using the merge function. However, the bcftools merge function works only with compressed and indexed VCF files, so first we will do this. In [ ]: Copied! ! bcftools view results / VCF_Files / HIFI_Contig_1_filtered . vcf - Oz - o results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz ! bcftools index results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz - f - o results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz . csi ! bcftools view results / VCF_Files / HIFI_Contig_2_filtered . vcf - Oz - o results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz ! bcftools index results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz - f - o results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz . csi ! bcftools view results / VCF_Files / RNA_TI_merged_filtered . vcf - Oz - o results / VCF_Files / RNA_TI_merged_filtered . vcf . gz ! bcftools index results / VCF_Files / RNA_TI_merged_filtered . vcf . gz - f - o results / VCF_Files / RNA_TI_merged_filtered . vcf . gz . csi !bcftools view results/VCF_Files/HIFI_Contig_1_filtered.vcf -Oz -o results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz !bcftools index results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz -f -o results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz.csi !bcftools view results/VCF_Files/HIFI_Contig_2_filtered.vcf -Oz -o results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz !bcftools index results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz -f -o results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz.csi !bcftools view results/VCF_Files/RNA_TI_merged_filtered.vcf -Oz -o results/VCF_Files/RNA_TI_merged_filtered.vcf.gz !bcftools index results/VCF_Files/RNA_TI_merged_filtered.vcf.gz -f -o results/VCF_Files/RNA_TI_merged_filtered.vcf.gz.csi Now we can merge Contig 1 VCF with the Ti RNA VCF and keep only positions that match between the two files by filtering out any positions with missing genotypes. In [ ]: Copied! ! bcftools merge results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz results / VCF_Files / RNA_TI_merged_filtered . vcf . gz | bcftools filter - e 'GT=\"./.\"' > results / VCF_Files / TI_Contig_1_merged . vcf !bcftools merge results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz results/VCF_Files/RNA_TI_merged_filtered.vcf.gz | bcftools filter -e 'GT=\"./.\"' > results/VCF_Files/TI_Contig_1_merged.vcf Merge Contig 2 VCF with the Ti RNA VCF and keep only positions that match between the two files. In [ ]: Copied! ! bcftools merge results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz results / VCF_Files / RNA_TI_merged_filtered . vcf . gz | bcftools filter - e 'GT=\"./.\"' > results / VCF_Files / TI_Contig_2_merged . vcf !bcftools merge results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz results/VCF_Files/RNA_TI_merged_filtered.vcf.gz | bcftools filter -e 'GT=\"./.\"' > results/VCF_Files/TI_Contig_2_merged.vcf Take a look at the merged VCF file. You can see that compared with the VCF files for just one sample, there is now a new column in the file, with the corresponding genotype for the second sample that we merged. In [ ]: Copied! ! cat results / VCF_Files / TI_Contig_1_merged . vcf | head - 50 !cat results/VCF_Files/TI_Contig_1_merged.vcf | head -50 In order to count the genotypes for the matching positions we can use once again bcftools filter command. Only that this time we will filter based on the genotype columns for the two samples (GT[0] and GT[1]). GT[0] repesents the genotype for the first sample in the merge command and GT[1] is the genotype for the second sample in the merge command. AR - heterozygous AA - heterozygous alternate For example, the filter GT[0]=\"AR\" && GT[1]=\"AR\" will keep only position in which both samples have a heterozygous genotype. A quick way to count how many positions are in the VCF file is by suing the simple bash command grep . This will count all the lines in the VCF files that do not start with a # , thus all the data lines. In [ ]: Copied! ! echo \"AR + AR:\" ! bcftools filter - i 'GT[0]=\"AR\" && GT[1]=\"AR\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" ! echo \"AR + AA:\" ! bcftools filter - i 'GT[0]=\"AR\" && GT[1]=\"AA\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" ! echo \"AA + AR:\" ! bcftools filter - i 'GT[0]=\"AA\" && GT[1]=\"AR\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" ! echo \"AA + AA:\" ! bcftools filter - i 'GT[0]=\"AA\" && GT[1]=\"AA\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" !echo \"AR + AR:\" !bcftools filter -i 'GT[0]=\"AR\" && GT[1]=\"AR\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" !echo \"AR + AA:\" !bcftools filter -i 'GT[0]=\"AR\" && GT[1]=\"AA\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" !echo \"AA + AR:\" !bcftools filter -i 'GT[0]=\"AA\" && GT[1]=\"AR\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" !echo \"AA + AA:\" !bcftools filter -i 'GT[0]=\"AA\" && GT[1]=\"AA\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" Use the same commands as above to count the positions marching the filters in the Contig2_Ti_merged.vcf. In [ ]: Copied! Wrapping up \ud83c\udf89 \ud83c\udf89 \ud83c\udf89 \u00b6 In this exercise, you identified polymorphisms between the two white clover subgenomes using Hifi reads alignment files and SNPs specific for the white clover Tienshan genotype using RNA-seq alignment files. You learned how to import VCF files in Python to extract data and created plots. You also filtered low-quality variants from the VCF files and counted genotypes.","title":"3.2.Variant calling analysis"},{"location":"nb/vcf.html#use-the-bam-files-generated-from-the-alignment-of-raw-reads","text":"We will use the data created during the alignment session of the course. You had two possibilities: using the interactive Galaxy platform as explained in the course webpage . In this case you will need to upload the some of the created data into the folder Data_for_VCF_analysis , renaming the files as follows: Hifi reads mapped to Contig1: HIFI_contig_1.bam Hifi reads mapped to Contig2: HIFI_contig_2.bam Hifi reads mapped to Contig 1 and 2: HIFI_contig_1_2_asm20.bam (you can of course choose the one with the map-pb alignment option from the alignment notebook) S10 RNAseq reads mapped to Contig 1 and 2 (After merging the bam files): RNA_S10_merged.bam Ti RNAseq reads mapped to Contig 1 and 2 (After merging the bam files): RNA_TI_merged.bam using the notebook 01_align.ipynb . In that case, you just need to run the command below, linking the data in the results folder into the folder Data_for_VCF_analysis . In [4]: Copied! %% bash ln - fs ` realpath results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam ` Data_for_VCF_analysis / HIFI_contig_1 . bam ln - fs ` realpath results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam ` Data_for_VCF_analysis / HIFI_contig_2 . bam ln - fs ` realpath results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam ` Data_for_VCF_analysis / HIFI_contig_1_2 . bam ln - fs ` realpath results / STAR_output / S10_align_contigs_1_2_merge / S10 . sorted . bam ` Data_for_VCF_analysis / RNA_S10_merged . bam ln - fs ` realpath results / STAR_output / TI_align_contigs_1_2_merge / TI . sorted . bam ` Data_for_VCF_analysis / RNA_TI_merged . bam %%bash ln -fs `realpath results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam` Data_for_VCF_analysis/HIFI_contig_1.bam ln -fs `realpath results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam` Data_for_VCF_analysis/HIFI_contig_2.bam ln -fs `realpath results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam` Data_for_VCF_analysis/HIFI_contig_1_2.bam ln -fs `realpath results/STAR_output/S10_align_contigs_1_2_merge/S10.sorted.bam` Data_for_VCF_analysis/RNA_S10_merged.bam ln -fs `realpath results/STAR_output/TI_align_contigs_1_2_merge/TI.sorted.bam` Data_for_VCF_analysis/RNA_TI_merged.bam","title":"Use the bam files generated from the alignment of raw reads"},{"location":"nb/vcf.html#variant-calling","text":"We will use a tool named bcftools to find variants in the alignment files ( .bam ) relative to the reference genome, in this case, the file DNA_Contig1_2.fasta . In this way we can identify positions in the genome where the sequence of the aligned reads differ from the reference genome they were mapped to. There are multiple types of genetic variation , e.g. SNPs (single nucleotide polymorphism), indels (insertions or deletions) and structural variations (such as chromosomal rearrangements). Here we will only be working with SNPs, thus we will use the terms variants and SNPs interchangeably. The called variants will be stored in a specific format file, the Variant Call Format ( VCF ). When looking at an alignment files in IGV you should easily recognize the SNP positions. You can see in the figure below that at the marked positions all the mapped reads have a mismatch compared to the reference. You will be calling two types of SNPs in this notebook. First, we will find subgenome SNPs using the Hifi reads mapped to white clover Contig1 and Contig2. These are positions in the contigs that are different between the two subgenomes of white clover. Secondly, we will find SNPs that differentiate between the sequenced white clover S10 (the reference genome) and Ti genotype, using RNA-seq read mapping. Create a folder that will store all the files generated in this notebook. In [5]: Copied! ! mkdir - p results / VCF_Files !mkdir -p results/VCF_Files","title":"Variant calling"},{"location":"nb/vcf.html#call-subgenome-snps-using-the-hifi-alignments-and-the-reference-genome","text":"We will use the bcftools software to call SNPs from alignment files. bcftools is a toolkit for variant calling and manipulating VCF files. If you are interested, you can find all the functionalities here http://samtools.github.io/bcftools/bcftools.html#call . We mainly need two commands for this step, first bcftools mpileup which takes as input the alignment and the genome reference files, followed by bcftools call to produce VCF files. The cell below will generate 3 VCF files stored in the folder results/VCF_Files using the Hifi alignment files uploaded from Galaxy. In [6]: Copied! ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f ../ Data / Clover_Data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / HIFI_contig_1 . bam | bcftools call - mv - Ov - o results / VCF_Files / HIFI_Contig_1 . vcf ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f ../ Data / Clover_Data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / HIFI_contig_2 . bam | bcftools call - mv - Ov - o results / VCF_Files / HIFI_Contig_2 . vcf ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f ../ Data / Clover_Data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / HIFI_contig_1_2 . bam | bcftools call - mv - Ov - o results / VCF_Files / HIFI_Contig_1_2 . vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f ../Data/Clover_Data/DNA_Contig1_2.fasta Data_for_VCF_analysis/HIFI_contig_1.bam | bcftools call -mv -Ov -o results/VCF_Files/HIFI_Contig_1.vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f ../Data/Clover_Data/DNA_Contig1_2.fasta Data_for_VCF_analysis/HIFI_contig_2.bam | bcftools call -mv -Ov -o results/VCF_Files/HIFI_Contig_2.vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f ../Data/Clover_Data/DNA_Contig1_2.fasta Data_for_VCF_analysis/HIFI_contig_1_2.bam | bcftools call -mv -Ov -o results/VCF_Files/HIFI_Contig_1_2.vcf Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 TASK Inspect the VCF files using IGV, comparing them to the specific alignment BAM files. (You can download the VCF files to your computer from the results/VCF_Files folder) Are there any problematic positions that may not represent true SNPs? How can you use the HiFi reads aligned to Contigs1+2 identify potential problems? Hint: Try scrolling to the ends of the contigs.","title":"Call subgenome SNPs using the Hifi alignments and the reference genome"},{"location":"nb/vcf.html#call-snps-using-the-two-rna-seq-genotypes-s10-and-ti","text":"We will repeat the same step as above this time using the RNA-seq alignment files for the two white clover genotypes (S10 and Ti). These commands will produce another two VCF files, stored in the same folder. In [7]: Copied! ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f reference_data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / RNA_S10_merged . bam | bcftools call - mv - Ov - o results / VCF_Files / RNA_S10_merged . vcf ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f reference_data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / RNA_TI_merged . bam | bcftools call - mv - Ov - o results / VCF_Files / RNA_TI_merged . vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f reference_data/DNA_Contig1_2.fasta Data_for_VCF_analysis/RNA_S10_merged.bam | bcftools call -mv -Ov -o results/VCF_Files/RNA_S10_merged.vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f reference_data/DNA_Contig1_2.fasta Data_for_VCF_analysis/RNA_TI_merged.bam | bcftools call -mv -Ov -o results/VCF_Files/RNA_TI_merged.vcf [mpileup] 1 samples in 1 input files Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] maximum number of reads per input file set to -d 250 Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 TASK Inspect one of the VCFs and the corresponding alignment files in IGV. Is it relevant to filter for false positives in this case, and what parameters would you look further into?","title":"Call SNPs using the two RNA-seq genotypes, S10 and Ti"},{"location":"nb/vcf.html#vcf-files-processing-and-filtering","text":"Take a look at what one of the previously generated VCF files contains by running the cell below. A VCF file begins with the meta-information lines starting with ## followed by one header line, starting with # . Next, there are the data lines, each describing a genetic variant for a certain position in the genome. Each data line contains multiple tab-delimited columns with information about the variant. There are 9 fixed columns, labelled \u201cCHROM\u201d, \u201cPOS\u201d, \u201cID\u201d, \u201cREF\u201d, \u201cALT\u201d, \u201cQUAL\u201d, \u201cFILTER\u201d, \u201cINFO\u201d and \u201cFORMAT\u201d. Find more about the content of those columns in this wikipedia page . In the sample column you can find the most likely genotype of the sample. For diploid organisms, there will be a 0 value for reference allele and 1 for the alternate allele. Figure: possible genotypes in the data. For example, here we read the first 50 rows from the RNAseq_S10_merged.vcf file. You can see that the file format follows the same pattern as explained above. In [8]: Copied! ! cat results / VCF_Files / RNA_S10_merged . vcf | head - 50 !cat results/VCF_Files/RNA_S10_merged.vcf | head -50 ##fileformat=VCFv4.2 ##FILTER=<ID=PASS,Description=\"All filters passed\"> ##bcftoolsVersion=1.16+htslib-1.17 ##bcftoolsCommand=mpileup --threads 4 -Ou --skip-indels -f reference_data/DNA_Contig1_2.fasta Data_for_VCF_analysis/RNA_S10_merged.bam ##reference=file://reference_data/DNA_Contig1_2.fasta ##contig=<ID=1,length=1031631> ##contig=<ID=2,length=1057923> ##ALT=<ID=*,Description=\"Represents allele(s) other than observed.\"> ##INFO=<ID=INDEL,Number=0,Type=Flag,Description=\"Indicates that the variant is an INDEL.\"> ##INFO=<ID=IDV,Number=1,Type=Integer,Description=\"Maximum number of raw reads supporting an indel\"> ##INFO=<ID=IMF,Number=1,Type=Float,Description=\"Maximum fraction of raw reads supporting an indel\"> ##INFO=<ID=DP,Number=1,Type=Integer,Description=\"Raw read depth\"> ##INFO=<ID=VDB,Number=1,Type=Float,Description=\"Variant Distance Bias for filtering splice-site artefacts in RNA-seq data (bigger is better)\",Version=\"3\"> ##INFO=<ID=RPBZ,Number=1,Type=Float,Description=\"Mann-Whitney U-z test of Read Position Bias (closer to 0 is better)\"> ##INFO=<ID=MQBZ,Number=1,Type=Float,Description=\"Mann-Whitney U-z test of Mapping Quality Bias (closer to 0 is better)\"> ##INFO=<ID=BQBZ,Number=1,Type=Float,Description=\"Mann-Whitney U-z test of Base Quality Bias (closer to 0 is better)\"> ##INFO=<ID=MQSBZ,Number=1,Type=Float,Description=\"Mann-Whitney U-z test of Mapping Quality vs Strand Bias (closer to 0 is better)\"> ##INFO=<ID=NMBZ,Number=1,Type=Float,Description=\"Mann-Whitney U-z test of Number of Mismatches within supporting reads (closer to 0 is better)\"> ##INFO=<ID=SCBZ,Number=1,Type=Float,Description=\"Mann-Whitney U-z test of Soft-Clip Length Bias (closer to 0 is better)\"> ##INFO=<ID=FS,Number=1,Type=Float,Description=\"Phred-scaled p-value using Fisher's exact test to detect strand bias\"> ##INFO=<ID=SGB,Number=1,Type=Float,Description=\"Segregation based metric.\"> ##INFO=<ID=MQ0F,Number=1,Type=Float,Description=\"Fraction of MQ0 reads (smaller is better)\"> ##FORMAT=<ID=PL,Number=G,Type=Integer,Description=\"List of Phred-scaled genotype likelihoods\"> ##FORMAT=<ID=GT,Number=1,Type=String,Description=\"Genotype\"> ##INFO=<ID=AC,Number=A,Type=Integer,Description=\"Allele count in genotypes for each ALT allele, in the same order as listed\"> ##INFO=<ID=AN,Number=1,Type=Integer,Description=\"Total number of alleles in called genotypes\"> ##INFO=<ID=DP4,Number=4,Type=Integer,Description=\"Number of high-quality ref-forward , ref-reverse, alt-forward and alt-reverse bases\"> ##INFO=<ID=MQ,Number=1,Type=Integer,Description=\"Average mapping quality\"> ##bcftools_callVersion=1.16+htslib-1.17 ##bcftools_callCommand=call -mv -Ov -o results/VCF_Files/RNA_S10_merged.vcf; Date=Mon Jun 19 10:48:17 2023 #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT Data_for_VCF_analysis/RNA_S10_merged.bam 1 33218 . G A 65.3262 . DP=88;VDB=0.0923504;SGB=-0.692914;RPBZ=3.99376;MQBZ=-1.86933;MQSBZ=-0.901828;BQBZ=-1.8368;SCBZ=2.6559;FS=0;MQ0F=0;AC=1;AN=2;DP4=15,13,16,9;MQ=19 GT:PL 0/1:98,0,138 1 33222 . C G 69.1967 . DP=56;VDB=4.89382e-06;SGB=-0.692352;RPBZ=3.47069;MQBZ=-1.73535;MQSBZ=-0.222384;BQBZ=-1.36705;SCBZ=2.16882;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,13,12,9;MQ=19 GT:PL 0/1:102,0,138 1 33223 . T G 61.1405 . DP=56;VDB=2.99077e-05;SGB=-0.692067;RPBZ=3.40644;MQBZ=-1.80665;MQSBZ=-0.222384;BQBZ=0.830878;SCBZ=2.63256;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,14,12,8;MQ=19 GT:PL 0/1:94,0,132 1 33224 . T G 65.1016 . DP=58;VDB=4.85907e-06;SGB=-0.692352;RPBZ=3.67489;MQBZ=-1.78978;MQSBZ=-0.159627;BQBZ=-1.43699;SCBZ=2.60016;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,15,12,9;MQ=19 GT:PL 0/1:98,0,144 1 33225 . G T 64.0746 . DP=58;VDB=4.36523e-06;SGB=-0.692352;RPBZ=3.67489;MQBZ=-1.78978;MQSBZ=-0.159627;BQBZ=-2.60059;SCBZ=2.60016;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,15,12,9;MQ=19 GT:PL 0/1:97,0,144 1 33227 . T A 64.067 . DP=59;VDB=4.09235e-06;SGB=-0.692352;RPBZ=4.13393;MQBZ=-1.81637;MQSBZ=-0.18317;BQBZ=-1.48941;SCBZ=2.64866;FS=0;MQ0F=0;AC=1;AN=2;DP4=19,15,12,9;MQ=19 GT:PL 0/1:97,0,144 1 49149 . C T 30.4183 . DP=4;VDB=0.0058656;SGB=-0.556411;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,0,4;MQ=20 GT:PL 1/1:60,12,0 1 83968 . C G 78.0347 . DP=10;VDB=5.52906e-06;SGB=-0.662043;RPBZ=-3;MQBZ=0;MQSBZ=0;BQBZ=-0.333333;SCBZ=1.77187;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 83970 . T C 78.0347 . DP=10;VDB=8.25997e-06;SGB=-0.662043;RPBZ=-1.79284;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=1.77187;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 83972 . G T 78.0347 . DP=10;VDB=8.25997e-06;SGB=-0.662043;RPBZ=-1.79284;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=1.57628;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 83975 . G A 78.0347 . DP=10;VDB=1.22485e-05;SGB=-0.662043;RPBZ=-1.79284;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=1.57628;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 174956 . C T 10.7923 . DP=2;VDB=0.14;SGB=-0.453602;MQSBZ=0;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,1,1;MQ=20 GT:PL 1/1:40,6,0 1 175456 . A G 7.30814 . DP=2;VDB=0.02;SGB=-0.453602;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,2,0;MQ=20 GT:PL 1/1:36,6,0 1 211292 . A T 16.7427 . DP=5;VDB=0.0058656;SGB=-0.556411;RPBZ=-2;MQBZ=0;MQSBZ=0;BQBZ=1.22474;SCBZ=2;FS=0;MQ0F=0;AC=1;AN=2;DP4=0,1,4,0;MQ=20 GT:PL 0/1:47,0,5 1 321659 . G A 13.5525 . DP=7;VDB=0.107235;SGB=-0.511536;RPBZ=1.34164;MQBZ=0;MQSBZ=0;BQBZ=1.73205;SCBZ=0.942809;FS=0;MQ0F=0;AC=1;AN=2;DP4=1,0,1,2;MQ=20 GT:PL 0/1:45,0,8 1 321661 . A C 13.5525 . DP=7;VDB=0.107235;SGB=-0.511536;RPBZ=1.34164;MQBZ=0;MQSBZ=0;BQBZ=1.73205;SCBZ=0.942809;FS=0;MQ0F=0;AC=1;AN=2;DP4=1,0,1,2;MQ=20 GT:PL 0/1:45,0,8 1 323166 . G A 7.30814 . DP=4;VDB=0.1;SGB=-0.453602;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,0,2;MQ=20 GT:PL 1/1:36,6,0 1 331785 . A T 7.30814 . DP=2;VDB=0.02;SGB=-0.453602;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,2,0;MQ=20 GT:PL 1/1:36,6,0 1 331890 . C T 39.4149 . DP=6;VDB=0.100421;SGB=-0.556411;MQSBZ=0;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,3,1;MQ=20 GT:PL 1/1:69,12,0 Next, in order to generate plots of the metrics stored in the VCF files, we will first import the VCF files in Python using the scikit-allel package. This package contains a series of function helpful for processing variant calling files. For now, we will import only the VCF files generated using the Hifi data alignments. You can ignore the warning. In [9]: Copied! #Import the VCF files Hifi_Contig_1_vcf = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_1.vcf' , fields = '*' ) Hifi_Contig_2_vcf = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_2.vcf' , fields = '*' ) Hifi_Contig_1_2_vcf = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_1_2.vcf' , fields = '*' ) #Import the VCF files Hifi_Contig_1_vcf = allel.read_vcf('results/VCF_Files/HIFI_Contig_1.vcf', fields='*') Hifi_Contig_2_vcf = allel.read_vcf('results/VCF_Files/HIFI_Contig_2.vcf', fields='*') Hifi_Contig_1_2_vcf = allel.read_vcf('results/VCF_Files/HIFI_Contig_1_2.vcf', fields='*') /work/Course_Env/lib/python3.10/site-packages/allel/io/vcf_read.py:1732: UserWarning: invalid INFO header: '##INFO=<ID=VDB,Number=1,Type=Float,Description=\"Variant Distance Bias for filtering splice-site artefacts in RNA-seq data (bigger is better)\",Version=\"3\">\\n' warnings.warn('invalid INFO header: %r' % header) Let's check the number of SNPs in each of the three VCF files. The first two VCF files contain subgenome SNPs. You can see that we identified a have a high number of polymorphisms between the two subgenomes. On the other hand the Hifi_Contig1_2_vcf file contains what we would call false positives, as we called SNPs using the same Hifi reads that were used for assembling the reference genome. As expected the number of SNPs in this file is much smaller. In [10]: Copied! print ( \"Number of SNPs in Hifi Contig 1 vcf file:\" , Hifi_Contig_1_vcf [ 'variants/POS' ] . size ) print ( \"Number of SNPs in Hifi Contig 2 vcf file:\" , Hifi_Contig_2_vcf [ 'variants/POS' ] . size ) print ( \"Number of SNPs in Hifi Contig 1 and 2 vcf file:\" , Hifi_Contig_1_2_vcf [ 'variants/POS' ] . size ) print(\"Number of SNPs in Hifi Contig 1 vcf file:\", Hifi_Contig_1_vcf['variants/POS'].size) print(\"Number of SNPs in Hifi Contig 2 vcf file:\", Hifi_Contig_2_vcf['variants/POS'].size) print(\"Number of SNPs in Hifi Contig 1 and 2 vcf file:\", Hifi_Contig_1_2_vcf['variants/POS'].size) Number of SNPs in Hifi Contig 1 vcf file: 31981 Number of SNPs in Hifi Contig 2 vcf file: 30823 Number of SNPs in Hifi Contig 1 and 2 vcf file: 1309 As an overview of the distribution of SNPs, we can plot the density of SNPs across the contigs. We have defined a function called plot_variant_density in the file ../Scripts/pythonScripts.sh that does exactly that. We use that to plot the SNP density across the contigs in the three VCF files in bins of 5000 bp. We can approximate that a SNP density of 0.1 SNPs/bp repesents one polymorphic site at every ten nucleotides. You can see that for the two contigs there are regions more conserved between the two subgenomes, which show a low density of SNPs and more variable regions, which have a higher SNP density. In the third plot we can mainly observe SNPs at the beging of the contigs, as you have probably also oberved when inspecting the files in IGV. Taking a look at the scale of the y-axis you can oberve that the SNP density for Hifi_Contig1_2_vcf is much smaller compared to the other two files. Note: For easier plotting of the files that contain SNPs from both Contig 1 and 2, the SNP density is plotted as an overlay of both contigs. In [11]: Copied! #Plot SNP density plot_variant_density ( Hifi_Contig_1_vcf , window_size = 5000 , title = 'Contig 1 Raw SNP density' ) plot_variant_density ( Hifi_Contig_2_vcf , window_size = 5000 , title = 'Contig 2 Raw SNP density' ) plot_variant_density ( Hifi_Contig_1_2_vcf , window_size = 5000 , title = 'Contig 1 and 2 Raw SNP density' ) #Plot SNP density plot_variant_density(Hifi_Contig_1_vcf, window_size=5000, title='Contig 1 Raw SNP density') plot_variant_density(Hifi_Contig_2_vcf, window_size=5000, title='Contig 2 Raw SNP density') plot_variant_density(Hifi_Contig_1_2_vcf, window_size=5000, title='Contig 1 and 2 Raw SNP density') We can also plot some of the most relevant parameters from the VCF files, such as the read depth and the quality, to have an overview of the quality of the SNPs in these samples. We defined for that the function plot_variant_hist in the file ../Scripts/pythonScripts.sh . We can plot the metrics \"DP\" (Read depth), \"QUAL\" (Quality score), and \"MQ\" (Mapping quality for Hifi_Contig1_vcf . The x-axis represents the chosen metric, while the y-axis represents the number of SNPs. You can observe that the majority of the SNPs from this file have a high quality. In [12]: Copied! plot_variant_hist ( Hifi_Contig_1_vcf , 'DP' , \"mediumblue\" ) plot_variant_hist ( Hifi_Contig_1_vcf , 'QUAL' , \"forestgreen\" ) plot_variant_hist ( Hifi_Contig_1_vcf , 'MQ' , \"darkred\" ) plot_variant_hist(Hifi_Contig_1_vcf,'DP', \"mediumblue\") plot_variant_hist(Hifi_Contig_1_vcf, 'QUAL', \"forestgreen\") plot_variant_hist(Hifi_Contig_1_vcf, 'MQ', \"darkred\") TASK In the two cells below generate the same three plots as above using the VCF files for Contig 2 and Contig 1+2. You will just need to copy the three command lines from the cell above and replace the name of the VCF files. In [ ]: Copied! #Plot the metrics for Contig 2 #Plot the metrics for Contig 2 In [ ]: Copied! #Plot the metrics for Contig 1 + 2 #Plot the metrics for Contig 1 + 2 As you can observe in the plots above, there are some positions with very low read depth and low quality. Those might represent false-positive variants. Thus, we will filter the VCF files in order to eliminate those positions. For this purpose, we will use another bcftools function bcftools filter . We can pass thresholds for all the info metrics to this function using the \"-i\" flag. Below is just an example of the thresholds, you can change the values to suit your files. Note that this command will generate a new filtered VCF file in the \"VCF_Files\" Folder. In [ ]: Copied! ! bcftools filter - i '(QUAL>150) & (DP > 20) & (MQ > 40)' results / VCF_Files / HIFI_Contig_1 . vcf - O v > results / VCF_Files / HIFI_Contig_1_filtered . vcf !bcftools filter -i '(QUAL>150) & (DP > 20) & (MQ > 40)' results/VCF_Files/HIFI_Contig_1.vcf -O v > results/VCF_Files/HIFI_Contig_1_filtered.vcf In [ ]: Copied! ! bcftools filter - i '(QUAL>150) & (DP > 20) & (MQ > 40)' results / VCF_Files / HIFI_Contig_2 . vcf - O v > results / VCF_Files / HIFI_Contig_2_filtered . vcf !bcftools filter -i '(QUAL>150) & (DP > 20) & (MQ > 40)' results/VCF_Files/HIFI_Contig_2.vcf -O v > results/VCF_Files/HIFI_Contig_2_filtered.vcf In [ ]: Copied! ! bcftools filter - i '(QUAL>150) & (DP > 20) & (MQ > 40)' results / VCF_Files / HIFI_Contig_1_2 . vcf - O v > results / VCF_Files / HIFI_Contig_1_2_filtered . vcf !bcftools filter -i '(QUAL>150) & (DP > 20) & (MQ > 40)' results/VCF_Files/HIFI_Contig_1_2.vcf -O v > results/VCF_Files/HIFI_Contig_1_2_filtered.vcf We read the filtered VCF file for Contig1 in python and generate the same plots as before. We can read the VCF file and generate all the plots in one cell this time. We check how many positions have been eliminated by the filter and compare the plots with the ones for the unfiltered VCFs. If you consider that there are still positions with with low quality you can change the filter and repeat the process. In [ ]: Copied! Hifi_Contig1_filtered = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_1_filtered.vcf' , fields = '*' ) print ( \"Number of SNPs in Contig 1 VCF filtered file:\" , Hifi_Contig1_filtered [ 'variants/POS' ] . size ) #Plot SNP density plot_variant_density ( Hifi_Contig1_filtered , window_size = 5000 , title = 'Contig 1 Filtered variant density' ) #Plot metrics plot_variant_hist ( Hifi_Contig1_filtered , 'DP' , \"mediumblue\" ) plot_variant_hist ( Hifi_Contig1_filtered , 'QUAL' , \"forestgreen\" ) plot_variant_hist ( Hifi_Contig1_filtered , 'MQ' , \"darkred\" ) Hifi_Contig1_filtered = allel.read_vcf('results/VCF_Files/HIFI_Contig_1_filtered.vcf', fields='*') print(\"Number of SNPs in Contig 1 VCF filtered file:\", Hifi_Contig1_filtered['variants/POS'].size) #Plot SNP density plot_variant_density(Hifi_Contig1_filtered, window_size=5000, title='Contig 1 Filtered variant density') #Plot metrics plot_variant_hist(Hifi_Contig1_filtered,'DP', \"mediumblue\") plot_variant_hist(Hifi_Contig1_filtered, 'QUAL', \"forestgreen\") plot_variant_hist(Hifi_Contig1_filtered, 'MQ', \"darkred\") TASK This exercise covers the rest of the notebook and contains explanations as well . The code will work only if you complete correctly the empty cells. Some cells are filled in to help. Compare the VCF files before and after filtering in IGV. In the two cells below generate the same plots as above using the filtered VCF files for Contig 2 and Contig 1 + 2. You will just need to copy the three command lines from the cell above and replace the name of the VCF files. In [ ]: Copied! #Figures for Contig 2 Filtered VCF #Figures for Contig 2 Filtered VCF In [ ]: Copied! #Figures for Contig 1 + 2 Filtered VCF #Figures for Contig 1 + 2 Filtered VCF Repeat the quality check and filtering as described above for the two RNA-seq VCF files: Read the VCF files in Python: In [ ]: Copied! vcf_S10_RNA = allel . read_vcf ( 'results/VCF_Files/RNA_S10_merged.vcf' , fields = '*' ) vcf_Ti_RNA = allel . read_vcf ( 'results/VCF_Files/RNA_TI_merged.vcf' , fields = '*' ) vcf_S10_RNA = allel.read_vcf('results/VCF_Files/RNA_S10_merged.vcf', fields='*') vcf_Ti_RNA = allel.read_vcf('results/VCF_Files/RNA_TI_merged.vcf', fields='*') Print the number of SNPs in the two RNA-seq VCF files. Note that the number of SNPs is much smaller than for the Hifi VCF files. Also, there is a difference in the number of SNPs for the two genotypes. As S10 RNA-seq data originates from the same genotype as the referece genome, one would expect a small number of SNPs to be identified in this case. In [ ]: Copied! print ( \"Number of SNPs in S10 RNA-seq VCF file:\" , vcf_S10_RNA [ 'variants/POS' ] . size ) print ( \"Number of SNPs in Ti RNA-seq VCF file:\" , vcf_Ti_RNA [ 'variants/POS' ] . size ) print(\"Number of SNPs in S10 RNA-seq VCF file:\", vcf_S10_RNA['variants/POS'].size) print(\"Number of SNPs in Ti RNA-seq VCF file:\", vcf_Ti_RNA['variants/POS'].size) Generate SNP density plots using the plot_variant_density() function: In [ ]: Copied! In [ ]: Copied! Generate info histograms using the plot_variant_hist() function for the same fields. You should observe that the metrics for the RNA-seq vcf files are quite different from the ones using Hifi reads. In [ ]: Copied! In [ ]: Copied! Filter the VCF files using bcftools and your selected tresholds after inspecting the VCF files. Add the filter thresholds before running the command. In [ ]: Copied! ! bcftools filter - i 'add filter thresholds here' results / VCF_Files / RNA_S10_merged . vcf - O v > results / VCF_Files / RNA_S10_merged_filtered . vcf ! bcftools filter - i 'add filter thresholds here' results / VCF_Files / RNA_TI_merged . vcf - O v > results / VCF_Files / RNA_TI_merged_filtered . vcf !bcftools filter -i 'add filter thresholds here' results/VCF_Files/RNA_S10_merged.vcf -O v > results/VCF_Files/RNA_S10_merged_filtered.vcf !bcftools filter -i 'add filter thresholds here' results/VCF_Files/RNA_TI_merged.vcf -O v > results/VCF_Files/RNA_TI_merged_filtered.vcf Read the filtered VCFs in python: In [ ]: Copied! vcf_S10_RNA_filtered = allel . read_vcf ( 'results/VCF_Files/RNA_S10_merged_filtered.vcf' , fields = '*' ) vcf_Ti_RNA_filtered = allel . read_vcf ( 'results/VCF_Files/RNA_TI_merged_filtered.vcf' , fields = '*' ) vcf_S10_RNA_filtered = allel.read_vcf('results/VCF_Files/RNA_S10_merged_filtered.vcf', fields='*') vcf_Ti_RNA_filtered = allel.read_vcf('results/VCF_Files/RNA_TI_merged_filtered.vcf', fields='*') Check the number of SNPs in the filtered VCF files: In [ ]: Copied! print ( \"Number of SNPs in the filtered S10 RNA-seq VCF file:\" , vcf_S10_RNA_filtered [ 'variants/POS' ] . size ) print ( \"Number of SNPs in the filtered Ti RNA-seq VCF file:\" , vcf_Ti_RNA_filtered [ 'variants/POS' ] . size ) print(\"Number of SNPs in the filtered S10 RNA-seq VCF file:\", vcf_S10_RNA_filtered['variants/POS'].size) print(\"Number of SNPs in the filtered Ti RNA-seq VCF file:\", vcf_Ti_RNA_filtered['variants/POS'].size) Plot same histograms for the filtered VCF files. In [ ]: Copied! In [ ]: Copied! Question: Is it relevant to filter for false positives in this case, and what parameters would you look further into?","title":"VCF files processing and filtering"},{"location":"nb/vcf.html#genotypes-counting","text":"We want to count the different genotypes in each of the five filtered VCF files generated previously. To make this task easier we can take advantage of the counting functions in the scikit-allel package. We have used those to implement a little function called position_count in the file ../Scripts/pythonScripts.sh . You can then use the function with each of the 5 filtered VCF files to count the number of positions. You need to fill the names of the VCF files imported in Python. In [ ]: Copied! #Contig 1 filtered VCF position_count ( Hifi_Contig1_filtered ) #Contig 1 filtered VCF position_count(Hifi_Contig1_filtered) In [ ]: Copied! #Contig 2 filtered VCF position_count () #Contig 2 filtered VCF position_count() In [ ]: Copied! #Contig 1 and 2 filtered VCF position_count () #Contig 1 and 2 filtered VCF position_count() In [ ]: Copied! #RNAseq S10 filtered VCF position_count () #RNAseq S10 filtered VCF position_count() In [ ]: Copied! #RNAseq Ti filtered VCF position_count () #RNAseq Ti filtered VCF position_count()","title":"Genotypes counting"},{"location":"nb/vcf.html#comparison-of-vcf-files","text":"Compare the VCF files with subgenome variants and the VCF files with S10/Tienshan genotype specific variants obtained from RNA-seq mapping We want now to find overlapping position between the Contig 1 and Contig 2 Hifi VCF files and the Ti RNAseq VCF file. To ease this task we will first create two merged VCF files containing: Hifi Contig 1 VCF + Ti RNAseq VCF Hifi Contig 2 VCF + Ti RNAseq VCF Once again, we will use bcftools for this task, using the merge function. However, the bcftools merge function works only with compressed and indexed VCF files, so first we will do this. In [ ]: Copied! ! bcftools view results / VCF_Files / HIFI_Contig_1_filtered . vcf - Oz - o results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz ! bcftools index results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz - f - o results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz . csi ! bcftools view results / VCF_Files / HIFI_Contig_2_filtered . vcf - Oz - o results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz ! bcftools index results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz - f - o results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz . csi ! bcftools view results / VCF_Files / RNA_TI_merged_filtered . vcf - Oz - o results / VCF_Files / RNA_TI_merged_filtered . vcf . gz ! bcftools index results / VCF_Files / RNA_TI_merged_filtered . vcf . gz - f - o results / VCF_Files / RNA_TI_merged_filtered . vcf . gz . csi !bcftools view results/VCF_Files/HIFI_Contig_1_filtered.vcf -Oz -o results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz !bcftools index results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz -f -o results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz.csi !bcftools view results/VCF_Files/HIFI_Contig_2_filtered.vcf -Oz -o results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz !bcftools index results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz -f -o results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz.csi !bcftools view results/VCF_Files/RNA_TI_merged_filtered.vcf -Oz -o results/VCF_Files/RNA_TI_merged_filtered.vcf.gz !bcftools index results/VCF_Files/RNA_TI_merged_filtered.vcf.gz -f -o results/VCF_Files/RNA_TI_merged_filtered.vcf.gz.csi Now we can merge Contig 1 VCF with the Ti RNA VCF and keep only positions that match between the two files by filtering out any positions with missing genotypes. In [ ]: Copied! ! bcftools merge results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz results / VCF_Files / RNA_TI_merged_filtered . vcf . gz | bcftools filter - e 'GT=\"./.\"' > results / VCF_Files / TI_Contig_1_merged . vcf !bcftools merge results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz results/VCF_Files/RNA_TI_merged_filtered.vcf.gz | bcftools filter -e 'GT=\"./.\"' > results/VCF_Files/TI_Contig_1_merged.vcf Merge Contig 2 VCF with the Ti RNA VCF and keep only positions that match between the two files. In [ ]: Copied! ! bcftools merge results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz results / VCF_Files / RNA_TI_merged_filtered . vcf . gz | bcftools filter - e 'GT=\"./.\"' > results / VCF_Files / TI_Contig_2_merged . vcf !bcftools merge results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz results/VCF_Files/RNA_TI_merged_filtered.vcf.gz | bcftools filter -e 'GT=\"./.\"' > results/VCF_Files/TI_Contig_2_merged.vcf Take a look at the merged VCF file. You can see that compared with the VCF files for just one sample, there is now a new column in the file, with the corresponding genotype for the second sample that we merged. In [ ]: Copied! ! cat results / VCF_Files / TI_Contig_1_merged . vcf | head - 50 !cat results/VCF_Files/TI_Contig_1_merged.vcf | head -50 In order to count the genotypes for the matching positions we can use once again bcftools filter command. Only that this time we will filter based on the genotype columns for the two samples (GT[0] and GT[1]). GT[0] repesents the genotype for the first sample in the merge command and GT[1] is the genotype for the second sample in the merge command. AR - heterozygous AA - heterozygous alternate For example, the filter GT[0]=\"AR\" && GT[1]=\"AR\" will keep only position in which both samples have a heterozygous genotype. A quick way to count how many positions are in the VCF file is by suing the simple bash command grep . This will count all the lines in the VCF files that do not start with a # , thus all the data lines. In [ ]: Copied! ! echo \"AR + AR:\" ! bcftools filter - i 'GT[0]=\"AR\" && GT[1]=\"AR\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" ! echo \"AR + AA:\" ! bcftools filter - i 'GT[0]=\"AR\" && GT[1]=\"AA\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" ! echo \"AA + AR:\" ! bcftools filter - i 'GT[0]=\"AA\" && GT[1]=\"AR\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" ! echo \"AA + AA:\" ! bcftools filter - i 'GT[0]=\"AA\" && GT[1]=\"AA\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" !echo \"AR + AR:\" !bcftools filter -i 'GT[0]=\"AR\" && GT[1]=\"AR\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" !echo \"AR + AA:\" !bcftools filter -i 'GT[0]=\"AR\" && GT[1]=\"AA\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" !echo \"AA + AR:\" !bcftools filter -i 'GT[0]=\"AA\" && GT[1]=\"AR\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" !echo \"AA + AA:\" !bcftools filter -i 'GT[0]=\"AA\" && GT[1]=\"AA\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" Use the same commands as above to count the positions marching the filters in the Contig2_Ti_merged.vcf. In [ ]: Copied!","title":"Comparison of VCF files"},{"location":"nb/vcf.html#wrapping-up","text":"In this exercise, you identified polymorphisms between the two white clover subgenomes using Hifi reads alignment files and SNPs specific for the white clover Tienshan genotype using RNA-seq alignment files. You learned how to import VCF files in Python to extract data and created plots. You also filtered low-quality variants from the VCF files and counted genotypes.","title":"Wrapping up  \ud83c\udf89 \ud83c\udf89 \ud83c\udf89"}]}
\ No newline at end of file
+{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"index.html","text":"Introduction to Next Generation Sequencing data \u00b6 A course of the Summer School of Aarhus University \u00b6 Computing and didactical support from the Danish Health Data Science Sandbox The material for this course is organized in four separated jupyter notebooks in both bash , python and R where you will benefit of an interactive coding setup on jupyterlab . Overview After the course, you will have knowledge of bioinformatics methods for analyzing genomes using NGS data, including knowledge of the existing types of genome data, how the different types of data can be displayed and analyzed, the current methods for genome assembly and analysis, their accuracy and how they can be used. The course will enable you to devise and run a project that makes use of NGS data. \ud83d\udcda Prerequisites: This is an introductory course that needs a basic understanding of the biology behind sequencing, and not necessarily programming experience (though this would help!). \ud83d\udcac Syllabus: 1. Describe key challenges in the analysis of NGS data 2. Explain the theoretical foundation for methods that use NGS for assembly and analysis of genomes 3. Discuss the bioinformatic methods for genome analysis and hypothesize what drives the outcome of the methods 4. Review original literature within the subjects and relate the discussed topics to analysis scenarios 5. Apply bioinformatics tools within the selected application areas and reflect on the results, formulating your own conclusion in the proposed tasks \ud83d\udd70 Total Time Estimation: 20 hours \ud83d\udcc1 Supporting Materials: jupyter notebooks for interactive coding lecture slides from the instructor You can find the links to the material in the table at the bottom of this page. \ud83d\udd8d Course authors and instructors: Mikkel H Schierup Stig U Andersen Samuele Soraggi Peter S Porsborg Adri\u00e1n G Repoll\u00e9s \ud83d\udccb License: Tutorial Content is licensed under Creative Commons Attribution 4.0 International License \ud83d\udcdd Citation: If you use any of this material for your research, please cite this course with the DOI below, and acknowledge the Health Data Science Sandbox project of the Novo Nordisk Foundation (grant number NNF20OC0063268). It is of great help to support the project. \ud83d\udce7 Contact: Samuele Soraggi (samuele at birc.au.dk). Course structure and Instructions The course exercises are organized in four exercise modules. The first one is executed with the web interface usegalaxy.org . Click on 1.Galaxy Exercise in the menu for instructions) Afterwards, we will work on a computing environment to use jupyterlab . Use 2.Instructions for instructions. The menu 3.Course exercises contains all the compiled exercises as a reference. Course material 2022 \u00b6 Here you find a table with the instructor's slides from 2022. Topic Slide Notebook Sequencing technologies link -- Mapping to reference link Notebook Data visualization link -- SNPs and structural variants link Notebook RNA sequencing link Notebook De-novo assembly link -- Microbiomes and metagenomics link -- Single cell RNA sequencing link Notebook Course material 2023 (on its way after the course's end) \u00b6 Here you find a table with the instructor's slides and a link to the compiled notebooks, that you can also run on your own following the instructions in this webpage. Data alignment can also be performed on the Galaxy interactive webpage (see the galaxy exercise in this webpage).","title":"Course Introduction"},{"location":"index.html#introduction-to-next-generation-sequencing-data","text":"","title":"Introduction to Next Generation Sequencing data"},{"location":"index.html#a-course-of-the-summer-school-of-aarhus-university","text":"Computing and didactical support from the Danish Health Data Science Sandbox The material for this course is organized in four separated jupyter notebooks in both bash , python and R where you will benefit of an interactive coding setup on jupyterlab . Overview After the course, you will have knowledge of bioinformatics methods for analyzing genomes using NGS data, including knowledge of the existing types of genome data, how the different types of data can be displayed and analyzed, the current methods for genome assembly and analysis, their accuracy and how they can be used. The course will enable you to devise and run a project that makes use of NGS data. \ud83d\udcda Prerequisites: This is an introductory course that needs a basic understanding of the biology behind sequencing, and not necessarily programming experience (though this would help!). \ud83d\udcac Syllabus: 1. Describe key challenges in the analysis of NGS data 2. Explain the theoretical foundation for methods that use NGS for assembly and analysis of genomes 3. Discuss the bioinformatic methods for genome analysis and hypothesize what drives the outcome of the methods 4. Review original literature within the subjects and relate the discussed topics to analysis scenarios 5. Apply bioinformatics tools within the selected application areas and reflect on the results, formulating your own conclusion in the proposed tasks \ud83d\udd70 Total Time Estimation: 20 hours \ud83d\udcc1 Supporting Materials: jupyter notebooks for interactive coding lecture slides from the instructor You can find the links to the material in the table at the bottom of this page. \ud83d\udd8d Course authors and instructors: Mikkel H Schierup Stig U Andersen Samuele Soraggi Peter S Porsborg Adri\u00e1n G Repoll\u00e9s \ud83d\udccb License: Tutorial Content is licensed under Creative Commons Attribution 4.0 International License \ud83d\udcdd Citation: If you use any of this material for your research, please cite this course with the DOI below, and acknowledge the Health Data Science Sandbox project of the Novo Nordisk Foundation (grant number NNF20OC0063268). It is of great help to support the project. \ud83d\udce7 Contact: Samuele Soraggi (samuele at birc.au.dk). Course structure and Instructions The course exercises are organized in four exercise modules. The first one is executed with the web interface usegalaxy.org . Click on 1.Galaxy Exercise in the menu for instructions) Afterwards, we will work on a computing environment to use jupyterlab . Use 2.Instructions for instructions. The menu 3.Course exercises contains all the compiled exercises as a reference.","title":"A course of the Summer School of Aarhus University"},{"location":"index.html#course-material-2022","text":"Here you find a table with the instructor's slides from 2022. Topic Slide Notebook Sequencing technologies link -- Mapping to reference link Notebook Data visualization link -- SNPs and structural variants link Notebook RNA sequencing link Notebook De-novo assembly link -- Microbiomes and metagenomics link -- Single cell RNA sequencing link Notebook","title":"Course material 2022"},{"location":"index.html#course-material-2023-on-its-way-after-the-courses-end","text":"Here you find a table with the instructor's slides and a link to the compiled notebooks, that you can also run on your own following the instructions in this webpage. Data alignment can also be performed on the Galaxy interactive webpage (see the galaxy exercise in this webpage).","title":"Course material 2023 (on its way after the course's end)"},{"location":"empty.html","text":"General Page \u00b6 Section Overview \ud83d\udd70 Time Estimation: X minutes \ud83d\udcac Learning Objectives: 1. First item 2. Second item Write your introduction to the page here. Useful functions \u00b6 You should continue to write your markdown document as normal. But here are some useful functions. You can find more in the Reference guide listed on the tab above. Be sure to delete the guide and the tips below when finished with them. Code \u00b6 Text will be highlighted appropriately when you include language abbreviation: import tensorflow as tf Admonitions \u00b6 The admonitions used for course/section overview and requirements should be consistent, though you can use any other admonitions freely. Examples (both drop-down and not): Quote Here is a quote What is the smallest country in the world? A: Vatican City Footnotes \u00b6 We can include footnotes like this one 1 . LaTeX \u00b6 You write an equation as normal: \\[ \\operatorname{ker} f=\\{g\\in G:f(g)=e_{H}\\}{\\mbox{.}} \\] Images \u00b6 Format is similar to links, but include an exclamation mark before: You can link to a URL or to somewhere locally. Remember to eat your vegetables. \u21a9","title":"General page"},{"location":"empty.html#general-page","text":"Section Overview \ud83d\udd70 Time Estimation: X minutes \ud83d\udcac Learning Objectives: 1. First item 2. Second item Write your introduction to the page here.","title":"General Page"},{"location":"empty.html#useful-functions","text":"You should continue to write your markdown document as normal. But here are some useful functions. You can find more in the Reference guide listed on the tab above. Be sure to delete the guide and the tips below when finished with them.","title":"Useful functions"},{"location":"empty.html#code","text":"Text will be highlighted appropriately when you include language abbreviation: import tensorflow as tf","title":"Code"},{"location":"empty.html#admonitions","text":"The admonitions used for course/section overview and requirements should be consistent, though you can use any other admonitions freely. Examples (both drop-down and not): Quote Here is a quote What is the smallest country in the world? A: Vatican City","title":"Admonitions"},{"location":"empty.html#footnotes","text":"We can include footnotes like this one 1 .","title":"Footnotes"},{"location":"empty.html#latex","text":"You write an equation as normal: \\[ \\operatorname{ker} f=\\{g\\in G:f(g)=e_{H}\\}{\\mbox{.}} \\]","title":"LaTeX"},{"location":"empty.html#images","text":"Format is similar to links, but include an exclamation mark before: You can link to a URL or to somewhere locally. Remember to eat your vegetables. \u21a9","title":"Images"},{"location":"filtering.html","text":"Data filtering \u00b6 Each person works on its own personal folder, which contains a dataset. As a reminder, these are the personal folders Name Sample folder Andersen, Albert Lund Gifu_ctr_1 Bagger, Andreas Gifu_ctr_2 Hansen, Mads W\u00fcrgler Gifu_ctr_ 3 Milo, Lasse Gifu_ctr_4 Reimick, Sebastian Haunstrup Gifu_R7A_1 Hemmingsen, Jonas Klejs Gifu_R7A_2 Skovm\u00f8ller, Emma Hvitfeldt Gifu_R7A_3 S\u00f8rensen, Emma Frasez Gifu_R7A_4 Agersnap, Simon N\u00f8rregaard HAR1_ctr_1 Schmidt, Alina HAR1_ctr_2 Henriksen, Frederik Oskar HAR1_ctr_3 Lundby, Josephine Marie HAR1_ctr_4 N\u00f8rholm, Anne HAR1_R7A_1 Odgaard, Louise Nyrup HAR1_R7A_2 S\u00f8rensen, Sara Sejer HAR1_R7A_3 L\u00f8nskov, Jonas HAR1_R7A_4 Niklassen, Jacob Hansen Gifu_ctr_1_bis \u00d8llgaard, Ann Mai Br\u00f8ndum Holm Gifu_ctr_2_bis Overgaard, Morten \u00d8gelund Gifu_R7A_1_bis S\u00f8rensen, Elisabeth Asta Gifu_R7A_2_bis Rey, Isabel HAR1_ctr_1_bis Instructions \u00b6 Open a new session of jupyterlab in the same way as in the tutorial session Find your way to your personal folder, which is inside 426401/Students_analysis Start filtering your data using the notebook filtering.ipynb . This is a copy of the first part of the tutorial, so be careful about small changes you have to made (e.g. condition name, thresholds). For the condition name created in the metadata at the beginning of the tutorial, here we use one of Gifu_Control , Gifu_Infected , HAR1_Control , HAR1_Infected (depending on the dataset you have assigned), so that it is easy to integrated all datasets at the end. When you are done and you saved your data at the end (keep the filename filtered.h5Seurat ), write when you are done at this google docs under the sheet Filtering , so we all know when we are ready to proceed to data integration.","title":"Data filtering"},{"location":"filtering.html#data-filtering","text":"Each person works on its own personal folder, which contains a dataset. As a reminder, these are the personal folders Name Sample folder Andersen, Albert Lund Gifu_ctr_1 Bagger, Andreas Gifu_ctr_2 Hansen, Mads W\u00fcrgler Gifu_ctr_ 3 Milo, Lasse Gifu_ctr_4 Reimick, Sebastian Haunstrup Gifu_R7A_1 Hemmingsen, Jonas Klejs Gifu_R7A_2 Skovm\u00f8ller, Emma Hvitfeldt Gifu_R7A_3 S\u00f8rensen, Emma Frasez Gifu_R7A_4 Agersnap, Simon N\u00f8rregaard HAR1_ctr_1 Schmidt, Alina HAR1_ctr_2 Henriksen, Frederik Oskar HAR1_ctr_3 Lundby, Josephine Marie HAR1_ctr_4 N\u00f8rholm, Anne HAR1_R7A_1 Odgaard, Louise Nyrup HAR1_R7A_2 S\u00f8rensen, Sara Sejer HAR1_R7A_3 L\u00f8nskov, Jonas HAR1_R7A_4 Niklassen, Jacob Hansen Gifu_ctr_1_bis \u00d8llgaard, Ann Mai Br\u00f8ndum Holm Gifu_ctr_2_bis Overgaard, Morten \u00d8gelund Gifu_R7A_1_bis S\u00f8rensen, Elisabeth Asta Gifu_R7A_2_bis Rey, Isabel HAR1_ctr_1_bis","title":"Data filtering"},{"location":"filtering.html#instructions","text":"Open a new session of jupyterlab in the same way as in the tutorial session Find your way to your personal folder, which is inside 426401/Students_analysis Start filtering your data using the notebook filtering.ipynb . This is a copy of the first part of the tutorial, so be careful about small changes you have to made (e.g. condition name, thresholds). For the condition name created in the metadata at the beginning of the tutorial, here we use one of Gifu_Control , Gifu_Infected , HAR1_Control , HAR1_Infected (depending on the dataset you have assigned), so that it is easy to integrated all datasets at the end. When you are done and you saved your data at the end (keep the filename filtered.h5Seurat ), write when you are done at this google docs under the sheet Filtering , so we all know when we are ready to proceed to data integration.","title":"Instructions"},{"location":"galaxy.html","text":"Quality Control and Alignment of raw reads \u00b6 This first exercise will be executed on Galaxy , an interactive platform to run bioinformatics workflows. We will replicate this lesson with computer code later in this course. Galaxy has the possibility of working with a free account. Biological introduction \u00b6 White clover ( Trifolium repens ) is an allotetraploid . This means that it contains genomes originating from two different species within the same nucleus. Normally, white clover is an outbreeding species, but a self-compatible line was used for sequencing the white clover genome . This line is designated S10 in your data, indicating that this is the 10 th self-fertilized generation. In addition, you have data from a wild clover accession (ecotype) called Tienshan ( Ti ), which is collected from Chinese mountains and is adapted to alpine conditions. Figure: Characterisation of the white clover population. T.Repens is a hybrid of T.Occidentale and T.Pallescens Exercise guide \u00b6 Prepare the tools and data \u00b6 Install IGV on your computer from here . This is a genome browser you will use to look at some files. Create an account at usegalaxy.org and log into galaxy. Find the course data by going to this web address and by clicking on Import this history (top left corner of the page). Note: If usegalaxy.org has availability problems, you can use the other server https://usegalaxy.eu/ and get the data at this link . You might need to create an account there as well. Data \u00b6 You will be working with two types of sequencing data. The first is PacBio Hifi reads , which are long and accurate. You can find them under Hifi_reads_white_clover.fastq. The second type is Illumina RNA-seq reads , which are short and accurate and should be aligned using a spliced aligner, such as STAR . There are 24 of these files, 12 for each of the two genotypes mentioned before. The files are named [genotype]_[treatment]_[replicate].fastq . Treatment 1 is before and treatment 2 is after exposure to frost, respectively. In addition to the sequencing data, there are also three reference files: one for homologous contig 1 (referencing T. occidentale-derived subgenome), one for contig 2 (T. pallescens-derived subgenome) and one for both Contigs 1 and 2. The reference files are in fasta format. The file white_clover_genes.gtf contains the gene annotations for the two contigs. Workflow illustration \u00b6 Through Galaxy , we build a workflow applying tools to the data. We will look at the quality of the raw reads for both PacBio HiFi and Illumina RNA-seq reads. Afterwards, we align to references, using two different tools for the two types of data. Finally, we will look at the alignments on a genome browser. We will work then on a computing cluster through uCloud to analyze the aligned data in some of the upcoming lessons of the course. Galaxy Workflow \u00b6 When you import the files, what you actually import is a History - a sequence of files and softwares applied on the data. You can see the history on the right side of your usegalaxy.org webpage with green panels. Here, we only have the starting data, and you will build the rest of your history through various tools. On the left side of the screen, you have a menu with various available tools organized by category. All those softwares are also available on a classical computing command line (we will try those as well). Quality control \u00b6 1 Run FastQC on the PacBio Hifi reads and on two of the Illumina RNA-seq libraries. FastQC does quality control of the raw sequence data, providing an overview of the data which can help identify if there are any problems that should be addressed before further analysis. In the tool menu, click on FASTQ quality control --> FASTQC read quality reports . You will see a window with tool parameters : for the first option (raw read data from history), choose multiple files and select Hifi_reads_white_clover.fastq plus other fastq files you want to see the quality of (example in figure below). Then click on the button Run Tool . You will notice that some new elements are added to your history. Part of them are FastQC producing a text file, while others are FastQC producing a webpage report. The reports are ready when coloured in green: click on the eye symbol of a history item to read a report. 2 FastQC provides a report for each sample. To have a better comparison between the Hifi and Illumina data, we would combine the three FastQC reports into one using MultiQC . Choose the MultiQC tool from FASTQ quality control --> MultiQC aggregate results from ... . In the options, select FastQC as the used tool for the logsselect FastQC as the tool used to generate the output, and then select the items of FastQC of your history producing RawData (Figure below). In this way, you build a pipeline from the previous reports to the new tool you are using. Now click on Run Tool . The tool will be now running in your history. When it is done, click on the eye symbol to see the report. select the three \u201cRawData\u201d outputs generated by FastQC. Visualize the Webpage generated by MultiQC. Hint: You can find a \u201cHelp\u201d button that offers additional information about the plots for each panel. Questions: Focus on the following panels: \u201cPer base sequence quality\u201d, \u201cPer sequence quality scores\u201d\u2026. (\u201cPer base sequence content\u201d always gives a FAIL for RNA-seq data). What do you notice with respect to the sequence quality scores? And are there any other quality issues worth noting? Hifi Data Alignment \u00b6 3 Map the PacBio Hifi reads ( Hifi_reads_white_clover.fastq ) to the white clover reference sequence (Contigs 1 and 2) using minimap2 (Map with minimap2). Find Genomics Analysis --> Mapping --> Map with minimap2 . In the options, do not leave Use a built-in genome index , but select the option for having a genome from history. Choose then DNA_Contig1_2.fasta as the reference sequence. Under the profile with preset options, choose PacBio/Oxford Nanopore read to reference mapping (map-pb) . Then click on Run Tool . 4 Run the same alignment, but choose as preset options Long assembly to reference mapping. Divergence is far below 20% (asm20) . Rename then the two alignments using the edit function ( pen symbol in the history). Use for example names Contig1_2_mappb and Contig1_2_asm20 , to distinguish alignment options and reference genome. 5 The aligned genomes are not sorted by coordinates. Sort the alignments using Samtools sort (Find the tool under Genomic file manipulation --> SAM/BAM --> Samtools sort ... ). In the options, choose the two aligned files with multiple selection. Then click on Run Tool . 6 Download the two alignments to your computer. To do so, click on the disk symbol of each file in your history, and for each download both the Dataset (alignments in bam format) and their index files (in bai format). Download as well the reference genome in fasta format ( DNA_Contig1_2.fasta from the history). 7 Open IGV on your computer. Load the reference first: go on Genome --> Load genome from file and select the fasta file you downloaded. Then load the two alignments: go on File --> Load from file and select the bam and bai files you downloaded, together. You can now visualize the alignments. Questions: Look at the alignments in IGV. What do you notice about the alignments? What is the difference between the two alignments? Do you think one of them is better than the other? Choose on of the two alignments for the next steps. 8 Repeat the alignment with Minimap2 (using the chosen alignment option from the question above) and the sorting, but using the reference genomes for Contig 1 and for Contig 2 searaately. Note: you can run all at once by choosing multiple reference genomes in the options! Questions: Download the two references for Contig 1 and 2, and the two sorted alignments. Load the references from the menu `Genomes` in IGV, and then open the two alignments using the menu `File` in IGV. - Why do you see fluctuations in coverage and large regions without any apparent subgenome SNPs? - What are the major differences between the stats for the reads mapped to Contigs1&2 versus contig1 and contig2? What is your interpretation of the differences? RNA Data Alignment \u00b6 9 First, group the 24 RNA-seq libraries into two dataset lists, one list of pairs for S10 libraries and another for Tienshan libraries. so we can work with multiple samples simultaneously. You can do this by selecting the libraries for each genotype and choosing Build Lists of Dataset Pairs . The grouping suggested by Galaxy is wrong, because the paired reads are paired according to _1 and _2 in their names. Change those two with R1 and R2 , click on Unpair all , and then click on the suggested corrected pairs with the buttons Pair these datasets . Your sequences will be substituted by two elements in your history. Here we chose for example to name them S10 and TI . 10 Do alignment of the RNA-seq lists of raw files to the reference DNA_Contig1_2.fasta using STAR . Go to Genomics analysis --> RNA-seq --> RNA STAR Gapped-read mapper for RNA-seq data . In the options use: as data, the parameter Paired-end (as collection) , and then choose one of the two collections (you cannot run them all at once) as reference, DNA_Contig1_2.fasta , with Length of SA pre-indexing string equal to 9 as index with gene-model, use white_clover_annotations.gtf as output, Per gene read counts (GeneCounts) . 11 Use MultiQC to see the quality of the output. The alignment of STAR produces log files which can be used for quality reports. Go on Genomic File Manipulation --> MultiQC . In the options select the tool STAR . Then Insert STAR output , as type of output the Log , and choose the two logs listing collections of STAR alignments. Then click on Run Tool . View the report to see the alignment statistics. Final note: `Galaxy can also be used to create an automatic workflow that will map the data. This workflow can be useful when running multiple samples. You can generate a workflow from the analysis already completed in a history, by going to Settings \u2192 Extract workflow. You can also create a workflow from scratch using the Workflow editor.","title":"1.Galaxy Exercise"},{"location":"galaxy.html#quality-control-and-alignment-of-raw-reads","text":"This first exercise will be executed on Galaxy , an interactive platform to run bioinformatics workflows. We will replicate this lesson with computer code later in this course. Galaxy has the possibility of working with a free account.","title":"Quality Control and Alignment of raw reads"},{"location":"galaxy.html#biological-introduction","text":"White clover ( Trifolium repens ) is an allotetraploid . This means that it contains genomes originating from two different species within the same nucleus. Normally, white clover is an outbreeding species, but a self-compatible line was used for sequencing the white clover genome . This line is designated S10 in your data, indicating that this is the 10 th self-fertilized generation. In addition, you have data from a wild clover accession (ecotype) called Tienshan ( Ti ), which is collected from Chinese mountains and is adapted to alpine conditions. Figure: Characterisation of the white clover population. T.Repens is a hybrid of T.Occidentale and T.Pallescens","title":"Biological introduction"},{"location":"galaxy.html#exercise-guide","text":"","title":"Exercise guide"},{"location":"galaxy.html#prepare-the-tools-and-data","text":"Install IGV on your computer from here . This is a genome browser you will use to look at some files. Create an account at usegalaxy.org and log into galaxy. Find the course data by going to this web address and by clicking on Import this history (top left corner of the page). Note: If usegalaxy.org has availability problems, you can use the other server https://usegalaxy.eu/ and get the data at this link . You might need to create an account there as well.","title":"Prepare the tools and data"},{"location":"galaxy.html#data","text":"You will be working with two types of sequencing data. The first is PacBio Hifi reads , which are long and accurate. You can find them under Hifi_reads_white_clover.fastq. The second type is Illumina RNA-seq reads , which are short and accurate and should be aligned using a spliced aligner, such as STAR . There are 24 of these files, 12 for each of the two genotypes mentioned before. The files are named [genotype]_[treatment]_[replicate].fastq . Treatment 1 is before and treatment 2 is after exposure to frost, respectively. In addition to the sequencing data, there are also three reference files: one for homologous contig 1 (referencing T. occidentale-derived subgenome), one for contig 2 (T. pallescens-derived subgenome) and one for both Contigs 1 and 2. The reference files are in fasta format. The file white_clover_genes.gtf contains the gene annotations for the two contigs.","title":"Data"},{"location":"galaxy.html#workflow-illustration","text":"Through Galaxy , we build a workflow applying tools to the data. We will look at the quality of the raw reads for both PacBio HiFi and Illumina RNA-seq reads. Afterwards, we align to references, using two different tools for the two types of data. Finally, we will look at the alignments on a genome browser. We will work then on a computing cluster through uCloud to analyze the aligned data in some of the upcoming lessons of the course.","title":"Workflow illustration"},{"location":"galaxy.html#galaxy-workflow","text":"When you import the files, what you actually import is a History - a sequence of files and softwares applied on the data. You can see the history on the right side of your usegalaxy.org webpage with green panels. Here, we only have the starting data, and you will build the rest of your history through various tools. On the left side of the screen, you have a menu with various available tools organized by category. All those softwares are also available on a classical computing command line (we will try those as well).","title":"Galaxy Workflow"},{"location":"galaxy.html#quality-control","text":"1 Run FastQC on the PacBio Hifi reads and on two of the Illumina RNA-seq libraries. FastQC does quality control of the raw sequence data, providing an overview of the data which can help identify if there are any problems that should be addressed before further analysis. In the tool menu, click on FASTQ quality control --> FASTQC read quality reports . You will see a window with tool parameters : for the first option (raw read data from history), choose multiple files and select Hifi_reads_white_clover.fastq plus other fastq files you want to see the quality of (example in figure below). Then click on the button Run Tool . You will notice that some new elements are added to your history. Part of them are FastQC producing a text file, while others are FastQC producing a webpage report. The reports are ready when coloured in green: click on the eye symbol of a history item to read a report. 2 FastQC provides a report for each sample. To have a better comparison between the Hifi and Illumina data, we would combine the three FastQC reports into one using MultiQC . Choose the MultiQC tool from FASTQ quality control --> MultiQC aggregate results from ... . In the options, select FastQC as the used tool for the logsselect FastQC as the tool used to generate the output, and then select the items of FastQC of your history producing RawData (Figure below). In this way, you build a pipeline from the previous reports to the new tool you are using. Now click on Run Tool . The tool will be now running in your history. When it is done, click on the eye symbol to see the report. select the three \u201cRawData\u201d outputs generated by FastQC. Visualize the Webpage generated by MultiQC. Hint: You can find a \u201cHelp\u201d button that offers additional information about the plots for each panel. Questions: Focus on the following panels: \u201cPer base sequence quality\u201d, \u201cPer sequence quality scores\u201d\u2026. (\u201cPer base sequence content\u201d always gives a FAIL for RNA-seq data). What do you notice with respect to the sequence quality scores? And are there any other quality issues worth noting?","title":"Quality control"},{"location":"galaxy.html#hifi-data-alignment","text":"3 Map the PacBio Hifi reads ( Hifi_reads_white_clover.fastq ) to the white clover reference sequence (Contigs 1 and 2) using minimap2 (Map with minimap2). Find Genomics Analysis --> Mapping --> Map with minimap2 . In the options, do not leave Use a built-in genome index , but select the option for having a genome from history. Choose then DNA_Contig1_2.fasta as the reference sequence. Under the profile with preset options, choose PacBio/Oxford Nanopore read to reference mapping (map-pb) . Then click on Run Tool . 4 Run the same alignment, but choose as preset options Long assembly to reference mapping. Divergence is far below 20% (asm20) . Rename then the two alignments using the edit function ( pen symbol in the history). Use for example names Contig1_2_mappb and Contig1_2_asm20 , to distinguish alignment options and reference genome. 5 The aligned genomes are not sorted by coordinates. Sort the alignments using Samtools sort (Find the tool under Genomic file manipulation --> SAM/BAM --> Samtools sort ... ). In the options, choose the two aligned files with multiple selection. Then click on Run Tool . 6 Download the two alignments to your computer. To do so, click on the disk symbol of each file in your history, and for each download both the Dataset (alignments in bam format) and their index files (in bai format). Download as well the reference genome in fasta format ( DNA_Contig1_2.fasta from the history). 7 Open IGV on your computer. Load the reference first: go on Genome --> Load genome from file and select the fasta file you downloaded. Then load the two alignments: go on File --> Load from file and select the bam and bai files you downloaded, together. You can now visualize the alignments. Questions: Look at the alignments in IGV. What do you notice about the alignments? What is the difference between the two alignments? Do you think one of them is better than the other? Choose on of the two alignments for the next steps. 8 Repeat the alignment with Minimap2 (using the chosen alignment option from the question above) and the sorting, but using the reference genomes for Contig 1 and for Contig 2 searaately. Note: you can run all at once by choosing multiple reference genomes in the options! Questions: Download the two references for Contig 1 and 2, and the two sorted alignments. Load the references from the menu `Genomes` in IGV, and then open the two alignments using the menu `File` in IGV. - Why do you see fluctuations in coverage and large regions without any apparent subgenome SNPs? - What are the major differences between the stats for the reads mapped to Contigs1&2 versus contig1 and contig2? What is your interpretation of the differences?","title":"Hifi Data Alignment"},{"location":"galaxy.html#rna-data-alignment","text":"9 First, group the 24 RNA-seq libraries into two dataset lists, one list of pairs for S10 libraries and another for Tienshan libraries. so we can work with multiple samples simultaneously. You can do this by selecting the libraries for each genotype and choosing Build Lists of Dataset Pairs . The grouping suggested by Galaxy is wrong, because the paired reads are paired according to _1 and _2 in their names. Change those two with R1 and R2 , click on Unpair all , and then click on the suggested corrected pairs with the buttons Pair these datasets . Your sequences will be substituted by two elements in your history. Here we chose for example to name them S10 and TI . 10 Do alignment of the RNA-seq lists of raw files to the reference DNA_Contig1_2.fasta using STAR . Go to Genomics analysis --> RNA-seq --> RNA STAR Gapped-read mapper for RNA-seq data . In the options use: as data, the parameter Paired-end (as collection) , and then choose one of the two collections (you cannot run them all at once) as reference, DNA_Contig1_2.fasta , with Length of SA pre-indexing string equal to 9 as index with gene-model, use white_clover_annotations.gtf as output, Per gene read counts (GeneCounts) . 11 Use MultiQC to see the quality of the output. The alignment of STAR produces log files which can be used for quality reports. Go on Genomic File Manipulation --> MultiQC . In the options select the tool STAR . Then Insert STAR output , as type of output the Log , and choose the two logs listing collections of STAR alignments. Then click on Run Tool . View the report to see the alignment statistics. Final note: `Galaxy can also be used to create an automatic workflow that will map the data. This workflow can be useful when running multiple samples. You can generate a workflow from the analysis already completed in a history, by going to Settings \u2192 Extract workflow. You can also create a workflow from scratch using the Workflow editor.","title":"RNA Data Alignment"},{"location":"instructions.html","text":"Instructions \u00b6 You have various options to run the course material. You can execute it on a supercomputing system or on your own computer. Access the material on uCloud \u00b6 Ucloud is an interactive online platform from the University of Southern Danmark eScience center that allows users to execute softwares and computer code directly from their browser. If you are enrolled in a danish university or hospital, you can access the platform for free and try the course. Please follow these instructions to access uCloud and get access to the course: To access uCloud for the first time, go to the ucloud webpage and log in by pressing on the green button, choosing your institution, and writing your institutional credentials. If you have a mail address from a danish institution or university, you should be able to login with your institutional credentials. If you are participating in the NGS Summer Course 2023 in Aarhus, click AFTER logging in on this link . This will add you to a project on uCloud, where we have data and extra computing credit for the course. You should see a message on your browser where you have to accept the invitation to the project. Click on the uCloud symbol on the top-left corner of the uCloud webpage. You should see a dashboard window similar to the one below. The dashboard is a home page you will always see when opening uCloud. You can use the menu on the left to navigate to different sections of uCloud (File explorer, applications, etc.) On top of the uCloud interface you have My Workspace . That is your private space where you have some free hundreds of CPU hours and GBs of memory to run different applications (that you can see by using the menu Apps on the left). Click on Apps and find the application called Genomics Courses . Here choose the course Intro to NGS data , and use the other options to choose, for example, how many resources you want to use (we suggest at least 8 vCPUs) Run the docker container \u00b6 With Docker , you can create containers that will work in the same way on all the machines. The results of the course are exactly reproducible if you use such a Docker container. We created a container for the course that already contains packages and course material installed. You need to download Docker Desktop at this link . We suggest you have at least 8GB of RAM and 20GB of hard disk space. When Docker Desktop is installed, you can use the command line to retrieve the container from the web: docker pull samuelesoraggi/ngs-summer-aarhus:2022.08.01 You can now execute the container. You will see a web address in the output of the command line: you need to copy that into your web browser to see jupyterlab . docker run --rm -it -p 8888:8888 samuelesoraggi/ngs-summer-aarhus:2022.08.01","title":"Instructions"},{"location":"instructions.html#instructions","text":"You have various options to run the course material. You can execute it on a supercomputing system or on your own computer.","title":"Instructions"},{"location":"instructions.html#access-the-material-on-ucloud","text":"Ucloud is an interactive online platform from the University of Southern Danmark eScience center that allows users to execute softwares and computer code directly from their browser. If you are enrolled in a danish university or hospital, you can access the platform for free and try the course. Please follow these instructions to access uCloud and get access to the course: To access uCloud for the first time, go to the ucloud webpage and log in by pressing on the green button, choosing your institution, and writing your institutional credentials. If you have a mail address from a danish institution or university, you should be able to login with your institutional credentials. If you are participating in the NGS Summer Course 2023 in Aarhus, click AFTER logging in on this link . This will add you to a project on uCloud, where we have data and extra computing credit for the course. You should see a message on your browser where you have to accept the invitation to the project. Click on the uCloud symbol on the top-left corner of the uCloud webpage. You should see a dashboard window similar to the one below. The dashboard is a home page you will always see when opening uCloud. You can use the menu on the left to navigate to different sections of uCloud (File explorer, applications, etc.) On top of the uCloud interface you have My Workspace . That is your private space where you have some free hundreds of CPU hours and GBs of memory to run different applications (that you can see by using the menu Apps on the left). Click on Apps and find the application called Genomics Courses . Here choose the course Intro to NGS data , and use the other options to choose, for example, how many resources you want to use (we suggest at least 8 vCPUs)","title":"Access the material on uCloud"},{"location":"instructions.html#run-the-docker-container","text":"With Docker , you can create containers that will work in the same way on all the machines. The results of the course are exactly reproducible if you use such a Docker container. We created a container for the course that already contains packages and course material installed. You need to download Docker Desktop at this link . We suggest you have at least 8GB of RAM and 20GB of hard disk space. When Docker Desktop is installed, you can use the command line to retrieve the container from the web: docker pull samuelesoraggi/ngs-summer-aarhus:2022.08.01 You can now execute the container. You will see a web address in the output of the command line: you need to copy that into your web browser to see jupyterlab . docker run --rm -it -p 8888:8888 samuelesoraggi/ngs-summer-aarhus:2022.08.01","title":"Run the docker container"},{"location":"ucloud.html","text":"Access through uCloud \u00b6 Ucloud is an interactive online platform from the University of Southern Danmark eScience center that allows users to execute softwares and computer code directly from their browser. If you have an account from a danish university, hospital, and other institutions, you can access the platform for free and try the course. Starting a session of the Introduction to NGS data analysis on uCloud \u00b6 Log onto ucloud at the address http://cloud.sdu.dk using the university/institution credentials you possess. If you are participating in the NGS Summer Course 2023 in Aarhus, click AFTER logging in on this link . You should see a message on your browser where you have to accept the invitation to the project. This will add you to a project on uCloud, where we have data and extra computing credit for the course. Be sure to be in the right project. If you are participating in the NGS Summer Course 2023 in Aarhus, choose NGS Summer Course from the project menu (red circle). Afterwards, click on the App menu (green circle) Note: The project My Workspace is instead your private space where you have some free hundreds of CPU hours and GBs of memory. Find the app Genomics Sandbox (red circle), which is under the title Featured . Click on it. You will get into the settings window. Choose any Job Name (Nr 1 in the figure below), how many hours you want to use for the job (Nr 2, choose at least 2 hours, you can increase this later), and how many CPUs (Nr 3, choose at least 4 CPUs for the first three exercises, but use at least 8 CPUs to run the single cell analysis). Select the Introduction to NGS Data Analysis as course (Nr 4). Then click on Submit (Nr 5). You will now enter a waiting queue. When the session starts, the timer begins to count down (red circle). In a couple of minutes you should be able to open the interface through the button (green circle). Note the buttons to add time to your session (blue circle) and the button to stop the session when you are done (pink circle) You will be able to see jupyterlab in your browser. On the left side of the screen you can see various folders. You can find the exercises inside the folder Notebooks . You can download any file by right-clicking on it and choosing Download . You can also upload files by clicking on the Upload button on the top left corner of the screen, if you want to experiment with your own data! Reuse your notebooks and data \u00b6 The first time you run the Genomics Sandbox, you download data and notebooks. You can reuse them! Open the Genomics Sandbox App and use the settings to run it. You can click on your previous job name to recycle the settings (red circle). Now click on Add Folder (red circle) and on the appearing browsing bar (green circle). You need to find a drive with your user name (red circle), written as Member Files: NameSurname#Number . Then, go into Jobs/Genomics Sandbox/JobID/Notebooks and choose the folder (green circle). JobID corresponds to the Job Name you used in the previous session, followed by some random number in parenthesis. Do the same procedure adding a folder again and finding Jobs/Genomics Sandbox/JobID/Data . At the end you should have two added folders like this: Click on Submit. Now you will be able to reuse data, results and exercises on which you worked before. Note: remember to recycle the settings of this session the next time, so you can actually reuse the data and notebooks without having to add folders.","title":"2.1.Ucloud Access"},{"location":"ucloud.html#access-through-ucloud","text":"Ucloud is an interactive online platform from the University of Southern Danmark eScience center that allows users to execute softwares and computer code directly from their browser. If you have an account from a danish university, hospital, and other institutions, you can access the platform for free and try the course.","title":"Access through uCloud"},{"location":"ucloud.html#starting-a-session-of-the-introduction-to-ngs-data-analysis-on-ucloud","text":"Log onto ucloud at the address http://cloud.sdu.dk using the university/institution credentials you possess. If you are participating in the NGS Summer Course 2023 in Aarhus, click AFTER logging in on this link . You should see a message on your browser where you have to accept the invitation to the project. This will add you to a project on uCloud, where we have data and extra computing credit for the course. Be sure to be in the right project. If you are participating in the NGS Summer Course 2023 in Aarhus, choose NGS Summer Course from the project menu (red circle). Afterwards, click on the App menu (green circle) Note: The project My Workspace is instead your private space where you have some free hundreds of CPU hours and GBs of memory. Find the app Genomics Sandbox (red circle), which is under the title Featured . Click on it. You will get into the settings window. Choose any Job Name (Nr 1 in the figure below), how many hours you want to use for the job (Nr 2, choose at least 2 hours, you can increase this later), and how many CPUs (Nr 3, choose at least 4 CPUs for the first three exercises, but use at least 8 CPUs to run the single cell analysis). Select the Introduction to NGS Data Analysis as course (Nr 4). Then click on Submit (Nr 5). You will now enter a waiting queue. When the session starts, the timer begins to count down (red circle). In a couple of minutes you should be able to open the interface through the button (green circle). Note the buttons to add time to your session (blue circle) and the button to stop the session when you are done (pink circle) You will be able to see jupyterlab in your browser. On the left side of the screen you can see various folders. You can find the exercises inside the folder Notebooks . You can download any file by right-clicking on it and choosing Download . You can also upload files by clicking on the Upload button on the top left corner of the screen, if you want to experiment with your own data!","title":"Starting a session of the Introduction to NGS data analysis on uCloud"},{"location":"ucloud.html#reuse-your-notebooks-and-data","text":"The first time you run the Genomics Sandbox, you download data and notebooks. You can reuse them! Open the Genomics Sandbox App and use the settings to run it. You can click on your previous job name to recycle the settings (red circle). Now click on Add Folder (red circle) and on the appearing browsing bar (green circle). You need to find a drive with your user name (red circle), written as Member Files: NameSurname#Number . Then, go into Jobs/Genomics Sandbox/JobID/Notebooks and choose the folder (green circle). JobID corresponds to the Job Name you used in the previous session, followed by some random number in parenthesis. Do the same procedure adding a folder again and finding Jobs/Genomics Sandbox/JobID/Data . At the end you should have two added folders like this: Click on Submit. Now you will be able to reuse data, results and exercises on which you worked before. Note: remember to recycle the settings of this session the next time, so you can actually reuse the data and notebooks without having to add folders.","title":"Reuse your notebooks and data"},{"location":"nb/align.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); 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padding-left: 5px; padding-right: 5px; } td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Raw data alignment This tutorial will cover the steps for performing the alignment of raw RNA- and HiFi-sequencing data. You will need to use the software IGV on your computer to visualize some of the output files, which can be easily downloaded once they are produced. At the end of this tutorial you will be able to: perform and discuss quality control on raw data in fastq format using FastQC and MultiQC align HiFi and RNA sequencing data with dedicated tools such as MiniMap2 and STAR analyze the quality the alignment with qualimap </div> The output of this notebook will be used for the Variant calling analysis and the bulk RNA-sequencing analysis. If you do not want to run this notebook, you can alternatively use the free interactive tool Galaxy to perform the alignment steps. We have uploaded the data on Galaxy, and the manual to perform the exercise is found at the course webpage . The present tutorial, like the rest of the course material, is available at our open-source github repository . A few introductory points to run this notebook (click to show): To use this notebook, use the NGS (python) kernel that contains the packages. Choose it by selecting Kernel -> Change Kernel in the menu on top of the window. In this notebook you will use only bash commands as you would do in the command line (this is why you read %%bash at the beginning of each piece of code). Those commands can be replicated in the command line, but we thought of integrating them in a notebook to make the tutorial understandable. The bash commands can also be marked with an ! sign at the beginning of the line On some computers, you might see the result of the commands once they are done running. This means you will wait some time while the computer is crunching, and only afterwards you will see the result of the command you have executed You can run the code in each cell by clicking on the run cell button, or by pressing Shift + Enter . When the code is done running, a small green check sign will appear on the left side You need to run the cells in sequential order, please do not run a cell until the one above finished running and do not skip any cells Each cell contains a short description of the code and the output you should get. Please try not to focus on understanding the code for each command in too much detail, but rather try to focus on the output You can create new code cells by pressing + in the Menu bar above. </details> Biological background \u00b6 White clover (Trifolium repens) is an allotetraploid. It is a relatively young, outcrossing species, which originated during the most recent glaciation around 20,000 years ago by hybridisation of two diploid species, T. occidentale and T. pallescens (see figure below). This means that it contains genomes originating from two different species within the same nucleus. Normally, white clover is an outbreeding species, but a self-compatible line was used for sequencing the white clover genome (Griffiths et al, 2019) . This line will be designated as S10 in the data, indicating that this is the 10th self-fertilized generation. In addition, we have data from a wild clover accession (ecotype) called Tienshan ( Ti ), which is collected from the Chinese mountains and is adapted to alpine conditions. We will perform alignment of the data to the white clover's reference genome containing both T. occidentale and T. pallescens (called contig 1 and contig 2 in the data). We will also perform alignment to each subgenome, and see which are the differences with the quality control tools. Quality control and mapping \u00b6 Quality Control \u00b6 We run FastQC on the PacBio Hifi reads and on two of the Illumina RNA-seq libraries. FastQC does quality control of the raw sequence data, providing an overview of the data which can help identify if there are any problems that should be addressed before further analysis. You can find the report for each file into the folder results/fastqc_output/ . The output is in HTML format and can be opened in any browser or in jupyterlab . It is however not easy to compare the various libraries by opening separate reports. To aggregate all the results, we apply the MultiQC software to the reports' folder. The output of MultiQC is in the directory results/multiqc_output/fastqc_data . In [1]: Copied! %% bash #run fastqc mkdir - p results / fastqc_output fastqc - q - o results / fastqc_output ../ Data / Clover_Data /*. fastq > / dev / null 2 >& 1 %%bash #run fastqc mkdir -p results/fastqc_output fastqc -q -o results/fastqc_output ../Data/Clover_Data/*.fastq > /dev/null 2>&1 Note: fastqc prints a lot of output conisting of a simple confirmation of execution without error, even when using the option -q , which means quiet . Therefore we added > /dev/null 2>&1 to the command to mute the printing of that output. In [2]: Copied! %% bash #run multiqc multiqc -- outdir results / multiqc_output / fastqc_data results / fastqc_output %%bash #run multiqc multiqc --outdir results/multiqc_output/fastqc_data results/fastqc_output / / / \u001b]8;id=903972;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/fastqc_output | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 50/50 .html | fastqc | Found 25 reports | multiqc | Compressing plot data | multiqc | Report : results/multiqc_output/fastqc_data/multiqc_report.html | multiqc | Data : results/multiqc_output/fastqc_data/multiqc_data | multiqc | MultiQC complete Questions Visualize the Webpage generated by MultiQC. Hint: You can find a Help button that offers additional information about the plots for each panel. Focus on the following panels: \u201cPer base sequence quality\u201d, \u201cPer sequence quality scores\u201d.... (\u201cPer base sequence content\u201d always gives a FAIL for RNA-seq data). What do you notice with respect to the sequence quality scores? Are there any other quality issues worth noting? Hifi data mapping \u00b6 We map the PacBio Hifi reads ( Hifi_reads_white_clover.fastq ) to the white clover reference sequence (Contig1&2) using minimap2 . We run two mapping rounds, using two different preset options ( -x in the command) for the technology: PacBio/Oxford Nanopore read to reference mapping: map-pb Long assembly to reference mapping. Divergence is below 20%\u201d settings asm20 . Next, we create reports of the mapping results by running QualiMap on the two obtained SAM files. We first need to index the reference fasta files using samtools faidx . This produces files in .fai format containing informations about length of the reference sequence, offset for the quality scores, name of the reference sequence. Click here for a detailed overview. In [3]: Copied! %% bash #copy the reference data in the folder reference_data, so that you can write the indexing files mkdir - p reference_data cp ../ Data / Clover_Data / DNA_Contig1_2 . fasta ../ Data / Clover_Data / DNA_Contig1 . fasta ../ Data / Clover_Data / DNA_Contig2 . fasta reference_data %%bash #copy the reference data in the folder reference_data, so that you can write the indexing files mkdir -p reference_data cp ../Data/Clover_Data/DNA_Contig1_2.fasta ../Data/Clover_Data/DNA_Contig1.fasta ../Data/Clover_Data/DNA_Contig2.fasta reference_data In [4]: Copied! %% bash samtools faidx reference_data / DNA_Contig1_2 . fasta samtools faidx reference_data / DNA_Contig1 . fasta samtools faidx reference_data / DNA_Contig2 . fasta %%bash samtools faidx reference_data/DNA_Contig1_2.fasta samtools faidx reference_data/DNA_Contig1.fasta samtools faidx reference_data/DNA_Contig2.fasta we create an output folder for the HIFI alignment, and run minimap2 with the settings explained before. In [5]: Copied! %% bash mkdir - p results / HIFI_alignment / minimap2 - a - x map - pb - o results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sam \\ reference_data / DNA_Contig1_2 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq minimap2 - a - x asm20 - o results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sam \\ reference_data / DNA_Contig1_2 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq %%bash mkdir -p results/HIFI_alignment/ minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sam \\ reference_data/DNA_Contig1_2.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq minimap2 -a -x asm20 -o results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sam \\ reference_data/DNA_Contig1_2.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::mm_idx_gen::0.075*1.04] collected minimizers [M::mm_idx_gen::0.099*1.52] sorted minimizers [M::main::0.099*1.52] loaded/built the index for 2 target sequence(s) [M::mm_mapopt_update::0.114*1.45] mid_occ = 11 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 1; #seq: 2 [M::mm_idx_stat::0.118*1.43] distinct minimizers: 203943 (79.05% are singletons); average occurrences: 1.273; average spacing: 8.047; total length: 2089554 [M::worker_pipeline::11.859*2.89] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sam reference_data/DNA_Contig1_2.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 11.873 sec; CPU: 34.246 sec; Peak RSS: 1.186 GB [M::mm_idx_gen::0.084*1.04] collected minimizers [M::mm_idx_gen::0.121*1.62] sorted minimizers [M::main::0.121*1.62] loaded/built the index for 2 target sequence(s) [M::mm_mapopt_update::0.138*1.54] mid_occ = 50 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 0; #seq: 2 [M::mm_idx_stat::0.143*1.52] distinct minimizers: 298340 (78.21% are singletons); average occurrences: 1.277; average spacing: 5.484; total length: 2089554 [M::worker_pipeline::18.366*2.88] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x asm20 -o results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sam reference_data/DNA_Contig1_2.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 18.394 sec; CPU: 52.932 sec; Peak RSS: 1.692 GB samtools sort is used to sort the alignment with left-to-right coordinates. The output is in .bam format, with .sam files in input (Note that you could have gotten .bam files from minimap2 with a specific option). In [6]: Copied! %% bash samtools sort results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sam \\ - o results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sort . bam samtools sort results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sam \\ - o results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam %%bash samtools sort results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sam \\ -o results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sort.bam samtools sort results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sam \\ -o results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam samtools index creates the index for the bam file, stored in .bai format. The index file lets programs access any position into the aligned data without reading the whole file, which would take too much time. In [7]: Copied! %% bash samtools index results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sort . bam samtools index results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam %%bash samtools index results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sort.bam samtools index results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam Run quality control on both files In [8]: Copied! %% bash qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sort . bam \\ - outdir results / qualimap_output / PacBio_clover_alignment_1_2_mappb qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam \\ - outdir results / qualimap_output / PacBio_clover_alignment_1_2_asm20 %%bash qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sort.bam \\ -outdir results/qualimap_output/PacBio_clover_alignment_1_2_mappb qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam \\ -outdir results/qualimap_output/PacBio_clover_alignment_1_2_asm20 Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 401 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 401 windows... Processed 100 out of 401 windows... Processed 150 out of 401 windows... Processed 200 out of 401 windows... Processed 250 out of 401 windows... Processed 300 out of 401 windows... Processed 350 out of 401 windows... Processed 400 out of 401 windows... Total processed windows:401 Number of reads: 4395 Number of valid reads: 4696 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4395 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 12 Computing descriptors... numberOfMappedBases: 70274383 referenceSize: 2089554 numberOfSequencedBases: 70039397 numberOfAs: 23621784 Computing per chromosome statistics... Computing histograms... Overall analysis time: 12 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 401 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 401 windows... Processed 100 out of 401 windows... Processed 150 out of 401 windows... Processed 200 out of 401 windows... Processed 250 out of 401 windows... Processed 300 out of 401 windows... Processed 350 out of 401 windows... Processed 400 out of 401 windows... Total processed windows:401 Number of reads: 4395 Number of valid reads: 4747 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4395 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 12 Computing descriptors... numberOfMappedBases: 69947563 referenceSize: 2089554 numberOfSequencedBases: 69812688 numberOfAs: 23541478 Computing per chromosome statistics... Computing histograms... Overall analysis time: 12 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished For easier comparison, we can again collapse the two reports into a single one using MultiQC , in the same way we did for putting together the other reports from fastQC . In [9]: Copied! %% bash #run multiqc multiqc -- outdir results / qualimap_output results / qualimap_output %%bash #run multiqc multiqc --outdir results/qualimap_output results/qualimap_output / / / \u001b]8;id=941002;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/qualimap_output | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 93/93 nt.t\u2026 | qualimap | Found 2 BamQC reports | multiqc | Compressing plot data | multiqc | Report : results/qualimap_output/multiqc_report.html | multiqc | Data : results/qualimap_output/multiqc_data | multiqc | MultiQC complete Now you can visualize the report generated, which is in results/qualimap_output/multiqc_report.html . Next, we map the white clover PacBio Hifi reads to contig1 and contig2 separately, using the setting you selected at the previous step (let's say map-pb was chosen, but you are free to change this setting in the commands). As the two contigs represent the two white clover subgenomes, this mapping will allow you to see the two subgenome haplotypes and call subgenome SNPs. In [10]: Copied! %% bash minimap2 - a - x map - pb - o results / HIFI_alignment / PacBio_clover_alignment_1 . sam \\ reference_data / DNA_Contig1 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq %%bash minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1.sam \\ reference_data/DNA_Contig1.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::mm_idx_gen::0.050*0.96] collected minimizers [M::mm_idx_gen::0.071*1.55] sorted minimizers [M::main::0.071*1.55] loaded/built the index for 1 target sequence(s) [M::mm_mapopt_update::0.079*1.50] mid_occ = 10 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 1; #seq: 1 [M::mm_idx_stat::0.082*1.48] distinct minimizers: 112547 (91.45% are singletons); average occurrences: 1.141; average spacing: 8.031; total length: 1031631 [M::worker_pipeline::27.024*2.90] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1.sam reference_data/DNA_Contig1.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 27.035 sec; CPU: 78.431 sec; Peak RSS: 2.258 GB In [11]: Copied! %% bash minimap2 - a - x map - pb - o results / HIFI_alignment / PacBio_clover_alignment_2 . sam \\ reference_data / DNA_Contig2 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq %%bash minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_2.sam \\ reference_data/DNA_Contig2.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::mm_idx_gen::0.048*1.04] collected minimizers [M::mm_idx_gen::0.069*1.61] sorted minimizers [M::main::0.069*1.61] loaded/built the index for 1 target sequence(s) [M::mm_mapopt_update::0.076*1.55] mid_occ = 10 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 1; #seq: 1 [M::mm_idx_stat::0.080*1.53] distinct minimizers: 120722 (93.18% are singletons); average occurrences: 1.087; average spacing: 8.062; total length: 1057923 [M::worker_pipeline::27.150*2.94] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_2.sam reference_data/DNA_Contig2.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 27.162 sec; CPU: 79.819 sec; Peak RSS: 2.058 GB Sort the bam files and create their index using samtools In [12]: Copied! %% bash samtools sort results / HIFI_alignment / PacBio_clover_alignment_1 . sam - o results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam samtools sort results / HIFI_alignment / PacBio_clover_alignment_2 . sam - o results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam %%bash samtools sort results/HIFI_alignment/PacBio_clover_alignment_1.sam -o results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam samtools sort results/HIFI_alignment/PacBio_clover_alignment_2.sam -o results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam In [13]: Copied! %% bash samtools index results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam samtools index results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam %%bash samtools index results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam samtools index results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam Perform quality control In [14]: Copied! %% bash mkdir - p results / qualimap_output qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam - outdir results / qualimap_output / PacBio_clover_alignment_1 %%bash mkdir -p results/qualimap_output qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam -outdir results/qualimap_output/PacBio_clover_alignment_1 Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 400 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 400 windows... Processed 100 out of 400 windows... Processed 150 out of 400 windows... Processed 200 out of 400 windows... Processed 250 out of 400 windows... Processed 300 out of 400 windows... Processed 350 out of 400 windows... Processed 400 out of 400 windows... Total processed windows:400 Number of reads: 4395 Number of valid reads: 9070 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4356 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 10 Computing descriptors... numberOfMappedBases: 59058058 referenceSize: 1031631 numberOfSequencedBases: 54917933 numberOfAs: 18508479 Computing per chromosome statistics... Computing histograms... Overall analysis time: 11 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished In [15]: Copied! %% bash qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam - outdir results / qualimap_output / PacBio_clover_alignment_2 %%bash qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam -outdir results/qualimap_output/PacBio_clover_alignment_2 Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 400 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 400 windows... Processed 100 out of 400 windows... Processed 150 out of 400 windows... Processed 200 out of 400 windows... Processed 250 out of 400 windows... Processed 300 out of 400 windows... Processed 350 out of 400 windows... Processed 400 out of 400 windows... Total processed windows:400 Number of reads: 4395 Number of valid reads: 9462 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4394 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 13 Computing descriptors... numberOfMappedBases: 59388453 referenceSize: 1057923 numberOfSequencedBases: 55867300 numberOfAs: 18822184 Computing per chromosome statistics... Computing histograms... Overall analysis time: 13 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished In [16]: Copied! %% bash #run multiqc multiqc -- outdir results / qualimap_output results / qualimap_output %%bash #run multiqc multiqc --outdir results/qualimap_output results/qualimap_output / / / \u001b]8;id=999624;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/qualimap_output | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 187/187 .t\u2026 | qualimap | Found 4 BamQC reports | multiqc | Compressing plot data | multiqc | Previous MultiQC output found! Adjusting filenames.. | multiqc | Use -f or --force to overwrite existing reports instead | multiqc | Report : results/qualimap_output/multiqc_report_1.html | multiqc | Data : results/qualimap_output/multiqc_data_1 | multiqc | MultiQC complete Task: IGV visualization and Questions Now you can inspect the alignment files in IGV . First, you will need to download the reference fasta sequence in ../Data/Clover_Data/DNA_Contig1_2.fasta and import it into IGV. You can do the same for the files DNA_Contig1.fasta and DNA_Contig2.fasta that you might need later. In IGV, this is done with the menu Genomes --> Load Genome from file menu and by selecting the relevant fasta file. Then, choose the reference you need from the drop-down menu (see figure below). You will not yet see much, but you can choose one of the two subgenomes (contig 1 or 2) and double click on a chromosome position to inspect the reference sequence. The next step will visualize the mapped files on IGV. Each mapped genome can be seen in IGV against the reference file of choice. To load an aligned file, first download it together with the index file in .bai format. For example, you need to download both results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam and results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam.bai to see this alignment (you need to open only the .bam file with IGV). If you open more files, their alignments will be distributed in the IGV interface, and you can change the size of each visualization yourself (below shown with only one opened alignment). Now compare in IGV the two bam files PacBio_clover_alignment_1.sort.bam and PacBio_clover_alignment_2.sort.bam . What do you observe when comparing the two BAM files? Have a look at the polymorphic regions in IGV. Are they true polymorphisms? Add to the visualization the third alignment PacBio_clover_alignment_1_2_mappb.sort.bam in IGV. Why do you see fluctuations in coverage and large regions without any apparent subgenome SNPs? What are the major differences between the stats for the reads mapped to Contigs1&2 versus contig1 and contig2? What is your interpretation of the differences? RNA-seq mapping \u00b6 In the ../Data folder you will find 24 RNA-seq libraries, 12 S10 libraries and 12 Tienshan libraries. Each library is paired-end, which is denoted by R1 and R2 at the end of two files having the same name, such as S10_1_1.R1.fastq and S10_1_1.R2.fastq . We will align each library separately and then merge the alignments to create two final samples for S10 and Tienshan . First, we need to create a genome file for the reference fasta file of contigs 1 and 2 . This is done with STAR , using the option --runMode genomeGenerate . We also need to convert the gene annotation from gff to gtf format with gffread to allow counting gene transcripts. STAR is a very complex tool with many options, so it is always useful to have a reference manual . In [17]: Copied! %% bash gffread - T - o reference_data / white_clover_genes . gtf ../ Data / Clover_Data / white_clover_genes . gff %%bash gffread -T -o reference_data/white_clover_genes.gtf ../Data/Clover_Data/white_clover_genes.gff In [18]: Copied! %% bash STAR -- runThreadN 8 \\ -- runMode genomeGenerate \\ -- genomeDir results / STAR_output / indexing_contigs_1_2 \\ -- genomeFastaFiles reference_data / DNA_Contig1_2 . fasta \\ -- sjdbGTFfile reference_data / white_clover_genes . gtf %%bash STAR --runThreadN 8 \\ --runMode genomeGenerate \\ --genomeDir results/STAR_output/indexing_contigs_1_2 \\ --genomeFastaFiles reference_data/DNA_Contig1_2.fasta \\ --sjdbGTFfile reference_data/white_clover_genes.gtf STAR --runThreadN 8 --runMode genomeGenerate --genomeDir results/STAR_output/indexing_contigs_1_2 --genomeFastaFiles reference_data/DNA_Contig1_2.fasta --sjdbGTFfile reference_data/white_clover_genes.gtf STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:03 ..... started STAR run Jun 19 10:23:03 ... starting to generate Genome files Jun 19 10:23:03 ..... processing annotations GTF !!!!! WARNING: --genomeSAindexNbases 14 is too large for the genome size=2089554, which may cause seg-fault at the mapping step. Re-run genome generation with recommended --genomeSAindexNbases 9 Jun 19 10:23:03 ... starting to sort Suffix Array. This may take a long time... Jun 19 10:23:03 ... sorting Suffix Array chunks and saving them to disk... Jun 19 10:23:04 ... loading chunks from disk, packing SA... Jun 19 10:23:04 ... finished generating suffix array Jun 19 10:23:04 ... generating Suffix Array index Jun 19 10:23:08 ... completed Suffix Array index Jun 19 10:23:08 ..... inserting junctions into the genome indices Jun 19 10:23:16 ... writing Genome to disk ... Jun 19 10:23:16 ... writing Suffix Array to disk ... Jun 19 10:23:17 ... writing SAindex to disk Jun 19 10:23:18 ..... finished successfully We got a warning saying !!!!! WARNING: --genomeSAindexNbases 14 is too large for the genome size=2089554, which may cause seg-fault at the mapping step. Re-run genome generation with recommended --genomeSAindexNbases 9 meaning we need shorter strings of bases (9 bases instead of 14) to be indexed, as our reference genome is very short, and too long strings would cause many alignment errors. So we rerun the command with the suggested option. In [19]: Copied! %% bash STAR -- runThreadN 8 \\ -- runMode genomeGenerate \\ -- genomeDir results / STAR_output / indexing_contigs_1_2 \\ -- genomeFastaFiles reference_data / DNA_Contig1_2 . fasta \\ -- sjdbGTFfile reference_data / white_clover_genes . gtf \\ -- genomeSAindexNbases 9 %%bash STAR --runThreadN 8 \\ --runMode genomeGenerate \\ --genomeDir results/STAR_output/indexing_contigs_1_2 \\ --genomeFastaFiles reference_data/DNA_Contig1_2.fasta \\ --sjdbGTFfile reference_data/white_clover_genes.gtf \\ --genomeSAindexNbases 9 STAR --runThreadN 8 --runMode genomeGenerate --genomeDir results/STAR_output/indexing_contigs_1_2 --genomeFastaFiles reference_data/DNA_Contig1_2.fasta --sjdbGTFfile reference_data/white_clover_genes.gtf --genomeSAindexNbases 9 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:18 ..... started STAR run Jun 19 10:23:18 ... starting to generate Genome files Jun 19 10:23:18 ..... processing annotations GTF Jun 19 10:23:20 ... starting to sort Suffix Array. This may take a long time... Jun 19 10:23:20 ... sorting Suffix Array chunks and saving them to disk... Jun 19 10:23:21 ... loading chunks from disk, packing SA... Jun 19 10:23:21 ... finished generating suffix array Jun 19 10:23:21 ... generating Suffix Array index Jun 19 10:23:21 ... completed Suffix Array index Jun 19 10:23:22 ..... inserting junctions into the genome indices Jun 19 10:23:23 ... writing Genome to disk ... Jun 19 10:23:23 ... writing Suffix Array to disk ... Jun 19 10:23:23 ... writing SAindex to disk Jun 19 10:23:24 ..... finished successfully We use again STAR to align every single library for S10 . We extract the library name of each file and run STAR through each pair of files. Note that plant introns are very rarely more than 5000 bp and that you are mapping to two homoeologous contigs that show high similarity, especially in genic regions. We set the maximum size to 5000 using --alignIntronMax 5000 . In [20]: Copied! %% bash for i in ` ls ../ Data / Clover_Data / S10 *. R1 . fastq ` do PREFIXNAME = ` basename $ i . R1 . fastq ` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \" $ PREFIXNAME echo \"###############################################\" STAR -- genomeDir results / STAR_output / indexing_contigs_1_2 / \\ -- runThreadN 8 \\ -- runMode alignReads \\ -- readFilesIn ../ Data / Clover_Data / $ PREFIXNAME . R1 . fastq ../ Data / Clover_Data / $ PREFIXNAME . R2 . fastq \\ -- outFileNamePrefix results / STAR_output / S10_align_contigs_1_2 / $ PREFIXNAME \\ -- outSAMtype BAM SortedByCoordinate \\ -- outSAMattributes Standard \\ -- quantMode GeneCounts \\ -- alignIntronMax 5000 done %%bash for i in `ls ../Data/Clover_Data/S10*.R1.fastq` do PREFIXNAME=`basename $i .R1.fastq` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \"$PREFIXNAME echo \"###############################################\" STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ \\ --runThreadN 8 \\ --runMode alignReads \\ --readFilesIn ../Data/Clover_Data/$PREFIXNAME.R1.fastq ../Data/Clover_Data/$PREFIXNAME.R2.fastq \\ --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/$PREFIXNAME \\ --outSAMtype BAM SortedByCoordinate \\ --outSAMattributes Standard \\ --quantMode GeneCounts \\ --alignIntronMax 5000 done ############################################### ##### ALIGNING PAIRED-END READS S10_1_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_1_1.R1.fastq ../Data/Clover_Data/S10_1_1.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_1_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:24 ..... started STAR run Jun 19 10:23:24 ..... loading genome Jun 19 10:23:24 ..... started mapping Jun 19 10:23:35 ..... finished mapping Jun 19 10:23:35 ..... started sorting BAM Jun 19 10:23:37 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_1_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_1_2.R1.fastq ../Data/Clover_Data/S10_1_2.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_1_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:38 ..... started STAR run Jun 19 10:23:38 ..... loading genome Jun 19 10:23:38 ..... started mapping Jun 19 10:23:48 ..... finished mapping Jun 19 10:23:48 ..... started sorting BAM Jun 19 10:23:51 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_1_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_1_3.R1.fastq ../Data/Clover_Data/S10_1_3.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_1_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:52 ..... started STAR run Jun 19 10:23:52 ..... loading genome Jun 19 10:23:52 ..... started mapping Jun 19 10:24:03 ..... finished mapping Jun 19 10:24:03 ..... started sorting BAM Jun 19 10:24:06 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_2_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_2_1.R1.fastq ../Data/Clover_Data/S10_2_1.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_2_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:06 ..... started STAR run Jun 19 10:24:06 ..... loading genome Jun 19 10:24:06 ..... started mapping Jun 19 10:24:16 ..... finished mapping Jun 19 10:24:16 ..... started sorting BAM Jun 19 10:24:18 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_2_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_2_2.R1.fastq ../Data/Clover_Data/S10_2_2.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_2_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:20 ..... started STAR run Jun 19 10:24:20 ..... loading genome Jun 19 10:24:20 ..... started mapping Jun 19 10:24:31 ..... finished mapping Jun 19 10:24:31 ..... started sorting BAM Jun 19 10:24:33 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_2_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_2_3.R1.fastq ../Data/Clover_Data/S10_2_3.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_2_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:33 ..... started STAR run Jun 19 10:24:33 ..... loading genome Jun 19 10:24:33 ..... started mapping Jun 19 10:24:45 ..... finished mapping Jun 19 10:24:45 ..... started sorting BAM Jun 19 10:24:48 ..... finished successfully Do the same alignment for Tienshan libraries In [21]: Copied! %% bash for i in ` ls ../ Data / Clover_Data / TI *. R1 . fastq ` do PREFIXNAME = ` basename $ i . R1 . fastq ` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \" $ PREFIXNAME echo \"###############################################\" STAR -- genomeDir results / STAR_output / indexing_contigs_1_2 / \\ -- runThreadN 8 \\ -- readFilesIn ../ Data / Clover_Data / $ PREFIXNAME . R1 . fastq ../ Data / Clover_Data / $ PREFIXNAME . R2 . fastq \\ -- outFileNamePrefix results / STAR_output / TI_align_contigs_1_2 / $ PREFIXNAME \\ -- outSAMtype BAM SortedByCoordinate \\ -- outSAMattributes Standard \\ -- quantMode GeneCounts \\ -- alignIntronMax 5000 done %%bash for i in `ls ../Data/Clover_Data/TI*.R1.fastq` do PREFIXNAME=`basename $i .R1.fastq` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \"$PREFIXNAME echo \"###############################################\" STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ \\ --runThreadN 8 \\ --readFilesIn ../Data/Clover_Data/$PREFIXNAME.R1.fastq ../Data/Clover_Data/$PREFIXNAME.R2.fastq \\ --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/$PREFIXNAME \\ --outSAMtype BAM SortedByCoordinate \\ --outSAMattributes Standard \\ --quantMode GeneCounts \\ --alignIntronMax 5000 done ############################################### ##### ALIGNING PAIRED-END READS TI_1_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_1_1.R1.fastq ../Data/Clover_Data/TI_1_1.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_1_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:50 ..... started STAR run Jun 19 10:24:50 ..... loading genome Jun 19 10:24:50 ..... started mapping Jun 19 10:25:03 ..... finished mapping Jun 19 10:25:04 ..... started sorting BAM Jun 19 10:25:18 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_1_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_1_2.R1.fastq ../Data/Clover_Data/TI_1_2.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_1_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:25:18 ..... started STAR run Jun 19 10:25:18 ..... loading genome Jun 19 10:25:18 ..... started mapping Jun 19 10:25:30 ..... finished mapping Jun 19 10:25:30 ..... started sorting BAM Jun 19 10:25:33 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_1_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_1_3.R1.fastq ../Data/Clover_Data/TI_1_3.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_1_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:25:36 ..... started STAR run Jun 19 10:25:36 ..... loading genome Jun 19 10:25:36 ..... started mapping Jun 19 10:25:50 ..... finished mapping Jun 19 10:25:50 ..... started sorting BAM Jun 19 10:25:51 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_2_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_2_1.R1.fastq ../Data/Clover_Data/TI_2_1.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_2_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:25:51 ..... started STAR run Jun 19 10:25:51 ..... loading genome Jun 19 10:25:52 ..... started mapping Jun 19 10:26:06 ..... finished mapping Jun 19 10:26:06 ..... started sorting BAM Jun 19 10:26:09 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_2_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_2_2.R1.fastq ../Data/Clover_Data/TI_2_2.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_2_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:26:09 ..... started STAR run Jun 19 10:26:09 ..... loading genome Jun 19 10:26:09 ..... started mapping Jun 19 10:26:23 ..... finished mapping Jun 19 10:26:23 ..... started sorting BAM Jun 19 10:26:31 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_2_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_2_3.R1.fastq ../Data/Clover_Data/TI_2_3.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_2_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:26:31 ..... started STAR run Jun 19 10:26:31 ..... loading genome Jun 19 10:26:31 ..... started mapping Jun 19 10:26:46 ..... finished mapping Jun 19 10:26:46 ..... started sorting BAM Jun 19 10:26:49 ..... finished successfully Run quality control on each aligned library with MultiQC . In this way there will be a whole report to compare S10 files and Tienshan files. In [22]: Copied! %% bash multiqc -- outdir results / multiqc_output / TI_STAR_align_1_2 \\ results / STAR_output / TI_align_contigs_1_2 / %%bash multiqc --outdir results/multiqc_output/TI_STAR_align_1_2 \\ results/STAR_output/TI_align_contigs_1_2/ / / / \u001b]8;id=121575;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/STAR_output/TI_align_contigs_1_2 | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 36/36 og.o\u2026 | star | Found 6 reports and 6 gene count files | multiqc | Compressing plot data | multiqc | Report : results/multiqc_output/TI_STAR_align_1_2/multiqc_report.html | multiqc | Data : results/multiqc_output/TI_STAR_align_1_2/multiqc_data | multiqc | MultiQC complete In [23]: Copied! %% bash multiqc -- outdir results / multiqc_output / S10_STAR_align_1_2 \\ results / STAR_output / S10_align_contigs_1_2 / %%bash multiqc --outdir results/multiqc_output/S10_STAR_align_1_2 \\ results/STAR_output/S10_align_contigs_1_2/ / / / \u001b]8;id=596946;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/STAR_output/S10_align_contigs_1_2 | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 36/36 ut.t\u2026 | star | Found 6 reports and 6 gene count files | multiqc | Compressing plot data | multiqc | Report : results/multiqc_output/S10_STAR_align_1_2/multiqc_report.html | multiqc | Data : results/multiqc_output/S10_STAR_align_1_2/multiqc_data | multiqc | MultiQC complete We merge the outputs of each group of aligned libraries. Here is how the files look like for the Tienshan . In [24]: Copied! ! ls - lh results / STAR_output / TI_align_contigs_1_2 / TI_ *. sortedByCoord . out . bam !ls -lh results/STAR_output/TI_align_contigs_1_2/TI_*.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 6.6M Jun 19 10:25 results/STAR_output/TI_align_contigs_1_2/TI_1_1Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 6.0M Jun 19 10:25 results/STAR_output/TI_align_contigs_1_2/TI_1_2Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 7.1M Jun 19 10:25 results/STAR_output/TI_align_contigs_1_2/TI_1_3Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 9.6M Jun 19 10:26 results/STAR_output/TI_align_contigs_1_2/TI_2_1Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 7.2M Jun 19 10:26 results/STAR_output/TI_align_contigs_1_2/TI_2_2Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 8.2M Jun 19 10:26 results/STAR_output/TI_align_contigs_1_2/TI_2_3Aligned.sortedByCoord.out.bam Apply samtools merge In [25]: Copied! %% bash mkdir - p results / STAR_output / TI_align_contigs_1_2_merge / samtools merge - f results / STAR_output / TI_align_contigs_1_2_merge / TI . sorted . bam results / STAR_output / TI_align_contigs_1_2 / TI_ *. sortedByCoord . out . bam %%bash mkdir -p results/STAR_output/TI_align_contigs_1_2_merge/ samtools merge -f results/STAR_output/TI_align_contigs_1_2_merge/TI.sorted.bam results/STAR_output/TI_align_contigs_1_2/TI_*.sortedByCoord.out.bam In [26]: Copied! %% bash mkdir - p results / STAR_output / S10_align_contigs_1_2_merge / samtools merge - f results / STAR_output / S10_align_contigs_1_2_merge / S10 . sorted . bam results / STAR_output / S10_align_contigs_1_2 / S10_ *. sortedByCoord . out . bam %%bash mkdir -p results/STAR_output/S10_align_contigs_1_2_merge/ samtools merge -f results/STAR_output/S10_align_contigs_1_2_merge/S10.sorted.bam results/STAR_output/S10_align_contigs_1_2/S10_*.sortedByCoord.out.bam Index both merging outputs. A file in format bam.bai will appear in their respective folders. In [27]: Copied! %% bash samtools index results / STAR_output / S10_align_contigs_1_2_merge / S10 . sorted . bam %%bash samtools index results/STAR_output/S10_align_contigs_1_2_merge/S10.sorted.bam In [28]: Copied! %% bash samtools index results / STAR_output / TI_align_contigs_1_2_merge / TI . sorted . bam %%bash samtools index results/STAR_output/TI_align_contigs_1_2_merge/TI.sorted.bam Wrapping up \ud83c\udf89 \ud83c\udf89 \ud83c\udf89 \u00b6 In this exercise, you learnt to align various types of data after performing quality control for raw data. We looked at some of the options for the aligners and at how to use some of the basic samtools manipulation programs. The outputs from the RNA alignments will be used for the VCF file analysis in the next notebook, and the RNA alignments will be use for the bulk RNA data analysis.","title":"3.1.Alignment of raw data"},{"location":"nb/align.html#biological-background","text":"White clover (Trifolium repens) is an allotetraploid. It is a relatively young, outcrossing species, which originated during the most recent glaciation around 20,000 years ago by hybridisation of two diploid species, T. occidentale and T. pallescens (see figure below). This means that it contains genomes originating from two different species within the same nucleus. Normally, white clover is an outbreeding species, but a self-compatible line was used for sequencing the white clover genome (Griffiths et al, 2019) . This line will be designated as S10 in the data, indicating that this is the 10th self-fertilized generation. In addition, we have data from a wild clover accession (ecotype) called Tienshan ( Ti ), which is collected from the Chinese mountains and is adapted to alpine conditions. We will perform alignment of the data to the white clover's reference genome containing both T. occidentale and T. pallescens (called contig 1 and contig 2 in the data). We will also perform alignment to each subgenome, and see which are the differences with the quality control tools.","title":"Biological background"},{"location":"nb/align.html#quality-control-and-mapping","text":"","title":"Quality control and mapping"},{"location":"nb/align.html#quality-control","text":"We run FastQC on the PacBio Hifi reads and on two of the Illumina RNA-seq libraries. FastQC does quality control of the raw sequence data, providing an overview of the data which can help identify if there are any problems that should be addressed before further analysis. You can find the report for each file into the folder results/fastqc_output/ . The output is in HTML format and can be opened in any browser or in jupyterlab . It is however not easy to compare the various libraries by opening separate reports. To aggregate all the results, we apply the MultiQC software to the reports' folder. The output of MultiQC is in the directory results/multiqc_output/fastqc_data . In [1]: Copied! %% bash #run fastqc mkdir - p results / fastqc_output fastqc - q - o results / fastqc_output ../ Data / Clover_Data /*. fastq > / dev / null 2 >& 1 %%bash #run fastqc mkdir -p results/fastqc_output fastqc -q -o results/fastqc_output ../Data/Clover_Data/*.fastq > /dev/null 2>&1 Note: fastqc prints a lot of output conisting of a simple confirmation of execution without error, even when using the option -q , which means quiet . Therefore we added > /dev/null 2>&1 to the command to mute the printing of that output. In [2]: Copied! %% bash #run multiqc multiqc -- outdir results / multiqc_output / fastqc_data results / fastqc_output %%bash #run multiqc multiqc --outdir results/multiqc_output/fastqc_data results/fastqc_output / / / \u001b]8;id=903972;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/fastqc_output | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 50/50 .html | fastqc | Found 25 reports | multiqc | Compressing plot data | multiqc | Report : results/multiqc_output/fastqc_data/multiqc_report.html | multiqc | Data : results/multiqc_output/fastqc_data/multiqc_data | multiqc | MultiQC complete Questions Visualize the Webpage generated by MultiQC. Hint: You can find a Help button that offers additional information about the plots for each panel. Focus on the following panels: \u201cPer base sequence quality\u201d, \u201cPer sequence quality scores\u201d.... (\u201cPer base sequence content\u201d always gives a FAIL for RNA-seq data). What do you notice with respect to the sequence quality scores? Are there any other quality issues worth noting?","title":"Quality Control"},{"location":"nb/align.html#hifi-data-mapping","text":"We map the PacBio Hifi reads ( Hifi_reads_white_clover.fastq ) to the white clover reference sequence (Contig1&2) using minimap2 . We run two mapping rounds, using two different preset options ( -x in the command) for the technology: PacBio/Oxford Nanopore read to reference mapping: map-pb Long assembly to reference mapping. Divergence is below 20%\u201d settings asm20 . Next, we create reports of the mapping results by running QualiMap on the two obtained SAM files. We first need to index the reference fasta files using samtools faidx . This produces files in .fai format containing informations about length of the reference sequence, offset for the quality scores, name of the reference sequence. Click here for a detailed overview. In [3]: Copied! %% bash #copy the reference data in the folder reference_data, so that you can write the indexing files mkdir - p reference_data cp ../ Data / Clover_Data / DNA_Contig1_2 . fasta ../ Data / Clover_Data / DNA_Contig1 . fasta ../ Data / Clover_Data / DNA_Contig2 . fasta reference_data %%bash #copy the reference data in the folder reference_data, so that you can write the indexing files mkdir -p reference_data cp ../Data/Clover_Data/DNA_Contig1_2.fasta ../Data/Clover_Data/DNA_Contig1.fasta ../Data/Clover_Data/DNA_Contig2.fasta reference_data In [4]: Copied! %% bash samtools faidx reference_data / DNA_Contig1_2 . fasta samtools faidx reference_data / DNA_Contig1 . fasta samtools faidx reference_data / DNA_Contig2 . fasta %%bash samtools faidx reference_data/DNA_Contig1_2.fasta samtools faidx reference_data/DNA_Contig1.fasta samtools faidx reference_data/DNA_Contig2.fasta we create an output folder for the HIFI alignment, and run minimap2 with the settings explained before. In [5]: Copied! %% bash mkdir - p results / HIFI_alignment / minimap2 - a - x map - pb - o results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sam \\ reference_data / DNA_Contig1_2 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq minimap2 - a - x asm20 - o results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sam \\ reference_data / DNA_Contig1_2 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq %%bash mkdir -p results/HIFI_alignment/ minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sam \\ reference_data/DNA_Contig1_2.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq minimap2 -a -x asm20 -o results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sam \\ reference_data/DNA_Contig1_2.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::mm_idx_gen::0.075*1.04] collected minimizers [M::mm_idx_gen::0.099*1.52] sorted minimizers [M::main::0.099*1.52] loaded/built the index for 2 target sequence(s) [M::mm_mapopt_update::0.114*1.45] mid_occ = 11 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 1; #seq: 2 [M::mm_idx_stat::0.118*1.43] distinct minimizers: 203943 (79.05% are singletons); average occurrences: 1.273; average spacing: 8.047; total length: 2089554 [M::worker_pipeline::11.859*2.89] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sam reference_data/DNA_Contig1_2.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 11.873 sec; CPU: 34.246 sec; Peak RSS: 1.186 GB [M::mm_idx_gen::0.084*1.04] collected minimizers [M::mm_idx_gen::0.121*1.62] sorted minimizers [M::main::0.121*1.62] loaded/built the index for 2 target sequence(s) [M::mm_mapopt_update::0.138*1.54] mid_occ = 50 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 0; #seq: 2 [M::mm_idx_stat::0.143*1.52] distinct minimizers: 298340 (78.21% are singletons); average occurrences: 1.277; average spacing: 5.484; total length: 2089554 [M::worker_pipeline::18.366*2.88] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x asm20 -o results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sam reference_data/DNA_Contig1_2.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 18.394 sec; CPU: 52.932 sec; Peak RSS: 1.692 GB samtools sort is used to sort the alignment with left-to-right coordinates. The output is in .bam format, with .sam files in input (Note that you could have gotten .bam files from minimap2 with a specific option). In [6]: Copied! %% bash samtools sort results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sam \\ - o results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sort . bam samtools sort results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sam \\ - o results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam %%bash samtools sort results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sam \\ -o results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sort.bam samtools sort results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sam \\ -o results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam samtools index creates the index for the bam file, stored in .bai format. The index file lets programs access any position into the aligned data without reading the whole file, which would take too much time. In [7]: Copied! %% bash samtools index results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sort . bam samtools index results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam %%bash samtools index results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sort.bam samtools index results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam Run quality control on both files In [8]: Copied! %% bash qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_1_2_mappb . sort . bam \\ - outdir results / qualimap_output / PacBio_clover_alignment_1_2_mappb qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam \\ - outdir results / qualimap_output / PacBio_clover_alignment_1_2_asm20 %%bash qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_1_2_mappb.sort.bam \\ -outdir results/qualimap_output/PacBio_clover_alignment_1_2_mappb qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam \\ -outdir results/qualimap_output/PacBio_clover_alignment_1_2_asm20 Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 401 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 401 windows... Processed 100 out of 401 windows... Processed 150 out of 401 windows... Processed 200 out of 401 windows... Processed 250 out of 401 windows... Processed 300 out of 401 windows... Processed 350 out of 401 windows... Processed 400 out of 401 windows... Total processed windows:401 Number of reads: 4395 Number of valid reads: 4696 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4395 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 12 Computing descriptors... numberOfMappedBases: 70274383 referenceSize: 2089554 numberOfSequencedBases: 70039397 numberOfAs: 23621784 Computing per chromosome statistics... Computing histograms... Overall analysis time: 12 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 401 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 401 windows... Processed 100 out of 401 windows... Processed 150 out of 401 windows... Processed 200 out of 401 windows... Processed 250 out of 401 windows... Processed 300 out of 401 windows... Processed 350 out of 401 windows... Processed 400 out of 401 windows... Total processed windows:401 Number of reads: 4395 Number of valid reads: 4747 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4395 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 12 Computing descriptors... numberOfMappedBases: 69947563 referenceSize: 2089554 numberOfSequencedBases: 69812688 numberOfAs: 23541478 Computing per chromosome statistics... Computing histograms... Overall analysis time: 12 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished For easier comparison, we can again collapse the two reports into a single one using MultiQC , in the same way we did for putting together the other reports from fastQC . In [9]: Copied! %% bash #run multiqc multiqc -- outdir results / qualimap_output results / qualimap_output %%bash #run multiqc multiqc --outdir results/qualimap_output results/qualimap_output / / / \u001b]8;id=941002;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/qualimap_output | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 93/93 nt.t\u2026 | qualimap | Found 2 BamQC reports | multiqc | Compressing plot data | multiqc | Report : results/qualimap_output/multiqc_report.html | multiqc | Data : results/qualimap_output/multiqc_data | multiqc | MultiQC complete Now you can visualize the report generated, which is in results/qualimap_output/multiqc_report.html . Next, we map the white clover PacBio Hifi reads to contig1 and contig2 separately, using the setting you selected at the previous step (let's say map-pb was chosen, but you are free to change this setting in the commands). As the two contigs represent the two white clover subgenomes, this mapping will allow you to see the two subgenome haplotypes and call subgenome SNPs. In [10]: Copied! %% bash minimap2 - a - x map - pb - o results / HIFI_alignment / PacBio_clover_alignment_1 . sam \\ reference_data / DNA_Contig1 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq %%bash minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1.sam \\ reference_data/DNA_Contig1.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::mm_idx_gen::0.050*0.96] collected minimizers [M::mm_idx_gen::0.071*1.55] sorted minimizers [M::main::0.071*1.55] loaded/built the index for 1 target sequence(s) [M::mm_mapopt_update::0.079*1.50] mid_occ = 10 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 1; #seq: 1 [M::mm_idx_stat::0.082*1.48] distinct minimizers: 112547 (91.45% are singletons); average occurrences: 1.141; average spacing: 8.031; total length: 1031631 [M::worker_pipeline::27.024*2.90] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_1.sam reference_data/DNA_Contig1.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 27.035 sec; CPU: 78.431 sec; Peak RSS: 2.258 GB In [11]: Copied! %% bash minimap2 - a - x map - pb - o results / HIFI_alignment / PacBio_clover_alignment_2 . sam \\ reference_data / DNA_Contig2 . fasta \\ ../ Data / Clover_Data / Hifi_reads_white_clover . fastq %%bash minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_2.sam \\ reference_data/DNA_Contig2.fasta \\ ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::mm_idx_gen::0.048*1.04] collected minimizers [M::mm_idx_gen::0.069*1.61] sorted minimizers [M::main::0.069*1.61] loaded/built the index for 1 target sequence(s) [M::mm_mapopt_update::0.076*1.55] mid_occ = 10 [M::mm_idx_stat] kmer size: 19; skip: 10; is_hpc: 1; #seq: 1 [M::mm_idx_stat::0.080*1.53] distinct minimizers: 120722 (93.18% are singletons); average occurrences: 1.087; average spacing: 8.062; total length: 1057923 [M::worker_pipeline::27.150*2.94] mapped 4395 sequences [M::main] Version: 2.24-r1122 [M::main] CMD: minimap2 -a -x map-pb -o results/HIFI_alignment/PacBio_clover_alignment_2.sam reference_data/DNA_Contig2.fasta ../Data/Clover_Data/Hifi_reads_white_clover.fastq [M::main] Real time: 27.162 sec; CPU: 79.819 sec; Peak RSS: 2.058 GB Sort the bam files and create their index using samtools In [12]: Copied! %% bash samtools sort results / HIFI_alignment / PacBio_clover_alignment_1 . sam - o results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam samtools sort results / HIFI_alignment / PacBio_clover_alignment_2 . sam - o results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam %%bash samtools sort results/HIFI_alignment/PacBio_clover_alignment_1.sam -o results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam samtools sort results/HIFI_alignment/PacBio_clover_alignment_2.sam -o results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam In [13]: Copied! %% bash samtools index results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam samtools index results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam %%bash samtools index results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam samtools index results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam Perform quality control In [14]: Copied! %% bash mkdir - p results / qualimap_output qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam - outdir results / qualimap_output / PacBio_clover_alignment_1 %%bash mkdir -p results/qualimap_output qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam -outdir results/qualimap_output/PacBio_clover_alignment_1 Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 400 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 400 windows... Processed 100 out of 400 windows... Processed 150 out of 400 windows... Processed 200 out of 400 windows... Processed 250 out of 400 windows... Processed 300 out of 400 windows... Processed 350 out of 400 windows... Processed 400 out of 400 windows... Total processed windows:400 Number of reads: 4395 Number of valid reads: 9070 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4356 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 10 Computing descriptors... numberOfMappedBases: 59058058 referenceSize: 1031631 numberOfSequencedBases: 54917933 numberOfAs: 18508479 Computing per chromosome statistics... Computing histograms... Overall analysis time: 11 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished In [15]: Copied! %% bash qualimap bamqc - bam results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam - outdir results / qualimap_output / PacBio_clover_alignment_2 %%bash qualimap bamqc -bam results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam -outdir results/qualimap_output/PacBio_clover_alignment_2 Java memory size is set to 1200M Launching application... QualiMap v.2.2.2-dev Built on 2019-11-11 14:05 Selected tool: bamqc Available memory (Mb): 33 Max memory (Mb): 1258 Starting bam qc.... Loading sam header... Loading locator... Loading reference... Number of windows: 400, effective number of windows: 400 Chunk of reads size: 1000 Number of threads: 8 Processed 50 out of 400 windows... Processed 100 out of 400 windows... Processed 150 out of 400 windows... Processed 200 out of 400 windows... Processed 250 out of 400 windows... Processed 300 out of 400 windows... Processed 350 out of 400 windows... Processed 400 out of 400 windows... Total processed windows:400 Number of reads: 4395 Number of valid reads: 9462 Number of correct strand reads:0 Inside of regions... Num mapped reads: 4394 Num mapped first of pair: 0 Num mapped second of pair: 0 Num singletons: 0 Time taken to analyze reads: 13 Computing descriptors... numberOfMappedBases: 59388453 referenceSize: 1057923 numberOfSequencedBases: 55867300 numberOfAs: 18822184 Computing per chromosome statistics... Computing histograms... Overall analysis time: 13 end of bam qc Computing report... Writing HTML report... HTML report created successfully Finished In [16]: Copied! %% bash #run multiqc multiqc -- outdir results / qualimap_output results / qualimap_output %%bash #run multiqc multiqc --outdir results/qualimap_output results/qualimap_output / / / \u001b]8;id=999624;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/qualimap_output | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 187/187 .t\u2026 | qualimap | Found 4 BamQC reports | multiqc | Compressing plot data | multiqc | Previous MultiQC output found! Adjusting filenames.. | multiqc | Use -f or --force to overwrite existing reports instead | multiqc | Report : results/qualimap_output/multiqc_report_1.html | multiqc | Data : results/qualimap_output/multiqc_data_1 | multiqc | MultiQC complete Task: IGV visualization and Questions Now you can inspect the alignment files in IGV . First, you will need to download the reference fasta sequence in ../Data/Clover_Data/DNA_Contig1_2.fasta and import it into IGV. You can do the same for the files DNA_Contig1.fasta and DNA_Contig2.fasta that you might need later. In IGV, this is done with the menu Genomes --> Load Genome from file menu and by selecting the relevant fasta file. Then, choose the reference you need from the drop-down menu (see figure below). You will not yet see much, but you can choose one of the two subgenomes (contig 1 or 2) and double click on a chromosome position to inspect the reference sequence. The next step will visualize the mapped files on IGV. Each mapped genome can be seen in IGV against the reference file of choice. To load an aligned file, first download it together with the index file in .bai format. For example, you need to download both results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam and results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam.bai to see this alignment (you need to open only the .bam file with IGV). If you open more files, their alignments will be distributed in the IGV interface, and you can change the size of each visualization yourself (below shown with only one opened alignment). Now compare in IGV the two bam files PacBio_clover_alignment_1.sort.bam and PacBio_clover_alignment_2.sort.bam . What do you observe when comparing the two BAM files? Have a look at the polymorphic regions in IGV. Are they true polymorphisms? Add to the visualization the third alignment PacBio_clover_alignment_1_2_mappb.sort.bam in IGV. Why do you see fluctuations in coverage and large regions without any apparent subgenome SNPs? What are the major differences between the stats for the reads mapped to Contigs1&2 versus contig1 and contig2? What is your interpretation of the differences?","title":"Hifi data mapping"},{"location":"nb/align.html#rna-seq-mapping","text":"In the ../Data folder you will find 24 RNA-seq libraries, 12 S10 libraries and 12 Tienshan libraries. Each library is paired-end, which is denoted by R1 and R2 at the end of two files having the same name, such as S10_1_1.R1.fastq and S10_1_1.R2.fastq . We will align each library separately and then merge the alignments to create two final samples for S10 and Tienshan . First, we need to create a genome file for the reference fasta file of contigs 1 and 2 . This is done with STAR , using the option --runMode genomeGenerate . We also need to convert the gene annotation from gff to gtf format with gffread to allow counting gene transcripts. STAR is a very complex tool with many options, so it is always useful to have a reference manual . In [17]: Copied! %% bash gffread - T - o reference_data / white_clover_genes . gtf ../ Data / Clover_Data / white_clover_genes . gff %%bash gffread -T -o reference_data/white_clover_genes.gtf ../Data/Clover_Data/white_clover_genes.gff In [18]: Copied! %% bash STAR -- runThreadN 8 \\ -- runMode genomeGenerate \\ -- genomeDir results / STAR_output / indexing_contigs_1_2 \\ -- genomeFastaFiles reference_data / DNA_Contig1_2 . fasta \\ -- sjdbGTFfile reference_data / white_clover_genes . gtf %%bash STAR --runThreadN 8 \\ --runMode genomeGenerate \\ --genomeDir results/STAR_output/indexing_contigs_1_2 \\ --genomeFastaFiles reference_data/DNA_Contig1_2.fasta \\ --sjdbGTFfile reference_data/white_clover_genes.gtf STAR --runThreadN 8 --runMode genomeGenerate --genomeDir results/STAR_output/indexing_contigs_1_2 --genomeFastaFiles reference_data/DNA_Contig1_2.fasta --sjdbGTFfile reference_data/white_clover_genes.gtf STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:03 ..... started STAR run Jun 19 10:23:03 ... starting to generate Genome files Jun 19 10:23:03 ..... processing annotations GTF !!!!! WARNING: --genomeSAindexNbases 14 is too large for the genome size=2089554, which may cause seg-fault at the mapping step. Re-run genome generation with recommended --genomeSAindexNbases 9 Jun 19 10:23:03 ... starting to sort Suffix Array. This may take a long time... Jun 19 10:23:03 ... sorting Suffix Array chunks and saving them to disk... Jun 19 10:23:04 ... loading chunks from disk, packing SA... Jun 19 10:23:04 ... finished generating suffix array Jun 19 10:23:04 ... generating Suffix Array index Jun 19 10:23:08 ... completed Suffix Array index Jun 19 10:23:08 ..... inserting junctions into the genome indices Jun 19 10:23:16 ... writing Genome to disk ... Jun 19 10:23:16 ... writing Suffix Array to disk ... Jun 19 10:23:17 ... writing SAindex to disk Jun 19 10:23:18 ..... finished successfully We got a warning saying !!!!! WARNING: --genomeSAindexNbases 14 is too large for the genome size=2089554, which may cause seg-fault at the mapping step. Re-run genome generation with recommended --genomeSAindexNbases 9 meaning we need shorter strings of bases (9 bases instead of 14) to be indexed, as our reference genome is very short, and too long strings would cause many alignment errors. So we rerun the command with the suggested option. In [19]: Copied! %% bash STAR -- runThreadN 8 \\ -- runMode genomeGenerate \\ -- genomeDir results / STAR_output / indexing_contigs_1_2 \\ -- genomeFastaFiles reference_data / DNA_Contig1_2 . fasta \\ -- sjdbGTFfile reference_data / white_clover_genes . gtf \\ -- genomeSAindexNbases 9 %%bash STAR --runThreadN 8 \\ --runMode genomeGenerate \\ --genomeDir results/STAR_output/indexing_contigs_1_2 \\ --genomeFastaFiles reference_data/DNA_Contig1_2.fasta \\ --sjdbGTFfile reference_data/white_clover_genes.gtf \\ --genomeSAindexNbases 9 STAR --runThreadN 8 --runMode genomeGenerate --genomeDir results/STAR_output/indexing_contigs_1_2 --genomeFastaFiles reference_data/DNA_Contig1_2.fasta --sjdbGTFfile reference_data/white_clover_genes.gtf --genomeSAindexNbases 9 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:18 ..... started STAR run Jun 19 10:23:18 ... starting to generate Genome files Jun 19 10:23:18 ..... processing annotations GTF Jun 19 10:23:20 ... starting to sort Suffix Array. This may take a long time... Jun 19 10:23:20 ... sorting Suffix Array chunks and saving them to disk... Jun 19 10:23:21 ... loading chunks from disk, packing SA... Jun 19 10:23:21 ... finished generating suffix array Jun 19 10:23:21 ... generating Suffix Array index Jun 19 10:23:21 ... completed Suffix Array index Jun 19 10:23:22 ..... inserting junctions into the genome indices Jun 19 10:23:23 ... writing Genome to disk ... Jun 19 10:23:23 ... writing Suffix Array to disk ... Jun 19 10:23:23 ... writing SAindex to disk Jun 19 10:23:24 ..... finished successfully We use again STAR to align every single library for S10 . We extract the library name of each file and run STAR through each pair of files. Note that plant introns are very rarely more than 5000 bp and that you are mapping to two homoeologous contigs that show high similarity, especially in genic regions. We set the maximum size to 5000 using --alignIntronMax 5000 . In [20]: Copied! %% bash for i in ` ls ../ Data / Clover_Data / S10 *. R1 . fastq ` do PREFIXNAME = ` basename $ i . R1 . fastq ` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \" $ PREFIXNAME echo \"###############################################\" STAR -- genomeDir results / STAR_output / indexing_contigs_1_2 / \\ -- runThreadN 8 \\ -- runMode alignReads \\ -- readFilesIn ../ Data / Clover_Data / $ PREFIXNAME . R1 . fastq ../ Data / Clover_Data / $ PREFIXNAME . R2 . fastq \\ -- outFileNamePrefix results / STAR_output / S10_align_contigs_1_2 / $ PREFIXNAME \\ -- outSAMtype BAM SortedByCoordinate \\ -- outSAMattributes Standard \\ -- quantMode GeneCounts \\ -- alignIntronMax 5000 done %%bash for i in `ls ../Data/Clover_Data/S10*.R1.fastq` do PREFIXNAME=`basename $i .R1.fastq` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \"$PREFIXNAME echo \"###############################################\" STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ \\ --runThreadN 8 \\ --runMode alignReads \\ --readFilesIn ../Data/Clover_Data/$PREFIXNAME.R1.fastq ../Data/Clover_Data/$PREFIXNAME.R2.fastq \\ --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/$PREFIXNAME \\ --outSAMtype BAM SortedByCoordinate \\ --outSAMattributes Standard \\ --quantMode GeneCounts \\ --alignIntronMax 5000 done ############################################### ##### ALIGNING PAIRED-END READS S10_1_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_1_1.R1.fastq ../Data/Clover_Data/S10_1_1.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_1_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:24 ..... started STAR run Jun 19 10:23:24 ..... loading genome Jun 19 10:23:24 ..... started mapping Jun 19 10:23:35 ..... finished mapping Jun 19 10:23:35 ..... started sorting BAM Jun 19 10:23:37 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_1_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_1_2.R1.fastq ../Data/Clover_Data/S10_1_2.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_1_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:38 ..... started STAR run Jun 19 10:23:38 ..... loading genome Jun 19 10:23:38 ..... started mapping Jun 19 10:23:48 ..... finished mapping Jun 19 10:23:48 ..... started sorting BAM Jun 19 10:23:51 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_1_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_1_3.R1.fastq ../Data/Clover_Data/S10_1_3.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_1_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:23:52 ..... started STAR run Jun 19 10:23:52 ..... loading genome Jun 19 10:23:52 ..... started mapping Jun 19 10:24:03 ..... finished mapping Jun 19 10:24:03 ..... started sorting BAM Jun 19 10:24:06 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_2_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_2_1.R1.fastq ../Data/Clover_Data/S10_2_1.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_2_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:06 ..... started STAR run Jun 19 10:24:06 ..... loading genome Jun 19 10:24:06 ..... started mapping Jun 19 10:24:16 ..... finished mapping Jun 19 10:24:16 ..... started sorting BAM Jun 19 10:24:18 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_2_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_2_2.R1.fastq ../Data/Clover_Data/S10_2_2.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_2_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:20 ..... started STAR run Jun 19 10:24:20 ..... loading genome Jun 19 10:24:20 ..... started mapping Jun 19 10:24:31 ..... finished mapping Jun 19 10:24:31 ..... started sorting BAM Jun 19 10:24:33 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS S10_2_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --runMode alignReads --readFilesIn ../Data/Clover_Data/S10_2_3.R1.fastq ../Data/Clover_Data/S10_2_3.R2.fastq --outFileNamePrefix results/STAR_output/S10_align_contigs_1_2/S10_2_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:33 ..... started STAR run Jun 19 10:24:33 ..... loading genome Jun 19 10:24:33 ..... started mapping Jun 19 10:24:45 ..... finished mapping Jun 19 10:24:45 ..... started sorting BAM Jun 19 10:24:48 ..... finished successfully Do the same alignment for Tienshan libraries In [21]: Copied! %% bash for i in ` ls ../ Data / Clover_Data / TI *. R1 . fastq ` do PREFIXNAME = ` basename $ i . R1 . fastq ` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \" $ PREFIXNAME echo \"###############################################\" STAR -- genomeDir results / STAR_output / indexing_contigs_1_2 / \\ -- runThreadN 8 \\ -- readFilesIn ../ Data / Clover_Data / $ PREFIXNAME . R1 . fastq ../ Data / Clover_Data / $ PREFIXNAME . R2 . fastq \\ -- outFileNamePrefix results / STAR_output / TI_align_contigs_1_2 / $ PREFIXNAME \\ -- outSAMtype BAM SortedByCoordinate \\ -- outSAMattributes Standard \\ -- quantMode GeneCounts \\ -- alignIntronMax 5000 done %%bash for i in `ls ../Data/Clover_Data/TI*.R1.fastq` do PREFIXNAME=`basename $i .R1.fastq` echo \"###############################################\" echo \"##### ALIGNING PAIRED-END READS \"$PREFIXNAME echo \"###############################################\" STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ \\ --runThreadN 8 \\ --readFilesIn ../Data/Clover_Data/$PREFIXNAME.R1.fastq ../Data/Clover_Data/$PREFIXNAME.R2.fastq \\ --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/$PREFIXNAME \\ --outSAMtype BAM SortedByCoordinate \\ --outSAMattributes Standard \\ --quantMode GeneCounts \\ --alignIntronMax 5000 done ############################################### ##### ALIGNING PAIRED-END READS TI_1_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_1_1.R1.fastq ../Data/Clover_Data/TI_1_1.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_1_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:24:50 ..... started STAR run Jun 19 10:24:50 ..... loading genome Jun 19 10:24:50 ..... started mapping Jun 19 10:25:03 ..... finished mapping Jun 19 10:25:04 ..... started sorting BAM Jun 19 10:25:18 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_1_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_1_2.R1.fastq ../Data/Clover_Data/TI_1_2.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_1_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:25:18 ..... started STAR run Jun 19 10:25:18 ..... loading genome Jun 19 10:25:18 ..... started mapping Jun 19 10:25:30 ..... finished mapping Jun 19 10:25:30 ..... started sorting BAM Jun 19 10:25:33 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_1_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_1_3.R1.fastq ../Data/Clover_Data/TI_1_3.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_1_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:25:36 ..... started STAR run Jun 19 10:25:36 ..... loading genome Jun 19 10:25:36 ..... started mapping Jun 19 10:25:50 ..... finished mapping Jun 19 10:25:50 ..... started sorting BAM Jun 19 10:25:51 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_2_1 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_2_1.R1.fastq ../Data/Clover_Data/TI_2_1.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_2_1 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:25:51 ..... started STAR run Jun 19 10:25:51 ..... loading genome Jun 19 10:25:52 ..... started mapping Jun 19 10:26:06 ..... finished mapping Jun 19 10:26:06 ..... started sorting BAM Jun 19 10:26:09 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_2_2 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_2_2.R1.fastq ../Data/Clover_Data/TI_2_2.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_2_2 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:26:09 ..... started STAR run Jun 19 10:26:09 ..... loading genome Jun 19 10:26:09 ..... started mapping Jun 19 10:26:23 ..... finished mapping Jun 19 10:26:23 ..... started sorting BAM Jun 19 10:26:31 ..... finished successfully ############################################### ##### ALIGNING PAIRED-END READS TI_2_3 ############################################### STAR --genomeDir results/STAR_output/indexing_contigs_1_2/ --runThreadN 8 --readFilesIn ../Data/Clover_Data/TI_2_3.R1.fastq ../Data/Clover_Data/TI_2_3.R2.fastq --outFileNamePrefix results/STAR_output/TI_align_contigs_1_2/TI_2_3 --outSAMtype BAM SortedByCoordinate --outSAMattributes Standard --quantMode GeneCounts --alignIntronMax 5000 STAR version: 2.7.10a compiled: 2022-01-14T18:50:00-05:00 :/home/dobin/data/STAR/STARcode/STAR.master/source Jun 19 10:26:31 ..... started STAR run Jun 19 10:26:31 ..... loading genome Jun 19 10:26:31 ..... started mapping Jun 19 10:26:46 ..... finished mapping Jun 19 10:26:46 ..... started sorting BAM Jun 19 10:26:49 ..... finished successfully Run quality control on each aligned library with MultiQC . In this way there will be a whole report to compare S10 files and Tienshan files. In [22]: Copied! %% bash multiqc -- outdir results / multiqc_output / TI_STAR_align_1_2 \\ results / STAR_output / TI_align_contigs_1_2 / %%bash multiqc --outdir results/multiqc_output/TI_STAR_align_1_2 \\ results/STAR_output/TI_align_contigs_1_2/ / / / \u001b]8;id=121575;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/STAR_output/TI_align_contigs_1_2 | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 36/36 og.o\u2026 | star | Found 6 reports and 6 gene count files | multiqc | Compressing plot data | multiqc | Report : results/multiqc_output/TI_STAR_align_1_2/multiqc_report.html | multiqc | Data : results/multiqc_output/TI_STAR_align_1_2/multiqc_data | multiqc | MultiQC complete In [23]: Copied! %% bash multiqc -- outdir results / multiqc_output / S10_STAR_align_1_2 \\ results / STAR_output / S10_align_contigs_1_2 / %%bash multiqc --outdir results/multiqc_output/S10_STAR_align_1_2 \\ results/STAR_output/S10_align_contigs_1_2/ / / / \u001b]8;id=596946;https://multiqc.info\u001b\\ MultiQC \u001b]8;;\u001b\\ \ud83d\udd0d | v1.14 | multiqc | Search path : /work/SamueleSoraggi/Notebooks/results/STAR_output/S10_align_contigs_1_2 | searching | \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 36/36 ut.t\u2026 | star | Found 6 reports and 6 gene count files | multiqc | Compressing plot data | multiqc | Report : results/multiqc_output/S10_STAR_align_1_2/multiqc_report.html | multiqc | Data : results/multiqc_output/S10_STAR_align_1_2/multiqc_data | multiqc | MultiQC complete We merge the outputs of each group of aligned libraries. Here is how the files look like for the Tienshan . In [24]: Copied! ! ls - lh results / STAR_output / TI_align_contigs_1_2 / TI_ *. sortedByCoord . out . bam !ls -lh results/STAR_output/TI_align_contigs_1_2/TI_*.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 6.6M Jun 19 10:25 results/STAR_output/TI_align_contigs_1_2/TI_1_1Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 6.0M Jun 19 10:25 results/STAR_output/TI_align_contigs_1_2/TI_1_2Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 7.1M Jun 19 10:25 results/STAR_output/TI_align_contigs_1_2/TI_1_3Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 9.6M Jun 19 10:26 results/STAR_output/TI_align_contigs_1_2/TI_2_1Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 7.2M Jun 19 10:26 results/STAR_output/TI_align_contigs_1_2/TI_2_2Aligned.sortedByCoord.out.bam -rw-r--r--. 1 ucloud users 8.2M Jun 19 10:26 results/STAR_output/TI_align_contigs_1_2/TI_2_3Aligned.sortedByCoord.out.bam Apply samtools merge In [25]: Copied! %% bash mkdir - p results / STAR_output / TI_align_contigs_1_2_merge / samtools merge - f results / STAR_output / TI_align_contigs_1_2_merge / TI . sorted . bam results / STAR_output / TI_align_contigs_1_2 / TI_ *. sortedByCoord . out . bam %%bash mkdir -p results/STAR_output/TI_align_contigs_1_2_merge/ samtools merge -f results/STAR_output/TI_align_contigs_1_2_merge/TI.sorted.bam results/STAR_output/TI_align_contigs_1_2/TI_*.sortedByCoord.out.bam In [26]: Copied! %% bash mkdir - p results / STAR_output / S10_align_contigs_1_2_merge / samtools merge - f results / STAR_output / S10_align_contigs_1_2_merge / S10 . sorted . bam results / STAR_output / S10_align_contigs_1_2 / S10_ *. sortedByCoord . out . bam %%bash mkdir -p results/STAR_output/S10_align_contigs_1_2_merge/ samtools merge -f results/STAR_output/S10_align_contigs_1_2_merge/S10.sorted.bam results/STAR_output/S10_align_contigs_1_2/S10_*.sortedByCoord.out.bam Index both merging outputs. A file in format bam.bai will appear in their respective folders. In [27]: Copied! %% bash samtools index results / STAR_output / S10_align_contigs_1_2_merge / S10 . sorted . bam %%bash samtools index results/STAR_output/S10_align_contigs_1_2_merge/S10.sorted.bam In [28]: Copied! %% bash samtools index results / STAR_output / TI_align_contigs_1_2_merge / TI . sorted . bam %%bash samtools index results/STAR_output/TI_align_contigs_1_2_merge/TI.sorted.bam","title":"RNA-seq mapping"},{"location":"nb/align.html#wrapping-up","text":"In this exercise, you learnt to align various types of data after performing quality control for raw data. We looked at some of the options for the aligners and at how to use some of the basic samtools manipulation programs. 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padding-left: 5px; padding-right: 5px; } td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); RNA-seq differential gene expression This tutorial will cover the steps for performing Differential Gene Expression on the RNA-seq data obtained from Galaxy. At the end of this tutorial you will be able to use R to Perform preliminary analyses of the RNA-seq results Find differentially expressed genes between two conditions using edgeR </div> The present tutorial, like the rest of the course material, is available at our open-source github repository . To use this notebook, use the NGS (R) kernel that contains the packages. Choose it by selecting Kernel -> Change Kernel in the menu on top of the window. A few introductory points to run this notebook (click to show): To use this notebook, use the NGS (R) kernel that contains the packages. Choose it by selecting Kernel -> Change Kernel in the menu on top of the window. In this notebook you will use only R commands On some computers, you might see the result of the commands once they are done running. This means you will wait some time while the computer is crunching, and only afterwards you will see the result of the command you have executed You can run the code in each cell by clicking on the run cell button, or by pressing Shift + Enter . When the code is done running, a small green check sign will appear on the left side You need to run the cells in sequential order, please do not run a cell until the one above finished running and do not skip any cells Each cell contains a short description of the code and the output you should get. Please try not to focus on understanding the code for each command in too much detail, but rather try to focus on the output You can create new code cells by pressing + in the Menu bar above. </details> Load the necessary R libraries In [1]: Copied! library ( VennDiagram ) library ( dplyr ) library ( tibble ) library ( formattable ) library ( mixOmics ) library ( pheatmap ) library ( edgeR ) library(VennDiagram) library(dplyr) library(tibble) library(formattable) library(mixOmics) library(pheatmap) library(edgeR) Warning message: \u201cpackage \u2018VennDiagram\u2019 was built under R version 4.1.3\u201d Loading required package: grid Loading required package: futile.logger Warning message: \u201cpackage \u2018futile.logger\u2019 was built under R version 4.1.3\u201d Warning message: \u201cpackage \u2018dplyr\u2019 was built under R version 4.1.3\u201d Attaching package: \u2018dplyr\u2019 The following objects are masked from \u2018package:stats\u2019: filter, lag The following objects are masked from \u2018package:base\u2019: intersect, setdiff, setequal, union Warning message: \u201cpackage \u2018tibble\u2019 was built under R version 4.1.3\u201d Warning message: \u201cpackage \u2018formattable\u2019 was built under R version 4.1.3\u201d Loading required package: MASS Warning message: \u201cpackage \u2018MASS\u2019 was built under R version 4.1.3\u201d Attaching package: \u2018MASS\u2019 The following object is masked from \u2018package:formattable\u2019: area The following object is masked from \u2018package:dplyr\u2019: select Loading required package: lattice Warning message: \u201cpackage \u2018lattice\u2019 was built under R version 4.1.3\u201d Loading required package: ggplot2 Warning message: \u201cpackage \u2018ggplot2\u2019 was built under R version 4.1.3\u201d Loaded mixOmics 6.17.26 Thank you for using mixOmics! Tutorials: http://mixomics.org Bookdown vignette: https://mixomicsteam.github.io/Bookdown Questions, issues: Follow the prompts at http://mixomics.org/contact-us Cite us: citation('mixOmics') Warning message: \u201cpackage \u2018pheatmap\u2019 was built under R version 4.1.3\u201d Warning message: \u201cpackage \u2018edgeR\u2019 was built under R version 4.1.3\u201d Loading required package: limma Warning message: \u201cpackage \u2018limma\u2019 was built under R version 4.1.3\u201d EdgeR - Data filtering and Normalization \u00b6 We will use the edgeR package to test for differentially expressed genes between our Control and Treatment Samples. You can read more about the edgeR package and its functionalities here . This exercise largely follows their user manual. File processing \u00b6 The data for this exercise comes from the 12 tabular files with Reads per Gene counts generated by STAR Mapping in the raw-data alignment part of this course. We want to create a table where each column is a sample, and the content of the table are the read counts from STAR. We must merge the 12 files with Reads per Gene information into a single file. If you aligned datasets in the first notebook with jupyterlab, then you will find the files using the following command: In [2]: Copied! samples <- sort ( system ( \"find results/STAR_output/*_align_contigs_1_2 -name \\\"*ReadsPerGene.out.tab\\\"\" , intern = TRUE )) print ( samples ) Read_counts <- do.call ( cbind , lapply ( samples , function ( x ) read.delim ( file = x , header = FALSE ))) samples < - sort(system(\"find results/STAR_output/*_align_contigs_1_2 -name \\\"*ReadsPerGene.out.tab\\\"\", intern=TRUE)) print(samples) Read_counts < - do.call(cbind, lapply(samples, function(x) read.delim(file=x, header = FALSE))) [1] \"results/STAR_output/S10_align_contigs_1_2/S10_1_1ReadsPerGene.out.tab\" [2] \"results/STAR_output/S10_align_contigs_1_2/S10_1_2ReadsPerGene.out.tab\" [3] \"results/STAR_output/S10_align_contigs_1_2/S10_1_3ReadsPerGene.out.tab\" [4] \"results/STAR_output/S10_align_contigs_1_2/S10_2_1ReadsPerGene.out.tab\" [5] \"results/STAR_output/S10_align_contigs_1_2/S10_2_2ReadsPerGene.out.tab\" [6] \"results/STAR_output/S10_align_contigs_1_2/S10_2_3ReadsPerGene.out.tab\" [7] \"results/STAR_output/TI_align_contigs_1_2/TI_1_1ReadsPerGene.out.tab\" [8] \"results/STAR_output/TI_align_contigs_1_2/TI_1_2ReadsPerGene.out.tab\" [9] \"results/STAR_output/TI_align_contigs_1_2/TI_1_3ReadsPerGene.out.tab\" [10] \"results/STAR_output/TI_align_contigs_1_2/TI_2_1ReadsPerGene.out.tab\" [11] \"results/STAR_output/TI_align_contigs_1_2/TI_2_2ReadsPerGene.out.tab\" [12] \"results/STAR_output/TI_align_contigs_1_2/TI_2_3ReadsPerGene.out.tab\" If you aligned datasets interactively with Galaxy , then you will need to create the folder tabular_files into the results folder copy the tabular files from STAR into the created folder. Each file must have the sample name and end by ReadsPerGene.out.tab . For example S10_1_1ReadsPerGene.out.tab run the following commands removing the # symbol In [ ]: Copied! #samples <- sort(system(\"find results/tabular_files/ -name \\\"*ReadsPerGene.out.tab\\\"\", intern=TRUE)) #print(samples) #Read_counts <- do.call(cbind, lapply(samples, function(x) read.delim(file=x, header = FALSE))) #samples < - sort(system(\"find results/tabular_files/ -name \\\"*ReadsPerGene.out.tab\\\"\", intern=TRUE)) #print(samples) #Read_counts < - do.call(cbind, lapply(samples, function(x) read.delim(file=x, header = FALSE))) The data frame has genes as rows and samples as columns and stores the gene expression counts (value representing the total number of sequence reads that originated from a particular gene in a sample) for each of the 12 samples. This data frame should have 12 columns and 366 rows. In [3]: Copied! rownames ( Read_counts ) <- Read_counts [, 1 ] Read_counts <- Read_counts [ c ( 5 : nrow ( Read_counts )), c ( seq ( 2 , 46 , by = 4 ))] colnames ( Read_counts ) <- c ( \"S10_1_1\" , \"S10_1_2\" , \"S10_1_3\" , \"S10_2_1\" , \"S10_2_2\" , \"S10_2_3\" , \"TI_1_1\" , \"TI_1_2\" , \"TI_1_3\" , \"TI_2_1\" , \"TI_2_2\" , \"TI_2_3\" ) head ( Read_counts , n = 10 ) dim ( Read_counts ) # dimensions of the data frame rownames(Read_counts) < - Read_counts[,1] Read_counts < - Read_counts[c(5:nrow(Read_counts)), c(seq(2, 46, by=4))] colnames(Read_counts) < - c(\"S10_1_1\", \"S10_1_2\", \"S10_1_3\", \"S10_2_1\", \"S10_2_2\", \"S10_2_3\", \"TI_1_1\", \"TI_1_2\", \"TI_1_3\", \"TI_2_1\", \"TI_2_2\", \"TI_2_3\") head(Read_counts, n=10) dim(Read_counts) # dimensions of the data frame A data.frame: 10 \u00d7 12 S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> g175 143 226 227 217 206 193 234 205 198 221 206 165 g176 17 12 15 8 14 18 29 19 30 6 11 23 g177 0 0 0 0 0 0 0 0 0 0 0 0 g178 0 0 0 0 0 0 0 0 2 2 1 2 g179 0 0 0 0 0 0 0 0 0 0 0 0 g180 2773 5223 4350 4139 3209 4644 1015 1313 889 2675 1023 2565 g181 473 679 583 437 390 450 642 700 697 650 459 410 g182 19 15 20 9 8 4 13 7 8 6 5 4 g183 0 0 0 0 0 0 0 0 0 0 0 0 g184 30 22 50 57 103 83 36 29 18 107 69 89 .list-inline {list-style: none; margin:0; padding: 0} .list-inline>li {display: inline-block} .list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex} 366 12 Import the Metadata table. This file, as its name suggests, contains information about each of the 12 RNA-seq samples, such as the treatment (Condition), genotype and replicate. Note that the order of the rows in the Metadata table should be the same as the columns in the Read_counts file generated above. In [4]: Copied! metadata <- read.csv ( \"../Data/Clover_Data/metadata.csv\" , sep = \";\" , row.names = 1 , stringsAsFactors = TRUE ) metadata metadata < - read.csv(\"../Data/Clover_Data/metadata.csv\", sep =\";\", row.names=1, stringsAsFactors=TRUE) metadata A data.frame: 12 \u00d7 3 Condition Genotype Replicate <fct> <fct> <int> S10_1_1 Control S10 1 S10_1_2 Control S10 2 S10_1_3 Control S10 3 S10_2_1 Treatment S10 1 S10_2_2 Treatment S10 2 S10_2_3 Treatment S10 3 TI_1_1 Control Tienshan 1 TI_1_2 Control Tienshan 2 TI_1_3 Control Tienshan 3 TI_2_1 Treatment Tienshan 1 TI_2_2 Treatment Tienshan 2 TI_2_3 Treatment Tienshan 3 In order to aid the following steps, we will create a Group for each sample (a new column in the metadata) based on the Genotype&Condition of each sample and assign the three replicates to this group. In [5]: Copied! Group <- factor ( paste ( metadata $ Genotype , metadata $ Condition , sep = \"_\" )) metadata <- cbind ( metadata , Group = Group ) metadata Group < - factor(paste(metadata$Genotype, metadata$Condition, sep=\"_\")) metadata < - cbind(metadata,Group=Group) metadata A data.frame: 12 \u00d7 4 Condition Genotype Replicate Group <fct> <fct> <int> <fct> S10_1_1 Control S10 1 S10_Control S10_1_2 Control S10 2 S10_Control S10_1_3 Control S10 3 S10_Control S10_2_1 Treatment S10 1 S10_Treatment S10_2_2 Treatment S10 2 S10_Treatment S10_2_3 Treatment S10 3 S10_Treatment TI_1_1 Control Tienshan 1 Tienshan_Control TI_1_2 Control Tienshan 2 Tienshan_Control TI_1_3 Control Tienshan 3 Tienshan_Control TI_2_1 Treatment Tienshan 1 Tienshan_Treatment TI_2_2 Treatment Tienshan 2 Tienshan_Treatment TI_2_3 Treatment Tienshan 3 Tienshan_Treatment Create the DGEList object \u00b6 We will merge the read counts and the metadata into a list-based data object named DGEList, which can be manipulated as any list object in R. The main components of the DGEList object are the matrix \"counts\" containing our read per gene counts and a data.frame \"samples\" containing the metadata. Note that all the genes with zero counts across all samples were eliminated. In [6]: Copied! DGEList <- DGEList ( Read_counts , remove.zeros = TRUE ) DGEList $ samples $ Condition <- relevel ( metadata $ Condition , ref = \"Control\" ) DGEList $ samples $ Genotype <- metadata $ Genotype DGEList $ samples $ group <- metadata $ Group DGEList DGEList < - DGEList(Read_counts, remove.zeros = TRUE) DGEList$samples$Condition < - relevel(metadata$Condition, ref = \"Control\") DGEList$samples$Genotype < - metadata$Genotype DGEList$samples$group < - metadata$Group DGEList Removing 99 rows with all zero counts $counts A matrix: 267 \u00d7 12 of type int S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g175 143 226 227 217 206 193 234 205 198 221 206 165 g176 17 12 15 8 14 18 29 19 30 6 11 23 g178 0 0 0 0 0 0 0 0 2 2 1 2 g180 2773 5223 4350 4139 3209 4644 1015 1313 889 2675 1023 2565 g181 473 679 583 437 390 450 642 700 697 650 459 410 g182 19 15 20 9 8 4 13 7 8 6 5 4 g184 30 22 50 57 103 83 36 29 18 107 69 89 g185 0 2 0 0 0 0 0 1 0 0 0 1 g186 1 0 0 0 0 0 0 0 0 0 0 0 g187 1 1 2 0 1 1 58 49 57 28 32 29 g188 316 285 417 200 184 322 1276 855 1341 402 809 547 g190 14 18 29 25 17 31 7 6 12 20 7 6 g191 2 2 1 1 0 0 0 0 0 0 0 0 g192 1 1 1 1 1 0 6 10 2 3 8 2 g193 208 231 301 289 161 279 70 61 60 128 160 91 g194 40 41 37 44 17 43 8 10 7 8 105 25 g195 54 85 85 71 54 43 56 54 42 67 68 56 g196 77 120 118 186 152 177 5 9 5 42 20 34 g197 1 6 5 18 14 21 15 44 12 32 16 17 g198 14 27 10 11 2 2 6 6 1 2 2 0 g199 0 0 0 0 0 0 0 0 0 0 1 0 g200 57 80 94 11 8 23 89 47 67 5 47 5 g203 815 1188 1412 1139 1034 1127 911 852 662 892 781 784 g204 250 312 348 277 275 264 125 104 133 119 91 130 g205 109 132 181 140 114 151 89 73 66 78 46 50 g206 0 1 2 7 4 9 4 0 7 0 1 2 g207 0 1 0 0 4 3 1 3 4 4 3 3 g208 0 1 0 0 0 0 0 1 0 1 0 0 g209 0 0 0 0 0 0 0 0 1 0 0 0 g210 52 76 60 12 12 13 61 73 94 54 45 21 \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee g135 0 0 0 1 0 0 5 1 0 2 0 0 g139 0 1 1 0 0 0 12 13 11 4 3 3 g140 0 5 1 0 0 2 5 4 0 0 2 0 g141 177 217 224 131 117 166 117 85 67 80 79 77 g142 488 713 557 241 154 228 983 1057 1023 1075 794 933 g144 3319 3987 4763 2249 1996 2260 4405 3880 4585 5072 3618 3590 g146 293 330 350 913 775 731 108 92 115 431 151 226 g147 6 3 3 0 8 10 1 1 1 0 1 0 g148 19 30 24 15 4 12 23 19 25 10 19 8 g149 35 62 65 57 58 53 114 103 91 92 79 93 g150 263 411 278 752 1378 1300 106 112 70 516 200 508 g151 44 44 50 92 92 84 23 26 25 56 43 31 g152 242 258 294 425 381 521 15 8 8 16 21 15 g153 142 144 181 233 177 233 31 49 50 50 45 57 g155 0 4 2 1 0 0 1 0 3 0 0 1 g156 0 0 0 0 0 0 0 3 2 0 0 1 g157 6 1 3 223 52 152 4 7 4 153 47 155 g158 0 0 0 7 1 3 0 0 1 1 0 0 g161 25 38 18 1 9 2 63 23 43 0 24 6 g162 31 43 38 43 25 28 0 0 0 0 0 0 g163 1 0 12 37 8 18 9 4 8 16 13 15 g164 69 58 97 294 123 168 56 58 60 146 101 135 g165 203 376 594 1343 2062 1610 265 229 214 488 270 490 g167 2 0 0 2 0 0 0 0 0 0 0 0 g168 2 14 6 0 0 5 31 26 38 3 27 10 g169 0 0 1 0 1 1 0 0 0 0 0 0 g170 0 0 0 0 0 0 0 1 0 0 0 0 g171 0 5 0 0 0 5 0 0 0 1 0 0 g172 1 2 2 6 4 3 3 1 2 3 3 0 g173 80 107 120 78 138 121 67 86 69 96 105 105 $samples A data.frame: 12 \u00d7 5 group lib.size norm.factors Condition Genotype <fct> <dbl> <dbl> <fct> <fct> S10_1_1 S10_Control 43614 1 Control S10 S10_1_2 S10_Control 58803 1 Control S10 S10_1_3 S10_Control 62654 1 Control S10 S10_2_1 S10_Treatment 59909 1 Treatment S10 S10_2_2 S10_Treatment 56761 1 Treatment S10 S10_2_3 S10_Treatment 63152 1 Treatment S10 TI_1_1 Tienshan_Control 43387 1 Control Tienshan TI_1_2 Tienshan_Control 38355 1 Control Tienshan TI_1_3 Tienshan_Control 40304 1 Control Tienshan TI_2_1 Tienshan_Treatment 55509 1 Treatment Tienshan TI_2_2 Tienshan_Treatment 39909 1 Treatment Tienshan TI_2_3 Tienshan_Treatment 45008 1 Treatment Tienshan Preliminary data analysis \u00b6 First, we will calculate the \"pseudoCounts\" as log2 of the reads per gene counts. This is not part of the actual differential gene expression analysis but is helpful for data exploration and quality assessment. We will look at a histogram of one of the samples and a boxplot representation of the log2 counts for all the 12 samples. Note that there are many genes with a low number of mapped reads and that there are differences between the average read counts for each library. In [7]: Copied! pseudoCounts <- log2 ( DGEList $ counts + 1 ) head ( pseudoCounts ) hist ( pseudoCounts [ , \"S10_1_1\" ], main = \"\" , xlab = \"Read counts\" ) boxplot ( pseudoCounts , col = \"gray\" , las = 3 , cex.names = 1 ) pseudoCounts < - log2(DGEList$counts + 1) head(pseudoCounts) hist(pseudoCounts[ ,\"S10_1_1\"], main = \"\", xlab = \"Read counts\") boxplot(pseudoCounts, col = \"gray\", las = 3, cex.names = 1) A matrix: 6 \u00d7 12 of type dbl S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g175 7.169925 7.826548 7.832890 7.768184 7.693487 7.599913 7.876517 7.686501 7.636625 7.794416 7.693487 7.375039 g176 4.169925 3.700440 4.000000 3.169925 3.906891 4.247928 4.906891 4.321928 4.954196 2.807355 3.584963 4.584963 g178 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.584963 1.584963 1.000000 1.584963 g180 11.437752 12.350939 12.087131 12.015415 11.648358 12.181463 9.988685 10.359750 9.797662 11.385862 10.000000 11.325305 g181 8.888743 9.409391 9.189825 8.774787 8.611025 8.816984 9.328675 9.453271 9.447083 9.346514 8.845490 8.682995 g182 4.321928 4.000000 4.392317 3.321928 3.169925 2.321928 3.807355 3.000000 3.169925 2.807355 2.584963 2.321928 We can also create a PCA plot of the samples in order to assess the differences between the Genotypes and Conditions, but also between the replicates. In this plot the samples that are similar cluster together, while samples that are different are further apart. In this type of plot, we would expect samples from the same group (the three replicates for each sample) to exhibit a similar gene expression profile thus clustering together while being separated from the other samples. In [8]: Copied! resPCA <- pca ( t ( pseudoCounts ), ncomp = 6 ) plotIndiv ( resPCA , group = metadata $ Genotype , pch = metadata $ Condition , legend = T , legend.title = 'Genotype' , legend.title.pch = 'Condition' , title = 'PCA plot raw counts' , style = 'ggplot2' , size.xlabel = 10 , size.ylabel = 10 ) resPCA < - pca(t(pseudoCounts), ncomp = 6) plotIndiv(resPCA, group = metadata$Genotype, pch=metadata$Condition, legend = T, legend.title = 'Genotype', legend.title.pch = 'Condition', title = 'PCA plot raw counts', style = 'ggplot2', size.xlabel = 10, size.ylabel = 10) Filtering the lowly expressed genes \u00b6 As seen previously, many genes have a low number of read counts in our samples. The genes with very low counts across all libraries provide little evidence for differential expression, thus we should eliminate these genes before the analysis. One of the filtering methods we can use is the \"filterByExpr\" function provided by the edgeR package. By default, this function keeps only the genes that have at least 10 reads per group, but other cutoffs can also be applied. In [9]: Copied! keep <- filterByExpr ( DGEList , group = metadata $ Group ) #create the filter DGEList <- DGEList [ keep , , keep.lib.sizes = FALSE ] #apply the filter to on the DGEList object table ( keep ) #Check the number of genes that passed the filter keep < - filterByExpr(DGEList, group=metadata$Group) #create the filter DGEList < - DGEList[keep, , keep.lib.sizes=FALSE] #apply the filter to on the DGEList object table(keep) #Check the number of genes that passed the filter keep FALSE TRUE 104 163 Normalization \u00b6 As we are working with multiple samples we need to normalize the read counts per gene in order to account for compositional and technical differences between the 12 RNA-seq libraries. For this, we will calculate normalization factors using the trimmed mean of M-values (TMM) method. You can read more about different normalization methods in the user manual. Note that running \"calcNormFactors\" does not change the actual reads per gene counts, it just fills the \"norm.factors\" column in DGEList$samples . These factors will be used to scale the read counts for each library. From the user guide: \"A normalization factor below one indicates that a small number of high count genes are monopolizing the sequencing, causing the counts for other genes to be lower than would be usual given the library size.\" In [10]: Copied! DGEList <- calcNormFactors ( DGEList , method = \"RLE\" ) DGEList $ samples DGEList < - calcNormFactors(DGEList, method=\"RLE\") DGEList$samples A data.frame: 12 \u00d7 5 group lib.size norm.factors Condition Genotype <fct> <dbl> <dbl> <fct> <fct> S10_1_1 S10_Control 43506 1.0897129 Control S10 S10_1_2 S10_Control 58660 1.0385093 Control S10 S10_1_3 S10_Control 62512 1.0362962 Control S10 S10_2_1 S10_Treatment 59741 0.9802860 Treatment S10 S10_2_2 S10_Treatment 56653 0.8418644 Treatment S10 S10_2_3 S10_Treatment 63008 0.8949144 Treatment S10 TI_1_1 Tienshan_Control 43174 1.0618440 Control Tienshan TI_1_2 Tienshan_Control 38203 1.0431678 Control Tienshan TI_1_3 Tienshan_Control 40178 1.0676351 Control Tienshan TI_2_1 Tienshan_Treatment 55391 0.9048196 Treatment Tienshan TI_2_2 Tienshan_Treatment 39810 1.1235077 Treatment Tienshan TI_2_3 Tienshan_Treatment 44910 0.9603761 Treatment Tienshan Normalized counts - Exploratory Data analysis \u00b6 For data analysis purposes normalized log2 counts can be extracted from the DGEList object using the function CPM (counts per million). We will generate the same plots as for the raw counts in order to compare the data before and after normalization. Do the plots for normalized counts look different compared with the plots computed before data filtering and normalization? In [11]: Copied! pseudoNormCounts <- cpm ( DGEList , log = TRUE , prior.count = 1 ) head ( pseudoNormCounts ) hist ( pseudoNormCounts [ , \"S10_1_1\" ], main = \"\" , xlab = \"counts\" ) boxplot ( pseudoNormCounts , col = \"gray\" , las = 3 , cex.names = 1 ) resPCA <- pca ( t ( pseudoNormCounts ), ncomp = 6 ) plotIndiv ( resPCA , group = metadata $ Genotype , pch = metadata $ Condition , legend = T , legend.title = 'Genotype' , legend.title.pch = 'Condition' , title = 'PCA plot normalized counts' , style = 'ggplot2' , size.xlabel = 10 , size.ylabel = 10 ) pseudoNormCounts < - cpm(DGEList, log = TRUE, prior.count = 1) head(pseudoNormCounts) hist(pseudoNormCounts[ ,\"S10_1_1\"], main = \"\", xlab = \"counts\") boxplot(pseudoNormCounts, col = \"gray\", las = 3, cex.names = 1) resPCA < - pca(t(pseudoNormCounts), ncomp = 6) plotIndiv(resPCA, group = metadata$Genotype, pch=metadata$Condition, legend = T, legend.title = 'Genotype', legend.title.pch = 'Condition', title = 'PCA plot normalized counts', style = 'ggplot2', size.xlabel = 10, size.ylabel = 10) A matrix: 6 \u00d7 12 of type dbl S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g175 11.568005 11.864813 11.782957 11.863090 12.083127 11.749272 12.323049 12.334198 12.178544 12.112851 12.175386 11.908906 g176 8.564108 7.760868 7.974199 7.290213 8.292080 8.405708 9.349782 8.956131 9.490388 7.125494 8.054467 9.111569 g180 15.836359 16.387906 16.035457 16.109276 16.038322 16.329943 14.435626 15.008665 14.340400 15.704307 14.482504 15.860317 g181 13.287211 13.446752 13.138771 12.869139 13.000828 12.965820 13.775545 14.101988 13.989757 13.664955 13.327806 13.217591 g182 8.716557 8.056048 8.360376 7.439590 7.551908 6.505541 8.245462 7.611500 7.689402 7.125494 7.041192 6.815835 g184 9.350249 8.573811 9.628852 9.955925 11.089735 10.542877 9.653135 9.546090 8.779819 11.073313 10.609695 11.024686 EdgeR - Testing for Differentially expressed genes (DEGs) \u00b6 We use edgeR for Differential Gene Expression using the GLM method (Generalized linear model) with treatment and genotype effects After we concluded the exploratory data analysis and the filtering and normalization steps we can now start testing for differentially expressed genes between our samples. For this, we first need to define a \"design matrix\" which describes our experimental design. We construct a matrix containing samples as rows and our previously defined groups (S10_Control, S10_Treatment, Tienshan_Control, Tienshan_Treatment) as columns. The samples belonging to each group are assigned a \"1\" in this matrix. In [12]: Copied! design.matrix <- model.matrix ( ~ 0 + Group ) rownames ( design.matrix ) <- colnames ( DGEList ) colnames ( design.matrix ) <- levels ( metadata $ Group ) design.matrix design.matrix < - model.matrix(~0+Group) rownames(design.matrix) < - colnames(DGEList) colnames(design.matrix) < - levels(metadata$Group) design.matrix A matrix: 12 \u00d7 4 of type dbl S10_Control S10_Treatment Tienshan_Control Tienshan_Treatment S10_1_1 1 0 0 0 S10_1_2 1 0 0 0 S10_1_3 1 0 0 0 S10_2_1 0 1 0 0 S10_2_2 0 1 0 0 S10_2_3 0 1 0 0 TI_1_1 0 0 1 0 TI_1_2 0 0 1 0 TI_1_3 0 0 1 0 TI_2_1 0 0 0 1 TI_2_2 0 0 0 1 TI_2_3 0 0 0 1 For easier pairwise comparisons between the groups we can create some \"contrasts\" using the \"makeContrasts\" function. This will tell the program which samples we will like to consider as our \"Control\" and which as our \"Treatment\" groups. The Control groups will be assigned a \"-1\" and the Treatment groups a \"1\". When using multiple Control and Treatment samples in the same contrast we need to devide by the number of samples used as for example in the contrast S10_Tienshan, where we use both S10 and Ti samples. For example, the contrast S10 = S10_Treatment - S10_Control will test for differentially expressed genes between the S10 treatment samples vs the S10 Control samples. In [13]: Copied! contrasts <- makeContrasts ( S10 = S10_Treatment - S10_Control , Tienshan = Tienshan_Treatment - Tienshan_Control , S10_Tienshan = ( S10_Treatment + Tienshan_Treatment ) / 2 - ( S10_Control + Tienshan_Control ) / 2 , levels = design.matrix ) contrasts contrasts < - makeContrasts( S10 = S10_Treatment-S10_Control, Tienshan = Tienshan_Treatment-Tienshan_Control, S10_Tienshan= (S10_Treatment+Tienshan_Treatment)/2 - (S10_Control+Tienshan_Control)/2, levels=design.matrix) contrasts A matrix: 4 \u00d7 3 of type dbl S10 Tienshan S10_Tienshan S10_Control -1 0 -0.5 S10_Treatment 1 0 0.5 Tienshan_Control 0 -1 -0.5 Tienshan_Treatment 0 1 0.5 Any ideas for other contrasts that migth be interesting to explore? The following cell will run two mandatory steps in order to fit a model in edgeR. First, the dispersion needs to be estimated, this is a measure of the degree of inter-library variation for each gene. Afterwards, a QL ( quasi-likelihood) model representing the sample design is fitted using the \"glmQLFit\" function. You can read more about these steps in the edgeR user guide. In [14]: Copied! DGEList <- estimateDisp ( DGEList , design.matrix ) fit <- glmQLFit ( DGEList , design.matrix ) DGEList < - estimateDisp(DGEList, design.matrix) fit < - glmQLFit(DGEList, design.matrix) DEGs for White clover S10 plants Treatment vs Control \u00b6 Find DEGs for White clover S10 plants in Treatment condition compared with the Control condition We will test for differentially expressed genes using the quasi-likelihood F-tests method. This is easily done just by running the \"glmQFTest\" function on the fit model and selecting one of the contrasts we created above (in this case the \"S10\" contrast). Next, we will use the topTags function to extract the information about all the genes. In [15]: Copied! glmqlf_S10 <- glmQLFTest ( fit , contrast = contrasts [, \"S10\" ]) DEG_S10 <- topTags ( glmqlf_S10 , n = nrow ( DGEList $ counts )) DEG_S10 glmqlf_S10 < - glmQLFTest(fit, contrast=contrasts[,\"S10\"]) DEG_S10 < - topTags(glmqlf_S10, n = nrow(DGEList$counts)) DEG_S10 $table A data.frame: 163 \u00d7 5 logFC logCPM F PValue FDR <dbl> <dbl> <dbl> <dbl> <dbl> g39 3.5861702 14.124630 336.66158 5.227331e-10 8.520550e-08 g232 4.0494125 11.950441 157.50997 3.685988e-08 2.591779e-06 g6 2.3225347 12.999541 150.27854 4.770146e-08 2.591779e-06 g350 2.0530029 13.324566 97.56453 4.890245e-07 1.992775e-05 g99 -1.5387874 11.681004 85.17478 9.976659e-07 3.195090e-05 g142 -1.4235276 13.830692 82.52308 1.176107e-06 3.195090e-05 g240 1.3603712 14.708794 79.98114 1.383201e-06 3.220882e-05 g269 -1.4191681 11.834217 75.66563 1.841279e-06 3.751606e-05 g26 2.1254241 11.366827 68.55898 3.047273e-06 5.518949e-05 g129 -5.3324794 8.679594 60.45459 5.738115e-06 9.353127e-05 g272 3.1405978 13.860652 59.08187 6.432977e-06 9.532503e-05 g157 5.3813801 10.414972 57.03712 7.659515e-06 1.040417e-04 g42 -1.9856547 9.962762 53.97164 1.005146e-05 1.260298e-04 g165 2.2666761 13.690016 51.66777 1.243606e-05 1.447913e-04 g101 3.1488142 13.181625 48.21727 1.736838e-05 1.887363e-04 g15 2.7410696 10.684840 47.29143 1.906062e-05 1.941800e-04 g210 -2.2430064 9.993002 46.52627 2.060621e-05 1.975772e-04 g299 -2.3509276 10.984491 45.75893 2.230566e-05 2.019901e-04 g103 2.6810410 10.579777 44.26963 2.609599e-05 2.238762e-04 g144 -0.7963477 16.186987 43.17707 2.936057e-05 2.392886e-04 g77 1.0984806 14.118231 41.46740 3.548119e-05 2.754016e-04 g125 -1.3099534 10.195970 39.85537 4.266319e-05 3.160955e-04 g298 -2.0471647 9.837072 33.49832 9.395967e-05 6.658881e-04 g104 2.4081173 9.219160 38.54240 1.121281e-04 7.615370e-04 g122 -0.6485441 12.457152 30.90261 1.341148e-04 8.744287e-04 g164 1.4415064 11.156457 30.50383 1.419308e-04 8.897970e-04 g146 1.3983889 12.824184 29.92911 1.541544e-04 9.306356e-04 g313 2.2492040 13.614815 28.41728 1.926822e-04 1.121686e-03 g150 1.9650641 13.229706 27.90020 2.083770e-04 1.171222e-03 g13 -0.6522187 13.101579 26.45593 2.608176e-04 1.417109e-03 \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee g96 -1.873792302 8.939397 0.4844512655 0.4999546 0.6041584 g10 0.233134670 14.382992 0.4834790913 0.5003766 0.6041584 g253 0.052426914 13.741219 0.3075042687 0.5896191 0.7066759 g289 -0.212573283 9.390864 0.2887857862 0.6010328 0.7150974 g312 0.093543041 10.849110 0.2666731965 0.6151559 0.7265972 g301 -0.102000599 10.135184 0.2572520652 0.6214023 0.7274503 g221 -0.277095720 8.469909 0.2522121463 0.6248039 0.7274503 g162 -0.145155616 8.517263 0.2278112475 0.6463283 0.7413085 g50 -0.170907352 8.749274 0.2158314533 0.6507284 0.7413085 g203 0.062836598 14.227601 0.2124612164 0.6532634 0.7413085 g88 0.045949497 14.235850 0.2103049178 0.6548983 0.7413085 g355 -0.287897714 8.890754 0.1803234432 0.6787703 0.7630314 g204 -0.055975779 11.947887 0.1408988543 0.7140799 0.7972262 g306 0.054269738 11.294732 0.0923937800 0.7664733 0.8498990 g193 0.049946343 11.682386 0.0642557849 0.8042743 0.8857886 g205 0.037877227 10.986961 0.0473457745 0.8314821 0.9029713 g194 -0.142990391 9.403497 0.0447552162 0.8360811 0.9029713 g362 0.025629967 11.336529 0.0415198735 0.8420254 0.9029713 g315 0.027958374 11.878331 0.0409766489 0.8430470 0.9029713 g180 0.059680256 15.711409 0.0386140448 0.8475743 0.9029713 g282 -0.026492114 11.756078 0.0247991469 0.8775434 0.9288284 g176 -0.061055115 8.587203 0.0144510429 0.9063467 0.9531258 g126 0.061707217 8.311212 0.0024786868 0.9648334 1.0000000 g128 0.063825269 7.671043 0.0019414812 0.9655950 1.0000000 g227 -0.025740630 7.939955 0.0011908008 0.9730516 1.0000000 g121 0.002660852 12.896620 0.0005233398 0.9821327 1.0000000 g284 0.000000000 8.538720 0.0000000000 1.0000000 1.0000000 g116 0.000000000 7.180297 0.0000000000 1.0000000 1.0000000 g117 0.000000000 7.033307 0.0000000000 1.0000000 1.0000000 g118 0.000000000 7.515646 0.0000000000 1.0000000 1.0000000 $adjust.method 'BH' $comparison '-1*S10_Control 1*S10_Treatment' $test 'glm' We have now created a table with certain parameters calculated for each of the genes analysed. logFC represents the base 2 logarithm of the fold change and it shows us how much the expression of the gene has changed between the two conditions. A logFC of 1 means a doubling in the read per gene count between the control and treatment samples. The genes with a logFC higher than 0 are upregulated while the genes with a logFC lower than 0 are downregulated between the control and treatment samples. logCPM represents the average log2-counts-per-million, the abundance of the gene. F - F-statistic. PValue is the raw p-value. FDR (The false discovery rate) represents the adjusted p-value and is calculated using Benjamini and Hochberg's algorithm. It controls the rate of false positive values under multiple testing. Usually, a threshold of under 5% is set for the FDR. The important information for us in this table is stored in the \"logFC\" and the \"FDR\" columns. The top DE genes have small FDR values and large fold changes Many of the genes in the samples are uninteresting for us, as they have a high FDR and/or low logFC values so we cannot consider them as differentially expressed. We will apply a filtering step in order to keep only the statistically significant genes. We will filter out the genes with an FDR higher than 0.05 and an absolute logFC lower than 1. In [16]: Copied! DEG_S10_filtered <- DEG_S10 $ table [ DEG_S10 $ table $ FDR < 0.05 & abs ( DEG_S10 $ table $ logFC ) > 1 ,] #Filtering DEG_S10_filtered <- rownames_to_column ( DEG_S10_filtered ) %>% rename ( gene_ID = rowname ) #Adding the gene_ID column head ( DEG_S10_filtered ) nrow ( DEG_S10_filtered ) # finding the number of genes that passed the filter DEG_S10_filtered < - DEG_S10$table[DEG_S10$table$FDR < 0.05 & abs(DEG_S10$table$logFC) > 1,] #Filtering DEG_S10_filtered < - rownames_to_column(DEG_S10_filtered) %>% rename(gene_ID = rowname) #Adding the gene_ID column head(DEG_S10_filtered) nrow(DEG_S10_filtered) # finding the number of genes that passed the filter A data.frame: 6 \u00d7 6 gene_ID logFC logCPM F PValue FDR <chr> <dbl> <dbl> <dbl> <dbl> <dbl> 1 g39 3.586170 14.12463 336.66158 5.227331e-10 8.520550e-08 2 g232 4.049413 11.95044 157.50997 3.685988e-08 2.591779e-06 3 g6 2.322535 12.99954 150.27854 4.770146e-08 2.591779e-06 4 g350 2.053003 13.32457 97.56453 4.890245e-07 1.992775e-05 5 g99 -1.538787 11.68100 85.17478 9.976659e-07 3.195090e-05 6 g142 -1.423528 13.83069 82.52308 1.176107e-06 3.195090e-05 52 We can also visualize the selected genes by plotting a Smear plot or a Volcano plot. The Genes that passed the filter are coloured in red, and the top 10 genes with the lowest FDR value are labelled with their gene ID. We can see that the majority of the genes analysed are either not statistically significant or have a very small logFC. In [17]: Copied! plotSmear ( glmqlf_S10 , de.tags = rownames ( DEG_S10 $ table )[ which ( DEG_S10 $ table $ FDR < 0.05 & abs ( DEG_S10 $ table $ logFC ) > 1 )]) text ( x = DEG_S10_filtered $ logCPM [ 1 : 10 ], y = DEG_S10_filtered $ logFC [ 1 : 10 ], labels = DEG_S10_filtered $ gene_ID [ 1 : 10 ], cex = 0.7 , pos = 1 ) abline ( h = c ( -1 , 1 ), col = \"blue\" ) plotSmear(glmqlf_S10, de.tags = rownames(DEG_S10$table)[which(DEG_S10$table$FDR < 0.05 & abs(DEG_S10$table$logFC) > 1)]) text(x=DEG_S10_filtered$logCPM[1:10], y=DEG_S10_filtered$logFC[1:10], labels=DEG_S10_filtered$gene_ID[1:10], cex=0.7, pos=1) abline(h = c(-1, 1), col = \"blue\") We can also create a heatmap with the log2 read counts of the selected differentially expressed genes so that we can visualise the differences in normalized counts between the Control and the Treatment samples. The genes are ordered by the FDR value. In [18]: Copied! annot_col <- data.frame ( row.names = colnames ( pseudoNormCounts )[ 1 : 6 ], Condition = c ( rep ( \"Control\" , 3 ), rep ( \"Treatment\" , 3 ))) pheatmap ( as.matrix ( pseudoNormCounts [ DEG_S10_filtered $ gene_ID , c ( 1 : 6 )]), cluster_rows = F , cluster_col = F , annotation_col = annot_col ) annot_col < - data.frame(row.names = colnames(pseudoNormCounts)[1:6], Condition = c(rep(\"Control\", 3),rep( \"Treatment\", 3))) pheatmap(as.matrix(pseudoNormCounts[DEG_S10_filtered$gene_ID,c(1:6)]), cluster_rows = F, cluster_col = F, annotation_col = annot_col) DEGs for White clover Tienshan Treatment vs Control \u00b6 We can do this using the same functions as above and changing the contrast. This time we will directly filter the differentially expressed genes using the same parameters as for the S10 samples. In [19]: Copied! glmqlf_Ti <- glmQLFTest ( fit , contrast = contrasts [, \"Tienshan\" ]) DEG_Ti <- topTags ( glmqlf_Ti , n = nrow ( DGEList $ counts )) DEG_Ti_filtered <- DEG_Ti $ table [ DEG_Ti $ table $ FDR < 0.05 & abs ( DEG_Ti $ table $ logFC ) > 1 ,] DEG_Ti_filtered <- rownames_to_column ( DEG_Ti_filtered ) %>% rename ( gene_ID = rowname ) print ( head ( DEG_Ti_filtered )) print ( \"Nr of differentially expressed genes:\" ) print ( nrow ( DEG_Ti_filtered )) glmqlf_Ti < - glmQLFTest(fit, contrast=contrasts[,\"Tienshan\"]) DEG_Ti < - topTags(glmqlf_Ti, n = nrow(DGEList$counts)) DEG_Ti_filtered < - DEG_Ti$table[DEG_Ti$table$FDR < 0.05 & abs(DEG_Ti$table$logFC) > 1,] DEG_Ti_filtered < - rownames_to_column(DEG_Ti_filtered) %>% rename(gene_ID = rowname) print(head(DEG_Ti_filtered)) print(\"Nr of differentially expressed genes:\") print(nrow(DEG_Ti_filtered)) gene_ID logFC logCPM F PValue FDR 1 g234 -3.348667 13.00417 278.01679 1.547626e-09 2.522630e-07 2 g269 -1.885843 11.83422 127.39949 1.172628e-07 9.556922e-06 3 g26 2.423725 11.36683 74.33983 2.016212e-06 8.910761e-05 4 g101 4.303290 13.18162 73.17140 2.186690e-06 8.910761e-05 5 g272 3.391225 13.86065 66.80131 3.475790e-06 9.840357e-05 6 g99 -1.430395 11.68100 66.25814 3.622217e-06 9.840357e-05 [1] \"Nr of differentially expressed genes:\" [1] 42 We can now plot a Venn diagram with the DEGs for the two genotypes in order to observe the number of common and specific differentially expressed genes between the two genotypes as response to the cold exposure. You can see that a high percentage of the identified genes are common for the two genotypes, while each genotype has also specific genes. In [20]: Copied! vd <- venn.diagram ( x = list ( DEG_S10_filtered $ gene_ID , DEG_Ti_filtered $ gene_ID ), category.names = c ( \"S10\" , \"Tienshan\" ), lwd = 4 , fill = c ( \"cornflowerblue\" , \"yellowgreen\" ), filename = NULL , cat.cex = 1 , cat.fontface = \"bold\" , output = TRUE ) grid.draw ( vd ) vd < - venn.diagram( x = list(DEG_S10_filtered$gene_ID, DEG_Ti_filtered$gene_ID), category.names = c(\"S10\" , \"Tienshan\"), lwd = 4, fill = c(\"cornflowerblue\", \"yellowgreen\"), filename = NULL, cat.cex = 1, cat.fontface = \"bold\", output=TRUE ) grid.draw(vd) DEGs for S10+Tienshan Treatment vs Control w/o genotype effects \u00b6 Until now we tested for DEGs specific for each of the two genotypes under cold treatment. We can also run a test where we ignore the genotype and just test for the differences in the cold response. Consider the counts for both genotypes as a single dataset using the previously created contrast \"S10_Tienshan\". Do the results look different compared with the previous tests? In [21]: Copied! glmqlf_S10_Ti <- glmQLFTest ( fit , contrast = contrasts [, \"S10_Tienshan\" ]) DEG_S10_Ti <- topTags ( glmqlf_S10_Ti , n = nrow ( DGEList $ counts )) DEG_S10_Ti_filtered <- DEG_S10_Ti $ table [ DEG_S10_Ti $ table $ FDR < 0.05 & abs ( DEG_S10_Ti $ table $ logFC ) > 1 ,] DEG_S10_Ti_filtered <- rownames_to_column ( DEG_S10_Ti_filtered ) %>% rename ( gene_ID = rowname ) print ( head ( DEG_S10_Ti_filtered )) print ( \"Nr of differentially expressed genes:\" ) print ( nrow ( DEG_S10_Ti_filtered )) glmqlf_S10_Ti < - glmQLFTest(fit, contrast=contrasts[,\"S10_Tienshan\"]) DEG_S10_Ti < - topTags(glmqlf_S10_Ti, n = nrow(DGEList$counts)) DEG_S10_Ti_filtered < - DEG_S10_Ti$table[DEG_S10_Ti$table$FDR < 0.05 & abs(DEG_S10_Ti$table$logFC) > 1,] DEG_S10_Ti_filtered < - rownames_to_column(DEG_S10_Ti_filtered) %>% rename(gene_ID = rowname) print(head(DEG_S10_Ti_filtered)) print(\"Nr of differentially expressed genes:\") print(nrow(DEG_S10_Ti_filtered)) gene_ID logFC logCPM F PValue FDR 1 g39 2.057128 14.12463 246.6519 3.039491e-09 4.954370e-07 2 g232 3.261008 11.95044 203.4580 8.934889e-09 5.288546e-07 3 g269 -1.652505 11.83422 200.3564 9.733520e-09 5.288546e-07 4 g6 1.714849 12.99954 166.2297 2.740066e-08 1.116577e-06 5 g99 -1.484591 11.68100 150.3246 4.762148e-08 1.552460e-06 6 g26 2.274575 11.36683 142.8625 6.290957e-08 1.623160e-06 [1] \"Nr of differentially expressed genes:\" [1] 52 Plot the results from the 3 tests in a Venn diagram to visualize the number of common and unique genes In [22]: Copied! vd <- venn.diagram ( x = list ( DEG_S10_filtered $ gene_ID , DEG_Ti_filtered $ gene_ID , DEG_S10_Ti_filtered $ gene_ID ), category.names = c ( \"S10\" , \"Tienshan\" , \"S10_Tienshan\" ), lwd = 3 , fill = c ( \"cornflowerblue\" , \"yellowgreen\" , \"thistle3\" ), filename = NULL , cat.cex = 1 , cat.fontface = \"bold\" , output = TRUE ) grid.draw ( vd ) vd < - venn.diagram( x = list(DEG_S10_filtered$gene_ID, DEG_Ti_filtered$gene_ID, DEG_S10_Ti_filtered$gene_ID), category.names = c(\"S10\" , \"Tienshan\", \"S10_Tienshan\"), lwd = 3, fill = c(\"cornflowerblue\", \"yellowgreen\", \"thistle3\"), filename = NULL, cat.cex = 1, cat.fontface = \"bold\", output=TRUE ) grid.draw(vd) Explore DGE results \u00b6 Select the genes which appear only in the analysis using both genotypes for further examination. \u00b6 We can use the \"anti_join\" function from the dplyr package to keep only the unique genes that appear only when using both genotypes. In [23]: Copied! S10_Ti_unique <- anti_join ( DEG_S10_Ti_filtered , DEG_S10_filtered , by = \"gene_ID\" ) %>% anti_join ( DEG_Ti_filtered , by = \"gene_ID\" ) S10_Ti_unique S10_Ti_unique < - anti_join(DEG_S10_Ti_filtered, DEG_S10_filtered, by=\"gene_ID\") %>% anti_join(DEG_Ti_filtered, by=\"gene_ID\") S10_Ti_unique A data.frame: 2 \u00d7 6 gene_ID logFC logCPM F PValue FDR <chr> <dbl> <dbl> <dbl> <dbl> <dbl> g247 -1.412652 8.424523 10.877350 0.006535994 0.01500517 g360 1.180849 7.900076 9.804067 0.013809376 0.02813660 Linking the genes selected as differentially expressed back to the raw read counts \u00b6 How do the counts look for these genes, does it make sense that they are differentially expressed only when using the two genotypes? In [24]: Copied! Read_counts <- rownames_to_column ( Read_counts ) %>% rename ( gene_ID = rowname ) S10_Ti_unique_counts <- inner_join ( S10_Ti_unique [, c ( 1 , 2 , 6 )], Read_counts , by = \"gene_ID\" ) S10_Ti_unique_counts Read_counts < - rownames_to_column(Read_counts) %>% rename(gene_ID = rowname) S10_Ti_unique_counts < - inner_join(S10_Ti_unique[,c(1,2,6)], Read_counts, by=\"gene_ID\") S10_Ti_unique_counts A data.frame: 2 \u00d7 15 gene_ID logFC FDR S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 <chr> <dbl> <dbl> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> g247 -1.412652 0.01500517 4 9 3 1 2 1 42 27 32 8 22 22 g360 1.180849 0.02813660 1 0 1 3 2 3 16 10 16 22 19 22 Adding functional annotation to DEGs \u00b6 Until now we looked only at gene IDs, but we can also add functional annotations to the DEGs. The functional annotations were generated using protein sequences and the EggNOG software. Identifying the molecular function of the differentially expressed genes can help us do a literature survey in order to check if any of the genes discovered have been previously identified as being involved in the cold response. In [25]: Copied! Functional_annotations <- read.delim ( \"../Data/Clover_Data/Functional_Annotations.txt\" ) S10_Ti_unique_FA <- inner_join ( S10_Ti_unique [, c ( 1 , 2 , 6 )], Functional_annotations , by = \"gene_ID\" ) S10_Ti_unique_FA Functional_annotations < - read.delim(\"../Data/Clover_Data/Functional_Annotations.txt\") S10_Ti_unique_FA < - inner_join(S10_Ti_unique[,c(1,2,6)], Functional_annotations, by=\"gene_ID\") S10_Ti_unique_FA A data.frame: 2 \u00d7 4 gene_ID logFC FDR Human.Readable.Description <chr> <dbl> <dbl> <chr> g247 -1.412652 0.01500517 Nitrogen regulatory protein P-II homolog g360 1.180849 0.02813660 Unknown protein Tasks and Questions Based on the results obtained in the analysis so far, would you change the cut-off for the FDR and logFC to be more strict or more permissive? Look back at the raw counts for different FDR and logFC values and set the thresholds as you find appropriate. You can also plot histograms with the FDR and logFC values. In [ ]: Copied! hist ( DEG_S10 $ table $ FDR , main = \"\" , xlab = \"FDR\" , breaks = 200 , xlim = range ( c ( 0 , 0.1 ))) hist(DEG_S10$table$FDR , main = \"\", xlab = \"FDR\", breaks= 200, xlim = range(c(0, 0.1))) In [ ]: Copied! hist ( DEG_S10 $ table $ logFC , main = \"\" , xlab = \"logFC\" , breaks = 50 , xlim = range ( c ( -6 , 6 ))) hist(DEG_S10$table$logFC , main = \"\", xlab = \"logFC\", breaks= 50, xlim = range(c(-6, 6))) Separate the upregulated and downregulated genes for each genotype and append functional annotations to them. Identify the genes that are commonly upregulated in S10 and Tienshan samples and the uniquely upregulated genes for each genotype. Why do you think some of the proteins appear in duplicates? To answer these questions, it may be convenient to save summary tables from R and open them in excel. See the code below for examples of how to do this. Files can be downloaded by right-clicking on the file name. If you are familiar with R functions, you are welcome to use those for counting. In [ ]: Copied! dir.create ( \"DEG_Output_tables\" , showWarnings = FALSE ) dir.create(\"DEG_Output_tables\", showWarnings = FALSE) In [ ]: Copied! #Create the table with the DEGs, Raw counts(just for the Ti samples in this case, change to columns (2:7) for the S10 samples) and Functional annotations DEG_Ti_counts_FA <- inner_join ( DEG_Ti_filtered [, c ( 1 , 2 , 6 )], Read_counts [, c ( 1 , 8 : 13 )], by = \"gene_ID\" ) %>% inner_join ( Functional_annotations , by = \"gene_ID\" ) #Write the table to file write.table ( DEG_Ti_counts_FA , file = \"DEG_Output_tables/Ti_Treatment_Control_DGE.txt\" , quote = FALSE , row.names = FALSE , sep = \"\\t\" ) #Display the first 10 rows of the table head ( DEG_Ti_counts_FA , n = 10 ) #Create the table with the DEGs, Raw counts(just for the Ti samples in this case, change to columns (2:7) for the S10 samples) and Functional annotations DEG_Ti_counts_FA < - inner_join(DEG_Ti_filtered[,c(1, 2, 6)], Read_counts[, c(1, 8:13)], by=\"gene_ID\") %>% inner_join(Functional_annotations, by=\"gene_ID\") #Write the table to file write.table(DEG_Ti_counts_FA, file = \"DEG_Output_tables/Ti_Treatment_Control_DGE.txt\", quote = FALSE, row.names = FALSE, sep = \"\\t\") #Display the first 10 rows of the table head(DEG_Ti_counts_FA, n=10) Example for filtering and counting the Up/Down genes. You can easily filter using the \"filter()\" function just by specifying the dataframe and a logical argument. In [ ]: Copied! DEG_Ti_up <- filter ( DEG_Ti_counts_FA , logFC > 0 ) print ( \"Nr of upregulated genes:\" ) print ( nrow ( DEG_Ti_up )) DEG_Ti_down <- filter ( DEG_Ti_counts_FA , logFC < 0 ) print ( \"Nr of downregulated genes:\" ) print ( nrow ( DEG_Ti_down )) DEG_Ti_up < - filter(DEG_Ti_counts_FA, logFC > 0) print(\"Nr of upregulated genes:\") print(nrow(DEG_Ti_up)) DEG_Ti_down < - filter(DEG_Ti_counts_FA, logFC < 0) print(\"Nr of downregulated genes:\") print(nrow(DEG_Ti_down)) You can use the \"inner_join\" and the \"anti_join\" functions from the dplyr package to select the common and unique genes for each genotype: example_file_joined <- inner_join(file1, file2, by=\"gene_ID\") Wrapping up \ud83c\udf89 \ud83c\udf89 \ud83c\udf89 \u00b6 In this notebook, you have worked with RNA-seq results for two white clover genotypes exposed to one night of cold treatment, aiming to identify genes that change their expression in response to the cold treatment. You have learned to perform exploratory data analysis of raw and normalized RNA-seq read counts. You have also performed differential gene expression using edgeR .","title":"3.3.Bulk RNA data analysis"},{"location":"nb/bulk.html#edger-data-filtering-and-normalization","text":"We will use the edgeR package to test for differentially expressed genes between our Control and Treatment Samples. You can read more about the edgeR package and its functionalities here . This exercise largely follows their user manual.","title":"EdgeR - Data filtering and Normalization"},{"location":"nb/bulk.html#file-processing","text":"The data for this exercise comes from the 12 tabular files with Reads per Gene counts generated by STAR Mapping in the raw-data alignment part of this course. We want to create a table where each column is a sample, and the content of the table are the read counts from STAR. We must merge the 12 files with Reads per Gene information into a single file. If you aligned datasets in the first notebook with jupyterlab, then you will find the files using the following command: In [2]: Copied! samples <- sort ( system ( \"find results/STAR_output/*_align_contigs_1_2 -name \\\"*ReadsPerGene.out.tab\\\"\" , intern = TRUE )) print ( samples ) Read_counts <- do.call ( cbind , lapply ( samples , function ( x ) read.delim ( file = x , header = FALSE ))) samples < - sort(system(\"find results/STAR_output/*_align_contigs_1_2 -name \\\"*ReadsPerGene.out.tab\\\"\", intern=TRUE)) print(samples) Read_counts < - do.call(cbind, lapply(samples, function(x) read.delim(file=x, header = FALSE))) [1] \"results/STAR_output/S10_align_contigs_1_2/S10_1_1ReadsPerGene.out.tab\" [2] \"results/STAR_output/S10_align_contigs_1_2/S10_1_2ReadsPerGene.out.tab\" [3] \"results/STAR_output/S10_align_contigs_1_2/S10_1_3ReadsPerGene.out.tab\" [4] \"results/STAR_output/S10_align_contigs_1_2/S10_2_1ReadsPerGene.out.tab\" [5] \"results/STAR_output/S10_align_contigs_1_2/S10_2_2ReadsPerGene.out.tab\" [6] \"results/STAR_output/S10_align_contigs_1_2/S10_2_3ReadsPerGene.out.tab\" [7] \"results/STAR_output/TI_align_contigs_1_2/TI_1_1ReadsPerGene.out.tab\" [8] \"results/STAR_output/TI_align_contigs_1_2/TI_1_2ReadsPerGene.out.tab\" [9] \"results/STAR_output/TI_align_contigs_1_2/TI_1_3ReadsPerGene.out.tab\" [10] \"results/STAR_output/TI_align_contigs_1_2/TI_2_1ReadsPerGene.out.tab\" [11] \"results/STAR_output/TI_align_contigs_1_2/TI_2_2ReadsPerGene.out.tab\" [12] \"results/STAR_output/TI_align_contigs_1_2/TI_2_3ReadsPerGene.out.tab\" If you aligned datasets interactively with Galaxy , then you will need to create the folder tabular_files into the results folder copy the tabular files from STAR into the created folder. Each file must have the sample name and end by ReadsPerGene.out.tab . For example S10_1_1ReadsPerGene.out.tab run the following commands removing the # symbol In [ ]: Copied! #samples <- sort(system(\"find results/tabular_files/ -name \\\"*ReadsPerGene.out.tab\\\"\", intern=TRUE)) #print(samples) #Read_counts <- do.call(cbind, lapply(samples, function(x) read.delim(file=x, header = FALSE))) #samples < - sort(system(\"find results/tabular_files/ -name \\\"*ReadsPerGene.out.tab\\\"\", intern=TRUE)) #print(samples) #Read_counts < - do.call(cbind, lapply(samples, function(x) read.delim(file=x, header = FALSE))) The data frame has genes as rows and samples as columns and stores the gene expression counts (value representing the total number of sequence reads that originated from a particular gene in a sample) for each of the 12 samples. This data frame should have 12 columns and 366 rows. In [3]: Copied! rownames ( Read_counts ) <- Read_counts [, 1 ] Read_counts <- Read_counts [ c ( 5 : nrow ( Read_counts )), c ( seq ( 2 , 46 , by = 4 ))] colnames ( Read_counts ) <- c ( \"S10_1_1\" , \"S10_1_2\" , \"S10_1_3\" , \"S10_2_1\" , \"S10_2_2\" , \"S10_2_3\" , \"TI_1_1\" , \"TI_1_2\" , \"TI_1_3\" , \"TI_2_1\" , \"TI_2_2\" , \"TI_2_3\" ) head ( Read_counts , n = 10 ) dim ( Read_counts ) # dimensions of the data frame rownames(Read_counts) < - Read_counts[,1] Read_counts < - Read_counts[c(5:nrow(Read_counts)), c(seq(2, 46, by=4))] colnames(Read_counts) < - c(\"S10_1_1\", \"S10_1_2\", \"S10_1_3\", \"S10_2_1\", \"S10_2_2\", \"S10_2_3\", \"TI_1_1\", \"TI_1_2\", \"TI_1_3\", \"TI_2_1\", \"TI_2_2\", \"TI_2_3\") head(Read_counts, n=10) dim(Read_counts) # dimensions of the data frame A data.frame: 10 \u00d7 12 S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> g175 143 226 227 217 206 193 234 205 198 221 206 165 g176 17 12 15 8 14 18 29 19 30 6 11 23 g177 0 0 0 0 0 0 0 0 0 0 0 0 g178 0 0 0 0 0 0 0 0 2 2 1 2 g179 0 0 0 0 0 0 0 0 0 0 0 0 g180 2773 5223 4350 4139 3209 4644 1015 1313 889 2675 1023 2565 g181 473 679 583 437 390 450 642 700 697 650 459 410 g182 19 15 20 9 8 4 13 7 8 6 5 4 g183 0 0 0 0 0 0 0 0 0 0 0 0 g184 30 22 50 57 103 83 36 29 18 107 69 89 .list-inline {list-style: none; margin:0; padding: 0} .list-inline>li {display: inline-block} .list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex} 366 12 Import the Metadata table. This file, as its name suggests, contains information about each of the 12 RNA-seq samples, such as the treatment (Condition), genotype and replicate. Note that the order of the rows in the Metadata table should be the same as the columns in the Read_counts file generated above. In [4]: Copied! metadata <- read.csv ( \"../Data/Clover_Data/metadata.csv\" , sep = \";\" , row.names = 1 , stringsAsFactors = TRUE ) metadata metadata < - read.csv(\"../Data/Clover_Data/metadata.csv\", sep =\";\", row.names=1, stringsAsFactors=TRUE) metadata A data.frame: 12 \u00d7 3 Condition Genotype Replicate <fct> <fct> <int> S10_1_1 Control S10 1 S10_1_2 Control S10 2 S10_1_3 Control S10 3 S10_2_1 Treatment S10 1 S10_2_2 Treatment S10 2 S10_2_3 Treatment S10 3 TI_1_1 Control Tienshan 1 TI_1_2 Control Tienshan 2 TI_1_3 Control Tienshan 3 TI_2_1 Treatment Tienshan 1 TI_2_2 Treatment Tienshan 2 TI_2_3 Treatment Tienshan 3 In order to aid the following steps, we will create a Group for each sample (a new column in the metadata) based on the Genotype&Condition of each sample and assign the three replicates to this group. In [5]: Copied! Group <- factor ( paste ( metadata $ Genotype , metadata $ Condition , sep = \"_\" )) metadata <- cbind ( metadata , Group = Group ) metadata Group < - factor(paste(metadata$Genotype, metadata$Condition, sep=\"_\")) metadata < - cbind(metadata,Group=Group) metadata A data.frame: 12 \u00d7 4 Condition Genotype Replicate Group <fct> <fct> <int> <fct> S10_1_1 Control S10 1 S10_Control S10_1_2 Control S10 2 S10_Control S10_1_3 Control S10 3 S10_Control S10_2_1 Treatment S10 1 S10_Treatment S10_2_2 Treatment S10 2 S10_Treatment S10_2_3 Treatment S10 3 S10_Treatment TI_1_1 Control Tienshan 1 Tienshan_Control TI_1_2 Control Tienshan 2 Tienshan_Control TI_1_3 Control Tienshan 3 Tienshan_Control TI_2_1 Treatment Tienshan 1 Tienshan_Treatment TI_2_2 Treatment Tienshan 2 Tienshan_Treatment TI_2_3 Treatment Tienshan 3 Tienshan_Treatment","title":"File processing"},{"location":"nb/bulk.html#create-the-dgelist-object","text":"We will merge the read counts and the metadata into a list-based data object named DGEList, which can be manipulated as any list object in R. The main components of the DGEList object are the matrix \"counts\" containing our read per gene counts and a data.frame \"samples\" containing the metadata. Note that all the genes with zero counts across all samples were eliminated. In [6]: Copied! DGEList <- DGEList ( Read_counts , remove.zeros = TRUE ) DGEList $ samples $ Condition <- relevel ( metadata $ Condition , ref = \"Control\" ) DGEList $ samples $ Genotype <- metadata $ Genotype DGEList $ samples $ group <- metadata $ Group DGEList DGEList < - DGEList(Read_counts, remove.zeros = TRUE) DGEList$samples$Condition < - relevel(metadata$Condition, ref = \"Control\") DGEList$samples$Genotype < - metadata$Genotype DGEList$samples$group < - metadata$Group DGEList Removing 99 rows with all zero counts $counts A matrix: 267 \u00d7 12 of type int S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g175 143 226 227 217 206 193 234 205 198 221 206 165 g176 17 12 15 8 14 18 29 19 30 6 11 23 g178 0 0 0 0 0 0 0 0 2 2 1 2 g180 2773 5223 4350 4139 3209 4644 1015 1313 889 2675 1023 2565 g181 473 679 583 437 390 450 642 700 697 650 459 410 g182 19 15 20 9 8 4 13 7 8 6 5 4 g184 30 22 50 57 103 83 36 29 18 107 69 89 g185 0 2 0 0 0 0 0 1 0 0 0 1 g186 1 0 0 0 0 0 0 0 0 0 0 0 g187 1 1 2 0 1 1 58 49 57 28 32 29 g188 316 285 417 200 184 322 1276 855 1341 402 809 547 g190 14 18 29 25 17 31 7 6 12 20 7 6 g191 2 2 1 1 0 0 0 0 0 0 0 0 g192 1 1 1 1 1 0 6 10 2 3 8 2 g193 208 231 301 289 161 279 70 61 60 128 160 91 g194 40 41 37 44 17 43 8 10 7 8 105 25 g195 54 85 85 71 54 43 56 54 42 67 68 56 g196 77 120 118 186 152 177 5 9 5 42 20 34 g197 1 6 5 18 14 21 15 44 12 32 16 17 g198 14 27 10 11 2 2 6 6 1 2 2 0 g199 0 0 0 0 0 0 0 0 0 0 1 0 g200 57 80 94 11 8 23 89 47 67 5 47 5 g203 815 1188 1412 1139 1034 1127 911 852 662 892 781 784 g204 250 312 348 277 275 264 125 104 133 119 91 130 g205 109 132 181 140 114 151 89 73 66 78 46 50 g206 0 1 2 7 4 9 4 0 7 0 1 2 g207 0 1 0 0 4 3 1 3 4 4 3 3 g208 0 1 0 0 0 0 0 1 0 1 0 0 g209 0 0 0 0 0 0 0 0 1 0 0 0 g210 52 76 60 12 12 13 61 73 94 54 45 21 \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee g135 0 0 0 1 0 0 5 1 0 2 0 0 g139 0 1 1 0 0 0 12 13 11 4 3 3 g140 0 5 1 0 0 2 5 4 0 0 2 0 g141 177 217 224 131 117 166 117 85 67 80 79 77 g142 488 713 557 241 154 228 983 1057 1023 1075 794 933 g144 3319 3987 4763 2249 1996 2260 4405 3880 4585 5072 3618 3590 g146 293 330 350 913 775 731 108 92 115 431 151 226 g147 6 3 3 0 8 10 1 1 1 0 1 0 g148 19 30 24 15 4 12 23 19 25 10 19 8 g149 35 62 65 57 58 53 114 103 91 92 79 93 g150 263 411 278 752 1378 1300 106 112 70 516 200 508 g151 44 44 50 92 92 84 23 26 25 56 43 31 g152 242 258 294 425 381 521 15 8 8 16 21 15 g153 142 144 181 233 177 233 31 49 50 50 45 57 g155 0 4 2 1 0 0 1 0 3 0 0 1 g156 0 0 0 0 0 0 0 3 2 0 0 1 g157 6 1 3 223 52 152 4 7 4 153 47 155 g158 0 0 0 7 1 3 0 0 1 1 0 0 g161 25 38 18 1 9 2 63 23 43 0 24 6 g162 31 43 38 43 25 28 0 0 0 0 0 0 g163 1 0 12 37 8 18 9 4 8 16 13 15 g164 69 58 97 294 123 168 56 58 60 146 101 135 g165 203 376 594 1343 2062 1610 265 229 214 488 270 490 g167 2 0 0 2 0 0 0 0 0 0 0 0 g168 2 14 6 0 0 5 31 26 38 3 27 10 g169 0 0 1 0 1 1 0 0 0 0 0 0 g170 0 0 0 0 0 0 0 1 0 0 0 0 g171 0 5 0 0 0 5 0 0 0 1 0 0 g172 1 2 2 6 4 3 3 1 2 3 3 0 g173 80 107 120 78 138 121 67 86 69 96 105 105 $samples A data.frame: 12 \u00d7 5 group lib.size norm.factors Condition Genotype <fct> <dbl> <dbl> <fct> <fct> S10_1_1 S10_Control 43614 1 Control S10 S10_1_2 S10_Control 58803 1 Control S10 S10_1_3 S10_Control 62654 1 Control S10 S10_2_1 S10_Treatment 59909 1 Treatment S10 S10_2_2 S10_Treatment 56761 1 Treatment S10 S10_2_3 S10_Treatment 63152 1 Treatment S10 TI_1_1 Tienshan_Control 43387 1 Control Tienshan TI_1_2 Tienshan_Control 38355 1 Control Tienshan TI_1_3 Tienshan_Control 40304 1 Control Tienshan TI_2_1 Tienshan_Treatment 55509 1 Treatment Tienshan TI_2_2 Tienshan_Treatment 39909 1 Treatment Tienshan TI_2_3 Tienshan_Treatment 45008 1 Treatment Tienshan","title":"Create the DGEList object"},{"location":"nb/bulk.html#preliminary-data-analysis","text":"First, we will calculate the \"pseudoCounts\" as log2 of the reads per gene counts. This is not part of the actual differential gene expression analysis but is helpful for data exploration and quality assessment. We will look at a histogram of one of the samples and a boxplot representation of the log2 counts for all the 12 samples. Note that there are many genes with a low number of mapped reads and that there are differences between the average read counts for each library. In [7]: Copied! pseudoCounts <- log2 ( DGEList $ counts + 1 ) head ( pseudoCounts ) hist ( pseudoCounts [ , \"S10_1_1\" ], main = \"\" , xlab = \"Read counts\" ) boxplot ( pseudoCounts , col = \"gray\" , las = 3 , cex.names = 1 ) pseudoCounts < - log2(DGEList$counts + 1) head(pseudoCounts) hist(pseudoCounts[ ,\"S10_1_1\"], main = \"\", xlab = \"Read counts\") boxplot(pseudoCounts, col = \"gray\", las = 3, cex.names = 1) A matrix: 6 \u00d7 12 of type dbl S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g175 7.169925 7.826548 7.832890 7.768184 7.693487 7.599913 7.876517 7.686501 7.636625 7.794416 7.693487 7.375039 g176 4.169925 3.700440 4.000000 3.169925 3.906891 4.247928 4.906891 4.321928 4.954196 2.807355 3.584963 4.584963 g178 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.584963 1.584963 1.000000 1.584963 g180 11.437752 12.350939 12.087131 12.015415 11.648358 12.181463 9.988685 10.359750 9.797662 11.385862 10.000000 11.325305 g181 8.888743 9.409391 9.189825 8.774787 8.611025 8.816984 9.328675 9.453271 9.447083 9.346514 8.845490 8.682995 g182 4.321928 4.000000 4.392317 3.321928 3.169925 2.321928 3.807355 3.000000 3.169925 2.807355 2.584963 2.321928 We can also create a PCA plot of the samples in order to assess the differences between the Genotypes and Conditions, but also between the replicates. In this plot the samples that are similar cluster together, while samples that are different are further apart. In this type of plot, we would expect samples from the same group (the three replicates for each sample) to exhibit a similar gene expression profile thus clustering together while being separated from the other samples. In [8]: Copied! resPCA <- pca ( t ( pseudoCounts ), ncomp = 6 ) plotIndiv ( resPCA , group = metadata $ Genotype , pch = metadata $ Condition , legend = T , legend.title = 'Genotype' , legend.title.pch = 'Condition' , title = 'PCA plot raw counts' , style = 'ggplot2' , size.xlabel = 10 , size.ylabel = 10 ) resPCA < - pca(t(pseudoCounts), ncomp = 6) plotIndiv(resPCA, group = metadata$Genotype, pch=metadata$Condition, legend = T, legend.title = 'Genotype', legend.title.pch = 'Condition', title = 'PCA plot raw counts', style = 'ggplot2', size.xlabel = 10, size.ylabel = 10)","title":"Preliminary data analysis"},{"location":"nb/bulk.html#filtering-the-lowly-expressed-genes","text":"As seen previously, many genes have a low number of read counts in our samples. The genes with very low counts across all libraries provide little evidence for differential expression, thus we should eliminate these genes before the analysis. One of the filtering methods we can use is the \"filterByExpr\" function provided by the edgeR package. By default, this function keeps only the genes that have at least 10 reads per group, but other cutoffs can also be applied. In [9]: Copied! keep <- filterByExpr ( DGEList , group = metadata $ Group ) #create the filter DGEList <- DGEList [ keep , , keep.lib.sizes = FALSE ] #apply the filter to on the DGEList object table ( keep ) #Check the number of genes that passed the filter keep < - filterByExpr(DGEList, group=metadata$Group) #create the filter DGEList < - DGEList[keep, , keep.lib.sizes=FALSE] #apply the filter to on the DGEList object table(keep) #Check the number of genes that passed the filter keep FALSE TRUE 104 163","title":"Filtering the lowly expressed genes"},{"location":"nb/bulk.html#normalization","text":"As we are working with multiple samples we need to normalize the read counts per gene in order to account for compositional and technical differences between the 12 RNA-seq libraries. For this, we will calculate normalization factors using the trimmed mean of M-values (TMM) method. You can read more about different normalization methods in the user manual. Note that running \"calcNormFactors\" does not change the actual reads per gene counts, it just fills the \"norm.factors\" column in DGEList$samples . These factors will be used to scale the read counts for each library. From the user guide: \"A normalization factor below one indicates that a small number of high count genes are monopolizing the sequencing, causing the counts for other genes to be lower than would be usual given the library size.\" In [10]: Copied! DGEList <- calcNormFactors ( DGEList , method = \"RLE\" ) DGEList $ samples DGEList < - calcNormFactors(DGEList, method=\"RLE\") DGEList$samples A data.frame: 12 \u00d7 5 group lib.size norm.factors Condition Genotype <fct> <dbl> <dbl> <fct> <fct> S10_1_1 S10_Control 43506 1.0897129 Control S10 S10_1_2 S10_Control 58660 1.0385093 Control S10 S10_1_3 S10_Control 62512 1.0362962 Control S10 S10_2_1 S10_Treatment 59741 0.9802860 Treatment S10 S10_2_2 S10_Treatment 56653 0.8418644 Treatment S10 S10_2_3 S10_Treatment 63008 0.8949144 Treatment S10 TI_1_1 Tienshan_Control 43174 1.0618440 Control Tienshan TI_1_2 Tienshan_Control 38203 1.0431678 Control Tienshan TI_1_3 Tienshan_Control 40178 1.0676351 Control Tienshan TI_2_1 Tienshan_Treatment 55391 0.9048196 Treatment Tienshan TI_2_2 Tienshan_Treatment 39810 1.1235077 Treatment Tienshan TI_2_3 Tienshan_Treatment 44910 0.9603761 Treatment Tienshan","title":"Normalization"},{"location":"nb/bulk.html#normalized-counts-exploratory-data-analysis","text":"For data analysis purposes normalized log2 counts can be extracted from the DGEList object using the function CPM (counts per million). We will generate the same plots as for the raw counts in order to compare the data before and after normalization. Do the plots for normalized counts look different compared with the plots computed before data filtering and normalization? In [11]: Copied! pseudoNormCounts <- cpm ( DGEList , log = TRUE , prior.count = 1 ) head ( pseudoNormCounts ) hist ( pseudoNormCounts [ , \"S10_1_1\" ], main = \"\" , xlab = \"counts\" ) boxplot ( pseudoNormCounts , col = \"gray\" , las = 3 , cex.names = 1 ) resPCA <- pca ( t ( pseudoNormCounts ), ncomp = 6 ) plotIndiv ( resPCA , group = metadata $ Genotype , pch = metadata $ Condition , legend = T , legend.title = 'Genotype' , legend.title.pch = 'Condition' , title = 'PCA plot normalized counts' , style = 'ggplot2' , size.xlabel = 10 , size.ylabel = 10 ) pseudoNormCounts < - cpm(DGEList, log = TRUE, prior.count = 1) head(pseudoNormCounts) hist(pseudoNormCounts[ ,\"S10_1_1\"], main = \"\", xlab = \"counts\") boxplot(pseudoNormCounts, col = \"gray\", las = 3, cex.names = 1) resPCA < - pca(t(pseudoNormCounts), ncomp = 6) plotIndiv(resPCA, group = metadata$Genotype, pch=metadata$Condition, legend = T, legend.title = 'Genotype', legend.title.pch = 'Condition', title = 'PCA plot normalized counts', style = 'ggplot2', size.xlabel = 10, size.ylabel = 10) A matrix: 6 \u00d7 12 of type dbl S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 g175 11.568005 11.864813 11.782957 11.863090 12.083127 11.749272 12.323049 12.334198 12.178544 12.112851 12.175386 11.908906 g176 8.564108 7.760868 7.974199 7.290213 8.292080 8.405708 9.349782 8.956131 9.490388 7.125494 8.054467 9.111569 g180 15.836359 16.387906 16.035457 16.109276 16.038322 16.329943 14.435626 15.008665 14.340400 15.704307 14.482504 15.860317 g181 13.287211 13.446752 13.138771 12.869139 13.000828 12.965820 13.775545 14.101988 13.989757 13.664955 13.327806 13.217591 g182 8.716557 8.056048 8.360376 7.439590 7.551908 6.505541 8.245462 7.611500 7.689402 7.125494 7.041192 6.815835 g184 9.350249 8.573811 9.628852 9.955925 11.089735 10.542877 9.653135 9.546090 8.779819 11.073313 10.609695 11.024686","title":"Normalized counts - Exploratory Data analysis"},{"location":"nb/bulk.html#edger-testing-for-differentially-expressed-genes-degs","text":"We use edgeR for Differential Gene Expression using the GLM method (Generalized linear model) with treatment and genotype effects After we concluded the exploratory data analysis and the filtering and normalization steps we can now start testing for differentially expressed genes between our samples. For this, we first need to define a \"design matrix\" which describes our experimental design. We construct a matrix containing samples as rows and our previously defined groups (S10_Control, S10_Treatment, Tienshan_Control, Tienshan_Treatment) as columns. The samples belonging to each group are assigned a \"1\" in this matrix. In [12]: Copied! design.matrix <- model.matrix ( ~ 0 + Group ) rownames ( design.matrix ) <- colnames ( DGEList ) colnames ( design.matrix ) <- levels ( metadata $ Group ) design.matrix design.matrix < - model.matrix(~0+Group) rownames(design.matrix) < - colnames(DGEList) colnames(design.matrix) < - levels(metadata$Group) design.matrix A matrix: 12 \u00d7 4 of type dbl S10_Control S10_Treatment Tienshan_Control Tienshan_Treatment S10_1_1 1 0 0 0 S10_1_2 1 0 0 0 S10_1_3 1 0 0 0 S10_2_1 0 1 0 0 S10_2_2 0 1 0 0 S10_2_3 0 1 0 0 TI_1_1 0 0 1 0 TI_1_2 0 0 1 0 TI_1_3 0 0 1 0 TI_2_1 0 0 0 1 TI_2_2 0 0 0 1 TI_2_3 0 0 0 1 For easier pairwise comparisons between the groups we can create some \"contrasts\" using the \"makeContrasts\" function. This will tell the program which samples we will like to consider as our \"Control\" and which as our \"Treatment\" groups. The Control groups will be assigned a \"-1\" and the Treatment groups a \"1\". When using multiple Control and Treatment samples in the same contrast we need to devide by the number of samples used as for example in the contrast S10_Tienshan, where we use both S10 and Ti samples. For example, the contrast S10 = S10_Treatment - S10_Control will test for differentially expressed genes between the S10 treatment samples vs the S10 Control samples. In [13]: Copied! contrasts <- makeContrasts ( S10 = S10_Treatment - S10_Control , Tienshan = Tienshan_Treatment - Tienshan_Control , S10_Tienshan = ( S10_Treatment + Tienshan_Treatment ) / 2 - ( S10_Control + Tienshan_Control ) / 2 , levels = design.matrix ) contrasts contrasts < - makeContrasts( S10 = S10_Treatment-S10_Control, Tienshan = Tienshan_Treatment-Tienshan_Control, S10_Tienshan= (S10_Treatment+Tienshan_Treatment)/2 - (S10_Control+Tienshan_Control)/2, levels=design.matrix) contrasts A matrix: 4 \u00d7 3 of type dbl S10 Tienshan S10_Tienshan S10_Control -1 0 -0.5 S10_Treatment 1 0 0.5 Tienshan_Control 0 -1 -0.5 Tienshan_Treatment 0 1 0.5 Any ideas for other contrasts that migth be interesting to explore? The following cell will run two mandatory steps in order to fit a model in edgeR. First, the dispersion needs to be estimated, this is a measure of the degree of inter-library variation for each gene. Afterwards, a QL ( quasi-likelihood) model representing the sample design is fitted using the \"glmQLFit\" function. You can read more about these steps in the edgeR user guide. In [14]: Copied! DGEList <- estimateDisp ( DGEList , design.matrix ) fit <- glmQLFit ( DGEList , design.matrix ) DGEList < - estimateDisp(DGEList, design.matrix) fit < - glmQLFit(DGEList, design.matrix)","title":"EdgeR - Testing for Differentially expressed genes (DEGs)"},{"location":"nb/bulk.html#degs-for-white-clover-s10-plants-treatment-vs-control","text":"Find DEGs for White clover S10 plants in Treatment condition compared with the Control condition We will test for differentially expressed genes using the quasi-likelihood F-tests method. This is easily done just by running the \"glmQFTest\" function on the fit model and selecting one of the contrasts we created above (in this case the \"S10\" contrast). Next, we will use the topTags function to extract the information about all the genes. In [15]: Copied! glmqlf_S10 <- glmQLFTest ( fit , contrast = contrasts [, \"S10\" ]) DEG_S10 <- topTags ( glmqlf_S10 , n = nrow ( DGEList $ counts )) DEG_S10 glmqlf_S10 < - glmQLFTest(fit, contrast=contrasts[,\"S10\"]) DEG_S10 < - topTags(glmqlf_S10, n = nrow(DGEList$counts)) DEG_S10 $table A data.frame: 163 \u00d7 5 logFC logCPM F PValue FDR <dbl> <dbl> <dbl> <dbl> <dbl> g39 3.5861702 14.124630 336.66158 5.227331e-10 8.520550e-08 g232 4.0494125 11.950441 157.50997 3.685988e-08 2.591779e-06 g6 2.3225347 12.999541 150.27854 4.770146e-08 2.591779e-06 g350 2.0530029 13.324566 97.56453 4.890245e-07 1.992775e-05 g99 -1.5387874 11.681004 85.17478 9.976659e-07 3.195090e-05 g142 -1.4235276 13.830692 82.52308 1.176107e-06 3.195090e-05 g240 1.3603712 14.708794 79.98114 1.383201e-06 3.220882e-05 g269 -1.4191681 11.834217 75.66563 1.841279e-06 3.751606e-05 g26 2.1254241 11.366827 68.55898 3.047273e-06 5.518949e-05 g129 -5.3324794 8.679594 60.45459 5.738115e-06 9.353127e-05 g272 3.1405978 13.860652 59.08187 6.432977e-06 9.532503e-05 g157 5.3813801 10.414972 57.03712 7.659515e-06 1.040417e-04 g42 -1.9856547 9.962762 53.97164 1.005146e-05 1.260298e-04 g165 2.2666761 13.690016 51.66777 1.243606e-05 1.447913e-04 g101 3.1488142 13.181625 48.21727 1.736838e-05 1.887363e-04 g15 2.7410696 10.684840 47.29143 1.906062e-05 1.941800e-04 g210 -2.2430064 9.993002 46.52627 2.060621e-05 1.975772e-04 g299 -2.3509276 10.984491 45.75893 2.230566e-05 2.019901e-04 g103 2.6810410 10.579777 44.26963 2.609599e-05 2.238762e-04 g144 -0.7963477 16.186987 43.17707 2.936057e-05 2.392886e-04 g77 1.0984806 14.118231 41.46740 3.548119e-05 2.754016e-04 g125 -1.3099534 10.195970 39.85537 4.266319e-05 3.160955e-04 g298 -2.0471647 9.837072 33.49832 9.395967e-05 6.658881e-04 g104 2.4081173 9.219160 38.54240 1.121281e-04 7.615370e-04 g122 -0.6485441 12.457152 30.90261 1.341148e-04 8.744287e-04 g164 1.4415064 11.156457 30.50383 1.419308e-04 8.897970e-04 g146 1.3983889 12.824184 29.92911 1.541544e-04 9.306356e-04 g313 2.2492040 13.614815 28.41728 1.926822e-04 1.121686e-03 g150 1.9650641 13.229706 27.90020 2.083770e-04 1.171222e-03 g13 -0.6522187 13.101579 26.45593 2.608176e-04 1.417109e-03 \u22ee \u22ee \u22ee \u22ee \u22ee \u22ee g96 -1.873792302 8.939397 0.4844512655 0.4999546 0.6041584 g10 0.233134670 14.382992 0.4834790913 0.5003766 0.6041584 g253 0.052426914 13.741219 0.3075042687 0.5896191 0.7066759 g289 -0.212573283 9.390864 0.2887857862 0.6010328 0.7150974 g312 0.093543041 10.849110 0.2666731965 0.6151559 0.7265972 g301 -0.102000599 10.135184 0.2572520652 0.6214023 0.7274503 g221 -0.277095720 8.469909 0.2522121463 0.6248039 0.7274503 g162 -0.145155616 8.517263 0.2278112475 0.6463283 0.7413085 g50 -0.170907352 8.749274 0.2158314533 0.6507284 0.7413085 g203 0.062836598 14.227601 0.2124612164 0.6532634 0.7413085 g88 0.045949497 14.235850 0.2103049178 0.6548983 0.7413085 g355 -0.287897714 8.890754 0.1803234432 0.6787703 0.7630314 g204 -0.055975779 11.947887 0.1408988543 0.7140799 0.7972262 g306 0.054269738 11.294732 0.0923937800 0.7664733 0.8498990 g193 0.049946343 11.682386 0.0642557849 0.8042743 0.8857886 g205 0.037877227 10.986961 0.0473457745 0.8314821 0.9029713 g194 -0.142990391 9.403497 0.0447552162 0.8360811 0.9029713 g362 0.025629967 11.336529 0.0415198735 0.8420254 0.9029713 g315 0.027958374 11.878331 0.0409766489 0.8430470 0.9029713 g180 0.059680256 15.711409 0.0386140448 0.8475743 0.9029713 g282 -0.026492114 11.756078 0.0247991469 0.8775434 0.9288284 g176 -0.061055115 8.587203 0.0144510429 0.9063467 0.9531258 g126 0.061707217 8.311212 0.0024786868 0.9648334 1.0000000 g128 0.063825269 7.671043 0.0019414812 0.9655950 1.0000000 g227 -0.025740630 7.939955 0.0011908008 0.9730516 1.0000000 g121 0.002660852 12.896620 0.0005233398 0.9821327 1.0000000 g284 0.000000000 8.538720 0.0000000000 1.0000000 1.0000000 g116 0.000000000 7.180297 0.0000000000 1.0000000 1.0000000 g117 0.000000000 7.033307 0.0000000000 1.0000000 1.0000000 g118 0.000000000 7.515646 0.0000000000 1.0000000 1.0000000 $adjust.method 'BH' $comparison '-1*S10_Control 1*S10_Treatment' $test 'glm' We have now created a table with certain parameters calculated for each of the genes analysed. logFC represents the base 2 logarithm of the fold change and it shows us how much the expression of the gene has changed between the two conditions. A logFC of 1 means a doubling in the read per gene count between the control and treatment samples. The genes with a logFC higher than 0 are upregulated while the genes with a logFC lower than 0 are downregulated between the control and treatment samples. logCPM represents the average log2-counts-per-million, the abundance of the gene. F - F-statistic. PValue is the raw p-value. FDR (The false discovery rate) represents the adjusted p-value and is calculated using Benjamini and Hochberg's algorithm. It controls the rate of false positive values under multiple testing. Usually, a threshold of under 5% is set for the FDR. The important information for us in this table is stored in the \"logFC\" and the \"FDR\" columns. The top DE genes have small FDR values and large fold changes Many of the genes in the samples are uninteresting for us, as they have a high FDR and/or low logFC values so we cannot consider them as differentially expressed. We will apply a filtering step in order to keep only the statistically significant genes. We will filter out the genes with an FDR higher than 0.05 and an absolute logFC lower than 1. In [16]: Copied! DEG_S10_filtered <- DEG_S10 $ table [ DEG_S10 $ table $ FDR < 0.05 & abs ( DEG_S10 $ table $ logFC ) > 1 ,] #Filtering DEG_S10_filtered <- rownames_to_column ( DEG_S10_filtered ) %>% rename ( gene_ID = rowname ) #Adding the gene_ID column head ( DEG_S10_filtered ) nrow ( DEG_S10_filtered ) # finding the number of genes that passed the filter DEG_S10_filtered < - DEG_S10$table[DEG_S10$table$FDR < 0.05 & abs(DEG_S10$table$logFC) > 1,] #Filtering DEG_S10_filtered < - rownames_to_column(DEG_S10_filtered) %>% rename(gene_ID = rowname) #Adding the gene_ID column head(DEG_S10_filtered) nrow(DEG_S10_filtered) # finding the number of genes that passed the filter A data.frame: 6 \u00d7 6 gene_ID logFC logCPM F PValue FDR <chr> <dbl> <dbl> <dbl> <dbl> <dbl> 1 g39 3.586170 14.12463 336.66158 5.227331e-10 8.520550e-08 2 g232 4.049413 11.95044 157.50997 3.685988e-08 2.591779e-06 3 g6 2.322535 12.99954 150.27854 4.770146e-08 2.591779e-06 4 g350 2.053003 13.32457 97.56453 4.890245e-07 1.992775e-05 5 g99 -1.538787 11.68100 85.17478 9.976659e-07 3.195090e-05 6 g142 -1.423528 13.83069 82.52308 1.176107e-06 3.195090e-05 52 We can also visualize the selected genes by plotting a Smear plot or a Volcano plot. The Genes that passed the filter are coloured in red, and the top 10 genes with the lowest FDR value are labelled with their gene ID. We can see that the majority of the genes analysed are either not statistically significant or have a very small logFC. In [17]: Copied! plotSmear ( glmqlf_S10 , de.tags = rownames ( DEG_S10 $ table )[ which ( DEG_S10 $ table $ FDR < 0.05 & abs ( DEG_S10 $ table $ logFC ) > 1 )]) text ( x = DEG_S10_filtered $ logCPM [ 1 : 10 ], y = DEG_S10_filtered $ logFC [ 1 : 10 ], labels = DEG_S10_filtered $ gene_ID [ 1 : 10 ], cex = 0.7 , pos = 1 ) abline ( h = c ( -1 , 1 ), col = \"blue\" ) plotSmear(glmqlf_S10, de.tags = rownames(DEG_S10$table)[which(DEG_S10$table$FDR < 0.05 & abs(DEG_S10$table$logFC) > 1)]) text(x=DEG_S10_filtered$logCPM[1:10], y=DEG_S10_filtered$logFC[1:10], labels=DEG_S10_filtered$gene_ID[1:10], cex=0.7, pos=1) abline(h = c(-1, 1), col = \"blue\") We can also create a heatmap with the log2 read counts of the selected differentially expressed genes so that we can visualise the differences in normalized counts between the Control and the Treatment samples. The genes are ordered by the FDR value. In [18]: Copied! annot_col <- data.frame ( row.names = colnames ( pseudoNormCounts )[ 1 : 6 ], Condition = c ( rep ( \"Control\" , 3 ), rep ( \"Treatment\" , 3 ))) pheatmap ( as.matrix ( pseudoNormCounts [ DEG_S10_filtered $ gene_ID , c ( 1 : 6 )]), cluster_rows = F , cluster_col = F , annotation_col = annot_col ) annot_col < - data.frame(row.names = colnames(pseudoNormCounts)[1:6], Condition = c(rep(\"Control\", 3),rep( \"Treatment\", 3))) pheatmap(as.matrix(pseudoNormCounts[DEG_S10_filtered$gene_ID,c(1:6)]), cluster_rows = F, cluster_col = F, annotation_col = annot_col)","title":"DEGs for White clover S10 plants Treatment vs Control"},{"location":"nb/bulk.html#degs-for-white-clover-tienshan-treatment-vs-control","text":"We can do this using the same functions as above and changing the contrast. This time we will directly filter the differentially expressed genes using the same parameters as for the S10 samples. In [19]: Copied! glmqlf_Ti <- glmQLFTest ( fit , contrast = contrasts [, \"Tienshan\" ]) DEG_Ti <- topTags ( glmqlf_Ti , n = nrow ( DGEList $ counts )) DEG_Ti_filtered <- DEG_Ti $ table [ DEG_Ti $ table $ FDR < 0.05 & abs ( DEG_Ti $ table $ logFC ) > 1 ,] DEG_Ti_filtered <- rownames_to_column ( DEG_Ti_filtered ) %>% rename ( gene_ID = rowname ) print ( head ( DEG_Ti_filtered )) print ( \"Nr of differentially expressed genes:\" ) print ( nrow ( DEG_Ti_filtered )) glmqlf_Ti < - glmQLFTest(fit, contrast=contrasts[,\"Tienshan\"]) DEG_Ti < - topTags(glmqlf_Ti, n = nrow(DGEList$counts)) DEG_Ti_filtered < - DEG_Ti$table[DEG_Ti$table$FDR < 0.05 & abs(DEG_Ti$table$logFC) > 1,] DEG_Ti_filtered < - rownames_to_column(DEG_Ti_filtered) %>% rename(gene_ID = rowname) print(head(DEG_Ti_filtered)) print(\"Nr of differentially expressed genes:\") print(nrow(DEG_Ti_filtered)) gene_ID logFC logCPM F PValue FDR 1 g234 -3.348667 13.00417 278.01679 1.547626e-09 2.522630e-07 2 g269 -1.885843 11.83422 127.39949 1.172628e-07 9.556922e-06 3 g26 2.423725 11.36683 74.33983 2.016212e-06 8.910761e-05 4 g101 4.303290 13.18162 73.17140 2.186690e-06 8.910761e-05 5 g272 3.391225 13.86065 66.80131 3.475790e-06 9.840357e-05 6 g99 -1.430395 11.68100 66.25814 3.622217e-06 9.840357e-05 [1] \"Nr of differentially expressed genes:\" [1] 42 We can now plot a Venn diagram with the DEGs for the two genotypes in order to observe the number of common and specific differentially expressed genes between the two genotypes as response to the cold exposure. You can see that a high percentage of the identified genes are common for the two genotypes, while each genotype has also specific genes. In [20]: Copied! vd <- venn.diagram ( x = list ( DEG_S10_filtered $ gene_ID , DEG_Ti_filtered $ gene_ID ), category.names = c ( \"S10\" , \"Tienshan\" ), lwd = 4 , fill = c ( \"cornflowerblue\" , \"yellowgreen\" ), filename = NULL , cat.cex = 1 , cat.fontface = \"bold\" , output = TRUE ) grid.draw ( vd ) vd < - venn.diagram( x = list(DEG_S10_filtered$gene_ID, DEG_Ti_filtered$gene_ID), category.names = c(\"S10\" , \"Tienshan\"), lwd = 4, fill = c(\"cornflowerblue\", \"yellowgreen\"), filename = NULL, cat.cex = 1, cat.fontface = \"bold\", output=TRUE ) grid.draw(vd)","title":"DEGs for White clover Tienshan Treatment vs Control"},{"location":"nb/bulk.html#degs-for-s10tienshan-treatment-vs-control-wo-genotype-effects","text":"Until now we tested for DEGs specific for each of the two genotypes under cold treatment. We can also run a test where we ignore the genotype and just test for the differences in the cold response. Consider the counts for both genotypes as a single dataset using the previously created contrast \"S10_Tienshan\". Do the results look different compared with the previous tests? In [21]: Copied! glmqlf_S10_Ti <- glmQLFTest ( fit , contrast = contrasts [, \"S10_Tienshan\" ]) DEG_S10_Ti <- topTags ( glmqlf_S10_Ti , n = nrow ( DGEList $ counts )) DEG_S10_Ti_filtered <- DEG_S10_Ti $ table [ DEG_S10_Ti $ table $ FDR < 0.05 & abs ( DEG_S10_Ti $ table $ logFC ) > 1 ,] DEG_S10_Ti_filtered <- rownames_to_column ( DEG_S10_Ti_filtered ) %>% rename ( gene_ID = rowname ) print ( head ( DEG_S10_Ti_filtered )) print ( \"Nr of differentially expressed genes:\" ) print ( nrow ( DEG_S10_Ti_filtered )) glmqlf_S10_Ti < - glmQLFTest(fit, contrast=contrasts[,\"S10_Tienshan\"]) DEG_S10_Ti < - topTags(glmqlf_S10_Ti, n = nrow(DGEList$counts)) DEG_S10_Ti_filtered < - DEG_S10_Ti$table[DEG_S10_Ti$table$FDR < 0.05 & abs(DEG_S10_Ti$table$logFC) > 1,] DEG_S10_Ti_filtered < - rownames_to_column(DEG_S10_Ti_filtered) %>% rename(gene_ID = rowname) print(head(DEG_S10_Ti_filtered)) print(\"Nr of differentially expressed genes:\") print(nrow(DEG_S10_Ti_filtered)) gene_ID logFC logCPM F PValue FDR 1 g39 2.057128 14.12463 246.6519 3.039491e-09 4.954370e-07 2 g232 3.261008 11.95044 203.4580 8.934889e-09 5.288546e-07 3 g269 -1.652505 11.83422 200.3564 9.733520e-09 5.288546e-07 4 g6 1.714849 12.99954 166.2297 2.740066e-08 1.116577e-06 5 g99 -1.484591 11.68100 150.3246 4.762148e-08 1.552460e-06 6 g26 2.274575 11.36683 142.8625 6.290957e-08 1.623160e-06 [1] \"Nr of differentially expressed genes:\" [1] 52 Plot the results from the 3 tests in a Venn diagram to visualize the number of common and unique genes In [22]: Copied! vd <- venn.diagram ( x = list ( DEG_S10_filtered $ gene_ID , DEG_Ti_filtered $ gene_ID , DEG_S10_Ti_filtered $ gene_ID ), category.names = c ( \"S10\" , \"Tienshan\" , \"S10_Tienshan\" ), lwd = 3 , fill = c ( \"cornflowerblue\" , \"yellowgreen\" , \"thistle3\" ), filename = NULL , cat.cex = 1 , cat.fontface = \"bold\" , output = TRUE ) grid.draw ( vd ) vd < - venn.diagram( x = list(DEG_S10_filtered$gene_ID, DEG_Ti_filtered$gene_ID, DEG_S10_Ti_filtered$gene_ID), category.names = c(\"S10\" , \"Tienshan\", \"S10_Tienshan\"), lwd = 3, fill = c(\"cornflowerblue\", \"yellowgreen\", \"thistle3\"), filename = NULL, cat.cex = 1, cat.fontface = \"bold\", output=TRUE ) grid.draw(vd)","title":"DEGs for S10+Tienshan Treatment vs Control w/o genotype effects"},{"location":"nb/bulk.html#explore-dge-results","text":"","title":"Explore DGE results"},{"location":"nb/bulk.html#select-the-genes-which-appear-only-in-the-analysis-using-both-genotypes-for-further-examination","text":"We can use the \"anti_join\" function from the dplyr package to keep only the unique genes that appear only when using both genotypes. In [23]: Copied! S10_Ti_unique <- anti_join ( DEG_S10_Ti_filtered , DEG_S10_filtered , by = \"gene_ID\" ) %>% anti_join ( DEG_Ti_filtered , by = \"gene_ID\" ) S10_Ti_unique S10_Ti_unique < - anti_join(DEG_S10_Ti_filtered, DEG_S10_filtered, by=\"gene_ID\") %>% anti_join(DEG_Ti_filtered, by=\"gene_ID\") S10_Ti_unique A data.frame: 2 \u00d7 6 gene_ID logFC logCPM F PValue FDR <chr> <dbl> <dbl> <dbl> <dbl> <dbl> g247 -1.412652 8.424523 10.877350 0.006535994 0.01500517 g360 1.180849 7.900076 9.804067 0.013809376 0.02813660","title":"Select the genes which appear only in the analysis using both genotypes for further examination."},{"location":"nb/bulk.html#linking-the-genes-selected-as-differentially-expressed-back-to-the-raw-read-counts","text":"How do the counts look for these genes, does it make sense that they are differentially expressed only when using the two genotypes? In [24]: Copied! Read_counts <- rownames_to_column ( Read_counts ) %>% rename ( gene_ID = rowname ) S10_Ti_unique_counts <- inner_join ( S10_Ti_unique [, c ( 1 , 2 , 6 )], Read_counts , by = \"gene_ID\" ) S10_Ti_unique_counts Read_counts < - rownames_to_column(Read_counts) %>% rename(gene_ID = rowname) S10_Ti_unique_counts < - inner_join(S10_Ti_unique[,c(1,2,6)], Read_counts, by=\"gene_ID\") S10_Ti_unique_counts A data.frame: 2 \u00d7 15 gene_ID logFC FDR S10_1_1 S10_1_2 S10_1_3 S10_2_1 S10_2_2 S10_2_3 TI_1_1 TI_1_2 TI_1_3 TI_2_1 TI_2_2 TI_2_3 <chr> <dbl> <dbl> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> g247 -1.412652 0.01500517 4 9 3 1 2 1 42 27 32 8 22 22 g360 1.180849 0.02813660 1 0 1 3 2 3 16 10 16 22 19 22","title":"Linking the genes selected as differentially expressed back to the raw read counts"},{"location":"nb/bulk.html#adding-functional-annotation-to-degs","text":"Until now we looked only at gene IDs, but we can also add functional annotations to the DEGs. The functional annotations were generated using protein sequences and the EggNOG software. Identifying the molecular function of the differentially expressed genes can help us do a literature survey in order to check if any of the genes discovered have been previously identified as being involved in the cold response. In [25]: Copied! Functional_annotations <- read.delim ( \"../Data/Clover_Data/Functional_Annotations.txt\" ) S10_Ti_unique_FA <- inner_join ( S10_Ti_unique [, c ( 1 , 2 , 6 )], Functional_annotations , by = \"gene_ID\" ) S10_Ti_unique_FA Functional_annotations < - read.delim(\"../Data/Clover_Data/Functional_Annotations.txt\") S10_Ti_unique_FA < - inner_join(S10_Ti_unique[,c(1,2,6)], Functional_annotations, by=\"gene_ID\") S10_Ti_unique_FA A data.frame: 2 \u00d7 4 gene_ID logFC FDR Human.Readable.Description <chr> <dbl> <dbl> <chr> g247 -1.412652 0.01500517 Nitrogen regulatory protein P-II homolog g360 1.180849 0.02813660 Unknown protein Tasks and Questions Based on the results obtained in the analysis so far, would you change the cut-off for the FDR and logFC to be more strict or more permissive? Look back at the raw counts for different FDR and logFC values and set the thresholds as you find appropriate. You can also plot histograms with the FDR and logFC values. In [ ]: Copied! hist ( DEG_S10 $ table $ FDR , main = \"\" , xlab = \"FDR\" , breaks = 200 , xlim = range ( c ( 0 , 0.1 ))) hist(DEG_S10$table$FDR , main = \"\", xlab = \"FDR\", breaks= 200, xlim = range(c(0, 0.1))) In [ ]: Copied! hist ( DEG_S10 $ table $ logFC , main = \"\" , xlab = \"logFC\" , breaks = 50 , xlim = range ( c ( -6 , 6 ))) hist(DEG_S10$table$logFC , main = \"\", xlab = \"logFC\", breaks= 50, xlim = range(c(-6, 6))) Separate the upregulated and downregulated genes for each genotype and append functional annotations to them. Identify the genes that are commonly upregulated in S10 and Tienshan samples and the uniquely upregulated genes for each genotype. Why do you think some of the proteins appear in duplicates? To answer these questions, it may be convenient to save summary tables from R and open them in excel. See the code below for examples of how to do this. Files can be downloaded by right-clicking on the file name. If you are familiar with R functions, you are welcome to use those for counting. In [ ]: Copied! dir.create ( \"DEG_Output_tables\" , showWarnings = FALSE ) dir.create(\"DEG_Output_tables\", showWarnings = FALSE) In [ ]: Copied! #Create the table with the DEGs, Raw counts(just for the Ti samples in this case, change to columns (2:7) for the S10 samples) and Functional annotations DEG_Ti_counts_FA <- inner_join ( DEG_Ti_filtered [, c ( 1 , 2 , 6 )], Read_counts [, c ( 1 , 8 : 13 )], by = \"gene_ID\" ) %>% inner_join ( Functional_annotations , by = \"gene_ID\" ) #Write the table to file write.table ( DEG_Ti_counts_FA , file = \"DEG_Output_tables/Ti_Treatment_Control_DGE.txt\" , quote = FALSE , row.names = FALSE , sep = \"\\t\" ) #Display the first 10 rows of the table head ( DEG_Ti_counts_FA , n = 10 ) #Create the table with the DEGs, Raw counts(just for the Ti samples in this case, change to columns (2:7) for the S10 samples) and Functional annotations DEG_Ti_counts_FA < - inner_join(DEG_Ti_filtered[,c(1, 2, 6)], Read_counts[, c(1, 8:13)], by=\"gene_ID\") %>% inner_join(Functional_annotations, by=\"gene_ID\") #Write the table to file write.table(DEG_Ti_counts_FA, file = \"DEG_Output_tables/Ti_Treatment_Control_DGE.txt\", quote = FALSE, row.names = FALSE, sep = \"\\t\") #Display the first 10 rows of the table head(DEG_Ti_counts_FA, n=10) Example for filtering and counting the Up/Down genes. You can easily filter using the \"filter()\" function just by specifying the dataframe and a logical argument. In [ ]: Copied! DEG_Ti_up <- filter ( DEG_Ti_counts_FA , logFC > 0 ) print ( \"Nr of upregulated genes:\" ) print ( nrow ( DEG_Ti_up )) DEG_Ti_down <- filter ( DEG_Ti_counts_FA , logFC < 0 ) print ( \"Nr of downregulated genes:\" ) print ( nrow ( DEG_Ti_down )) DEG_Ti_up < - filter(DEG_Ti_counts_FA, logFC > 0) print(\"Nr of upregulated genes:\") print(nrow(DEG_Ti_up)) DEG_Ti_down < - filter(DEG_Ti_counts_FA, logFC < 0) print(\"Nr of downregulated genes:\") print(nrow(DEG_Ti_down)) You can use the \"inner_join\" and the \"anti_join\" functions from the dplyr package to select the common and unique genes for each genotype: example_file_joined <- inner_join(file1, file2, by=\"gene_ID\")","title":"Adding functional annotation to DEGs"},{"location":"nb/bulk.html#wrapping-up","text":"In this notebook, you have worked with RNA-seq results for two white clover genotypes exposed to one night of cold treatment, aiming to identify genes that change their expression in response to the cold treatment. 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We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Variant calling and VCF processing This tutorial will cover the steps for performing Variant calling and working on the resulting VCF file format. We will be using the Hifi and RNA-seq mapping data obtained from Galaxy. At the end of this tutorial you will be able to: call variants using alignment files extract data from VCF files using Python filter and count variants </div> The present tutorial, like the rest of the course material, is available at our open-source github repository . To use this notebook, use the NGS (python) kernel that contains the packages. Choose it by selecting Kernel -> Change Kernel in the menu on top of the window. A few introductory points to run this notebook (click to show): To use this notebook, use the NGS (python) kernel that contains the packages. Choose it by selecting Kernel -> Change Kernel in the menu on top of the window. In this notebook you will use both bash and python commands On some computers, you might see the result of the commands once they are done running. This means you will wait some time while the computer is crunching, and only afterwards you will see the result of the command you have executed You can run the code in each cell by clicking on the run cell button, or by pressing Shift + Enter . When the code is done running, a small green check sign will appear on the left side You need to run the cells in sequential order, please do not run a cell until the one above finished running and do not skip any cells Each cell contains a short description of the code and the output you should get. Please try not to focus on understanding the code for each command in too much detail, but rather try to focus on the output You can create new code cells by pressing + in the Menu bar above. </details> Import the necessary Python libraries: In [1]: Copied! import pandas import allel import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt import seaborn as sns import pandas import allel import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt import seaborn as sns Some of the commands used in the course are functions we implement to simplify reading the code of this course. Mostly, those are commands requiring lines of code that would not add anything to your learning curve (management of plots, trivial calculations, few management of the notebook layout). However, you are free to look at the code into the file Scripts/pythonScripts.py and to reuse our code in your own work (citing our course). In [2]: Copied! % run ../ Scripts / pythonScripts . py %run ../Scripts/pythonScripts.py Create a folder for the data necessary to VCF data analysis In [3]: Copied! %% bash mkdir - p Data_for_VCF_analysis %%bash mkdir -p Data_for_VCF_analysis Use the bam files generated from the alignment of raw reads \u00b6 We will use the data created during the alignment session of the course. You had two possibilities: using the interactive Galaxy platform as explained in the course webpage . In this case you will need to upload the some of the created data into the folder Data_for_VCF_analysis , renaming the files as follows: Hifi reads mapped to Contig1: HIFI_contig_1.bam Hifi reads mapped to Contig2: HIFI_contig_2.bam Hifi reads mapped to Contig 1 and 2: HIFI_contig_1_2_asm20.bam (you can of course choose the one with the map-pb alignment option from the alignment notebook) S10 RNAseq reads mapped to Contig 1 and 2 (After merging the bam files): RNA_S10_merged.bam Ti RNAseq reads mapped to Contig 1 and 2 (After merging the bam files): RNA_TI_merged.bam using the notebook 01_align.ipynb . In that case, you just need to run the command below, linking the data in the results folder into the folder Data_for_VCF_analysis . In [4]: Copied! %% bash ln - fs ` realpath results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam ` Data_for_VCF_analysis / HIFI_contig_1 . bam ln - fs ` realpath results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam ` Data_for_VCF_analysis / HIFI_contig_2 . bam ln - fs ` realpath results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam ` Data_for_VCF_analysis / HIFI_contig_1_2 . bam ln - fs ` realpath results / STAR_output / S10_align_contigs_1_2_merge / S10 . sorted . bam ` Data_for_VCF_analysis / RNA_S10_merged . bam ln - fs ` realpath results / STAR_output / TI_align_contigs_1_2_merge / TI . sorted . bam ` Data_for_VCF_analysis / RNA_TI_merged . bam %%bash ln -fs `realpath results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam` Data_for_VCF_analysis/HIFI_contig_1.bam ln -fs `realpath results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam` Data_for_VCF_analysis/HIFI_contig_2.bam ln -fs `realpath results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam` Data_for_VCF_analysis/HIFI_contig_1_2.bam ln -fs `realpath results/STAR_output/S10_align_contigs_1_2_merge/S10.sorted.bam` Data_for_VCF_analysis/RNA_S10_merged.bam ln -fs `realpath results/STAR_output/TI_align_contigs_1_2_merge/TI.sorted.bam` Data_for_VCF_analysis/RNA_TI_merged.bam Variant calling \u00b6 We will use a tool named bcftools to find variants in the alignment files ( .bam ) relative to the reference genome, in this case, the file DNA_Contig1_2.fasta . In this way we can identify positions in the genome where the sequence of the aligned reads differ from the reference genome they were mapped to. There are multiple types of genetic variation , e.g. SNPs (single nucleotide polymorphism), indels (insertions or deletions) and structural variations (such as chromosomal rearrangements). Here we will only be working with SNPs, thus we will use the terms variants and SNPs interchangeably. The called variants will be stored in a specific format file, the Variant Call Format ( VCF ). When looking at an alignment files in IGV you should easily recognize the SNP positions. You can see in the figure below that at the marked positions all the mapped reads have a mismatch compared to the reference. You will be calling two types of SNPs in this notebook. First, we will find subgenome SNPs using the Hifi reads mapped to white clover Contig1 and Contig2. These are positions in the contigs that are different between the two subgenomes of white clover. Secondly, we will find SNPs that differentiate between the sequenced white clover S10 (the reference genome) and Ti genotype, using RNA-seq read mapping. Create a folder that will store all the files generated in this notebook. In [5]: Copied! ! mkdir - p results / VCF_Files !mkdir -p results/VCF_Files Call subgenome SNPs using the Hifi alignments and the reference genome \u00b6 We will use the bcftools software to call SNPs from alignment files. bcftools is a toolkit for variant calling and manipulating VCF files. If you are interested, you can find all the functionalities here http://samtools.github.io/bcftools/bcftools.html#call . We mainly need two commands for this step, first bcftools mpileup which takes as input the alignment and the genome reference files, followed by bcftools call to produce VCF files. The cell below will generate 3 VCF files stored in the folder results/VCF_Files using the Hifi alignment files uploaded from Galaxy. In [6]: Copied! ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f ../ Data / Clover_Data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / HIFI_contig_1 . bam | bcftools call - mv - Ov - o results / VCF_Files / HIFI_Contig_1 . vcf ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f ../ Data / Clover_Data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / HIFI_contig_2 . bam | bcftools call - mv - Ov - o results / VCF_Files / HIFI_Contig_2 . vcf ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f ../ Data / Clover_Data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / HIFI_contig_1_2 . bam | bcftools call - mv - Ov - o results / VCF_Files / HIFI_Contig_1_2 . vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f ../Data/Clover_Data/DNA_Contig1_2.fasta Data_for_VCF_analysis/HIFI_contig_1.bam | bcftools call -mv -Ov -o results/VCF_Files/HIFI_Contig_1.vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f ../Data/Clover_Data/DNA_Contig1_2.fasta Data_for_VCF_analysis/HIFI_contig_2.bam | bcftools call -mv -Ov -o results/VCF_Files/HIFI_Contig_2.vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f ../Data/Clover_Data/DNA_Contig1_2.fasta Data_for_VCF_analysis/HIFI_contig_1_2.bam | bcftools call -mv -Ov -o results/VCF_Files/HIFI_Contig_1_2.vcf Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 TASK Inspect the VCF files using IGV, comparing them to the specific alignment BAM files. (You can download the VCF files to your computer from the results/VCF_Files folder) Are there any problematic positions that may not represent true SNPs? How can you use the HiFi reads aligned to Contigs1+2 identify potential problems? Hint: Try scrolling to the ends of the contigs. Call SNPs using the two RNA-seq genotypes, S10 and Ti \u00b6 We will repeat the same step as above this time using the RNA-seq alignment files for the two white clover genotypes (S10 and Ti). These commands will produce another two VCF files, stored in the same folder. In [7]: Copied! ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f reference_data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / RNA_S10_merged . bam | bcftools call - mv - Ov - o results / VCF_Files / RNA_S10_merged . vcf ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f reference_data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / RNA_TI_merged . bam | bcftools call - mv - Ov - o results / VCF_Files / RNA_TI_merged . vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f reference_data/DNA_Contig1_2.fasta Data_for_VCF_analysis/RNA_S10_merged.bam | bcftools call -mv -Ov -o results/VCF_Files/RNA_S10_merged.vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f reference_data/DNA_Contig1_2.fasta Data_for_VCF_analysis/RNA_TI_merged.bam | bcftools call -mv -Ov -o results/VCF_Files/RNA_TI_merged.vcf [mpileup] 1 samples in 1 input files Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] maximum number of reads per input file set to -d 250 Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 TASK Inspect one of the VCFs and the corresponding alignment files in IGV. Is it relevant to filter for false positives in this case, and what parameters would you look further into? VCF files processing and filtering \u00b6 Take a look at what one of the previously generated VCF files contains by running the cell below. A VCF file begins with the meta-information lines starting with ## followed by one header line, starting with # . Next, there are the data lines, each describing a genetic variant for a certain position in the genome. Each data line contains multiple tab-delimited columns with information about the variant. There are 9 fixed columns, labelled \u201cCHROM\u201d, \u201cPOS\u201d, \u201cID\u201d, \u201cREF\u201d, \u201cALT\u201d, \u201cQUAL\u201d, \u201cFILTER\u201d, \u201cINFO\u201d and \u201cFORMAT\u201d. Find more about the content of those columns in this wikipedia page . In the sample column you can find the most likely genotype of the sample. For diploid organisms, there will be a 0 value for reference allele and 1 for the alternate allele. Figure: possible genotypes in the data. For example, here we read the first 50 rows from the RNAseq_S10_merged.vcf file. You can see that the file format follows the same pattern as explained above. In [8]: Copied! ! cat results / VCF_Files / RNA_S10_merged . vcf | head - 50 !cat results/VCF_Files/RNA_S10_merged.vcf | head -50 ##fileformat=VCFv4.2 ##FILTER=<ID=PASS,Description=\"All filters passed\"> ##bcftoolsVersion=1.16+htslib-1.17 ##bcftoolsCommand=mpileup --threads 4 -Ou --skip-indels -f reference_data/DNA_Contig1_2.fasta Data_for_VCF_analysis/RNA_S10_merged.bam ##reference=file://reference_data/DNA_Contig1_2.fasta ##contig=<ID=1,length=1031631> ##contig=<ID=2,length=1057923> ##ALT=<ID=*,Description=\"Represents allele(s) other than observed.\"> ##INFO=<ID=INDEL,Number=0,Type=Flag,Description=\"Indicates that the variant is an INDEL.\"> ##INFO=<ID=IDV,Number=1,Type=Integer,Description=\"Maximum number of raw reads supporting an indel\"> ##INFO=<ID=IMF,Number=1,Type=Float,Description=\"Maximum fraction of raw reads supporting an indel\"> ##INFO=<ID=DP,Number=1,Type=Integer,Description=\"Raw read depth\"> ##INFO=<ID=VDB,Number=1,Type=Float,Description=\"Variant Distance Bias for filtering splice-site artefacts in RNA-seq data (bigger is better)\",Version=\"3\"> ##INFO=<ID=RPBZ,Number=1,Type=Float,Description=\"Mann-Whitney U-z test of Read Position Bias (closer to 0 is better)\"> ##INFO=<ID=MQBZ,Number=1,Type=Float,Description=\"Mann-Whitney U-z test of Mapping Quality Bias (closer to 0 is better)\"> ##INFO=<ID=BQBZ,Number=1,Type=Float,Description=\"Mann-Whitney U-z test of Base Quality Bias (closer to 0 is better)\"> ##INFO=<ID=MQSBZ,Number=1,Type=Float,Description=\"Mann-Whitney U-z test of Mapping Quality vs Strand Bias (closer to 0 is better)\"> ##INFO=<ID=NMBZ,Number=1,Type=Float,Description=\"Mann-Whitney U-z test of Number of Mismatches within supporting reads (closer to 0 is better)\"> ##INFO=<ID=SCBZ,Number=1,Type=Float,Description=\"Mann-Whitney U-z test of Soft-Clip Length Bias (closer to 0 is better)\"> ##INFO=<ID=FS,Number=1,Type=Float,Description=\"Phred-scaled p-value using Fisher's exact test to detect strand bias\"> ##INFO=<ID=SGB,Number=1,Type=Float,Description=\"Segregation based metric.\"> ##INFO=<ID=MQ0F,Number=1,Type=Float,Description=\"Fraction of MQ0 reads (smaller is better)\"> ##FORMAT=<ID=PL,Number=G,Type=Integer,Description=\"List of Phred-scaled genotype likelihoods\"> ##FORMAT=<ID=GT,Number=1,Type=String,Description=\"Genotype\"> ##INFO=<ID=AC,Number=A,Type=Integer,Description=\"Allele count in genotypes for each ALT allele, in the same order as listed\"> ##INFO=<ID=AN,Number=1,Type=Integer,Description=\"Total number of alleles in called genotypes\"> ##INFO=<ID=DP4,Number=4,Type=Integer,Description=\"Number of high-quality ref-forward , ref-reverse, alt-forward and alt-reverse bases\"> ##INFO=<ID=MQ,Number=1,Type=Integer,Description=\"Average mapping quality\"> ##bcftools_callVersion=1.16+htslib-1.17 ##bcftools_callCommand=call -mv -Ov -o results/VCF_Files/RNA_S10_merged.vcf; Date=Mon Jun 19 10:48:17 2023 #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT Data_for_VCF_analysis/RNA_S10_merged.bam 1 33218 . G A 65.3262 . DP=88;VDB=0.0923504;SGB=-0.692914;RPBZ=3.99376;MQBZ=-1.86933;MQSBZ=-0.901828;BQBZ=-1.8368;SCBZ=2.6559;FS=0;MQ0F=0;AC=1;AN=2;DP4=15,13,16,9;MQ=19 GT:PL 0/1:98,0,138 1 33222 . C G 69.1967 . DP=56;VDB=4.89382e-06;SGB=-0.692352;RPBZ=3.47069;MQBZ=-1.73535;MQSBZ=-0.222384;BQBZ=-1.36705;SCBZ=2.16882;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,13,12,9;MQ=19 GT:PL 0/1:102,0,138 1 33223 . T G 61.1405 . DP=56;VDB=2.99077e-05;SGB=-0.692067;RPBZ=3.40644;MQBZ=-1.80665;MQSBZ=-0.222384;BQBZ=0.830878;SCBZ=2.63256;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,14,12,8;MQ=19 GT:PL 0/1:94,0,132 1 33224 . T G 65.1016 . DP=58;VDB=4.85907e-06;SGB=-0.692352;RPBZ=3.67489;MQBZ=-1.78978;MQSBZ=-0.159627;BQBZ=-1.43699;SCBZ=2.60016;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,15,12,9;MQ=19 GT:PL 0/1:98,0,144 1 33225 . G T 64.0746 . DP=58;VDB=4.36523e-06;SGB=-0.692352;RPBZ=3.67489;MQBZ=-1.78978;MQSBZ=-0.159627;BQBZ=-2.60059;SCBZ=2.60016;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,15,12,9;MQ=19 GT:PL 0/1:97,0,144 1 33227 . T A 64.067 . DP=59;VDB=4.09235e-06;SGB=-0.692352;RPBZ=4.13393;MQBZ=-1.81637;MQSBZ=-0.18317;BQBZ=-1.48941;SCBZ=2.64866;FS=0;MQ0F=0;AC=1;AN=2;DP4=19,15,12,9;MQ=19 GT:PL 0/1:97,0,144 1 49149 . C T 30.4183 . DP=4;VDB=0.0058656;SGB=-0.556411;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,0,4;MQ=20 GT:PL 1/1:60,12,0 1 83968 . C G 78.0347 . DP=10;VDB=5.52906e-06;SGB=-0.662043;RPBZ=-3;MQBZ=0;MQSBZ=0;BQBZ=-0.333333;SCBZ=1.77187;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 83970 . T C 78.0347 . DP=10;VDB=8.25997e-06;SGB=-0.662043;RPBZ=-1.79284;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=1.77187;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 83972 . G T 78.0347 . DP=10;VDB=8.25997e-06;SGB=-0.662043;RPBZ=-1.79284;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=1.57628;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 83975 . G A 78.0347 . DP=10;VDB=1.22485e-05;SGB=-0.662043;RPBZ=-1.79284;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=1.57628;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 174956 . C T 10.7923 . DP=2;VDB=0.14;SGB=-0.453602;MQSBZ=0;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,1,1;MQ=20 GT:PL 1/1:40,6,0 1 175456 . A G 7.30814 . DP=2;VDB=0.02;SGB=-0.453602;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,2,0;MQ=20 GT:PL 1/1:36,6,0 1 211292 . A T 16.7427 . DP=5;VDB=0.0058656;SGB=-0.556411;RPBZ=-2;MQBZ=0;MQSBZ=0;BQBZ=1.22474;SCBZ=2;FS=0;MQ0F=0;AC=1;AN=2;DP4=0,1,4,0;MQ=20 GT:PL 0/1:47,0,5 1 321659 . G A 13.5525 . DP=7;VDB=0.107235;SGB=-0.511536;RPBZ=1.34164;MQBZ=0;MQSBZ=0;BQBZ=1.73205;SCBZ=0.942809;FS=0;MQ0F=0;AC=1;AN=2;DP4=1,0,1,2;MQ=20 GT:PL 0/1:45,0,8 1 321661 . A C 13.5525 . DP=7;VDB=0.107235;SGB=-0.511536;RPBZ=1.34164;MQBZ=0;MQSBZ=0;BQBZ=1.73205;SCBZ=0.942809;FS=0;MQ0F=0;AC=1;AN=2;DP4=1,0,1,2;MQ=20 GT:PL 0/1:45,0,8 1 323166 . G A 7.30814 . DP=4;VDB=0.1;SGB=-0.453602;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,0,2;MQ=20 GT:PL 1/1:36,6,0 1 331785 . A T 7.30814 . DP=2;VDB=0.02;SGB=-0.453602;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,2,0;MQ=20 GT:PL 1/1:36,6,0 1 331890 . C T 39.4149 . DP=6;VDB=0.100421;SGB=-0.556411;MQSBZ=0;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,3,1;MQ=20 GT:PL 1/1:69,12,0 Next, in order to generate plots of the metrics stored in the VCF files, we will first import the VCF files in Python using the scikit-allel package. This package contains a series of function helpful for processing variant calling files. For now, we will import only the VCF files generated using the Hifi data alignments. You can ignore the warning. In [9]: Copied! #Import the VCF files Hifi_Contig_1_vcf = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_1.vcf' , fields = '*' ) Hifi_Contig_2_vcf = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_2.vcf' , fields = '*' ) Hifi_Contig_1_2_vcf = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_1_2.vcf' , fields = '*' ) #Import the VCF files Hifi_Contig_1_vcf = allel.read_vcf('results/VCF_Files/HIFI_Contig_1.vcf', fields='*') Hifi_Contig_2_vcf = allel.read_vcf('results/VCF_Files/HIFI_Contig_2.vcf', fields='*') Hifi_Contig_1_2_vcf = allel.read_vcf('results/VCF_Files/HIFI_Contig_1_2.vcf', fields='*') /work/Course_Env/lib/python3.10/site-packages/allel/io/vcf_read.py:1732: UserWarning: invalid INFO header: '##INFO=<ID=VDB,Number=1,Type=Float,Description=\"Variant Distance Bias for filtering splice-site artefacts in RNA-seq data (bigger is better)\",Version=\"3\">\\n' warnings.warn('invalid INFO header: %r' % header) Let's check the number of SNPs in each of the three VCF files. The first two VCF files contain subgenome SNPs. You can see that we identified a have a high number of polymorphisms between the two subgenomes. On the other hand the Hifi_Contig1_2_vcf file contains what we would call false positives, as we called SNPs using the same Hifi reads that were used for assembling the reference genome. As expected the number of SNPs in this file is much smaller. In [10]: Copied! print ( \"Number of SNPs in Hifi Contig 1 vcf file:\" , Hifi_Contig_1_vcf [ 'variants/POS' ] . size ) print ( \"Number of SNPs in Hifi Contig 2 vcf file:\" , Hifi_Contig_2_vcf [ 'variants/POS' ] . size ) print ( \"Number of SNPs in Hifi Contig 1 and 2 vcf file:\" , Hifi_Contig_1_2_vcf [ 'variants/POS' ] . size ) print(\"Number of SNPs in Hifi Contig 1 vcf file:\", Hifi_Contig_1_vcf['variants/POS'].size) print(\"Number of SNPs in Hifi Contig 2 vcf file:\", Hifi_Contig_2_vcf['variants/POS'].size) print(\"Number of SNPs in Hifi Contig 1 and 2 vcf file:\", Hifi_Contig_1_2_vcf['variants/POS'].size) Number of SNPs in Hifi Contig 1 vcf file: 31981 Number of SNPs in Hifi Contig 2 vcf file: 30823 Number of SNPs in Hifi Contig 1 and 2 vcf file: 1309 As an overview of the distribution of SNPs, we can plot the density of SNPs across the contigs. We have defined a function called plot_variant_density in the file ../Scripts/pythonScripts.sh that does exactly that. We use that to plot the SNP density across the contigs in the three VCF files in bins of 5000 bp. We can approximate that a SNP density of 0.1 SNPs/bp repesents one polymorphic site at every ten nucleotides. You can see that for the two contigs there are regions more conserved between the two subgenomes, which show a low density of SNPs and more variable regions, which have a higher SNP density. In the third plot we can mainly observe SNPs at the beging of the contigs, as you have probably also oberved when inspecting the files in IGV. Taking a look at the scale of the y-axis you can oberve that the SNP density for Hifi_Contig1_2_vcf is much smaller compared to the other two files. Note: For easier plotting of the files that contain SNPs from both Contig 1 and 2, the SNP density is plotted as an overlay of both contigs. In [11]: Copied! #Plot SNP density plot_variant_density ( Hifi_Contig_1_vcf , window_size = 5000 , title = 'Contig 1 Raw SNP density' ) plot_variant_density ( Hifi_Contig_2_vcf , window_size = 5000 , title = 'Contig 2 Raw SNP density' ) plot_variant_density ( Hifi_Contig_1_2_vcf , window_size = 5000 , title = 'Contig 1 and 2 Raw SNP density' ) #Plot SNP density plot_variant_density(Hifi_Contig_1_vcf, window_size=5000, title='Contig 1 Raw SNP density') plot_variant_density(Hifi_Contig_2_vcf, window_size=5000, title='Contig 2 Raw SNP density') plot_variant_density(Hifi_Contig_1_2_vcf, window_size=5000, title='Contig 1 and 2 Raw SNP density') We can also plot some of the most relevant parameters from the VCF files, such as the read depth and the quality, to have an overview of the quality of the SNPs in these samples. We defined for that the function plot_variant_hist in the file ../Scripts/pythonScripts.sh . We can plot the metrics \"DP\" (Read depth), \"QUAL\" (Quality score), and \"MQ\" (Mapping quality for Hifi_Contig1_vcf . The x-axis represents the chosen metric, while the y-axis represents the number of SNPs. You can observe that the majority of the SNPs from this file have a high quality. In [12]: Copied! plot_variant_hist ( Hifi_Contig_1_vcf , 'DP' , \"mediumblue\" ) plot_variant_hist ( Hifi_Contig_1_vcf , 'QUAL' , \"forestgreen\" ) plot_variant_hist ( Hifi_Contig_1_vcf , 'MQ' , \"darkred\" ) plot_variant_hist(Hifi_Contig_1_vcf,'DP', \"mediumblue\") plot_variant_hist(Hifi_Contig_1_vcf, 'QUAL', \"forestgreen\") plot_variant_hist(Hifi_Contig_1_vcf, 'MQ', \"darkred\") TASK In the two cells below generate the same three plots as above using the VCF files for Contig 2 and Contig 1+2. You will just need to copy the three command lines from the cell above and replace the name of the VCF files. In [ ]: Copied! #Plot the metrics for Contig 2 #Plot the metrics for Contig 2 In [ ]: Copied! #Plot the metrics for Contig 1 + 2 #Plot the metrics for Contig 1 + 2 As you can observe in the plots above, there are some positions with very low read depth and low quality. Those might represent false-positive variants. Thus, we will filter the VCF files in order to eliminate those positions. For this purpose, we will use another bcftools function bcftools filter . We can pass thresholds for all the info metrics to this function using the \"-i\" flag. Below is just an example of the thresholds, you can change the values to suit your files. Note that this command will generate a new filtered VCF file in the \"VCF_Files\" Folder. In [ ]: Copied! ! bcftools filter - i '(QUAL>150) & (DP > 20) & (MQ > 40)' results / VCF_Files / HIFI_Contig_1 . vcf - O v > results / VCF_Files / HIFI_Contig_1_filtered . vcf !bcftools filter -i '(QUAL>150) & (DP > 20) & (MQ > 40)' results/VCF_Files/HIFI_Contig_1.vcf -O v > results/VCF_Files/HIFI_Contig_1_filtered.vcf In [ ]: Copied! ! bcftools filter - i '(QUAL>150) & (DP > 20) & (MQ > 40)' results / VCF_Files / HIFI_Contig_2 . vcf - O v > results / VCF_Files / HIFI_Contig_2_filtered . vcf !bcftools filter -i '(QUAL>150) & (DP > 20) & (MQ > 40)' results/VCF_Files/HIFI_Contig_2.vcf -O v > results/VCF_Files/HIFI_Contig_2_filtered.vcf In [ ]: Copied! ! bcftools filter - i '(QUAL>150) & (DP > 20) & (MQ > 40)' results / VCF_Files / HIFI_Contig_1_2 . vcf - O v > results / VCF_Files / HIFI_Contig_1_2_filtered . vcf !bcftools filter -i '(QUAL>150) & (DP > 20) & (MQ > 40)' results/VCF_Files/HIFI_Contig_1_2.vcf -O v > results/VCF_Files/HIFI_Contig_1_2_filtered.vcf We read the filtered VCF file for Contig1 in python and generate the same plots as before. We can read the VCF file and generate all the plots in one cell this time. We check how many positions have been eliminated by the filter and compare the plots with the ones for the unfiltered VCFs. If you consider that there are still positions with with low quality you can change the filter and repeat the process. In [ ]: Copied! Hifi_Contig1_filtered = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_1_filtered.vcf' , fields = '*' ) print ( \"Number of SNPs in Contig 1 VCF filtered file:\" , Hifi_Contig1_filtered [ 'variants/POS' ] . size ) #Plot SNP density plot_variant_density ( Hifi_Contig1_filtered , window_size = 5000 , title = 'Contig 1 Filtered variant density' ) #Plot metrics plot_variant_hist ( Hifi_Contig1_filtered , 'DP' , \"mediumblue\" ) plot_variant_hist ( Hifi_Contig1_filtered , 'QUAL' , \"forestgreen\" ) plot_variant_hist ( Hifi_Contig1_filtered , 'MQ' , \"darkred\" ) Hifi_Contig1_filtered = allel.read_vcf('results/VCF_Files/HIFI_Contig_1_filtered.vcf', fields='*') print(\"Number of SNPs in Contig 1 VCF filtered file:\", Hifi_Contig1_filtered['variants/POS'].size) #Plot SNP density plot_variant_density(Hifi_Contig1_filtered, window_size=5000, title='Contig 1 Filtered variant density') #Plot metrics plot_variant_hist(Hifi_Contig1_filtered,'DP', \"mediumblue\") plot_variant_hist(Hifi_Contig1_filtered, 'QUAL', \"forestgreen\") plot_variant_hist(Hifi_Contig1_filtered, 'MQ', \"darkred\") TASK This exercise covers the rest of the notebook and contains explanations as well . The code will work only if you complete correctly the empty cells. Some cells are filled in to help. Compare the VCF files before and after filtering in IGV. In the two cells below generate the same plots as above using the filtered VCF files for Contig 2 and Contig 1 + 2. You will just need to copy the three command lines from the cell above and replace the name of the VCF files. In [ ]: Copied! #Figures for Contig 2 Filtered VCF #Figures for Contig 2 Filtered VCF In [ ]: Copied! #Figures for Contig 1 + 2 Filtered VCF #Figures for Contig 1 + 2 Filtered VCF Repeat the quality check and filtering as described above for the two RNA-seq VCF files: Read the VCF files in Python: In [ ]: Copied! vcf_S10_RNA = allel . read_vcf ( 'results/VCF_Files/RNA_S10_merged.vcf' , fields = '*' ) vcf_Ti_RNA = allel . read_vcf ( 'results/VCF_Files/RNA_TI_merged.vcf' , fields = '*' ) vcf_S10_RNA = allel.read_vcf('results/VCF_Files/RNA_S10_merged.vcf', fields='*') vcf_Ti_RNA = allel.read_vcf('results/VCF_Files/RNA_TI_merged.vcf', fields='*') Print the number of SNPs in the two RNA-seq VCF files. Note that the number of SNPs is much smaller than for the Hifi VCF files. Also, there is a difference in the number of SNPs for the two genotypes. As S10 RNA-seq data originates from the same genotype as the referece genome, one would expect a small number of SNPs to be identified in this case. In [ ]: Copied! print ( \"Number of SNPs in S10 RNA-seq VCF file:\" , vcf_S10_RNA [ 'variants/POS' ] . size ) print ( \"Number of SNPs in Ti RNA-seq VCF file:\" , vcf_Ti_RNA [ 'variants/POS' ] . size ) print(\"Number of SNPs in S10 RNA-seq VCF file:\", vcf_S10_RNA['variants/POS'].size) print(\"Number of SNPs in Ti RNA-seq VCF file:\", vcf_Ti_RNA['variants/POS'].size) Generate SNP density plots using the plot_variant_density() function: In [ ]: Copied! In [ ]: Copied! Generate info histograms using the plot_variant_hist() function for the same fields. You should observe that the metrics for the RNA-seq vcf files are quite different from the ones using Hifi reads. In [ ]: Copied! In [ ]: Copied! Filter the VCF files using bcftools and your selected tresholds after inspecting the VCF files. Add the filter thresholds before running the command. In [ ]: Copied! ! bcftools filter - i 'add filter thresholds here' results / VCF_Files / RNA_S10_merged . vcf - O v > results / VCF_Files / RNA_S10_merged_filtered . vcf ! bcftools filter - i 'add filter thresholds here' results / VCF_Files / RNA_TI_merged . vcf - O v > results / VCF_Files / RNA_TI_merged_filtered . vcf !bcftools filter -i 'add filter thresholds here' results/VCF_Files/RNA_S10_merged.vcf -O v > results/VCF_Files/RNA_S10_merged_filtered.vcf !bcftools filter -i 'add filter thresholds here' results/VCF_Files/RNA_TI_merged.vcf -O v > results/VCF_Files/RNA_TI_merged_filtered.vcf Read the filtered VCFs in python: In [ ]: Copied! vcf_S10_RNA_filtered = allel . read_vcf ( 'results/VCF_Files/RNA_S10_merged_filtered.vcf' , fields = '*' ) vcf_Ti_RNA_filtered = allel . read_vcf ( 'results/VCF_Files/RNA_TI_merged_filtered.vcf' , fields = '*' ) vcf_S10_RNA_filtered = allel.read_vcf('results/VCF_Files/RNA_S10_merged_filtered.vcf', fields='*') vcf_Ti_RNA_filtered = allel.read_vcf('results/VCF_Files/RNA_TI_merged_filtered.vcf', fields='*') Check the number of SNPs in the filtered VCF files: In [ ]: Copied! print ( \"Number of SNPs in the filtered S10 RNA-seq VCF file:\" , vcf_S10_RNA_filtered [ 'variants/POS' ] . size ) print ( \"Number of SNPs in the filtered Ti RNA-seq VCF file:\" , vcf_Ti_RNA_filtered [ 'variants/POS' ] . size ) print(\"Number of SNPs in the filtered S10 RNA-seq VCF file:\", vcf_S10_RNA_filtered['variants/POS'].size) print(\"Number of SNPs in the filtered Ti RNA-seq VCF file:\", vcf_Ti_RNA_filtered['variants/POS'].size) Plot same histograms for the filtered VCF files. In [ ]: Copied! In [ ]: Copied! Question: Is it relevant to filter for false positives in this case, and what parameters would you look further into? Genotypes counting \u00b6 We want to count the different genotypes in each of the five filtered VCF files generated previously. To make this task easier we can take advantage of the counting functions in the scikit-allel package. We have used those to implement a little function called position_count in the file ../Scripts/pythonScripts.sh . You can then use the function with each of the 5 filtered VCF files to count the number of positions. You need to fill the names of the VCF files imported in Python. In [ ]: Copied! #Contig 1 filtered VCF position_count ( Hifi_Contig1_filtered ) #Contig 1 filtered VCF position_count(Hifi_Contig1_filtered) In [ ]: Copied! #Contig 2 filtered VCF position_count () #Contig 2 filtered VCF position_count() In [ ]: Copied! #Contig 1 and 2 filtered VCF position_count () #Contig 1 and 2 filtered VCF position_count() In [ ]: Copied! #RNAseq S10 filtered VCF position_count () #RNAseq S10 filtered VCF position_count() In [ ]: Copied! #RNAseq Ti filtered VCF position_count () #RNAseq Ti filtered VCF position_count() Comparison of VCF files \u00b6 Compare the VCF files with subgenome variants and the VCF files with S10/Tienshan genotype specific variants obtained from RNA-seq mapping We want now to find overlapping position between the Contig 1 and Contig 2 Hifi VCF files and the Ti RNAseq VCF file. To ease this task we will first create two merged VCF files containing: Hifi Contig 1 VCF + Ti RNAseq VCF Hifi Contig 2 VCF + Ti RNAseq VCF Once again, we will use bcftools for this task, using the merge function. However, the bcftools merge function works only with compressed and indexed VCF files, so first we will do this. In [ ]: Copied! ! bcftools view results / VCF_Files / HIFI_Contig_1_filtered . vcf - Oz - o results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz ! bcftools index results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz - f - o results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz . csi ! bcftools view results / VCF_Files / HIFI_Contig_2_filtered . vcf - Oz - o results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz ! bcftools index results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz - f - o results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz . csi ! bcftools view results / VCF_Files / RNA_TI_merged_filtered . vcf - Oz - o results / VCF_Files / RNA_TI_merged_filtered . vcf . gz ! bcftools index results / VCF_Files / RNA_TI_merged_filtered . vcf . gz - f - o results / VCF_Files / RNA_TI_merged_filtered . vcf . gz . csi !bcftools view results/VCF_Files/HIFI_Contig_1_filtered.vcf -Oz -o results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz !bcftools index results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz -f -o results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz.csi !bcftools view results/VCF_Files/HIFI_Contig_2_filtered.vcf -Oz -o results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz !bcftools index results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz -f -o results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz.csi !bcftools view results/VCF_Files/RNA_TI_merged_filtered.vcf -Oz -o results/VCF_Files/RNA_TI_merged_filtered.vcf.gz !bcftools index results/VCF_Files/RNA_TI_merged_filtered.vcf.gz -f -o results/VCF_Files/RNA_TI_merged_filtered.vcf.gz.csi Now we can merge Contig 1 VCF with the Ti RNA VCF and keep only positions that match between the two files by filtering out any positions with missing genotypes. In [ ]: Copied! ! bcftools merge results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz results / VCF_Files / RNA_TI_merged_filtered . vcf . gz | bcftools filter - e 'GT=\"./.\"' > results / VCF_Files / TI_Contig_1_merged . vcf !bcftools merge results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz results/VCF_Files/RNA_TI_merged_filtered.vcf.gz | bcftools filter -e 'GT=\"./.\"' > results/VCF_Files/TI_Contig_1_merged.vcf Merge Contig 2 VCF with the Ti RNA VCF and keep only positions that match between the two files. In [ ]: Copied! ! bcftools merge results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz results / VCF_Files / RNA_TI_merged_filtered . vcf . gz | bcftools filter - e 'GT=\"./.\"' > results / VCF_Files / TI_Contig_2_merged . vcf !bcftools merge results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz results/VCF_Files/RNA_TI_merged_filtered.vcf.gz | bcftools filter -e 'GT=\"./.\"' > results/VCF_Files/TI_Contig_2_merged.vcf Take a look at the merged VCF file. You can see that compared with the VCF files for just one sample, there is now a new column in the file, with the corresponding genotype for the second sample that we merged. In [ ]: Copied! ! cat results / VCF_Files / TI_Contig_1_merged . vcf | head - 50 !cat results/VCF_Files/TI_Contig_1_merged.vcf | head -50 In order to count the genotypes for the matching positions we can use once again bcftools filter command. Only that this time we will filter based on the genotype columns for the two samples (GT[0] and GT[1]). GT[0] repesents the genotype for the first sample in the merge command and GT[1] is the genotype for the second sample in the merge command. AR - heterozygous AA - homozygous alternate For example, the filter GT[0]=\"AR\" && GT[1]=\"AR\" will keep only position in which both samples have a heterozygous genotype. A quick way to count how many positions are in the VCF file is by suing the simple bash command grep . This will count all the lines in the VCF files that do not start with a # , thus all the data lines. In [ ]: Copied! ! echo \"AR + AR:\" ! bcftools filter - i 'GT[0]=\"AR\" && GT[1]=\"AR\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" ! echo \"AR + AA:\" ! bcftools filter - i 'GT[0]=\"AR\" && GT[1]=\"AA\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" ! echo \"AA + AR:\" ! bcftools filter - i 'GT[0]=\"AA\" && GT[1]=\"AR\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" ! echo \"AA + AA:\" ! bcftools filter - i 'GT[0]=\"AA\" && GT[1]=\"AA\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" !echo \"AR + AR:\" !bcftools filter -i 'GT[0]=\"AR\" && GT[1]=\"AR\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" !echo \"AR + AA:\" !bcftools filter -i 'GT[0]=\"AR\" && GT[1]=\"AA\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" !echo \"AA + AR:\" !bcftools filter -i 'GT[0]=\"AA\" && GT[1]=\"AR\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" !echo \"AA + AA:\" !bcftools filter -i 'GT[0]=\"AA\" && GT[1]=\"AA\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" Use the same commands as above to count the positions marching the filters in the Contig2_Ti_merged.vcf. In [ ]: Copied! Wrapping up \ud83c\udf89 \ud83c\udf89 \ud83c\udf89 \u00b6 In this exercise, you identified polymorphisms between the two white clover subgenomes using Hifi reads alignment files and SNPs specific for the white clover Tienshan genotype using RNA-seq alignment files. You learned how to import VCF files in Python to extract data and created plots. You also filtered low-quality variants from the VCF files and counted genotypes.","title":"3.2.Variant calling analysis"},{"location":"nb/vcf.html#use-the-bam-files-generated-from-the-alignment-of-raw-reads","text":"We will use the data created during the alignment session of the course. You had two possibilities: using the interactive Galaxy platform as explained in the course webpage . In this case you will need to upload the some of the created data into the folder Data_for_VCF_analysis , renaming the files as follows: Hifi reads mapped to Contig1: HIFI_contig_1.bam Hifi reads mapped to Contig2: HIFI_contig_2.bam Hifi reads mapped to Contig 1 and 2: HIFI_contig_1_2_asm20.bam (you can of course choose the one with the map-pb alignment option from the alignment notebook) S10 RNAseq reads mapped to Contig 1 and 2 (After merging the bam files): RNA_S10_merged.bam Ti RNAseq reads mapped to Contig 1 and 2 (After merging the bam files): RNA_TI_merged.bam using the notebook 01_align.ipynb . In that case, you just need to run the command below, linking the data in the results folder into the folder Data_for_VCF_analysis . In [4]: Copied! %% bash ln - fs ` realpath results / HIFI_alignment / PacBio_clover_alignment_1 . sort . bam ` Data_for_VCF_analysis / HIFI_contig_1 . bam ln - fs ` realpath results / HIFI_alignment / PacBio_clover_alignment_2 . sort . bam ` Data_for_VCF_analysis / HIFI_contig_2 . bam ln - fs ` realpath results / HIFI_alignment / PacBio_clover_alignment_1_2_asm20 . sort . bam ` Data_for_VCF_analysis / HIFI_contig_1_2 . bam ln - fs ` realpath results / STAR_output / S10_align_contigs_1_2_merge / S10 . sorted . bam ` Data_for_VCF_analysis / RNA_S10_merged . bam ln - fs ` realpath results / STAR_output / TI_align_contigs_1_2_merge / TI . sorted . bam ` Data_for_VCF_analysis / RNA_TI_merged . bam %%bash ln -fs `realpath results/HIFI_alignment/PacBio_clover_alignment_1.sort.bam` Data_for_VCF_analysis/HIFI_contig_1.bam ln -fs `realpath results/HIFI_alignment/PacBio_clover_alignment_2.sort.bam` Data_for_VCF_analysis/HIFI_contig_2.bam ln -fs `realpath results/HIFI_alignment/PacBio_clover_alignment_1_2_asm20.sort.bam` Data_for_VCF_analysis/HIFI_contig_1_2.bam ln -fs `realpath results/STAR_output/S10_align_contigs_1_2_merge/S10.sorted.bam` Data_for_VCF_analysis/RNA_S10_merged.bam ln -fs `realpath results/STAR_output/TI_align_contigs_1_2_merge/TI.sorted.bam` Data_for_VCF_analysis/RNA_TI_merged.bam","title":"Use the bam files generated from the alignment of raw reads"},{"location":"nb/vcf.html#variant-calling","text":"We will use a tool named bcftools to find variants in the alignment files ( .bam ) relative to the reference genome, in this case, the file DNA_Contig1_2.fasta . In this way we can identify positions in the genome where the sequence of the aligned reads differ from the reference genome they were mapped to. There are multiple types of genetic variation , e.g. SNPs (single nucleotide polymorphism), indels (insertions or deletions) and structural variations (such as chromosomal rearrangements). Here we will only be working with SNPs, thus we will use the terms variants and SNPs interchangeably. The called variants will be stored in a specific format file, the Variant Call Format ( VCF ). When looking at an alignment files in IGV you should easily recognize the SNP positions. You can see in the figure below that at the marked positions all the mapped reads have a mismatch compared to the reference. You will be calling two types of SNPs in this notebook. First, we will find subgenome SNPs using the Hifi reads mapped to white clover Contig1 and Contig2. These are positions in the contigs that are different between the two subgenomes of white clover. Secondly, we will find SNPs that differentiate between the sequenced white clover S10 (the reference genome) and Ti genotype, using RNA-seq read mapping. Create a folder that will store all the files generated in this notebook. In [5]: Copied! ! mkdir - p results / VCF_Files !mkdir -p results/VCF_Files","title":"Variant calling"},{"location":"nb/vcf.html#call-subgenome-snps-using-the-hifi-alignments-and-the-reference-genome","text":"We will use the bcftools software to call SNPs from alignment files. bcftools is a toolkit for variant calling and manipulating VCF files. If you are interested, you can find all the functionalities here http://samtools.github.io/bcftools/bcftools.html#call . We mainly need two commands for this step, first bcftools mpileup which takes as input the alignment and the genome reference files, followed by bcftools call to produce VCF files. The cell below will generate 3 VCF files stored in the folder results/VCF_Files using the Hifi alignment files uploaded from Galaxy. In [6]: Copied! ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f ../ Data / Clover_Data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / HIFI_contig_1 . bam | bcftools call - mv - Ov - o results / VCF_Files / HIFI_Contig_1 . vcf ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f ../ Data / Clover_Data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / HIFI_contig_2 . bam | bcftools call - mv - Ov - o results / VCF_Files / HIFI_Contig_2 . vcf ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f ../ Data / Clover_Data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / HIFI_contig_1_2 . bam | bcftools call - mv - Ov - o results / VCF_Files / HIFI_Contig_1_2 . vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f ../Data/Clover_Data/DNA_Contig1_2.fasta Data_for_VCF_analysis/HIFI_contig_1.bam | bcftools call -mv -Ov -o results/VCF_Files/HIFI_Contig_1.vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f ../Data/Clover_Data/DNA_Contig1_2.fasta Data_for_VCF_analysis/HIFI_contig_2.bam | bcftools call -mv -Ov -o results/VCF_Files/HIFI_Contig_2.vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f ../Data/Clover_Data/DNA_Contig1_2.fasta Data_for_VCF_analysis/HIFI_contig_1_2.bam | bcftools call -mv -Ov -o results/VCF_Files/HIFI_Contig_1_2.vcf Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 TASK Inspect the VCF files using IGV, comparing them to the specific alignment BAM files. (You can download the VCF files to your computer from the results/VCF_Files folder) Are there any problematic positions that may not represent true SNPs? How can you use the HiFi reads aligned to Contigs1+2 identify potential problems? Hint: Try scrolling to the ends of the contigs.","title":"Call subgenome SNPs using the Hifi alignments and the reference genome"},{"location":"nb/vcf.html#call-snps-using-the-two-rna-seq-genotypes-s10-and-ti","text":"We will repeat the same step as above this time using the RNA-seq alignment files for the two white clover genotypes (S10 and Ti). These commands will produce another two VCF files, stored in the same folder. In [7]: Copied! ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f reference_data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / RNA_S10_merged . bam | bcftools call - mv - Ov - o results / VCF_Files / RNA_S10_merged . vcf ! bcftools mpileup -- threads 4 - Ou -- skip - indels - f reference_data / DNA_Contig1_2 . fasta Data_for_VCF_analysis / RNA_TI_merged . bam | bcftools call - mv - Ov - o results / VCF_Files / RNA_TI_merged . vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f reference_data/DNA_Contig1_2.fasta Data_for_VCF_analysis/RNA_S10_merged.bam | bcftools call -mv -Ov -o results/VCF_Files/RNA_S10_merged.vcf !bcftools mpileup --threads 4 -Ou --skip-indels -f reference_data/DNA_Contig1_2.fasta Data_for_VCF_analysis/RNA_TI_merged.bam | bcftools call -mv -Ov -o results/VCF_Files/RNA_TI_merged.vcf [mpileup] 1 samples in 1 input files Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] maximum number of reads per input file set to -d 250 Note: none of --samples-file, --ploidy or --ploidy-file given, assuming all sites are diploid [mpileup] 1 samples in 1 input files [mpileup] maximum number of reads per input file set to -d 250 TASK Inspect one of the VCFs and the corresponding alignment files in IGV. Is it relevant to filter for false positives in this case, and what parameters would you look further into?","title":"Call SNPs using the two RNA-seq genotypes, S10 and Ti"},{"location":"nb/vcf.html#vcf-files-processing-and-filtering","text":"Take a look at what one of the previously generated VCF files contains by running the cell below. A VCF file begins with the meta-information lines starting with ## followed by one header line, starting with # . Next, there are the data lines, each describing a genetic variant for a certain position in the genome. Each data line contains multiple tab-delimited columns with information about the variant. There are 9 fixed columns, labelled \u201cCHROM\u201d, \u201cPOS\u201d, \u201cID\u201d, \u201cREF\u201d, \u201cALT\u201d, \u201cQUAL\u201d, \u201cFILTER\u201d, \u201cINFO\u201d and \u201cFORMAT\u201d. Find more about the content of those columns in this wikipedia page . In the sample column you can find the most likely genotype of the sample. For diploid organisms, there will be a 0 value for reference allele and 1 for the alternate allele. Figure: possible genotypes in the data. For example, here we read the first 50 rows from the RNAseq_S10_merged.vcf file. You can see that the file format follows the same pattern as explained above. In [8]: Copied! ! cat results / VCF_Files / RNA_S10_merged . vcf | head - 50 !cat results/VCF_Files/RNA_S10_merged.vcf | head -50 ##fileformat=VCFv4.2 ##FILTER=<ID=PASS,Description=\"All filters passed\"> ##bcftoolsVersion=1.16+htslib-1.17 ##bcftoolsCommand=mpileup --threads 4 -Ou --skip-indels -f reference_data/DNA_Contig1_2.fasta Data_for_VCF_analysis/RNA_S10_merged.bam ##reference=file://reference_data/DNA_Contig1_2.fasta ##contig=<ID=1,length=1031631> ##contig=<ID=2,length=1057923> ##ALT=<ID=*,Description=\"Represents allele(s) other than observed.\"> ##INFO=<ID=INDEL,Number=0,Type=Flag,Description=\"Indicates that the variant is an INDEL.\"> ##INFO=<ID=IDV,Number=1,Type=Integer,Description=\"Maximum number of raw reads supporting an indel\"> ##INFO=<ID=IMF,Number=1,Type=Float,Description=\"Maximum fraction of raw reads supporting an indel\"> ##INFO=<ID=DP,Number=1,Type=Integer,Description=\"Raw read depth\"> ##INFO=<ID=VDB,Number=1,Type=Float,Description=\"Variant Distance Bias for filtering splice-site artefacts in RNA-seq data (bigger is better)\",Version=\"3\"> ##INFO=<ID=RPBZ,Number=1,Type=Float,Description=\"Mann-Whitney U-z test of Read Position Bias (closer to 0 is better)\"> ##INFO=<ID=MQBZ,Number=1,Type=Float,Description=\"Mann-Whitney U-z test of Mapping Quality Bias (closer to 0 is better)\"> ##INFO=<ID=BQBZ,Number=1,Type=Float,Description=\"Mann-Whitney U-z test of Base Quality Bias (closer to 0 is better)\"> ##INFO=<ID=MQSBZ,Number=1,Type=Float,Description=\"Mann-Whitney U-z test of Mapping Quality vs Strand Bias (closer to 0 is better)\"> ##INFO=<ID=NMBZ,Number=1,Type=Float,Description=\"Mann-Whitney U-z test of Number of Mismatches within supporting reads (closer to 0 is better)\"> ##INFO=<ID=SCBZ,Number=1,Type=Float,Description=\"Mann-Whitney U-z test of Soft-Clip Length Bias (closer to 0 is better)\"> ##INFO=<ID=FS,Number=1,Type=Float,Description=\"Phred-scaled p-value using Fisher's exact test to detect strand bias\"> ##INFO=<ID=SGB,Number=1,Type=Float,Description=\"Segregation based metric.\"> ##INFO=<ID=MQ0F,Number=1,Type=Float,Description=\"Fraction of MQ0 reads (smaller is better)\"> ##FORMAT=<ID=PL,Number=G,Type=Integer,Description=\"List of Phred-scaled genotype likelihoods\"> ##FORMAT=<ID=GT,Number=1,Type=String,Description=\"Genotype\"> ##INFO=<ID=AC,Number=A,Type=Integer,Description=\"Allele count in genotypes for each ALT allele, in the same order as listed\"> ##INFO=<ID=AN,Number=1,Type=Integer,Description=\"Total number of alleles in called genotypes\"> ##INFO=<ID=DP4,Number=4,Type=Integer,Description=\"Number of high-quality ref-forward , ref-reverse, alt-forward and alt-reverse bases\"> ##INFO=<ID=MQ,Number=1,Type=Integer,Description=\"Average mapping quality\"> ##bcftools_callVersion=1.16+htslib-1.17 ##bcftools_callCommand=call -mv -Ov -o results/VCF_Files/RNA_S10_merged.vcf; Date=Mon Jun 19 10:48:17 2023 #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT Data_for_VCF_analysis/RNA_S10_merged.bam 1 33218 . G A 65.3262 . DP=88;VDB=0.0923504;SGB=-0.692914;RPBZ=3.99376;MQBZ=-1.86933;MQSBZ=-0.901828;BQBZ=-1.8368;SCBZ=2.6559;FS=0;MQ0F=0;AC=1;AN=2;DP4=15,13,16,9;MQ=19 GT:PL 0/1:98,0,138 1 33222 . C G 69.1967 . DP=56;VDB=4.89382e-06;SGB=-0.692352;RPBZ=3.47069;MQBZ=-1.73535;MQSBZ=-0.222384;BQBZ=-1.36705;SCBZ=2.16882;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,13,12,9;MQ=19 GT:PL 0/1:102,0,138 1 33223 . T G 61.1405 . DP=56;VDB=2.99077e-05;SGB=-0.692067;RPBZ=3.40644;MQBZ=-1.80665;MQSBZ=-0.222384;BQBZ=0.830878;SCBZ=2.63256;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,14,12,8;MQ=19 GT:PL 0/1:94,0,132 1 33224 . T G 65.1016 . DP=58;VDB=4.85907e-06;SGB=-0.692352;RPBZ=3.67489;MQBZ=-1.78978;MQSBZ=-0.159627;BQBZ=-1.43699;SCBZ=2.60016;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,15,12,9;MQ=19 GT:PL 0/1:98,0,144 1 33225 . G T 64.0746 . DP=58;VDB=4.36523e-06;SGB=-0.692352;RPBZ=3.67489;MQBZ=-1.78978;MQSBZ=-0.159627;BQBZ=-2.60059;SCBZ=2.60016;FS=0;MQ0F=0;AC=1;AN=2;DP4=18,15,12,9;MQ=19 GT:PL 0/1:97,0,144 1 33227 . T A 64.067 . DP=59;VDB=4.09235e-06;SGB=-0.692352;RPBZ=4.13393;MQBZ=-1.81637;MQSBZ=-0.18317;BQBZ=-1.48941;SCBZ=2.64866;FS=0;MQ0F=0;AC=1;AN=2;DP4=19,15,12,9;MQ=19 GT:PL 0/1:97,0,144 1 49149 . C T 30.4183 . DP=4;VDB=0.0058656;SGB=-0.556411;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,0,4;MQ=20 GT:PL 1/1:60,12,0 1 83968 . C G 78.0347 . DP=10;VDB=5.52906e-06;SGB=-0.662043;RPBZ=-3;MQBZ=0;MQSBZ=0;BQBZ=-0.333333;SCBZ=1.77187;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 83970 . T C 78.0347 . DP=10;VDB=8.25997e-06;SGB=-0.662043;RPBZ=-1.79284;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=1.77187;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 83972 . G T 78.0347 . DP=10;VDB=8.25997e-06;SGB=-0.662043;RPBZ=-1.79284;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=1.57628;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 83975 . G A 78.0347 . DP=10;VDB=1.22485e-05;SGB=-0.662043;RPBZ=-1.79284;MQBZ=0;MQSBZ=0;BQBZ=0;SCBZ=1.57628;FS=0;MQ0F=0;AC=2;AN=2;DP4=1,0,2,7;MQ=20 GT:PL 1/1:105,10,0 1 174956 . C T 10.7923 . DP=2;VDB=0.14;SGB=-0.453602;MQSBZ=0;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,1,1;MQ=20 GT:PL 1/1:40,6,0 1 175456 . A G 7.30814 . DP=2;VDB=0.02;SGB=-0.453602;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,2,0;MQ=20 GT:PL 1/1:36,6,0 1 211292 . A T 16.7427 . DP=5;VDB=0.0058656;SGB=-0.556411;RPBZ=-2;MQBZ=0;MQSBZ=0;BQBZ=1.22474;SCBZ=2;FS=0;MQ0F=0;AC=1;AN=2;DP4=0,1,4,0;MQ=20 GT:PL 0/1:47,0,5 1 321659 . G A 13.5525 . DP=7;VDB=0.107235;SGB=-0.511536;RPBZ=1.34164;MQBZ=0;MQSBZ=0;BQBZ=1.73205;SCBZ=0.942809;FS=0;MQ0F=0;AC=1;AN=2;DP4=1,0,1,2;MQ=20 GT:PL 0/1:45,0,8 1 321661 . A C 13.5525 . DP=7;VDB=0.107235;SGB=-0.511536;RPBZ=1.34164;MQBZ=0;MQSBZ=0;BQBZ=1.73205;SCBZ=0.942809;FS=0;MQ0F=0;AC=1;AN=2;DP4=1,0,1,2;MQ=20 GT:PL 0/1:45,0,8 1 323166 . G A 7.30814 . DP=4;VDB=0.1;SGB=-0.453602;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,0,2;MQ=20 GT:PL 1/1:36,6,0 1 331785 . A T 7.30814 . DP=2;VDB=0.02;SGB=-0.453602;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,2,0;MQ=20 GT:PL 1/1:36,6,0 1 331890 . C T 39.4149 . DP=6;VDB=0.100421;SGB=-0.556411;MQSBZ=0;FS=0;MQ0F=0;AC=2;AN=2;DP4=0,0,3,1;MQ=20 GT:PL 1/1:69,12,0 Next, in order to generate plots of the metrics stored in the VCF files, we will first import the VCF files in Python using the scikit-allel package. This package contains a series of function helpful for processing variant calling files. For now, we will import only the VCF files generated using the Hifi data alignments. You can ignore the warning. In [9]: Copied! #Import the VCF files Hifi_Contig_1_vcf = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_1.vcf' , fields = '*' ) Hifi_Contig_2_vcf = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_2.vcf' , fields = '*' ) Hifi_Contig_1_2_vcf = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_1_2.vcf' , fields = '*' ) #Import the VCF files Hifi_Contig_1_vcf = allel.read_vcf('results/VCF_Files/HIFI_Contig_1.vcf', fields='*') Hifi_Contig_2_vcf = allel.read_vcf('results/VCF_Files/HIFI_Contig_2.vcf', fields='*') Hifi_Contig_1_2_vcf = allel.read_vcf('results/VCF_Files/HIFI_Contig_1_2.vcf', fields='*') /work/Course_Env/lib/python3.10/site-packages/allel/io/vcf_read.py:1732: UserWarning: invalid INFO header: '##INFO=<ID=VDB,Number=1,Type=Float,Description=\"Variant Distance Bias for filtering splice-site artefacts in RNA-seq data (bigger is better)\",Version=\"3\">\\n' warnings.warn('invalid INFO header: %r' % header) Let's check the number of SNPs in each of the three VCF files. The first two VCF files contain subgenome SNPs. You can see that we identified a have a high number of polymorphisms between the two subgenomes. On the other hand the Hifi_Contig1_2_vcf file contains what we would call false positives, as we called SNPs using the same Hifi reads that were used for assembling the reference genome. As expected the number of SNPs in this file is much smaller. In [10]: Copied! print ( \"Number of SNPs in Hifi Contig 1 vcf file:\" , Hifi_Contig_1_vcf [ 'variants/POS' ] . size ) print ( \"Number of SNPs in Hifi Contig 2 vcf file:\" , Hifi_Contig_2_vcf [ 'variants/POS' ] . size ) print ( \"Number of SNPs in Hifi Contig 1 and 2 vcf file:\" , Hifi_Contig_1_2_vcf [ 'variants/POS' ] . size ) print(\"Number of SNPs in Hifi Contig 1 vcf file:\", Hifi_Contig_1_vcf['variants/POS'].size) print(\"Number of SNPs in Hifi Contig 2 vcf file:\", Hifi_Contig_2_vcf['variants/POS'].size) print(\"Number of SNPs in Hifi Contig 1 and 2 vcf file:\", Hifi_Contig_1_2_vcf['variants/POS'].size) Number of SNPs in Hifi Contig 1 vcf file: 31981 Number of SNPs in Hifi Contig 2 vcf file: 30823 Number of SNPs in Hifi Contig 1 and 2 vcf file: 1309 As an overview of the distribution of SNPs, we can plot the density of SNPs across the contigs. We have defined a function called plot_variant_density in the file ../Scripts/pythonScripts.sh that does exactly that. We use that to plot the SNP density across the contigs in the three VCF files in bins of 5000 bp. We can approximate that a SNP density of 0.1 SNPs/bp repesents one polymorphic site at every ten nucleotides. You can see that for the two contigs there are regions more conserved between the two subgenomes, which show a low density of SNPs and more variable regions, which have a higher SNP density. In the third plot we can mainly observe SNPs at the beging of the contigs, as you have probably also oberved when inspecting the files in IGV. Taking a look at the scale of the y-axis you can oberve that the SNP density for Hifi_Contig1_2_vcf is much smaller compared to the other two files. Note: For easier plotting of the files that contain SNPs from both Contig 1 and 2, the SNP density is plotted as an overlay of both contigs. In [11]: Copied! #Plot SNP density plot_variant_density ( Hifi_Contig_1_vcf , window_size = 5000 , title = 'Contig 1 Raw SNP density' ) plot_variant_density ( Hifi_Contig_2_vcf , window_size = 5000 , title = 'Contig 2 Raw SNP density' ) plot_variant_density ( Hifi_Contig_1_2_vcf , window_size = 5000 , title = 'Contig 1 and 2 Raw SNP density' ) #Plot SNP density plot_variant_density(Hifi_Contig_1_vcf, window_size=5000, title='Contig 1 Raw SNP density') plot_variant_density(Hifi_Contig_2_vcf, window_size=5000, title='Contig 2 Raw SNP density') plot_variant_density(Hifi_Contig_1_2_vcf, window_size=5000, title='Contig 1 and 2 Raw SNP density') We can also plot some of the most relevant parameters from the VCF files, such as the read depth and the quality, to have an overview of the quality of the SNPs in these samples. We defined for that the function plot_variant_hist in the file ../Scripts/pythonScripts.sh . We can plot the metrics \"DP\" (Read depth), \"QUAL\" (Quality score), and \"MQ\" (Mapping quality for Hifi_Contig1_vcf . The x-axis represents the chosen metric, while the y-axis represents the number of SNPs. You can observe that the majority of the SNPs from this file have a high quality. In [12]: Copied! plot_variant_hist ( Hifi_Contig_1_vcf , 'DP' , \"mediumblue\" ) plot_variant_hist ( Hifi_Contig_1_vcf , 'QUAL' , \"forestgreen\" ) plot_variant_hist ( Hifi_Contig_1_vcf , 'MQ' , \"darkred\" ) plot_variant_hist(Hifi_Contig_1_vcf,'DP', \"mediumblue\") plot_variant_hist(Hifi_Contig_1_vcf, 'QUAL', \"forestgreen\") plot_variant_hist(Hifi_Contig_1_vcf, 'MQ', \"darkred\") TASK In the two cells below generate the same three plots as above using the VCF files for Contig 2 and Contig 1+2. You will just need to copy the three command lines from the cell above and replace the name of the VCF files. In [ ]: Copied! #Plot the metrics for Contig 2 #Plot the metrics for Contig 2 In [ ]: Copied! #Plot the metrics for Contig 1 + 2 #Plot the metrics for Contig 1 + 2 As you can observe in the plots above, there are some positions with very low read depth and low quality. Those might represent false-positive variants. Thus, we will filter the VCF files in order to eliminate those positions. For this purpose, we will use another bcftools function bcftools filter . We can pass thresholds for all the info metrics to this function using the \"-i\" flag. Below is just an example of the thresholds, you can change the values to suit your files. Note that this command will generate a new filtered VCF file in the \"VCF_Files\" Folder. In [ ]: Copied! ! bcftools filter - i '(QUAL>150) & (DP > 20) & (MQ > 40)' results / VCF_Files / HIFI_Contig_1 . vcf - O v > results / VCF_Files / HIFI_Contig_1_filtered . vcf !bcftools filter -i '(QUAL>150) & (DP > 20) & (MQ > 40)' results/VCF_Files/HIFI_Contig_1.vcf -O v > results/VCF_Files/HIFI_Contig_1_filtered.vcf In [ ]: Copied! ! bcftools filter - i '(QUAL>150) & (DP > 20) & (MQ > 40)' results / VCF_Files / HIFI_Contig_2 . vcf - O v > results / VCF_Files / HIFI_Contig_2_filtered . vcf !bcftools filter -i '(QUAL>150) & (DP > 20) & (MQ > 40)' results/VCF_Files/HIFI_Contig_2.vcf -O v > results/VCF_Files/HIFI_Contig_2_filtered.vcf In [ ]: Copied! ! bcftools filter - i '(QUAL>150) & (DP > 20) & (MQ > 40)' results / VCF_Files / HIFI_Contig_1_2 . vcf - O v > results / VCF_Files / HIFI_Contig_1_2_filtered . vcf !bcftools filter -i '(QUAL>150) & (DP > 20) & (MQ > 40)' results/VCF_Files/HIFI_Contig_1_2.vcf -O v > results/VCF_Files/HIFI_Contig_1_2_filtered.vcf We read the filtered VCF file for Contig1 in python and generate the same plots as before. We can read the VCF file and generate all the plots in one cell this time. We check how many positions have been eliminated by the filter and compare the plots with the ones for the unfiltered VCFs. If you consider that there are still positions with with low quality you can change the filter and repeat the process. In [ ]: Copied! Hifi_Contig1_filtered = allel . read_vcf ( 'results/VCF_Files/HIFI_Contig_1_filtered.vcf' , fields = '*' ) print ( \"Number of SNPs in Contig 1 VCF filtered file:\" , Hifi_Contig1_filtered [ 'variants/POS' ] . size ) #Plot SNP density plot_variant_density ( Hifi_Contig1_filtered , window_size = 5000 , title = 'Contig 1 Filtered variant density' ) #Plot metrics plot_variant_hist ( Hifi_Contig1_filtered , 'DP' , \"mediumblue\" ) plot_variant_hist ( Hifi_Contig1_filtered , 'QUAL' , \"forestgreen\" ) plot_variant_hist ( Hifi_Contig1_filtered , 'MQ' , \"darkred\" ) Hifi_Contig1_filtered = allel.read_vcf('results/VCF_Files/HIFI_Contig_1_filtered.vcf', fields='*') print(\"Number of SNPs in Contig 1 VCF filtered file:\", Hifi_Contig1_filtered['variants/POS'].size) #Plot SNP density plot_variant_density(Hifi_Contig1_filtered, window_size=5000, title='Contig 1 Filtered variant density') #Plot metrics plot_variant_hist(Hifi_Contig1_filtered,'DP', \"mediumblue\") plot_variant_hist(Hifi_Contig1_filtered, 'QUAL', \"forestgreen\") plot_variant_hist(Hifi_Contig1_filtered, 'MQ', \"darkred\") TASK This exercise covers the rest of the notebook and contains explanations as well . The code will work only if you complete correctly the empty cells. Some cells are filled in to help. Compare the VCF files before and after filtering in IGV. In the two cells below generate the same plots as above using the filtered VCF files for Contig 2 and Contig 1 + 2. You will just need to copy the three command lines from the cell above and replace the name of the VCF files. In [ ]: Copied! #Figures for Contig 2 Filtered VCF #Figures for Contig 2 Filtered VCF In [ ]: Copied! #Figures for Contig 1 + 2 Filtered VCF #Figures for Contig 1 + 2 Filtered VCF Repeat the quality check and filtering as described above for the two RNA-seq VCF files: Read the VCF files in Python: In [ ]: Copied! vcf_S10_RNA = allel . read_vcf ( 'results/VCF_Files/RNA_S10_merged.vcf' , fields = '*' ) vcf_Ti_RNA = allel . read_vcf ( 'results/VCF_Files/RNA_TI_merged.vcf' , fields = '*' ) vcf_S10_RNA = allel.read_vcf('results/VCF_Files/RNA_S10_merged.vcf', fields='*') vcf_Ti_RNA = allel.read_vcf('results/VCF_Files/RNA_TI_merged.vcf', fields='*') Print the number of SNPs in the two RNA-seq VCF files. Note that the number of SNPs is much smaller than for the Hifi VCF files. Also, there is a difference in the number of SNPs for the two genotypes. As S10 RNA-seq data originates from the same genotype as the referece genome, one would expect a small number of SNPs to be identified in this case. In [ ]: Copied! print ( \"Number of SNPs in S10 RNA-seq VCF file:\" , vcf_S10_RNA [ 'variants/POS' ] . size ) print ( \"Number of SNPs in Ti RNA-seq VCF file:\" , vcf_Ti_RNA [ 'variants/POS' ] . size ) print(\"Number of SNPs in S10 RNA-seq VCF file:\", vcf_S10_RNA['variants/POS'].size) print(\"Number of SNPs in Ti RNA-seq VCF file:\", vcf_Ti_RNA['variants/POS'].size) Generate SNP density plots using the plot_variant_density() function: In [ ]: Copied! In [ ]: Copied! Generate info histograms using the plot_variant_hist() function for the same fields. You should observe that the metrics for the RNA-seq vcf files are quite different from the ones using Hifi reads. In [ ]: Copied! In [ ]: Copied! Filter the VCF files using bcftools and your selected tresholds after inspecting the VCF files. Add the filter thresholds before running the command. In [ ]: Copied! ! bcftools filter - i 'add filter thresholds here' results / VCF_Files / RNA_S10_merged . vcf - O v > results / VCF_Files / RNA_S10_merged_filtered . vcf ! bcftools filter - i 'add filter thresholds here' results / VCF_Files / RNA_TI_merged . vcf - O v > results / VCF_Files / RNA_TI_merged_filtered . vcf !bcftools filter -i 'add filter thresholds here' results/VCF_Files/RNA_S10_merged.vcf -O v > results/VCF_Files/RNA_S10_merged_filtered.vcf !bcftools filter -i 'add filter thresholds here' results/VCF_Files/RNA_TI_merged.vcf -O v > results/VCF_Files/RNA_TI_merged_filtered.vcf Read the filtered VCFs in python: In [ ]: Copied! vcf_S10_RNA_filtered = allel . read_vcf ( 'results/VCF_Files/RNA_S10_merged_filtered.vcf' , fields = '*' ) vcf_Ti_RNA_filtered = allel . read_vcf ( 'results/VCF_Files/RNA_TI_merged_filtered.vcf' , fields = '*' ) vcf_S10_RNA_filtered = allel.read_vcf('results/VCF_Files/RNA_S10_merged_filtered.vcf', fields='*') vcf_Ti_RNA_filtered = allel.read_vcf('results/VCF_Files/RNA_TI_merged_filtered.vcf', fields='*') Check the number of SNPs in the filtered VCF files: In [ ]: Copied! print ( \"Number of SNPs in the filtered S10 RNA-seq VCF file:\" , vcf_S10_RNA_filtered [ 'variants/POS' ] . size ) print ( \"Number of SNPs in the filtered Ti RNA-seq VCF file:\" , vcf_Ti_RNA_filtered [ 'variants/POS' ] . size ) print(\"Number of SNPs in the filtered S10 RNA-seq VCF file:\", vcf_S10_RNA_filtered['variants/POS'].size) print(\"Number of SNPs in the filtered Ti RNA-seq VCF file:\", vcf_Ti_RNA_filtered['variants/POS'].size) Plot same histograms for the filtered VCF files. In [ ]: Copied! In [ ]: Copied! Question: Is it relevant to filter for false positives in this case, and what parameters would you look further into?","title":"VCF files processing and filtering"},{"location":"nb/vcf.html#genotypes-counting","text":"We want to count the different genotypes in each of the five filtered VCF files generated previously. To make this task easier we can take advantage of the counting functions in the scikit-allel package. We have used those to implement a little function called position_count in the file ../Scripts/pythonScripts.sh . You can then use the function with each of the 5 filtered VCF files to count the number of positions. You need to fill the names of the VCF files imported in Python. In [ ]: Copied! #Contig 1 filtered VCF position_count ( Hifi_Contig1_filtered ) #Contig 1 filtered VCF position_count(Hifi_Contig1_filtered) In [ ]: Copied! #Contig 2 filtered VCF position_count () #Contig 2 filtered VCF position_count() In [ ]: Copied! #Contig 1 and 2 filtered VCF position_count () #Contig 1 and 2 filtered VCF position_count() In [ ]: Copied! #RNAseq S10 filtered VCF position_count () #RNAseq S10 filtered VCF position_count() In [ ]: Copied! #RNAseq Ti filtered VCF position_count () #RNAseq Ti filtered VCF position_count()","title":"Genotypes counting"},{"location":"nb/vcf.html#comparison-of-vcf-files","text":"Compare the VCF files with subgenome variants and the VCF files with S10/Tienshan genotype specific variants obtained from RNA-seq mapping We want now to find overlapping position between the Contig 1 and Contig 2 Hifi VCF files and the Ti RNAseq VCF file. To ease this task we will first create two merged VCF files containing: Hifi Contig 1 VCF + Ti RNAseq VCF Hifi Contig 2 VCF + Ti RNAseq VCF Once again, we will use bcftools for this task, using the merge function. However, the bcftools merge function works only with compressed and indexed VCF files, so first we will do this. In [ ]: Copied! ! bcftools view results / VCF_Files / HIFI_Contig_1_filtered . vcf - Oz - o results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz ! bcftools index results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz - f - o results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz . csi ! bcftools view results / VCF_Files / HIFI_Contig_2_filtered . vcf - Oz - o results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz ! bcftools index results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz - f - o results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz . csi ! bcftools view results / VCF_Files / RNA_TI_merged_filtered . vcf - Oz - o results / VCF_Files / RNA_TI_merged_filtered . vcf . gz ! bcftools index results / VCF_Files / RNA_TI_merged_filtered . vcf . gz - f - o results / VCF_Files / RNA_TI_merged_filtered . vcf . gz . csi !bcftools view results/VCF_Files/HIFI_Contig_1_filtered.vcf -Oz -o results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz !bcftools index results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz -f -o results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz.csi !bcftools view results/VCF_Files/HIFI_Contig_2_filtered.vcf -Oz -o results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz !bcftools index results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz -f -o results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz.csi !bcftools view results/VCF_Files/RNA_TI_merged_filtered.vcf -Oz -o results/VCF_Files/RNA_TI_merged_filtered.vcf.gz !bcftools index results/VCF_Files/RNA_TI_merged_filtered.vcf.gz -f -o results/VCF_Files/RNA_TI_merged_filtered.vcf.gz.csi Now we can merge Contig 1 VCF with the Ti RNA VCF and keep only positions that match between the two files by filtering out any positions with missing genotypes. In [ ]: Copied! ! bcftools merge results / VCF_Files / HIFI_Contig_1_filtered . vcf . gz results / VCF_Files / RNA_TI_merged_filtered . vcf . gz | bcftools filter - e 'GT=\"./.\"' > results / VCF_Files / TI_Contig_1_merged . vcf !bcftools merge results/VCF_Files/HIFI_Contig_1_filtered.vcf.gz results/VCF_Files/RNA_TI_merged_filtered.vcf.gz | bcftools filter -e 'GT=\"./.\"' > results/VCF_Files/TI_Contig_1_merged.vcf Merge Contig 2 VCF with the Ti RNA VCF and keep only positions that match between the two files. In [ ]: Copied! ! bcftools merge results / VCF_Files / HIFI_Contig_2_filtered . vcf . gz results / VCF_Files / RNA_TI_merged_filtered . vcf . gz | bcftools filter - e 'GT=\"./.\"' > results / VCF_Files / TI_Contig_2_merged . vcf !bcftools merge results/VCF_Files/HIFI_Contig_2_filtered.vcf.gz results/VCF_Files/RNA_TI_merged_filtered.vcf.gz | bcftools filter -e 'GT=\"./.\"' > results/VCF_Files/TI_Contig_2_merged.vcf Take a look at the merged VCF file. You can see that compared with the VCF files for just one sample, there is now a new column in the file, with the corresponding genotype for the second sample that we merged. In [ ]: Copied! ! cat results / VCF_Files / TI_Contig_1_merged . vcf | head - 50 !cat results/VCF_Files/TI_Contig_1_merged.vcf | head -50 In order to count the genotypes for the matching positions we can use once again bcftools filter command. Only that this time we will filter based on the genotype columns for the two samples (GT[0] and GT[1]). GT[0] repesents the genotype for the first sample in the merge command and GT[1] is the genotype for the second sample in the merge command. AR - heterozygous AA - homozygous alternate For example, the filter GT[0]=\"AR\" && GT[1]=\"AR\" will keep only position in which both samples have a heterozygous genotype. A quick way to count how many positions are in the VCF file is by suing the simple bash command grep . This will count all the lines in the VCF files that do not start with a # , thus all the data lines. In [ ]: Copied! ! echo \"AR + AR:\" ! bcftools filter - i 'GT[0]=\"AR\" && GT[1]=\"AR\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" ! echo \"AR + AA:\" ! bcftools filter - i 'GT[0]=\"AR\" && GT[1]=\"AA\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" ! echo \"AA + AR:\" ! bcftools filter - i 'GT[0]=\"AA\" && GT[1]=\"AR\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" ! echo \"AA + AA:\" ! bcftools filter - i 'GT[0]=\"AA\" && GT[1]=\"AA\"' results / VCF_Files / TI_Contig_1_merged . vcf | grep - c - v \"^#\" !echo \"AR + AR:\" !bcftools filter -i 'GT[0]=\"AR\" && GT[1]=\"AR\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" !echo \"AR + AA:\" !bcftools filter -i 'GT[0]=\"AR\" && GT[1]=\"AA\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" !echo \"AA + AR:\" !bcftools filter -i 'GT[0]=\"AA\" && GT[1]=\"AR\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" !echo \"AA + AA:\" !bcftools filter -i 'GT[0]=\"AA\" && GT[1]=\"AA\"' results/VCF_Files/TI_Contig_1_merged.vcf | grep -c -v \"^#\" Use the same commands as above to count the positions marching the filters in the Contig2_Ti_merged.vcf. In [ ]: Copied!","title":"Comparison of VCF files"},{"location":"nb/vcf.html#wrapping-up","text":"In this exercise, you identified polymorphisms between the two white clover subgenomes using Hifi reads alignment files and SNPs specific for the white clover Tienshan genotype using RNA-seq alignment files. You learned how to import VCF files in Python to extract data and created plots. You also filtered low-quality variants from the VCF files and counted genotypes.","title":"Wrapping up  \ud83c\udf89 \ud83c\udf89 \ud83c\udf89"}]}
\ No newline at end of file
diff --git a/sitemap.xml b/sitemap.xml
index 4fd341a..9c8f64c 100644
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          <loc>None</loc>
-         <lastmod>2023-06-27</lastmod>
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-         <lastmod>2023-06-27</lastmod>
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     <url>
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-         <lastmod>2023-06-27</lastmod>
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