Refseq Vizualization.ipynb

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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Analysis of Refseq annotations\n",
      "Unfortunately, due to the fact that on VEP's web interface, the refseq transcript set is grouped with \"other transcripts\" makes this analysis invalid. I've included this for completeness however."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "import pandas as pd\n",
      "import numpy as np\n",
      "import os\n",
      "import matplotlib.pyplot as plt\n",
      "from matplotlib_venn import venn3, venn3_circles, venn3_unweighted\n",
      "import seaborn\n",
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#These are defined by the way annovar defines precedence. I found empirically that stop_gain > frame_shift in annovar, hence the reverse\n",
      "precedence_dict = {\n",
      "\"splicing_variant\": 1,\n",
      "\"frameshift_variant\": 4,\n",
      "\"stop_gained\": 2,\n",
      "\"stop_lost\": 3,\n",
      "\"inframe_variant\": 5,\n",
      "\"nonsynonymous_variant\": 6,\n",
      "\"synonymous_variant\": 7,\n",
      "\"5_prime_UTR_variant\": 8,\n",
      "\"3_prime_UTR_variant\": 9,\n",
      "\"intron_variant\": 10,\n",
      "\"upstream_gene_variant\": 11,\n",
      "\"downstream_gene_variant\": 12,\n",
      "\"intergenic_variant\": 13,\n",
      "\"intron_variant\": 14,\n",
      "\"upstream_gene_variant\": 15,\n",
      "\"regulatory_region_variant\": 16,\n",
      "\"ignored\": 17\n",
      "}\n",
      "\n",
      "def ranked(col):\n",
      "    return max(col, key=lambda val: -1*precedence_dict[val])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with pd.get_store('classified_variant_store.h5') as store:\n",
      "    snpeff_subset = store.get(\"cftr_snpeff_refseq_subset\")\n",
      "grouped_snpeff_subset = snpeff_subset.groupby([\"Gene_Name\", \"POS\", \"REF\", \"ALT\"])\n",
      "grouped_snpeff_subset = grouped_snpeff_subset.agg({\"normalized_so_snpeff\": ranked})\n",
      "grouped_snpeff_subset = grouped_snpeff_subset.rename(columns={\"normalized_so_snpeff\": \"normalized_so_snpeff_max\"}).reset_index()\n",
      "grouped_snpeff_subset = pd.merge(grouped_snpeff_subset, snpeff_subset, how=\"left\", on=[\"POS\", \"REF\", \"ALT\", \"Gene_Name\"])\n",
      "grouped_snpeff_subset = grouped_snpeff_subset[grouped_snpeff_subset[\"normalized_so_snpeff_max\"] == grouped_snpeff_subset[\"normalized_so_snpeff\"]]\n",
      "#kludge ties are broken by taking the first element in the group (ie randomly; this should only really effect the transcript level comparisons, ie hgvs etc)\n",
      "grouped_snpeff_subset = grouped_snpeff_subset.groupby([\"Gene_Name\", \"POS\", \"REF\", \"ALT\"]).first()\n",
      "agg_snpeff = grouped_snpeff_subset.reset_index()\n",
      "del agg_snpeff[\"normalized_so_snpeff_max\"]\n",
      "del grouped_snpeff_subset\n",
      "del snpeff_subset\n",
      "agg_snpeff.rename(columns={\"Gene_Name\":\"Gene\"}, inplace=True)\n",
      "agg_snpeff[100000:100050]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>100000</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199805</td>\n",
        "      <td>    A</td>\n",
        "      <td>   AT</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>    c.1584+97*&gt;+T</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100001</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199805</td>\n",
        "      <td>    A</td>\n",
        "      <td> ATCA</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>  c.1584+97*&gt;+TCA</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100002</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199805</td>\n",
        "      <td>    A</td>\n",
        "      <td>    C</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>     c.1584+96A&gt;C</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100003</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199805</td>\n",
        "      <td>    A</td>\n",
        "      <td>    G</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>     c.1584+96A&gt;G</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100004</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199805</td>\n",
        "      <td>    A</td>\n",
        "      <td>    T</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>     c.1584+96A&gt;T</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100005</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199805</td>\n",
        "      <td>   AT</td>\n",
        "      <td>    A</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>    c.1584+97*&gt;-T</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100006</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199805</td>\n",
        "      <td>  ATA</td>\n",
        "      <td>    A</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>   c.1584+97*&gt;-TA</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100007</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199805</td>\n",
        "      <td> ATAT</td>\n",
        "      <td>    A</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>  c.1584+97*&gt;-TAT</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100008</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199806</td>\n",
        "      <td>    T</td>\n",
        "      <td>    A</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>     c.1584+97T&gt;A</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100009</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199806</td>\n",
        "      <td>    T</td>\n",
        "      <td>    C</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>     c.1584+97T&gt;C</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100010</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199806</td>\n",
        "      <td>    T</td>\n",
        "      <td>    G</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>     c.1584+97T&gt;G</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100011</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199806</td>\n",
        "      <td>    T</td>\n",
        "      <td>   TA</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>    c.1584+98*&gt;+A</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100012</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199806</td>\n",
        "      <td>    T</td>\n",
        "      <td> TAGT</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>  c.1584+98*&gt;+AGT</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100013</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199806</td>\n",
        "      <td>    T</td>\n",
        "      <td>   TC</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>    c.1584+98*&gt;+C</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100014</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199806</td>\n",
        "      <td>    T</td>\n",
        "      <td>   TG</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>    c.1584+98*&gt;+G</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100015</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199806</td>\n",
        "      <td>    T</td>\n",
        "      <td>  TGA</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>   c.1584+98*&gt;+GA</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100016</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199806</td>\n",
        "      <td>    T</td>\n",
        "      <td> TGAC</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>  c.1584+98*&gt;+GAC</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100017</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199806</td>\n",
        "      <td>    T</td>\n",
        "      <td>   TT</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>    c.1584+98*&gt;+T</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100018</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199806</td>\n",
        "      <td>    T</td>\n",
        "      <td>  TTA</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>   c.1584+98*&gt;+TA</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100019</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199806</td>\n",
        "      <td>   TA</td>\n",
        "      <td>    T</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>    c.1584+98*&gt;-A</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100020</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199806</td>\n",
        "      <td>  TAT</td>\n",
        "      <td>    T</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>   c.1584+98*&gt;-AT</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100021</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199806</td>\n",
        "      <td> TATA</td>\n",
        "      <td>    T</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>  c.1584+98*&gt;-ATA</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100022</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199807</td>\n",
        "      <td>    A</td>\n",
        "      <td>   AA</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>    c.1584+99*&gt;+A</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100023</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199807</td>\n",
        "      <td>    A</td>\n",
        "      <td>   AC</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>    c.1584+99*&gt;+C</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100024</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199807</td>\n",
        "      <td>    A</td>\n",
        "      <td>  ACG</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>   c.1584+99*&gt;+CG</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100025</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199807</td>\n",
        "      <td>    A</td>\n",
        "      <td> ACTG</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>  c.1584+99*&gt;+CTG</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100026</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199807</td>\n",
        "      <td>    A</td>\n",
        "      <td>   AG</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>    c.1584+99*&gt;+G</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100027</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199807</td>\n",
        "      <td>    A</td>\n",
        "      <td> AGTC</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>  c.1584+99*&gt;+GTC</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100028</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199807</td>\n",
        "      <td>    A</td>\n",
        "      <td>   AT</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>    c.1584+99*&gt;+T</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100029</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199807</td>\n",
        "      <td>    A</td>\n",
        "      <td>  ATA</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>   c.1584+99*&gt;+TA</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100030</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199807</td>\n",
        "      <td>    A</td>\n",
        "      <td>    C</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>     c.1584+98A&gt;C</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100031</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199807</td>\n",
        "      <td>    A</td>\n",
        "      <td>    G</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>     c.1584+98A&gt;G</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100032</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199807</td>\n",
        "      <td>    A</td>\n",
        "      <td>    T</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>     c.1584+98A&gt;T</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100033</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199807</td>\n",
        "      <td>   AT</td>\n",
        "      <td>    A</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>    c.1584+99*&gt;-T</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100034</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199807</td>\n",
        "      <td>  ATA</td>\n",
        "      <td>    A</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>   c.1584+99*&gt;-TA</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100035</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199807</td>\n",
        "      <td> ATAT</td>\n",
        "      <td>    A</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>  c.1584+99*&gt;-TAT</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100036</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199808</td>\n",
        "      <td>    T</td>\n",
        "      <td>    A</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>     c.1584+99T&gt;A</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100037</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199808</td>\n",
        "      <td>    T</td>\n",
        "      <td>    C</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>     c.1584+99T&gt;C</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100038</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199808</td>\n",
        "      <td>    T</td>\n",
        "      <td>    G</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>     c.1584+99T&gt;G</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100039</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199808</td>\n",
        "      <td>    T</td>\n",
        "      <td>   TA</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>   c.1584+100*&gt;+A</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100040</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199808</td>\n",
        "      <td>    T</td>\n",
        "      <td>   TC</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>   c.1584+100*&gt;+C</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100041</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199808</td>\n",
        "      <td>    T</td>\n",
        "      <td>   TG</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>   c.1584+100*&gt;+G</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100042</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199808</td>\n",
        "      <td>    T</td>\n",
        "      <td>  TGA</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>  c.1584+100*&gt;+GA</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100043</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199808</td>\n",
        "      <td>    T</td>\n",
        "      <td>  TGC</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>  c.1584+100*&gt;+GC</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100044</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199808</td>\n",
        "      <td>    T</td>\n",
        "      <td> TGCT</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> c.1584+100*&gt;+GCT</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100045</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199808</td>\n",
        "      <td>    T</td>\n",
        "      <td>   TT</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>   c.1584+100*&gt;+T</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100046</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199808</td>\n",
        "      <td>    T</td>\n",
        "      <td> TTAC</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> c.1584+100*&gt;+TAC</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100047</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199808</td>\n",
        "      <td>   TA</td>\n",
        "      <td>    T</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>   c.1584+100*&gt;-A</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100048</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199808</td>\n",
        "      <td>  TAT</td>\n",
        "      <td>    T</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>  c.1584+100*&gt;-AT</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100049</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199808</td>\n",
        "      <td> TATT</td>\n",
        "      <td>    T</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> c.1584+100*&gt;-ATT</td>\n",
        "      <td> intron_variant</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>50 rows \u00d7 9 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "        Gene        POS   REF   ALT ID  Effect Transcript_ID  \\\n",
        "100000  CFTR  117199805     A    AT  .  INTRON   NM_000492.3   \n",
        "100001  CFTR  117199805     A  ATCA  .  INTRON   NM_000492.3   \n",
        "100002  CFTR  117199805     A     C  .  INTRON   NM_000492.3   \n",
        "100003  CFTR  117199805     A     G  .  INTRON   NM_000492.3   \n",
        "100004  CFTR  117199805     A     T  .  INTRON   NM_000492.3   \n",
        "100005  CFTR  117199805    AT     A  .  INTRON   NM_000492.3   \n",
        "100006  CFTR  117199805   ATA     A  .  INTRON   NM_000492.3   \n",
        "100007  CFTR  117199805  ATAT     A  .  INTRON   NM_000492.3   \n",
        "100008  CFTR  117199806     T     A  .  INTRON   NM_000492.3   \n",
        "100009  CFTR  117199806     T     C  .  INTRON   NM_000492.3   \n",
        "100010  CFTR  117199806     T     G  .  INTRON   NM_000492.3   \n",
        "100011  CFTR  117199806     T    TA  .  INTRON   NM_000492.3   \n",
        "100012  CFTR  117199806     T  TAGT  .  INTRON   NM_000492.3   \n",
        "100013  CFTR  117199806     T    TC  .  INTRON   NM_000492.3   \n",
        "100014  CFTR  117199806     T    TG  .  INTRON   NM_000492.3   \n",
        "100015  CFTR  117199806     T   TGA  .  INTRON   NM_000492.3   \n",
        "100016  CFTR  117199806     T  TGAC  .  INTRON   NM_000492.3   \n",
        "100017  CFTR  117199806     T    TT  .  INTRON   NM_000492.3   \n",
        "100018  CFTR  117199806     T   TTA  .  INTRON   NM_000492.3   \n",
        "100019  CFTR  117199806    TA     T  .  INTRON   NM_000492.3   \n",
        "100020  CFTR  117199806   TAT     T  .  INTRON   NM_000492.3   \n",
        "100021  CFTR  117199806  TATA     T  .  INTRON   NM_000492.3   \n",
        "100022  CFTR  117199807     A    AA  .  INTRON   NM_000492.3   \n",
        "100023  CFTR  117199807     A    AC  .  INTRON   NM_000492.3   \n",
        "100024  CFTR  117199807     A   ACG  .  INTRON   NM_000492.3   \n",
        "100025  CFTR  117199807     A  ACTG  .  INTRON   NM_000492.3   \n",
        "100026  CFTR  117199807     A    AG  .  INTRON   NM_000492.3   \n",
        "100027  CFTR  117199807     A  AGTC  .  INTRON   NM_000492.3   \n",
        "100028  CFTR  117199807     A    AT  .  INTRON   NM_000492.3   \n",
        "100029  CFTR  117199807     A   ATA  .  INTRON   NM_000492.3   \n",
        "100030  CFTR  117199807     A     C  .  INTRON   NM_000492.3   \n",
        "100031  CFTR  117199807     A     G  .  INTRON   NM_000492.3   \n",
        "100032  CFTR  117199807     A     T  .  INTRON   NM_000492.3   \n",
        "100033  CFTR  117199807    AT     A  .  INTRON   NM_000492.3   \n",
        "100034  CFTR  117199807   ATA     A  .  INTRON   NM_000492.3   \n",
        "100035  CFTR  117199807  ATAT     A  .  INTRON   NM_000492.3   \n",
        "100036  CFTR  117199808     T     A  .  INTRON   NM_000492.3   \n",
        "100037  CFTR  117199808     T     C  .  INTRON   NM_000492.3   \n",
        "100038  CFTR  117199808     T     G  .  INTRON   NM_000492.3   \n",
        "100039  CFTR  117199808     T    TA  .  INTRON   NM_000492.3   \n",
        "100040  CFTR  117199808     T    TC  .  INTRON   NM_000492.3   \n",
        "100041  CFTR  117199808     T    TG  .  INTRON   NM_000492.3   \n",
        "100042  CFTR  117199808     T   TGA  .  INTRON   NM_000492.3   \n",
        "100043  CFTR  117199808     T   TGC  .  INTRON   NM_000492.3   \n",
        "100044  CFTR  117199808     T  TGCT  .  INTRON   NM_000492.3   \n",
        "100045  CFTR  117199808     T    TT  .  INTRON   NM_000492.3   \n",
        "100046  CFTR  117199808     T  TTAC  .  INTRON   NM_000492.3   \n",
        "100047  CFTR  117199808    TA     T  .  INTRON   NM_000492.3   \n",
        "100048  CFTR  117199808   TAT     T  .  INTRON   NM_000492.3   \n",
        "100049  CFTR  117199808  TATT     T  .  INTRON   NM_000492.3   \n",
        "\n",
        "             hgvs_snpeff normalized_so_snpeff  \n",
        "100000     c.1584+97*>+T       intron_variant  \n",
        "100001   c.1584+97*>+TCA       intron_variant  \n",
        "100002      c.1584+96A>C       intron_variant  \n",
        "100003      c.1584+96A>G       intron_variant  \n",
        "100004      c.1584+96A>T       intron_variant  \n",
        "100005     c.1584+97*>-T       intron_variant  \n",
        "100006    c.1584+97*>-TA       intron_variant  \n",
        "100007   c.1584+97*>-TAT       intron_variant  \n",
        "100008      c.1584+97T>A       intron_variant  \n",
        "100009      c.1584+97T>C       intron_variant  \n",
        "100010      c.1584+97T>G       intron_variant  \n",
        "100011     c.1584+98*>+A       intron_variant  \n",
        "100012   c.1584+98*>+AGT       intron_variant  \n",
        "100013     c.1584+98*>+C       intron_variant  \n",
        "100014     c.1584+98*>+G       intron_variant  \n",
        "100015    c.1584+98*>+GA       intron_variant  \n",
        "100016   c.1584+98*>+GAC       intron_variant  \n",
        "100017     c.1584+98*>+T       intron_variant  \n",
        "100018    c.1584+98*>+TA       intron_variant  \n",
        "100019     c.1584+98*>-A       intron_variant  \n",
        "100020    c.1584+98*>-AT       intron_variant  \n",
        "100021   c.1584+98*>-ATA       intron_variant  \n",
        "100022     c.1584+99*>+A       intron_variant  \n",
        "100023     c.1584+99*>+C       intron_variant  \n",
        "100024    c.1584+99*>+CG       intron_variant  \n",
        "100025   c.1584+99*>+CTG       intron_variant  \n",
        "100026     c.1584+99*>+G       intron_variant  \n",
        "100027   c.1584+99*>+GTC       intron_variant  \n",
        "100028     c.1584+99*>+T       intron_variant  \n",
        "100029    c.1584+99*>+TA       intron_variant  \n",
        "100030      c.1584+98A>C       intron_variant  \n",
        "100031      c.1584+98A>G       intron_variant  \n",
        "100032      c.1584+98A>T       intron_variant  \n",
        "100033     c.1584+99*>-T       intron_variant  \n",
        "100034    c.1584+99*>-TA       intron_variant  \n",
        "100035   c.1584+99*>-TAT       intron_variant  \n",
        "100036      c.1584+99T>A       intron_variant  \n",
        "100037      c.1584+99T>C       intron_variant  \n",
        "100038      c.1584+99T>G       intron_variant  \n",
        "100039    c.1584+100*>+A       intron_variant  \n",
        "100040    c.1584+100*>+C       intron_variant  \n",
        "100041    c.1584+100*>+G       intron_variant  \n",
        "100042   c.1584+100*>+GA       intron_variant  \n",
        "100043   c.1584+100*>+GC       intron_variant  \n",
        "100044  c.1584+100*>+GCT       intron_variant  \n",
        "100045    c.1584+100*>+T       intron_variant  \n",
        "100046  c.1584+100*>+TAC       intron_variant  \n",
        "100047    c.1584+100*>-A       intron_variant  \n",
        "100048   c.1584+100*>-AT       intron_variant  \n",
        "100049  c.1584+100*>-ATT       intron_variant  \n",
        "\n",
        "[50 rows x 9 columns]"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with pd.get_store('classified_variant_store.h5') as store:\n",
      "    vep_subset = store.get(\"cftr_vep_refseq_subset\")\n",
      "del vep_subset[\"Feature\"]\n",
      "vep_subset.drop_duplicates(inplace=True)\n",
      "grouped_vep_subset = vep_subset.groupby([\"Gene\", \"POS\", \"REF\", \"ALT\"])\n",
      "grouped_vep_subset = grouped_vep_subset.agg({\"normalized_so_vep\": ranked})\n",
      "grouped_vep_subset = grouped_vep_subset.rename(columns={\"normalized_so_vep\": \"normalized_so_vep_max\"}).reset_index()\n",
      "grouped_vep_subset = pd.merge(grouped_vep_subset, vep_subset, how=\"left\", on=[\"POS\", \"REF\", \"ALT\", \"Gene\"])\n",
      "grouped_vep_subset = grouped_vep_subset[grouped_vep_subset[\"normalized_so_vep_max\"] == grouped_vep_subset[\"normalized_so_vep\"]]\n",
      "grouped_vep_subset = grouped_vep_subset.groupby([\"Gene\", \"POS\", \"REF\", \"ALT\"]).first()\n",
      "agg_vep = grouped_vep_subset.reset_index()\n",
      "del grouped_vep_subset\n",
      "del vep_subset\n",
      "del agg_vep[\"normalized_so_vep_max\"]\n",
      "agg_vep[80000:80023]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>80000</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117182109</td>\n",
        "      <td>    A</td>\n",
        "      <td>    C</td>\n",
        "      <td> .</td>\n",
        "      <td>   missense_variant</td>\n",
        "      <td>      NP_000483.3:p.Asn386HisNM_000492.3:c.1156A&gt;C</td>\n",
        "      <td> nonsynonymous_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>80001</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117182109</td>\n",
        "      <td>    A</td>\n",
        "      <td>    G</td>\n",
        "      <td> .</td>\n",
        "      <td>   missense_variant</td>\n",
        "      <td>      NP_000483.3:p.Asn386AspNM_000492.3:c.1156A&gt;G</td>\n",
        "      <td> nonsynonymous_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>80002</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117182109</td>\n",
        "      <td>    A</td>\n",
        "      <td>    T</td>\n",
        "      <td> .</td>\n",
        "      <td>   missense_variant</td>\n",
        "      <td>      NP_000483.3:p.Asn386TyrNM_000492.3:c.1156A&gt;T</td>\n",
        "      <td> nonsynonymous_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>80003</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117182109</td>\n",
        "      <td>   AA</td>\n",
        "      <td>    A</td>\n",
        "      <td> .</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td> NP_000483.3:p.Asn386ThrfsTer2NM_000492.3:c.115...</td>\n",
        "      <td>    frameshift_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>80004</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117182109</td>\n",
        "      <td>  AAC</td>\n",
        "      <td>    A</td>\n",
        "      <td> .</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td> NP_000483.3:p.Asn386IlefsTer24NM_000492.3:c.11...</td>\n",
        "      <td>    frameshift_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>80005</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117182109</td>\n",
        "      <td> AACT</td>\n",
        "      <td>    A</td>\n",
        "      <td> .</td>\n",
        "      <td>   inframe_deletion</td>\n",
        "      <td> NP_000483.3:p.Asn386_Leu387delinsIleNM_000492....</td>\n",
        "      <td>       inframe_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>80006</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117182110</td>\n",
        "      <td>    A</td>\n",
        "      <td>   AA</td>\n",
        "      <td> .</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td> NP_000483.3:p.Asn386LysfsTer25NM_000492.3:c.11...</td>\n",
        "      <td>    frameshift_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>80007</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117182110</td>\n",
        "      <td>    A</td>\n",
        "      <td>  AAC</td>\n",
        "      <td> .</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td> NP_000483.3:p.Asn386LysfsTer3NM_000492.3:c.115...</td>\n",
        "      <td>    frameshift_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>80008</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117182110</td>\n",
        "      <td>    A</td>\n",
        "      <td>  AAG</td>\n",
        "      <td> .</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td> NP_000483.3:p.Asn386LysfsTer3NM_000492.3:c.115...</td>\n",
        "      <td>    frameshift_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>80009</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117182110</td>\n",
        "      <td>    A</td>\n",
        "      <td> AATC</td>\n",
        "      <td> .</td>\n",
        "      <td>  inframe_insertion</td>\n",
        "      <td> NP_000483.3:p.Asn386delinsLysSerNM_000492.3:c....</td>\n",
        "      <td>       inframe_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>80010</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117182110</td>\n",
        "      <td>    A</td>\n",
        "      <td>   AC</td>\n",
        "      <td> .</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td> NP_000483.3:p.Thr388AsnfsTer23NM_000492.3:c.11...</td>\n",
        "      <td>    frameshift_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>80011</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117182110</td>\n",
        "      <td>    A</td>\n",
        "      <td> ACAT</td>\n",
        "      <td> .</td>\n",
        "      <td>  inframe_insertion</td>\n",
        "      <td> NP_000483.3:p.Asn386_Leu387insIleNM_000492.3:c...</td>\n",
        "      <td>       inframe_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>80012</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117182110</td>\n",
        "      <td>    A</td>\n",
        "      <td>   AG</td>\n",
        "      <td> .</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td> NP_000483.3:p.Asn386LysfsTer25NM_000492.3:c.11...</td>\n",
        "      <td>    frameshift_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>80013</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117182110</td>\n",
        "      <td>    A</td>\n",
        "      <td>   AT</td>\n",
        "      <td> .</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td> NP_000483.3:p.Thr388AsnfsTer23NM_000492.3:c.11...</td>\n",
        "      <td>    frameshift_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>80014</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117182110</td>\n",
        "      <td>    A</td>\n",
        "      <td>    C</td>\n",
        "      <td> .</td>\n",
        "      <td>   missense_variant</td>\n",
        "      <td>      NP_000483.3:p.Asn386ThrNM_000492.3:c.1157A&gt;C</td>\n",
        "      <td> nonsynonymous_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>80015</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117182110</td>\n",
        "      <td>    A</td>\n",
        "      <td>    G</td>\n",
        "      <td> .</td>\n",
        "      <td>   missense_variant</td>\n",
        "      <td>      NP_000483.3:p.Asn386SerNM_000492.3:c.1157A&gt;G</td>\n",
        "      <td> nonsynonymous_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>80016</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117182110</td>\n",
        "      <td>    A</td>\n",
        "      <td>    T</td>\n",
        "      <td> .</td>\n",
        "      <td>   missense_variant</td>\n",
        "      <td>      NP_000483.3:p.Asn386IleNM_000492.3:c.1157A&gt;T</td>\n",
        "      <td> nonsynonymous_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>80017</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117182110</td>\n",
        "      <td>   AC</td>\n",
        "      <td>    A</td>\n",
        "      <td> .</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td>     NP_000483.3:p.Leu387TerNM_000492.3:c.1158delC</td>\n",
        "      <td>    frameshift_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>80018</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117182110</td>\n",
        "      <td>  ACT</td>\n",
        "      <td>    A</td>\n",
        "      <td> .</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td> NP_000483.3:p.Leu387AsnfsTer23NM_000492.3:c.11...</td>\n",
        "      <td>    frameshift_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>80019</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117182110</td>\n",
        "      <td> ACTT</td>\n",
        "      <td>    A</td>\n",
        "      <td> .</td>\n",
        "      <td>   inframe_deletion</td>\n",
        "      <td> NP_000483.3:p.Asn386_Leu387delinsLysNM_000492....</td>\n",
        "      <td>       inframe_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>80020</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117182111</td>\n",
        "      <td>    C</td>\n",
        "      <td>    A</td>\n",
        "      <td> .</td>\n",
        "      <td>   missense_variant</td>\n",
        "      <td>      NP_000483.3:p.Asn386LysNM_000492.3:c.1158C&gt;A</td>\n",
        "      <td> nonsynonymous_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>80021</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117182111</td>\n",
        "      <td>    C</td>\n",
        "      <td>   CA</td>\n",
        "      <td> .</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td> NP_000483.3:p.Leu387IlefsTer24NM_000492.3:c.11...</td>\n",
        "      <td>    frameshift_variant</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>80022</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117182111</td>\n",
        "      <td>    C</td>\n",
        "      <td>  CAC</td>\n",
        "      <td> .</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td> NP_000483.3:p.Leu387ThrfsTer2NM_000492.3:c.115...</td>\n",
        "      <td>    frameshift_variant</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>23 rows \u00d7 8 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "       Gene        POS   REF   ALT ID         Consequence  \\\n",
        "80000  1080  117182109     A     C  .    missense_variant   \n",
        "80001  1080  117182109     A     G  .    missense_variant   \n",
        "80002  1080  117182109     A     T  .    missense_variant   \n",
        "80003  1080  117182109    AA     A  .  frameshift_variant   \n",
        "80004  1080  117182109   AAC     A  .  frameshift_variant   \n",
        "80005  1080  117182109  AACT     A  .    inframe_deletion   \n",
        "80006  1080  117182110     A    AA  .  frameshift_variant   \n",
        "80007  1080  117182110     A   AAC  .  frameshift_variant   \n",
        "80008  1080  117182110     A   AAG  .  frameshift_variant   \n",
        "80009  1080  117182110     A  AATC  .   inframe_insertion   \n",
        "80010  1080  117182110     A    AC  .  frameshift_variant   \n",
        "80011  1080  117182110     A  ACAT  .   inframe_insertion   \n",
        "80012  1080  117182110     A    AG  .  frameshift_variant   \n",
        "80013  1080  117182110     A    AT  .  frameshift_variant   \n",
        "80014  1080  117182110     A     C  .    missense_variant   \n",
        "80015  1080  117182110     A     G  .    missense_variant   \n",
        "80016  1080  117182110     A     T  .    missense_variant   \n",
        "80017  1080  117182110    AC     A  .  frameshift_variant   \n",
        "80018  1080  117182110   ACT     A  .  frameshift_variant   \n",
        "80019  1080  117182110  ACTT     A  .    inframe_deletion   \n",
        "80020  1080  117182111     C     A  .    missense_variant   \n",
        "80021  1080  117182111     C    CA  .  frameshift_variant   \n",
        "80022  1080  117182111     C   CAC  .  frameshift_variant   \n",
        "\n",
        "                                                hgvs_vep  \\\n",
        "80000       NP_000483.3:p.Asn386HisNM_000492.3:c.1156A>C   \n",
        "80001       NP_000483.3:p.Asn386AspNM_000492.3:c.1156A>G   \n",
        "80002       NP_000483.3:p.Asn386TyrNM_000492.3:c.1156A>T   \n",
        "80003  NP_000483.3:p.Asn386ThrfsTer2NM_000492.3:c.115...   \n",
        "80004  NP_000483.3:p.Asn386IlefsTer24NM_000492.3:c.11...   \n",
        "80005  NP_000483.3:p.Asn386_Leu387delinsIleNM_000492....   \n",
        "80006  NP_000483.3:p.Asn386LysfsTer25NM_000492.3:c.11...   \n",
        "80007  NP_000483.3:p.Asn386LysfsTer3NM_000492.3:c.115...   \n",
        "80008  NP_000483.3:p.Asn386LysfsTer3NM_000492.3:c.115...   \n",
        "80009  NP_000483.3:p.Asn386delinsLysSerNM_000492.3:c....   \n",
        "80010  NP_000483.3:p.Thr388AsnfsTer23NM_000492.3:c.11...   \n",
        "80011  NP_000483.3:p.Asn386_Leu387insIleNM_000492.3:c...   \n",
        "80012  NP_000483.3:p.Asn386LysfsTer25NM_000492.3:c.11...   \n",
        "80013  NP_000483.3:p.Thr388AsnfsTer23NM_000492.3:c.11...   \n",
        "80014       NP_000483.3:p.Asn386ThrNM_000492.3:c.1157A>C   \n",
        "80015       NP_000483.3:p.Asn386SerNM_000492.3:c.1157A>G   \n",
        "80016       NP_000483.3:p.Asn386IleNM_000492.3:c.1157A>T   \n",
        "80017      NP_000483.3:p.Leu387TerNM_000492.3:c.1158delC   \n",
        "80018  NP_000483.3:p.Leu387AsnfsTer23NM_000492.3:c.11...   \n",
        "80019  NP_000483.3:p.Asn386_Leu387delinsLysNM_000492....   \n",
        "80020       NP_000483.3:p.Asn386LysNM_000492.3:c.1158C>A   \n",
        "80021  NP_000483.3:p.Leu387IlefsTer24NM_000492.3:c.11...   \n",
        "80022  NP_000483.3:p.Leu387ThrfsTer2NM_000492.3:c.115...   \n",
        "\n",
        "           normalized_so_vep  \n",
        "80000  nonsynonymous_variant  \n",
        "80001  nonsynonymous_variant  \n",
        "80002  nonsynonymous_variant  \n",
        "80003     frameshift_variant  \n",
        "80004     frameshift_variant  \n",
        "80005        inframe_variant  \n",
        "80006     frameshift_variant  \n",
        "80007     frameshift_variant  \n",
        "80008     frameshift_variant  \n",
        "80009        inframe_variant  \n",
        "80010     frameshift_variant  \n",
        "80011        inframe_variant  \n",
        "80012     frameshift_variant  \n",
        "80013     frameshift_variant  \n",
        "80014  nonsynonymous_variant  \n",
        "80015  nonsynonymous_variant  \n",
        "80016  nonsynonymous_variant  \n",
        "80017     frameshift_variant  \n",
        "80018     frameshift_variant  \n",
        "80019        inframe_variant  \n",
        "80020  nonsynonymous_variant  \n",
        "80021     frameshift_variant  \n",
        "80022     frameshift_variant  \n",
        "\n",
        "[23 rows x 8 columns]"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with pd.get_store('classified_variant_store.h5') as store:\n",
      "    annovar_subset = store.get(\"cftr_annovar_ensembl_subset\")\n",
      "grouped_annovar_subset = annovar_subset.groupby([\"Gene\", \"POS\", \"REF\", \"ALT\"])\n",
      "agg_annovar = grouped_annovar_subset.agg({\"normalized_so_annovar\": ranked}).reset_index()\n",
      "del annovar_subset['normalized_so_annovar']\n",
      "agg_annovar = pd.merge(agg_annovar, annovar_subset, on=[\"Gene\", \"POS\", \"REF\", \"ALT\"])\n",
      "del grouped_annovar_subset\n",
      "del annovar_subset\n",
      "agg_annovar[2000:2050]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2000</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119399</td>\n",
        "      <td>  GGT</td>\n",
        "      <td>    G</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> splicing</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2001</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119399</td>\n",
        "      <td> GGTA</td>\n",
        "      <td>    G</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> splicing</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2002</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119400</td>\n",
        "      <td>    G</td>\n",
        "      <td>    A</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> splicing</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2003</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119400</td>\n",
        "      <td>    G</td>\n",
        "      <td>    C</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> splicing</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119400</td>\n",
        "      <td>    G</td>\n",
        "      <td>   GA</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> splicing</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2005</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119400</td>\n",
        "      <td>    G</td>\n",
        "      <td> GAGC</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> splicing</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2006</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119400</td>\n",
        "      <td>    G</td>\n",
        "      <td>   GC</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> splicing</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119400</td>\n",
        "      <td>    G</td>\n",
        "      <td>   GG</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> splicing</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119400</td>\n",
        "      <td>    G</td>\n",
        "      <td> GGCA</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> splicing</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119400</td>\n",
        "      <td>    G</td>\n",
        "      <td>   GT</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> splicing</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119400</td>\n",
        "      <td>    G</td>\n",
        "      <td>  GTA</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> splicing</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119400</td>\n",
        "      <td>    G</td>\n",
        "      <td>  GTG</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> splicing</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119400</td>\n",
        "      <td>    G</td>\n",
        "      <td>    T</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> splicing</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119400</td>\n",
        "      <td>   GT</td>\n",
        "      <td>    G</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> splicing</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119400</td>\n",
        "      <td>  GTA</td>\n",
        "      <td>    G</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> splicing</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119400</td>\n",
        "      <td> GTAA</td>\n",
        "      <td>    G</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> splicing</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2016</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119401</td>\n",
        "      <td>    T</td>\n",
        "      <td>    A</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> splicing</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2017</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119401</td>\n",
        "      <td>    T</td>\n",
        "      <td>    C</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> splicing</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2018</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119401</td>\n",
        "      <td>    T</td>\n",
        "      <td>    G</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> splicing</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2019</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119401</td>\n",
        "      <td>    T</td>\n",
        "      <td>   TA</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> splicing</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2020</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119401</td>\n",
        "      <td>    T</td>\n",
        "      <td>  TAT</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> splicing</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2021</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119401</td>\n",
        "      <td>    T</td>\n",
        "      <td>   TC</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> splicing</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2022</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119401</td>\n",
        "      <td>    T</td>\n",
        "      <td> TCGT</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> splicing</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2023</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119401</td>\n",
        "      <td>    T</td>\n",
        "      <td>   TG</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> splicing</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2024</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119401</td>\n",
        "      <td>    T</td>\n",
        "      <td> TGCT</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> splicing</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2025</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119401</td>\n",
        "      <td>    T</td>\n",
        "      <td>  TGT</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> splicing</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2026</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119401</td>\n",
        "      <td>    T</td>\n",
        "      <td>   TT</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> splicing</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2027</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119401</td>\n",
        "      <td>   TA</td>\n",
        "      <td>    T</td>\n",
        "      <td>   intron_variant</td>\n",
        "      <td> intronic</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2028</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119401</td>\n",
        "      <td>  TAA</td>\n",
        "      <td>    T</td>\n",
        "      <td>   intron_variant</td>\n",
        "      <td> intronic</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2029</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119401</td>\n",
        "      <td> TAAA</td>\n",
        "      <td>    T</td>\n",
        "      <td>   intron_variant</td>\n",
        "      <td> intronic</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2030</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119402</td>\n",
        "      <td>    A</td>\n",
        "      <td>   AA</td>\n",
        "      <td>   intron_variant</td>\n",
        "      <td> intronic</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2031</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119402</td>\n",
        "      <td>    A</td>\n",
        "      <td>  AAG</td>\n",
        "      <td>   intron_variant</td>\n",
        "      <td> intronic</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2032</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119402</td>\n",
        "      <td>    A</td>\n",
        "      <td> AAGT</td>\n",
        "      <td>   intron_variant</td>\n",
        "      <td> intronic</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2033</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119402</td>\n",
        "      <td>    A</td>\n",
        "      <td>   AC</td>\n",
        "      <td>   intron_variant</td>\n",
        "      <td> intronic</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2034</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119402</td>\n",
        "      <td>    A</td>\n",
        "      <td> ACAG</td>\n",
        "      <td>   intron_variant</td>\n",
        "      <td> intronic</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2035</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119402</td>\n",
        "      <td>    A</td>\n",
        "      <td>   AG</td>\n",
        "      <td>   intron_variant</td>\n",
        "      <td> intronic</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2036</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119402</td>\n",
        "      <td>    A</td>\n",
        "      <td>  AGT</td>\n",
        "      <td>   intron_variant</td>\n",
        "      <td> intronic</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2037</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119402</td>\n",
        "      <td>    A</td>\n",
        "      <td>   AT</td>\n",
        "      <td>   intron_variant</td>\n",
        "      <td> intronic</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2038</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119402</td>\n",
        "      <td>    A</td>\n",
        "      <td>    C</td>\n",
        "      <td>   intron_variant</td>\n",
        "      <td> intronic</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2039</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119402</td>\n",
        "      <td>    A</td>\n",
        "      <td>    G</td>\n",
        "      <td>   intron_variant</td>\n",
        "      <td> intronic</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2040</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119402</td>\n",
        "      <td>    A</td>\n",
        "      <td>    T</td>\n",
        "      <td>   intron_variant</td>\n",
        "      <td> intronic</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2041</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119402</td>\n",
        "      <td>   AA</td>\n",
        "      <td>    A</td>\n",
        "      <td>   intron_variant</td>\n",
        "      <td> intronic</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2042</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119402</td>\n",
        "      <td>  AAA</td>\n",
        "      <td>    A</td>\n",
        "      <td>   intron_variant</td>\n",
        "      <td> intronic</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2043</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119402</td>\n",
        "      <td> AAAT</td>\n",
        "      <td>    A</td>\n",
        "      <td>   intron_variant</td>\n",
        "      <td> intronic</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2044</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119403</td>\n",
        "      <td>    A</td>\n",
        "      <td>   AA</td>\n",
        "      <td>   intron_variant</td>\n",
        "      <td> intronic</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2045</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119403</td>\n",
        "      <td>    A</td>\n",
        "      <td>   AC</td>\n",
        "      <td>   intron_variant</td>\n",
        "      <td> intronic</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2046</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119403</td>\n",
        "      <td>    A</td>\n",
        "      <td> ACAG</td>\n",
        "      <td>   intron_variant</td>\n",
        "      <td> intronic</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2047</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119403</td>\n",
        "      <td>    A</td>\n",
        "      <td>  ACT</td>\n",
        "      <td>   intron_variant</td>\n",
        "      <td> intronic</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2048</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119403</td>\n",
        "      <td>    A</td>\n",
        "      <td> ACTG</td>\n",
        "      <td>   intron_variant</td>\n",
        "      <td> intronic</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2049</th>\n",
        "      <td> ENSG00000001626</td>\n",
        "      <td> 117119403</td>\n",
        "      <td>    A</td>\n",
        "      <td>   AG</td>\n",
        "      <td>   intron_variant</td>\n",
        "      <td> intronic</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>50 rows \u00d7 7 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "                 Gene        POS   REF   ALT normalized_so_annovar  \\\n",
        "2000  ENSG00000001626  117119399   GGT     G      splicing_variant   \n",
        "2001  ENSG00000001626  117119399  GGTA     G      splicing_variant   \n",
        "2002  ENSG00000001626  117119400     G     A      splicing_variant   \n",
        "2003  ENSG00000001626  117119400     G     C      splicing_variant   \n",
        "2004  ENSG00000001626  117119400     G    GA      splicing_variant   \n",
        "2005  ENSG00000001626  117119400     G  GAGC      splicing_variant   \n",
        "2006  ENSG00000001626  117119400     G    GC      splicing_variant   \n",
        "2007  ENSG00000001626  117119400     G    GG      splicing_variant   \n",
        "2008  ENSG00000001626  117119400     G  GGCA      splicing_variant   \n",
        "2009  ENSG00000001626  117119400     G    GT      splicing_variant   \n",
        "2010  ENSG00000001626  117119400     G   GTA      splicing_variant   \n",
        "2011  ENSG00000001626  117119400     G   GTG      splicing_variant   \n",
        "2012  ENSG00000001626  117119400     G     T      splicing_variant   \n",
        "2013  ENSG00000001626  117119400    GT     G      splicing_variant   \n",
        "2014  ENSG00000001626  117119400   GTA     G      splicing_variant   \n",
        "2015  ENSG00000001626  117119400  GTAA     G      splicing_variant   \n",
        "2016  ENSG00000001626  117119401     T     A      splicing_variant   \n",
        "2017  ENSG00000001626  117119401     T     C      splicing_variant   \n",
        "2018  ENSG00000001626  117119401     T     G      splicing_variant   \n",
        "2019  ENSG00000001626  117119401     T    TA      splicing_variant   \n",
        "2020  ENSG00000001626  117119401     T   TAT      splicing_variant   \n",
        "2021  ENSG00000001626  117119401     T    TC      splicing_variant   \n",
        "2022  ENSG00000001626  117119401     T  TCGT      splicing_variant   \n",
        "2023  ENSG00000001626  117119401     T    TG      splicing_variant   \n",
        "2024  ENSG00000001626  117119401     T  TGCT      splicing_variant   \n",
        "2025  ENSG00000001626  117119401     T   TGT      splicing_variant   \n",
        "2026  ENSG00000001626  117119401     T    TT      splicing_variant   \n",
        "2027  ENSG00000001626  117119401    TA     T        intron_variant   \n",
        "2028  ENSG00000001626  117119401   TAA     T        intron_variant   \n",
        "2029  ENSG00000001626  117119401  TAAA     T        intron_variant   \n",
        "2030  ENSG00000001626  117119402     A    AA        intron_variant   \n",
        "2031  ENSG00000001626  117119402     A   AAG        intron_variant   \n",
        "2032  ENSG00000001626  117119402     A  AAGT        intron_variant   \n",
        "2033  ENSG00000001626  117119402     A    AC        intron_variant   \n",
        "2034  ENSG00000001626  117119402     A  ACAG        intron_variant   \n",
        "2035  ENSG00000001626  117119402     A    AG        intron_variant   \n",
        "2036  ENSG00000001626  117119402     A   AGT        intron_variant   \n",
        "2037  ENSG00000001626  117119402     A    AT        intron_variant   \n",
        "2038  ENSG00000001626  117119402     A     C        intron_variant   \n",
        "2039  ENSG00000001626  117119402     A     G        intron_variant   \n",
        "2040  ENSG00000001626  117119402     A     T        intron_variant   \n",
        "2041  ENSG00000001626  117119402    AA     A        intron_variant   \n",
        "2042  ENSG00000001626  117119402   AAA     A        intron_variant   \n",
        "2043  ENSG00000001626  117119402  AAAT     A        intron_variant   \n",
        "2044  ENSG00000001626  117119403     A    AA        intron_variant   \n",
        "2045  ENSG00000001626  117119403     A    AC        intron_variant   \n",
        "2046  ENSG00000001626  117119403     A  ACAG        intron_variant   \n",
        "2047  ENSG00000001626  117119403     A   ACT        intron_variant   \n",
        "2048  ENSG00000001626  117119403     A  ACTG        intron_variant   \n",
        "2049  ENSG00000001626  117119403     A    AG        intron_variant   \n",
        "\n",
        "     combined_effect hgvs  \n",
        "2000        splicing  NaN  \n",
        "2001        splicing  NaN  \n",
        "2002        splicing  NaN  \n",
        "2003        splicing  NaN  \n",
        "2004        splicing  NaN  \n",
        "2005        splicing  NaN  \n",
        "2006        splicing  NaN  \n",
        "2007        splicing  NaN  \n",
        "2008        splicing  NaN  \n",
        "2009        splicing  NaN  \n",
        "2010        splicing  NaN  \n",
        "2011        splicing  NaN  \n",
        "2012        splicing  NaN  \n",
        "2013        splicing  NaN  \n",
        "2014        splicing  NaN  \n",
        "2015        splicing  NaN  \n",
        "2016        splicing  NaN  \n",
        "2017        splicing  NaN  \n",
        "2018        splicing  NaN  \n",
        "2019        splicing  NaN  \n",
        "2020        splicing  NaN  \n",
        "2021        splicing  NaN  \n",
        "2022        splicing  NaN  \n",
        "2023        splicing  NaN  \n",
        "2024        splicing  NaN  \n",
        "2025        splicing  NaN  \n",
        "2026        splicing  NaN  \n",
        "2027        intronic  NaN  \n",
        "2028        intronic  NaN  \n",
        "2029        intronic  NaN  \n",
        "2030        intronic  NaN  \n",
        "2031        intronic  NaN  \n",
        "2032        intronic  NaN  \n",
        "2033        intronic  NaN  \n",
        "2034        intronic  NaN  \n",
        "2035        intronic  NaN  \n",
        "2036        intronic  NaN  \n",
        "2037        intronic  NaN  \n",
        "2038        intronic  NaN  \n",
        "2039        intronic  NaN  \n",
        "2040        intronic  NaN  \n",
        "2041        intronic  NaN  \n",
        "2042        intronic  NaN  \n",
        "2043        intronic  NaN  \n",
        "2044        intronic  NaN  \n",
        "2045        intronic  NaN  \n",
        "2046        intronic  NaN  \n",
        "2047        intronic  NaN  \n",
        "2048        intronic  NaN  \n",
        "2049        intronic  NaN  \n",
        "\n",
        "[50 rows x 7 columns]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vc_snpeff = agg_snpeff.groupby([\"normalized_so_snpeff\"]).size()\n",
      "vc_snpeff.name = \"SNPeff\"\n",
      "vc_vep = agg_vep.groupby([\"normalized_so_vep\"]).size()\n",
      "vc_vep.name = \"VEP\"\n",
      "vc_annovar = agg_annovar.groupby([\"normalized_so_annovar\"]).size()\n",
      "vc_annovar.name = \"Annovar\"\n",
      "vc_df = pd.DataFrame([vc_snpeff, vc_vep, vc_annovar])\n",
      "vc_df.transpose().plot(kind=\"barh\", fontsize=13, figsize=(16,8))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x13f517990>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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EoijsEBERERERERGLorBDSixPT8/iLkFERERERERKIYUdIiIiIiIiImJR9OhZ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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x13f507990>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "It's worth noting that the refseq + other transcripts set was used in VEP's web app. This explain the huge number of extra annotations in "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "master_df = pd.merge(agg_snpeff, agg_vep, how=\"outer\", on=[\"Gene\", \"POS\", \"REF\", \"ALT\"])\n",
      "master_df = pd.merge(master_df, agg_annovar, how=\"outer\", on=[\"Gene\", \"POS\", \"REF\", \"ALT\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "master_df[100000:100001]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>100000</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117199805</td>\n",
        "      <td> A</td>\n",
        "      <td> AT</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> c.1584+97*&gt;+T</td>\n",
        "      <td> intron_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>1 rows \u00d7 16 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "        Gene        POS REF ALT ID_x  Effect Transcript_ID    hgvs_snpeff  \\\n",
        "100000  CFTR  117199805   A  AT    .  INTRON   NM_000492.3  c.1584+97*>+T   \n",
        "\n",
        "       normalized_so_snpeff ID_y Consequence hgvs_vep normalized_so_vep  \\\n",
        "100000       intron_variant  NaN         NaN      NaN               NaN   \n",
        "\n",
        "       normalized_so_annovar combined_effect hgvs  \n",
        "100000                   NaN             NaN  NaN  \n",
        "\n",
        "[1 rows x 16 columns]"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for effect in master_df[\"normalized_so_snpeff\"].unique():\n",
      "    vep_effect = master_df[master_df[\"normalized_so_vep\"] == effect]\n",
      "    annovar_effect = master_df[master_df[\"normalized_so_annovar\"] == effect]\n",
      "    snpeff_effect = master_df[master_df[\"normalized_so_snpeff\"] == effect]\n",
      "    fig = plt.figure(figsize=(10,10), dpi=300)\n",
      "    fig.suptitle(effect, fontsize=14, fontweight='bold')\n",
      "    v = venn3_unweighted([set(vep_effect.index.values), set(snpeff_effect.index.values), set(annovar_effect.index.values)], set_labels=(\"VEP\", \"SNPeff\", \"Annovar\"))\n",
      "    plt.plot(fontsize=24)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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iYcJCLQKiSyEH25fch9/v/D0sm8sZKomBp5J6eoDVq0VXQQ7XNy2ClMrjItxK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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x13f238550>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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pdJQYeOwqmUz36zQ1mV0JWVh0WADthUkIW+6ZnFvhYABve5JQ2ddDGfhcPpwx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\ngYeIiIgi7/8DqGdoOcmaPTMAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x13f4e6610>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GThre9HHTcWHzhQw9FsHAYxX79+eOimDYoWHoXhnHJnmgcmTBEkxJws5yP/az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\n6DHwEBERUej9fxvJ8q+ETGKEAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x13f485810>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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dUingV78CcmwaR+MbnhXFUNh/i5Rt28Gdv3gSO7p7EdB1XHf1hWhrqhNdVlUV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\nSOgp8IiIiEjoKfCIiIhI6P1/A7Zbb9DKqvkAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x110d31310>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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KmCZmRjNogF92KWRhe3r34JkdzzD0SMQpLVneeQf461/zZ2QR9cNUBA43+ZHm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\nEZHIU+ARERGRyFPgERERkcj7/0hjZmTk96CsAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x13e82f9d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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e6UXAE+CsyWcxvmq806XIMVDgKUbNzfDYY9DZ6XQlUqBsoG1CmA6N1xmQpN/P\nS2GLDrWGSS8MDBaNXcS8hnlOlyIDpMBTbLZuza2voynn0ous30PTOB8JjUs5JpZpsrHCr6nrktcJ\ntSdw+sTT8Zgep0uRflLgKRa2ndvd/KWXnK5ECliq3MfBBsjYmnl0XAyDXeUhXvOohUx6NzI8kuVT\nl2svriKhwFMM0mlYvRq2b3e6Eilg8dogTTWpklg1ebi0lYV53hfTX1R6FfKGOGfqOTSUNzhdivRB\ngafQtbfnxuu0tjpdiRSw3ODkuIYmD4FIKMSaQIKM/rrSC9MwWTp+KTPrZjpdiuShwFPIDh6Ehx+G\npNZOkd51jAnTGlLXy1CKB4KsCaZIGmrrkd7Nq5/H4nGLnS5DeqHAU6j274dHHoGUO3ewlsHROj5M\nh19hZzgk/X7WhrPEtPmo5HHiqBM5ZfwpTpchPVDgKUR798Kjj+bG7oj0wAZaJoaIeEtr80+nZbxe\nXigztB2F5DWrbhZLxy/VdhQFRoGn0OzeDX/6k6adS69s06BpYoCYqWnTTsh6PLxc4aEJtb5K7xpH\nNrJswjKFngKiwFNIdu7MDVDWnljSC8trcnCC9sRymmWavFbpZw8KndK76SOmc/rE0xV6CoQCT6HY\nvj23VYTCjvQi6zM5MMFLSi0LBcE2DN6oDLBdCxRKHlNrpnLm5DMxDdPpUkqeAk8h2LYN/vzn3Gag\nIj3I+kz2T/CSVtgpLAZsqgyyQ6FH8phcPZmzp5yt0OMw/fWdtmWLwo7kdagbS2GnANnQ2JFkLEGn\nK5ECtq1tG49veZyspRZ8JynwOOmtt3IrKCvsSC8s0+DgBB9JNGanUBm2zeyOJA0EnC5FCtj29u08\ntuUxhR4HqUvLKW+8AX/7W26PLJEe2KbBgYkBEpqNVRQs0+TlCi8HDbXESe/GVozlvSe8F6/pdbqU\nkqPA44SNG+Gpp5yuQgqYbRg0TQoSM7XOTjGxPB5erDBpQev0SO9Gl4/m3BPOxefxOV1KSVGX1nDb\nskVhR/KygeZJIYWdImRms5zcaVGN3+lSpIDtjezl8a2PY9kazjCcFHiG0/798OSTTlchBa5lUpio\nqe0iipUnm2VBxKbCVpeF9G5Xxy6e2fmM02WUFAWe4dLRkVtBWevsSB4tE8JEPAo7xc6TSbMoZlBm\ne5wuRQrYxoMbeXX/q06XUTIUeIZDMpnbGyuhwafSu44xYTp9Cjtu4U2nWRj34kWr7Erv1uxaw9tt\nbztdRkn8rxfaAAAgAElEQVRQ4BlqlgWPPw5tbU5XIgUsNiJIa0hhx20CqSQLk5quLr2zsVm9bTUH\nowedLsX1FHiG2v/9H+zZ43QVUsBS5T6aqzWV2a0q4wnmZ8JOlyEFLGNl+NOWPxFJRZwuxdUUeIbS\nyy/n1tsR6UXW7+FgvY2FZmu4WX0kxgm2Qo/0LpaO8ejmR0lntaTBUFHgGSpbt8LatU5XIQXMNg0O\njvOSIeN0KTIMpnTEadAWFJJHS7yFJ7Y+gZbHGxoKPEPhwAH4y1+crkIKXPOEIElDW0aUCsO2mdOZ\n1nR1yWtnx05NVx8iWml5sHV2wh/+AHEtGie9ax8bpi1YeoOULctm1b1PsG33QXxeL/966XLG1FU7\nXdawSvr9PB1Kkzb00Su9O3X8qcwZNcfpMlxFLTyDKZXKTT9X2JE8YiODJRl2AJ55ZTOZbJYffPkS\nPvn+Zdz2uyedLmnYBVIpFqUCKO9IPs/ufJbtbdudLsNVFHgGi23DE09Aa6vTlUgBS5X7aKoq3W6s\n17buZsGsyQDMnDyat3bsd7giZ5THE8zLahCz9O7QdPXmWLPTpbiGAs9geekl2LXL6SqkgFkeg6YG\nA5vSvbSPJVKEg+/sM2WaBpZVmn+P+kiM8bYGMUvv0laaJ7Y+oZlbg0SBZzDs358LPCJ5tIwPkrZL\ne72dcNBPPPnO38CybEyzdFciboykKUeDmKV37cl2ntv1nNNluIICz/FKp3MzsjT2W/KI1IeIejS2\na/aUsazdsA2ATdv2MGVsncMVOcvMZjk57sHU9hOSx6amTdp+YhBoltbx+utftbig5JUq97GvPlPS\nXVmH2LbNqnv/zNbduWX0/23FexlXX+twVc7bXx7mZW9pDmSX/gl6g3x41ocJ+zT261gp8ByPrVtz\nA5VFemF7TPZO8pBGffCS34aqILsNbTAsvRtXOY7zTjgPw1CL4LFQl9axikZz+2SJ5NE6NqCwI/0y\nM5ImqI9kyWNXxy5eO/ia02UULb27joVtw5NPQrJ0pxdL32IjgnT6NG5H+seTzXJy0t/3HaWkrdm1\nhpZ4i9NlFCUFnmOxfj3s3u10FVLAsgEPLdVq2ZGBqYgnmGGFnC5DCljWzrJ622qyVtbpUoqOAs9A\nNTdrU1DpU/MYH1n0gSQDNzGSoBq19EjvWuItPL/neafLKDoKPAORzcLq1WBZTlciBSxSHyZuavCp\nHBvDsjkxrkGpkt+r+19lV4cWux0IBZ6BWLNGW0dIXlmfSVu5xnbJ8Qknk0xT15b04cm3nySR0cVV\nfynw9NfOnbBhg9NVSIFrHRtQV5YMikmRJGGtwix5xNIx/m+7Zgv3lwJPfySTuQUGRfKI1wS1mrIM\nGtOymJvyOV2GFLhtbdt4o0mL3/aHAk9/PP88xLQKqvTOMg1aRqhlRwZXVSzOBG0wKn1Ys3sNyYy6\n0vuiwNOXgwdh0yanq5AC1z42RMbWNHQZfNOiGXy2BjFL7xKZhGZt9YMCTz62DU8/rY1BJa9URYDO\ngLqyZGh4MxlOzGoAs+S36eAmmmJNTpdR0BR48nnzTThwwOkqpIDZQHOdjbakk6FUF4kxSmvzSB42\nNk/teEqfRXko8PQmmcxNQxfJo3N0mJSRcroMKQGzYvrAlvwORA/wZvObTpdRsPT+6c0LL0BC6xtI\n77I+k/awXiMyPAKpFNO1No/0QQOYe6fA05PmZti40ekqpMC1jwlioVW3ZfiMi6Y0gFnySmQSvLj3\nRafLKEgKPD1Zs0YDlSWvdMhLxKulCmR4ebJZZllhp8uQArfx4EbaE+1Ol1FwFHiOtHMn7NL+JJJf\nW70PRWJxQn00RhiP02VIAbNsizW7NQb1SAo8h7NteO45p6uQApes9BPTisriEMOymZ3WjC3J7+22\nt9nbudfpMgqKAs/hXn9dm4NKn9pGagyFOKs2lqDG1rYTkt9zu57TNPXDKPAckk7nZmaJ5BEbGSJh\naAaEOMy2mZVSt5bkdzB2kC2tW5wuo2Ao8BzyyisQVzeF9M4G2qoyTpchAkB5PMEYtM+W5Ld291qy\nlvb5AwWenEQCXn3V6SqkwEUaQqTRfllSOKbF1V0h+UVSETYe1DIroMCT89prkNGVu/TOMg3ay7Si\nshSWYDLJZC1GKH1Yf2A9lq01wxR4Mplc4BHJI1ofIouahaXwTEzodSn5RVIRtrZudboMxynwvPGG\ntpCQvGzDoCOs1h0pTIFUinG2xvJIfq/se8XpEhxX2oHHtmH9eqerkAIXGxkkg7o8pXBN1sRB6UNz\nvJldHaW9qG5pB56tW6Gjw+kqpMB1VKrLQApbOJFgFFqMUPIr9Vae0g48r5T2P770LV4bJIW6s6Tw\nTU2W9se59G13524ORg86XYZjSvcdsns3NDU5XYUUuI4apysQ6Z/KeIJqtfJIH17dX7pLsJRu4FHr\njvQhWRkggQa0S/GYltbqy5Lf1tatdCY7nS7DEaUZeJqbtSO69KljhPbMkuJSE0toJ3XJy8Yu2Vae\n0gw8WlVZ+pAO+4iZat2R4mLYNtMzAafLkAL3RvMbJDKl9/lWeoEnEoEt2kxN8uuo8zpdgsgxqYsl\n8JfgR7v0X8bK8NqB0ltwt/TeFevXg6UltqV3ltck6i29qx9xB9OymGyplUfye+3ga2Ss0lpfrLQC\nTzIJmzY5XYUUuOjIADbalFGKV4O2m5A+JDIJ3mh6w+kyhlVpBZ7XX9cmodKnaJlaAKW4BVMpRtia\noi75rT9QWjsNlFbgeestpyuQApcq95FE6/RL8ZuY1Tg0ya8j2cH+yH6nyxg2pRN4WlpyXyJ5RGp9\nTpcgMihGxBJ4bC2tIPltaS2dSTylE3g2b3a6AilwtmkQ86t1R9zBtCwmEnK6DClwW1q2YNml0Y1f\nGoHHthV4pE+xEUGytgZ7inuMSZbGiUyOXTwTZ3fHbqfLGBalEXj278+tvyOSR6RcM7PEXcoSCSps\njeWR/Da3lEaDQGkEHrXuSB8yQS8JrawsLjTZ0mwtye/ttrdLYk0e9wcey4KtW52uQgpcZKROCuJO\no+JpDDVeSh5pK832tu1OlzHk3B94du2ChK7cJb9oIOV0CSJDwpNJMxqtvCz5lUK3lvs7d0ukOyud\nzXLdn/7Eno4OUtks/7hkCfNGj+b6xx6jM5nEtm1uOu88Iskk33nyya7fe2XvXm65+GIWjB3Llx95\nhJZYjDK/nxvPPZfacJgV//u/Xffd2tzMh+bM4aplyxx4hkMnVe4jQ9rpMkSGzOishz0u/rTPZrI8\ndvtjtO5vxePxcOYnzmTtg2uJtccAaD/QzpjpY7jgcxew7rF1bPzbRjBgwQULmLFkBulUmkd+/Aix\nzhj+oJ9zrzyXcGXY4Wc1vHZ27CSZSRLwujccu/gtAKTT8PbbTlcxLP64aRO14TD/ef75tCcSXPyL\nX3DKhAlcPGsW506fzpqdO3mrqYkzp07l7o9+FIBH3niDhvJyTps0iTteeIEZdXV87pRTePj117l1\nzRq+euaZXffd2dbGvz70EP+4ZImTT3NIxKt8oMAjLladSEG501UMnVdXv4o34OWSGy6hZW8LD616\niBX/sQKARDTB/377fzljxRnEOmK8+udXWfHdFWRSGe788p3MWDKDVx5/hbqJdZzywVN4/dnXWfOH\nNZy58kyHn9XwsmyLra1bmVk30+lShoy7u7TefrtktpI4d8YMPn/qqQBYto3XNHlpzx72dXZy+W9+\nwx83bWLJhAld94+l0/x/zz7LV8/Mvalf2rOHd0+aBMCyyZN5dnv3/tzv/OUv/NuyZYR87luYLxYs\njdeIlC5vJsNI3DtOrXl3M5PmTgKgdnQtkdYIyVhuTa1nfvsMJ7/3ZMqqyghXhln53ZWYpkmkNYLX\nn7vm3/Pmnq7fnzxvMts3uH88S0/c3q3l7sCzpXRWkAz7fJT5/URSKb7wxz/yxaVL2d3eTlUwyB0f\n+QijKyr46dq1Xff/7fr1nDd9OtWh3MJkkWSS8kCuKbPM76cz+c4CfK8fPEg0ne4WmNwiE/SSQuN3\nxP3GZt13sXLIqImj2LouNzllz1t7iHfESSfTxNpj7HhtB7NPn911X8M0WPfYOn79jV8zc2muNSMZ\nTxII5z7//EF/V1gqNXsje4mk3LuEi3u7tOLx3IDlErK3o4PPPfggH58/nwtnzuTGv/6Vs6ZOBeCs\nqVP576ee6rrv///666y66KKu/y8PBIikcif+aCpFZTDY9bMHN27kYyeeOEzPYnjFavyAWniOlW14\nsfBj2T4sw4dle3NfeMlauespGwPsv3/HwLZtwMD4+470hklucVCD3D0MC4+ZxUMWw8jgIYVBGtNO\nYRrp3H1lwGqTaXDpsJQ5Z8yhZXcL995wL2Omj6FmdA3BsiAb/rqBmUtnYhjdt9g4aflJzD1rLr+/\n6ffs3LiTQChAKp77/EslUgTLgj09TEnY0rKFeQ3znC5jSLg38GzdmpuSXiKaolE++bvf8Y2zz+5q\niTl57Fie3LqVi2fNYu2uXUwbORKAzmSSVCZDfUVF1++fPGYMf9u6lbkNDfxt2zYWjB3b9bPndu7k\ns4sXD+8TGibxkFZW7okNWITJGGWk7QCW7SeTNclaBlbWJGuZZLO57tPhZBgGpglej43HY2MaWTxm\nLiB5jSReM47PiqIQezR/KkVVyEe74b7xavs272P87PGcseIM9m3dx74t+/D6vWzfsJ1TPnBK1/1a\n9rTwf/f9Hxf/68WYHhOPz4NhGIyZPoatL2+lYWoD217extjGsXkezd02t2xW4Ck620urD/Yna9bQ\nmUzy4+ee48fPPYcB3HjuuVz/2GP8+pVXqAwEuPmCCwDY1trKuKqqbr//D/PmcfWjj3LJvffi93i6\n7gvQHI1SFXTfFU/W7yFppqCEGwxsTLJGGVnCpKwA6ayfdMZLKm1gWfn+MM780WzbJpuFbBZy7UGH\nf4SFgGoMw8DrsfH7LXyeFD4zhc+I4yWGYZdmV8Uh4ywf7R73BZ6aMTU8/aOnWfPAGrw+L8s/vRyA\n1r2tVI1657OudkwtoyaO4ldf/xUYMGX+FMbNHEf91HoevfVR7r3hXjxeDxd87oLeHsr1muPNdCY7\nqQhU9H3nImPYtgvbhy0L7ryzZAYsy7GJjArRXBF3uoxhYxkhUlSRzgZJZX0k0x7S7jv35eXxGPh9\nWQLezN+DUCc+o6NkusnigSB/C2ldMsnv9ImnM2PkDKfLGHTubOE5cEBhR/oUL3P3SS5rlpO0Kkhm\nwsSTPtJ6S5DN2sSzJnH8gB8oxzTHEAxkCPmSBMwIXtoxXLqJbCiZIBzyElOXn+SxN7JXgado7Nnj\ndAVS4GyPSdx0T/eGDWSNCpJ25d8DjpdMxt2BbrBYlk0s7iEWDwNhTKOeQCBLyJ8LQD7aMWz3BIRx\nlo83Tfc8Hxl8ezrdeQ5V4JGSlKj0YVPcgSdtVJK0q0img8STXrLZwwOOws6xsmybeMIknggBIQxj\nFAGfRTCQJOiJErBbKeZB0bUZcPGSPDIIIqkIHckOKgOVTpcyqNwXeLJZ2L/f6SqkwCXLPE6XMHCG\nQZoq4tlqookgqW7jbxRwhopt2yRSBolUEAhimiMpC6YJ+zsJ0Fx0rT/lyZQCj/RpT+ceBZ6Ct3//\noSkcIr1KBopjyQIbyBg1xLLVROMBjcMpAJZl0xnz0hmrwTRqCIeyhH2dBI1mDLvwR4F7slmqbHdO\nT5fBs6dzD40jG50uY1C5L/CoO0v6YJsGSaOAp6MbBilqiGeqiCSCGotTwCwbIjEPEaoxqCYczhL2\nRQjRjFHAK3iPsr0KPJKXG8fxKPBIyUlW+LELbT0WwyBFLbFMFZG4/7BGSoWdYmED0ZiHKFUYRjWh\nYIYyf5QgTZgFFn40jkf6EkvHaEu0UR2sdrqUQeOuwJPJ5Kaki+SRLC+c8TuWESBq1dMRDWslBRex\n7UMzvyoxqKS8LEOFrxmf3ep0aQCUp9IKPNKnPZ17FHgK1v79JbWdhBybhN/510jaqKUzVUsk5lUb\njsvZQGfUSyf1BPz1VIY6CLEfA+fGGnozGcrxEini2WYy9PZ07mFW3Synyxg07go86s6SPtiGQcp0\npnvBNrzE7Xo64hUkC6uHQ4ZJMgUHU5V4zCoqyhKUeQ7itZ3ZnbrB9rPZUOCR3u3t3Ot0CYNKgUdK\nSqrCjzXM6+9kzAqimTo6owGyefenklKRtWzaOgO0MY5wyKLC30bQODisW1zUZGzwDdvDSRGKZ+K0\nxlupCdU4XcqgcE/gSafh4EGnq5ACN3zjdwwS1NGZqiYWN/9+m8KOHC0WN4nFa/F5R1ARjlJmHsC0\nh36/q4pURoFH+rSnc49rAo/Z912KxL59Gr8jfUoGhjh0GAYxYwx7ojPY3157WNgRyS+dsWnpCLOr\nbRJt2SlYRmhIH8+XThN20TWvDA03TU93z6tds7OkH1LmEK09YhjE7XraotVHrIAsMjA20B7x02lM\npKoiRbm5G3OIllGosb3ENI5H8jgQdc+51T2Bp63N6QqkwFlek8wgz0qxgaTRQFusWgORZVBZNrR2\n+Gk3p1BVFqfCsxdjkINPlW2y2xjUQ4rLRNNR0tk0Pk/x938q8EjJSJf7gUEaG2Hkxui0xWoVdGRI\nWZZNa2eQdnMy1eUJys09GPbgvOjKs7abBjbIEGlLtFFXVud0GcfNHYHHtqG93ekqpMClgoPzyZ40\n6miL15JI6tJYho9lQUtHkHZzKlXlUcqNPRjH2WIZTmc1cFn61JpoVeApGJEIWqZW+pL2H9+A5SQj\naE+MIJ7UJbE4J2vlBje3mydQXR6lzNhzzIsY+jMZDBtsZXfJoy3hjh4UdwSe1sJYrl0KW9p7bLP4\nsmY5LYnRxOKFsyWFSNaC5o4y2j3TqC1vI8S+AR/DsCyqDD9tBbbXlxQWBZ5CovE70g+pAe4ObRse\nItY42trCWMO4IJzIQGSycKC9mnCoktrAHjwDXLm52vbQphYeyUOBp5Ao8EgfMgEP1gCa/VPGSJqi\nI0mnQQsGSjGIxU3i8XHUVCYoN3di2P17vVdaBqjxUvLoSHZg2RamUdzd+cVd/SEKPNKHdFn/RmZa\nRpCW9AnsbTsUdkSKh01uYPO+6DRS5sh+/U5ZRgu2Sn6WbdGeKP6JQe4IPBrDI31I9zVDyzCIMpY9\nHZPpjLmj4VNKVyoNe1tH0pI+oc8Vm0MZ53Ztl+Lhhm6t4v9kj8chObybQUrxSfl675bKGFU0xxv+\nPs1c3VfiHp0xL9HEREZURAgbu3vcnNSXTuOxDbKGXvvSOzcEnuJv4VF3lvRD1nN0s72Nl/bsZHa3\njdaaOuJalgUH28vZH5tO2qju8T4VhhbjkfwUeAqBAo/0Q8bo3myfMkeyJzqNtkjAoYpEhlciZbC3\nfTTt1mRso/so5TIXnApkaLUmin/oSPG/yjV+R/pgA9m/r0hrGx7arcnsbR1JJqMmfCkttm3T1hlg\nf2waGaOi6/awpRZOya8t0YZd5MtzFH/gUQuP9CEb9GIDGbOS/bFptHWqVUdKWzIFe9vHErPHgWEQ\n0lLL0oeMlSGajjpdxnEp/sDT0eF0BVLgskEvMcaxt22MNvoU+TvLhoMd5TSlpuHLagyP9K3Yp6YX\nf+BJDNLu1+JKWTPIXs9MDraXYxV3a6zIkIjGTDrbJxG0xzpdihS4ZLa4Z0QXd+CxbUjpkl16lgiM\nZq+5lDjlTpciUtCMDCTa5xDOzNdOotKrZEaBxzkKO9IDG4P28BwOpOaSzXjJGBmnSxIpaEYm9x6J\nReoJJJbhscscrkgKUSpb3Ofc4g48WnBQjpA1AxwILqUtNrZrjbWsqZVkRfKysl0ng2QyBJFTCKIu\nLulOXVpOUuCRw6R9Vez3LCWR6H51mh3ApqEipSpgvLM4ZzbrIdE+h5DV6GBFUmjUpeUkdWnJ38UD\nY9iXXUQ6ffRsEwUekb75OWI1chviHRMJpd+FUeSnChkcauFxkmZoCdAZmsHB5IlYVs8v5wwawyPS\nF38vFwbx6EgCiVMx8Q9zRVJo1MLjJLXwlDQbg5bwu2iJT8q75adlH72Ploh058nzPkkkyvBEl+Kl\nchgrkkKjFh4naQxPybJMPweDp9IZG9nnfW3tgC7SJ7OP2ejptB+7czFBxgxPQVJwNEvLSQo8JSnt\nrWSfZynxRD/W1zEo+v1fRIaD0Y/3STZrkmg/kZA1bRgqkkKTyBT3MBIFHikqiUAD++1FpNP9G09g\nGwo7Iv3R7+UGbYh3TCGcOXkoy5EClMqmivoCsrgDj8bwlJR4cGxuMcGsp/+/VNyvcJFhYw6w6zcW\nqSOUXqSVmUtMMXdrFffpQC08JSMWnMjB5GzsAX64WqYGLIv0yzEM7o9HawilF1PspxLpv2IeuFzc\nr1IFnpIQDU2hKdE44LADFPsrXGTY9GcMT0/isSqCySUY9gBaXqVoFfPU9OI+HSjwuF4kNI2m+LRj\nnmelMTwi/WMcx2zGRLyCQPIUDLyDWJEUIrXwiAyBjnAjzfEpx3cQDS8Q6ZfjvTZIJMrwx7VAoRSu\n4g48ZnGXL71rD8+mNTbxuI9jm2rhEemPgQ5a7kkyGcIbOwUPwUGoSAqRxyjersviTgye4v3DS+/a\nwvNoi40bnIOphUekX46nS+twqVQQT2wJHjs0KMeTwmIaxRsbirdyUAuPC7WG59Meaxi041lHbogo\nIj061kHLPUmlAnhipyj0uJACj1MUeFylIzybjlj94B5ULxGR/hnkBeVSaR+e+GKN6XEZj1m8PSvF\nfTpQl5ZrRMLTaB2sbqzDqUtLpF+G4q2SSgXwJRZryrqLaAyPUxR4XCEWnEjL8c7G6oVhKfGI9Ic1\nRFcHyUSYQEorMruFurScoi6topcIjqEpOWOwW9O7KPCI9I89hJ+niXgl4czCITu+DB8FHqco8BS1\nVGAkB1MD3y5iIBR4RPrHMob2vRKL1hDOzB/Sx5ChpzE8TlGXVtFK+6o4kJmPZQ3tS9DMFPdLXGS4\nWMNw5R6L1BO2Zg3548jQ0Rgep6iFpyhlPWEO2AsGtuv5sdKsdJF+GeoWnkNiHeMJWdOG5bFk8KlL\nyylq4Sk6lulnv2cRmczw7LljZov7JS4yXLLDeDpIdE4haI8ftseTwaPA4xS18BSdJv8C0qnA8D2g\nXdxvUJHhMlSztHpi25DqbMRH9bA9phw/0zAxhqklcCgU95lALTxFpS08l3iiYtgfV4FHpG/ZYX6f\nWJaJET1ZCxMWkWL/LC3y6ou7/FISC06kPTbakccu9jepyHAYrjE8h0ulffiTC7VGT5Eo5gHLUOyB\nxzs840Dk+KR8NTSnZjj2+KZd3C9zkeGQceh9koiXU2bNdeSxZWC8ZnGfc4v7TFBe7nQF0gfL9NPE\nSVgOrodTzH3OIsMl4+A+LNHOBoL2JMceX/qnzF/mdAnHRYFHhoyNQZN/Iem0z9E6PBR3M6zIcMgO\n0Wrn/ZXqnI6fWmeLkLzK/cV9zlXgkSHTFp5HPOH8v5Gh8QEifco4PNbNsgys6HxMexhnccqAKPA4\nqay4m9fcLBqaREes3ukyAPBS3P3OIkPNMD1kCuDCIKNBzAVNgcdJXi8Eg05XIUdI+6poSU53uowu\n3mFa5FCkWFleZ7udD5dIlBG2TnS6DOmBAo/T1K1VUGzDpMmY7+gg5SMp8Ijkly2wGa/xyGgCdmG0\nEMs7FHicpsBTUNrDJ5JKFVarmyetQcsi+WQ9hdPCA7mVmLOx2Rh2YdVV6hR4nKbAUzCSwQY6Yg1O\nl3EUM1n8L3ORoZTyFFYLD+TG8wTT850uQ/7Oa3oJegvrYnagiv9MoMBTECzDR1N2NrbDU1t7YlhG\n0S+YJTKUkgW6dEM8Vqv1eQpEsbfugBsCj2ZqFYTW4Hwy6cINFX5D+/WI9CZRoIEHIB05AQ9hp8so\neWW+4j/XFn/gUQuP42LBSUTihb1gmKami/Qubhdu4MlmPfgSJzldRslTC08hUOBxVNYTpiV9gtNl\n9MlrKfCI9MTwesk6uK1EfyQS5YStRqfLKGkKPIUgHNau6Q5q8p1MNlu4V4eHaGq6SM+yBbQGTz7x\nyAR8do3TZZQsBZ5CYBgax+OQzvAMEoni+Nsr8Ij0rNCmpPfGtgyM+FytwuwQBZ5CoW6tYZf1hGhL\nTHC6jH7zpAq/FUrECZkCnJLem1QqSMie4XQZJanYd0oHtwSeGjVzDrfWwFwsq3hePmbKxCjwcQoi\nTkgX2ZINych4PHbI6TJKisfwUBmodLqM41Y8Z6x8RoxwuoKSkgiMJhqrdrqMgbHBb2pqusiRYkU2\ng9GyTPzpuU6XUVJqQ7WYRvHHheJ/BgAjRzpdQcmwDZMWuzhnSwQp7lVCRQafQYddXIEHIB6rJmCP\ndrqMkjEy7I5zrDsCT22tZmoNk87QTNKp4mwp8WeKs26RoWIHAlhF2tVrxRpxyyms0CnwFBKPR+N4\nhkHGU0Z7YqzTZRwzf0KBR+RwKV/xtnqm037C1kynyygJCjyFRt1aQ67VNxfLKs6rQQBv3OuKfmiR\nwRL3FPdFQCIyFi+apTuUTMOkNlTYK+n3l3s+/RV4hlQ8OI5YoshH6dsQNIv3ilZksEWM4liDpzeW\nZeBNnuh0Ga5WE6zBY7pjWQ8FHumTbXhoyUx3uoxB4beK+4pWZLAYpodIAe+h1V+JeCVBe7zTZbiW\nW7qzgCKbj5jPyJG5gcuW5XQlrtMZmk4mVtxXgocEMgEKeGNo17Msi3seuIfd+3bj9XpZ+cGVjBox\nyumySlI2EIAiHbB8pGzsBAjvAsN2uhTXGVXmnvene1p4PB6oq3O6CtexDB8dyeIdqHwkX9wdwa1Y\nvbzxZbLZLNf84zV88L0f5DcP/8bpkkpW0htwuoRBk077CdlTnC7DlRrKG5wuYdC4J/AA1Nc7XYHr\ndIYai2Jz0P7yJDx4DPc8n2KzeftmZk+fDcCUCVN4e9fbzhZUwop9wPKR0vGJuO2U5jS/x091sMgW\nmQbvce0AACAASURBVM3DXa8OBZ5BlTUDdCTck+4PCZlalt4piWSCUOCdv79pmljqhnZEh+2u1s5M\n2kfImuZ0Ga5SX1aPYbij2xMUeCSPjuDMotovq7/8WXdd2RaTYCBIIpXo+n/btjG1aOiwM7w+Ei4c\nzJaOjsN00dBUp9WXu+uc6q5PmnAYKot86nSByHrDdMbdM1jtcIGUe8YuFJsTJp7A+jfWA7BlxxbG\nNYxzuKLSlPa78z2QyXoJWu6YUVoI6svcFXjcF4Xr66Gjw+kqil67byZ2xj1NmYfzxXwYAQPb1oyO\n4XbS7JPYuHkjN/7kRgAu+9BlzhZUohI+93brJqNjMMrfwjbSTpdS1AwMV83QAjcGntGj4a23nK6i\nqGW8FUQS7t2B3sgYhMwQsWzM6VJKjmEYXPr+S50uo+S1mQFw6dCpbNZD2JpJzPOq06UUtbqyOnwe\nd43zcleXFsDEieCiQVZOaPc1Ytvu/huGMu69whXJy+cnYrlv/M7hktEGPGhV9eMxsWqi0yUMOvcF\nnlAIRrmrGW44pf3VRBPu2Dcln2BMH4ZSmhLBsNMlDLmsZRDINDpdRlGbVD3J6RIGnfsCD8CkSU5X\nULQ6vNMphaEt3rgXv8vWIRHpj05PabRuJmKjMNF7/FhUBiqpCdU4Xcagc2fgmei+prjhkPWGicbd\ns8hUX8rsMqdLEBlWhumh1S6NEGBZBkGty3NM3Ni6A24NPNXVuS8ZkE7/NNeP3TlcMKFuLSkt6WDY\nrWOVe5SKNuDW09xQcuP4HXDzK0HdWgNiGT46E6U19snX6dOid1JSYv7S6M46JJP1ao+tAQp6g67a\nP+tw7v20V7fWgERCU125qnI+hm0QNtw/gFMEcksCNOPOBQfzyca1uOVATKia4KrtJA7n3jPcqFG5\nGVvSJxuDztQYp8twRCit14iUBssfIGm79yO/N6lUgIBdmp9vx8Kt43fAzYHHMNTK00/x4HgyGXct\nMNVfwYjG8UhpiAdKtzXTSE1yuoSi4DW9jKt0b4uYewMPaBxPP3UywekSHGOmTIIehR5xv3azdF/n\niXgFPrTPYl/GVozFa7pvA4ZD3B14xo4Fr3v/8QZD2ldNIlHa07PD2dK98pXSYHi9dFCarbiH+DJT\nnS6h4Lm5OwvcHng8Hhg/3ukqClqnVx8CoZjG8Yi7JULlJbGgaD6J2EgMF24fOVgMDCZUubu1392B\nBzSOJw/b9BJNun8bib54Y15CJbL6rJSmJk9pt+ICWJZJ0J7kdBkFq768npDP3Z+DpRF4XDrF7njF\nAuNLbip6byrSFU6XIDIkbH+QTlstGwB20p3rywwGty42eDj3n+0CARjn3lHnxyOKpmoeEmwPYhru\nfztI6YmEFOYPSSTK8FLudBkFx8BgSo37F2gsjU/4WbOcrqDgZD0hEgm98Q8xsgblpv4e4jKGyQGj\ndGdn9cSfdX9LxkCNrxpPRcD9wbg0As+ECVCuk9nhooFJJT+I8UhlMY1zEHfJhMtIl+Big/mkS2wL\nnf6YXTfb6RKGRWm8EwwDZs50uoqCEs3UO11CwfFH/PjN0thJWkpDq1ch/kjptB8/I50uo2BUBipd\nvdjg4Uoj8AA0NoI2igQg5ashlSq9PXX6ZEO5pZZAcQmvj2YU4Hvizbh7+vVAzBw507V7Zx2pdBJA\nKART3D8oqz+iPvVh96asQ1fE4g7xcAVQGieygfp/7d15jJ3lfS/w7/Ou5z37mTO7xzOesfGGNxwD\nxthgMNg4tJTcVNyIxFWL+kelVmqj2+j2KlUqVfyRqk2Xm0pXuVKlVhUJaZRCoUlpSmguISQQEoIB\np8HGxjZeZ9/O+i73jzdeGXu2c87zLt9PNLJjZub87DnvOd/3WX5PtdwGEaO3vxtRhYp17etkl9Ey\n8fqJc/EyPAjM1oqyywgspaYgxZ4lFHJCCAwr7CB+I46jwvRWyC5DuqHCEBJafBa1xyvwdHcDbfFu\ntFcxe+DY7MlxM+kap7Uo3JyEhZKnyi4j0ESdgWdjR7wGAeIVeIDYj/KUFF7k8zEnTagK3ywovKYM\nhvb5VCtZCC++54u1J9vRlY7X5pX4BZ5bbgH0+D7Jy7Wc7BICT7gCWZ6sTGGlqBhm7515ua6AGePm\nqxva47dzOX6BR9f90BNDVbMTjsORi4VITaZis3OBoqWUzsHx+NxdCGHHsyePoRpY07ZGdhktF7/A\nA8R2Wqus8hyZhVKrKjJK9DuPUsQoCs6zY/iC1SvxHMldW1wLXY3fTEc8A09bm7+AOWbKdkF2CaGS\nncpylIdCpZrKocbRnQWzHQ1mDJsQxm2x8iXxDDxA7EZ5HC2JWo3z+ouhllWkBe+WKRyEUHBO5fN1\nsTS3R3YJLdWb6UU+kZddhhTxDTyDg0AyPn0qyjp3Zy1FZobTWhQO1VQGFZ6btWh2NV4j33E5N2su\n8b06VBW47TbZVbRMOYbDto2gl3Q2IqTAE0LgvMpwvhTVqgUF8Rj9bk+2Y1V+lewypIlv4AH8A0Uz\n0X+R8KCgUuVQ91JlZ+O5sJHCo5bMoATuwFwq043HCPjtvbfHel1ivAOPogAf+5jsKpquanbDdeP9\no14OY8aApViyyyCakxACF/Xo37g1k7A7ZJfQdN3pbqzMrZRdhlR8F7zlFqAQ7TncshqvbprNkKuw\nYSMFk22lMO3xuJjlqFQyQMR3t93ee7vsEqRj4BEC2LFDdhVNVbH5Zr1c5rSJhBqPeX4Kl2GDU67L\n5boKTBHdUZ6V2ZXoycRrN9pcGHgAf8dWRzSf7K7QUauZsssIPw/I8VgOChjXSmIixudBNZLqFGWX\n0DS3r+DoDsDAc8Xt0XxCVI1oBjkZEhMJGIohuwyiy0ZNhvBG8Zxo/lsOFYbQnuQuXYCB54q+PqA3\negfJ1dTo3rW0nAcUatFe70Xh4VgpjHoM4I1Sr0Wv/YSAwI7eaC/ZWAwGnqtFcJSn6nF+v5ESEwlY\nKndskXznzXh2y20W29agetEKPWuLa2PbVXkuDDxX6+oC+vtlV9FQ1Wp8ukm3SttMW6x7WZB8tXSO\nO7OawEB0lgAoQsH2nu2yywgUBp7rRWiUp67n2X+nCbRZjSepkzyKgjMaR26bQTjRmbLe2LERGZOv\nU1fju+H1ikVg9WrZVTREVYvO3UrQ5CZyUAQvH2q9UqaAKs/MagqnHo2AoCkabuuOz9FJC8WrZi47\ndvhdmEOuimjuOggCpaogD86NU4tpOs6IaK0zCZJaLQHhhf+Ijk2dm2DpXGt4vfC/qzdDLuefsxVy\nVYfnZzVTZiwDTeE6CmqdsVQbnIh3BJbJ8wSMkB+0bGkWtnZtlV1GIDHw3MgddwDp8AYGVzFQZ8PB\n5nKAYo3b/qk1HCuJizE51Vsm1Qv3NX13/90wNb72z4WB50Z0Hdi9W3YVS1bT22SXEAuJiQSSKnfC\nUbMJnDN5TbeEE951PKvyqzBUGJJdRmAx8NxMfz+wZo3sKpbE5i6ililMF7hNnZqqksljJgJrS8LA\nscM5imaoBnb3h/cmvRUYeOazaxeQCN8FUBfhnY4LG62kIS+4gJmaRNPwocobmFap18PZvXpn304k\ndY423wwDz3wSCT/0hEzd5RO/lTKjGegKD3GkxhtPF2FzoXLLuK4CFeF6/ezN9GJ9+3rZZQQeA89C\nrFkTug7MtsNFa60kHIH2Sjuntqih7FQGF7zwjTCHneaFZ8RWUzTcM3CP7DJCgYFnofbs8Rcyh4AH\nBbYdjlqjxJgyOLVFDSM0Haf06HT+DRPFC88U4o7eHcia7Ly9EAw8C5VKAXfeKbuKBanrOXie7Cri\nKTOSgalydI2W72K6HTVOZUkh3HA0d+xIdmBz52bZZYQGA89ibNgAdHfLrmJetsoOy7IIV6B9hlNb\ntDy1TB6jXjgXz0aBawe/S7EiFNwzcA9faxaBgWcxhADuvRdQg709tK5wh5ZM2qyGgsepCFoiXccp\nhTctMtkh2Jq+rXsbislwN0lsNQaexcrlgO3bZVdxU3WEYzg2yjKjGSSU4L9oUrAIIXAh3QFbdiEx\nZ9saFAT32Jh8Is/DQZeAgWcptm71T1UPqDDcnUSeBxSnizxRnRallM5j3OWGgyDQvGCOsgkI3Dtw\nL1Ql2DMNQcRX46VQFGDvXkAL5h2AzRfMQNDKGopucIMxBYtnJHBa4W6boFACOlK+uWszutJdsssI\nJQaepSoWA3nWlgcB12HyD4rkWJJnbdG8hFBwNlmEK7sQukx4wdtt2ZvpxZ0rwrFbOIgYeJZj7Vpg\n40bZVVzDVRPckh4kHlCcKHL4mW5qJlvAtBfMEeO4ClrgSekp7Bvcx11Zy8DAs1x33QV0dsqu4jJH\nCf52yrhRqgqKdU5t0dxcK4nTAZ0+ibUAtQVQhIIHhh6ApfP1fTkYeJZLVYEHHwSsYDwRHe4MCiRr\n3GIXZvooTcdJsx0A79qDxnODE3h2rdzFdTsNwMDTCKkUsG+f36dHMpeBJ7ByIzmkVN7J0y8JBWfT\nnaiym3IgeQHZ/LG2uBYbO4K1dCKsGHgapbc3EEdPOCJY8850FQ8ojhZhqMG5cyR5xnMdmOK6ncBy\nHPk/m6JVxO7+4G2OCSsGnkbasgUYGpJaggMGniATtkDHVAcUhZdenJUzBZ6CHnCuKzfwmKqJB1c/\nCE2RH7yigq+6jXbvvUBB3rECrheMYVi6Ma2soaPawd0WMeUk0ziphOc07rhyHAWy3iIFBO4fvJ+n\noDcYo2Oj6bq/iPmZZ4B6veUP7wRoZ8FCuJ6LL7/4ZZwYPgFd0/HZ/Z9Fb75XdllNl5hMoNBewBjG\nZJdCrWSYOGG0ATFZt+O5Ll58+ssYPnMCmqZj/2c+i3xHeK5v1bPgiNmWP+72nu1YmVvZ8seNOo7w\nNEM+73dilsCRPAy7WK8eexW2Y+OvH/9rPLHnCXzle1+RXVLLZEYzSKs86DU2FBUfpjpgxyTsAMCx\nt16F49h4/HN/jT2PPoHvfTNc17cqWr/7tj/Xj+09wT6vMawYeJplcBDYtq3lD+t54Wpw9+6Zd7Fj\ncAcAYEPPBhy9cFRyRS3kAW0jbTBVrruKg5FcJ2bccF2fy3Xm+LsY3Ohf3z2DG3DhVLiub9HiEfOs\nmcV9q+7jdHeTMPA00+23AytbOyzphezusVQrIWlcOXpBEQpcLz4N9oUj0DHRAVXE640wbmazRYwE\nqK9Lq9QqJRiJK9e3UBR4bniub9HC/kiGauDBoQdharwBahYGnmYSwl/P093dsocMW+BJGkmUa+XL\n/9/13NidMK5WVXRWO3lXF1F2OovTIp5Tl0YiiVr1yvXtuS5EqHYotuZGRFM0HFh9AMUkO7I3U5ie\neeGkacBDDwHt7S16wHC9ad664la8fuJ1AMDPz/4cQx1yt/XLYkwZ6LC5cytq3GQKx9X4dtheMXQr\nTrzjX99nT/wcHSvCdn03/y1SEQoeHHoQPZmepj9W3AnP41GTLVEuA88/D0xMNPVhTiv74brhedP0\nPA9f/u6XcXz4OADgDw/8Ifra+iRXJU+pWMKwGJZdBjWAZyXxvtEBW3YhEnmeh+8+/WUMn/Gv7wOH\n/hBtXeG5vpO5oyiJ4037/gIC+4b2YagQtiAYTgw8rTQ7Czz3HDA93bSHOCX2h25ai641U5zBqBiV\nXQYtg2daeD/RATtkI650LSv3PsriWNO+/70D92Jd+7qmfX+6Fqe0WimVAh5+GEgm5//cJWLYCb/0\naBptaJNdBi2VYeKExbATCU18Pb2r7y6GnRZj4Gm1bNYPPYnGt5X3+OOMjMxIBgVFXsduWiLdwIlk\nJ2q88YiI5ixa3tG7A5u7Njfle9ON8R1ShkIBOHjQ78rcQF7MdjdFXfZiFnklvgteQ0fTcTLVharH\n6zAqRBOC65auLWwsKAmvTFk6OvzdW1rjOiN77OUSOdmLWWRVnqcTdELTcDrdhTLDTrQ0+CZyfft6\n7Ozb2dDvSQvHq1Omnh7ggQeARvWl4AhP5AgIFC4WkFbi2cclDISq4sNMF2ZD1uWcFqCBAXZ1YTX2\n9O9p2PejxeM7pGz9/cB99/lNConm4gHFkSJSakp2JXQdoag4m+nCdMjOsKPW6s/1475BHhkhGwNP\nEKxeDexZfvIXbpw7fkScCxSHi0iqzdvhR4sjFBXnsp2Y9Bq7Fo8CRDjL/ha9mV48OPRg7DrIBxF/\nAkGxfj2wbx+gLn1YXHgMPFEmXIH2i+3IqBnZpcSe0DScyXZjosWHS1KLLTPwDOYHcXDNQagKpzuD\ngIEnSFavBj7+ccBY2ouogAehsI9klAlPoHChwC3rEnm6gZPpbkx5nMaKOm8ZN5GbOjfhgaEHGHYC\nhIEnaHp6gF/7NSC9tEWqimDgiToBgezFLIrgQYOt5iUsfJDqRokLlGPBW+IIz86+ndi1chfX7AQM\nA08QFQp+6Cku/g1NCLcJBVEQpUfS6HK7+KLaIm4yjffNTlTZVDBG6ov6bFWo2De4D1u6tjSpHloO\nBp6gSqWARx4B+hZ30B4DT7wkxhLornZzQWST1dNZHDWKsT4INI4WM8JjqiY+fsvHsbptdRMrouXg\nq2SQ6brfnHDt2gV/CQNP/BhTBnpKPVDZeLIpytk2vK8WwGOW48dbYMRNG2k8su4R9GR6mlwRLQcD\nT9ApCrB3L7B9Ya3IFQaeWNJmNfTM9MBQuWuokabzHTgpuCsuvuYPPEWriEfXP4qCxY0EQcfAExY7\ndvi9euZZr8ERnvhSyyq6xruQUBt/MG3sCAXj+S6c8dj3KM4c7+ZrePqyfXhk3SNI6nyehAEDT5hs\n2AAcOHDT87cYeOJNqSnoHOlkV+blUFVczHXjgsfgGHc3m9JaW1yLh9Y8BF1l48mwYOAJm/5+4Fd/\nFbCsOf+zwmWVsSccgeKFItrQJruU0HETFk5mejHG7skEwBVzj/Bs79mOvav2crNAyPCnFUYdHcCj\nj/q/XkcVNQkFUdAICGRGMuipcTHzQlUyeRw1O3niOQEAVNUBcO2IuaZouG/VfdjRu0NOUbQsvLLD\nKpPxt61v2nTNH6uoSiqIgsiYMtA71cszuG5CKCrG8134QMmBG7HoElW9drS8kCjgE+s/gVuKt0iq\niJaLvdHDTFWBXbuA7m7g5ZeBWg2KV5FdFQWMUlXQcbEDk+2TmPAmZJcTKJ5h4sNUB2ZcjoLRtRTl\nSuBZW1yL3f27oSl8ywwz/vSiYGjI78r84otQZxh4aA4ekBvOwcybGDFG4LjLPwU67OrpLD7Q8nBc\ndk6mjxJqDZqi4e6Vd2Nd+zrZ5VADCM9jO63IcBzUf/gWzh7hGUt0Y07CwUh2BBU3nuFYKAoms+04\n68298J8IADoKM7h3Qw/aLC7+jwqu4YkSVYVy13b8wuqAq/BHS3NTKyo6RzqRU3KyS2k9XceZbA/D\nDt2UZaexztrAsBMxfFeMGFUFLookXhU9KJnsI0JzE65A/mIenU4nlJiEYyeVwbFkD6Y8zuTT3FSh\nIjHZgfLJItKpeFwXccKfaARlMsCso+GVaic+TOYBcI0Czc0at9A72YuUEt1GhULTMJbrwlGtDTav\nBboB00sAp7tRGfF3NGZ4okjkMPBE0KUL1YPAu6UcfmJ2o2rwjCWam1pV0X6xHZ1OJ1QlWruV6uks\n3k/34iI42klzU6AgWSqgerwTTvXK6F86LbEoagoGngi6/kIdqRr4f7VunE4W4M1zFhfFlzVuoXei\nFxklAre2uoHhXDfeVwuoeXzO09wSrgXlTA9K57K4eiQ8kQB0NtuOHE5mR9BcQ7EeBI6UsjitJbFF\nG0O6Um59YRR4Sk1B28U2pHIpjJqjqLs3PzwxiKqZPE4pWTicvqIbUKHCmCygPDL3VC5Hd6KJgSeC\nbnaxTtsafmB3YsiaxVBtHKrDfiz0UeakiR61B1PFKUx6kwhF9wrDxPlkERM8B4tuwrLTqJ4poGzf\neIKD63eiiYEngrLZ+T/neDmFs2oCW6xxFMqzzS+KQkc4ArmLOVhpCyPJkQCP9giUswWcFhm4Ichl\nJIcOHcpoG8oT86/nYuCJJgaeCMpmAU0D7HkOTq84Kl4vt6PXTGG9MwZ9vi+gWDJmDPSUejBTnME4\nxgM12uMlLJxNtGGaW83phgSS1SzKZ3PwFthVu8jerZHEV4kIEgJobwfOn1/Y55+tWrggerE+OYXe\nyhQU153/iyhWhCuQGc7ASlqYTE9ixp2RW5CmYyJVwHkkAC5Kphuw3CTsC3mUSoub5mxvb1JBJBUD\nT0QtJvAAgOP5W9jfV9LYmJxER2kG4NnRdB2tpKFYKiKbyWLcGkfZbe3id6GqmE4VcE5JwmHQoRsw\nPBNiNI/y5OLbEeg6kM83oSiSjoEnojo6lvZ1FVfFT0ttyGoZbNQnkeP6HpqDPq2jc7oT1XwV4+Y4\nqk61qY8nFAXldB5nlDTqnmAWpznp0KFN5VEeTi75e7S3+6PkFD0MPBG11MBzyZSt40d2OzrNDNaJ\nCSQr8Txokm7OnDDRLbpRKpQwro3Ddhu7DkwIgWoqi3NaFmVPYdChOalQYczmUL7wy0C8DJzOii4G\nnojK5fyh2foyN9ZcrJq4iC70J0pY7UzAWO43pOjxgORYEpZiYbZtFhPKBBx3+e0OnGQa580cpl2N\nQYfmJCCQrGVROptF2WlMH93l3ixScDHwRNSlhcvnzjXm+52qJHEaFtYkZ7GyPgWdwYeuI1yB9Ega\nKS2FqcIUpjAF11v8Ang3YWE4UcC4pwNcP09zUISAWUujdiGH2Wpjj0Nh4IkuBp4I6+hoXOAB/G7N\nR0tpHEMKA1YJA84UErVa4x6AIkHYArnhHNJmGjO5GUx5Cws+XsLCWCKHYc/kiA7NSYECq5ZB5UIG\n5Vrjz30zjIX1MaNwYuCJsGbNRXsQ+KCcwgdIodcsY0hMIcU1PnQdtaoidzGHjJHBbHYWk2ISjnft\nVJcQAraVwoiR9Ud0GHRoDrrQoJcyKF9IY7ZBU1dz4YLlaGPgibBWDM2erVo4CwvtRg1r1CnkKiUg\nQI3pSD6lpiAzkkFaSaNcKGNSm0TdtVFLZXBBy2DGUxl0aE46dGjTWZSHU8tejLwQnM6KNgaeCMtm\n/SHaVsw6jdQMjKAdWa2Otfo02qozEOzzT1cRroA5kYFn9uB0UkFdKaHqNXc7O4VTAiYwkUVlNIlW\nrhbkDq1oY+CJsEsLl8+ebd1jTtk63rDbkFByWJ2cRWd9hju7CBXDwIdqBifKKbglAZQAjGRhZGpQ\n26ZQ0UrwOMwTawoUmHYSzngalSlTSg0c4Yk2Bp6I6+hobeC5pOKqeLeUxbvIojtRxUoxg0K1BMFj\nK2LDUxWMG0mcctO4UJ37Daw2bQDT7VB0B1bHDOrWLOotvacn2RIwIWbSqIwkG7a1fClMkwuWo46B\nJ+KCcMdyvmLiPEzoooBVVgm9zgwSNU5lRJIQmEkkcE6kcKqShF1e2LoLt66idDYHIAcjW4WWn0VV\nL8HB8vv5UPCoUGHWUqiPplFZ5DlXzRKE10pqLgaeiAvSnHTdU3C0nMZRpFHQaxjUZ9FWnYXq8E0t\n7KqmgQtqCidrKZTKy9suXJsyUZsyIUQBiUIFIjuDqlaGy8XwoSaEgOkkgOk0KqMWSgE7Cy1Ir5XU\nHAw8EZfNAuk0MCP5cOvrjdcNjNcNKMijL1FGt1JGtlpm+AkRW1MxaqRwyk5hrGo0/Pt7nkBlzALG\nLCiaC6tYgpuaQVVwdDA8BBKeCaWcRHXMQqUa3Lecnh7ZFVCzBffZRw3T3w8cOSK7irm5EDhVSeIU\nkhDw0G1W0aOWUaiVoNmNPZeJlq9u6JjQLJx3LJyrmvDs1tylu7aC8oU0gDQ0y4aRL8G1yqiIKrin\nPVgEBEzXglKyUBmzUKk3vkFgo+k60NsruwpqNgaeGBgYCG7guZoHgXPVBM4hAaCAolHDCq2ENrsM\nkx2dpfCEQMk0MaJYOFuzMFXTAck/CruswS5nAWQhVBdmoQKRKqOmlbnmRxIVKgzbAmYtVMctVJxg\nTVfNp68PUIOfy2iZGHhioLcX0DQgbAMmozUDozUDQB4ZzUafUULBrSBVq0Lhbq+msTUVk7qFYc/C\n2WoC9Yq8nTPz8RwFlZEkMJIEABiZGrRsGbZZRo1TX00jhIDu6tDqCbgzFirjJsoIV8i5Wn+/7Aqo\nFRh4YkBV/TuYDz6QXcnSTdsafm7/8q4eHtqMOjq0CgpuFal6lWt/lsFRVczqJiYVExfshB8yQxaO\nL6lNG/5Wd+Sg6A7MfAWwqnD0KuqiDo8Ln5dIIAEDSs2EO5vwF5bbiuzBvoYQgoEnLhh4YmJgINyB\n52oexFWjP768XkeXXkHeqyJdr0CzGYDmJlAxdExrJiZgYrhmYtrWEMWZILeuojycApACAH/6K1OD\nmqrCNaqoq7WPnO1FPgEBwzOh1kw4MwnUp83QTVMtVEcHYFmyq6BWYOCJiajfwUzUdUzUdQAZAEBG\ns9Gm1ZBV6kh5NVh2HYZtx+6cL1vTUNIMTCkmxhwDw3UTdk1IX4cjg+coqE4kgInE5T8zUnWoqSpg\nVeFqNdRQj13HZ02o0FwdSt2AV9PhlHXUpg1UA7ZtvFkGBmRXQK3CwBMTlgV0dgIXL8qupDWmbc0f\nubiKKjzkjTryag0ZUUfKrSNh10I/GuQpCmqahrKqoyw0THs6phwdk7YO2xahnZ5qhdqsDszqANKX\n/0yzbGiJOpREHZ5uw9XqsEU99AuiFSjQPB2qo0PUdDhlA/VZHXZdjfVTJOo3g3QFA0+MDAzEJ/DM\nxfF+ORWGa3vGGIqLjGYjpdhIKg4SsJHwbBiuA91xoDoOhKyRISHgKApsVYWtKKgrKqpQUYGGaVfD\npK1jxtZiOWLTLP4uMA3AtfMciuZCT/pBSBg2PNWBp7jwhANXcWB7jtTRIfXS/zwVwtGAugqvrsGt\nqXCqGuyKxqfJddJpoFiUXQW1CgNPjPT3Az/+sewqgqfmKnMGoauZioOk6sBSXOjChSo8aPB/125Y\n/QAAElpJREFUVXHpw4XqeVB++f8Vz99J5kHAhYAnBDz4W71dT8AVfh8iD/6f16H4YcZRUXYVlB0V\nFVf119eEe3AhElxbQXXKBG5ysKVQXGgJF4ruQGgOhO5AqC4gPP8DgHf59x6EArj+s8L/EAA8AeEJ\nwBOAp1z5vavAcwXg+n/uOQKurcCpqn6o8QSfJovE6ax4YeCJkWIxmF2Xw6Dqqqi6KsZlF0KB5rkK\n6iUFfGkNB05nxUtwG2xQU/ACJyJid+U4YuCJGQ7hEhEBK1awu3LcMPDETG+vf2dDRBRnvPmLHwae\nmFFV/86GiCiu2F05nhh4YuiWW2RXQEQkz4oV7K4cRww8MTQwACSTsqsgIpJjwwbZFZAMDDwxpCjA\nunWyqyAiar1kkut34oqBJ6bWr/fnsYmI4mT9ev+mj+KHP/aYymSAvj7ZVRARtY4QfuCheGLgiTHO\nYxNRnKxc6Xebp3hi4ImxgQEglZJdBRFRa/AmL94YeGJMCC5eJqJ4SKXYeyfuGHhijouXiSgO+FpH\nDDwxl07789pERFHFxcoEMPAQOK9NRNHW38/1isTAQ+CLARFFG2/qCGDgIXC4l4iii9P2dAkDDwHg\ngj4iiia+ttElDDwEwJ/SGhyUXQURUeNoGkev6QoGHrrsYx/jnRARRcfGjf5hoUQAAw9dpVAAVq+W\nXQUR0fJpGrB1q+wqKEgYeOgaHOUhoijYtAmwLNlVUJAw8NA1cjlg7VrZVRARLZ1hcHSHPoqBhz5i\n+3ZA4TODiEJq82bANGVXQUHDtzX6iEyGh4oSUTiZph94iK7HwENz2r4dUFXZVRARLc6WLf6UFtH1\nGHhoTqkU27ETUbgkEv5iZaK5MPDQDW3b5m/tJCIKg23bAF2XXQUFFQMP3VAy6TfuIiIKOr5e0XwY\neOimeMdERGHAEWmaDwMP3RTnxIko6NJprjmk+THw0Ly464GIguy227irlObHwEPzMk3/yAkioqBp\na2PfMFoYBh5akE2bgPZ22VUQEV0hBHDPPewMTwvDpwktyKUXFh4sSkRBceutQGen7CooLBh4aMHa\n29mynYiCIZUCbr9ddhUUJgw8tCg7dvhnbRERybR7N1tm0OIw8NCiaJr/QkNEJMvgIDAwILsKChsG\nHlq0lSuBNWtkV0FEcWQYwK5dsqugMGLgoSW56y5/uzoRUSvdcYe/fodosRh4aEksC9i5U3YVRBQn\n3d3sqExLx8BDS7ZuHdDbK7sKIooDRQH27GFrDFo6Bh5alj172NKdiJpv2zagUJBdBYUZAw8tSy4H\nbN8uuwoiirJ83j8vi2g5GHho2bZu9c+zISJqBo4kUyMw8NCyKQqwdy9fkIio8TZtAnp6ZFdBUcDA\nQw3R3s7eGETUWF1d3A1KjaPJLoCiY8MG4MIF4L33ZFdCMjhODd/5zh9jfPwUVFXDfff9MV5//f+i\nVBoGAExOnkFv7214+OEv4c03n8KRI88AENix4wmsW3cQ9XoF//Zvn0OpNAbDSOGhh76IZJJzpXGV\nSAAPPMCT0KlxGHiooXbvBkZGgLEx2ZVQqx0+/A1oWgKPP/40xsZO4Fvf+h84dOifAQCVyhT+6Z9+\nA3v3/i+USmM4fPhpHDr0LGy7gr//+4exbt1BvPXW19DRsQ533fV7+K//+jZee+3/4L77Pi/5b0Uy\nCAHs28cGg9RYzM7UUJoGPPig3/6d4mV09BhWrboHANDWNoiZmQuoVmcAAK+++r+xffshpFLtSCbb\n8Bu/8S9QFBUzMxehaX7L7rNnf3r56wcH9+DkyR/K+YuQdB/7GLBihewqKGoYeKjhcjng3ntlV0Gt\n1tm5AceP/ycA4OzZn6FcHkO9XkKpNIpTp36EW2/9b5c/VwgFb775FL72tU9hw4ZHAADV6gxMMw0A\nMIwUqtXp1v8lSLr+fm5Bp+Zg4KGmGBwEtmyRXQW10qZNn4RppvH004/j2LEXUSisQiKRw3vvvYAN\nG34V4roWubfd9mn8zu+8gg8//DFOn34NpplGreaPCNVqs0gksjL+GiRROg3cdx+7KVNzMPBQ09xx\nh3/2DcXD+fOHsXLlTnzqU1/F2rUHkEp1QNNMnDz5QwwO3nP588bGjuNf/uX3AACKokFVDQihoLd3\nO44ffxkAcOLEy1ixYoeUvwfJoSj+dDgPJaZm4aJlahpF8Rce/vM/A+Wy7Gqo2QqFQfzgB5/Fa699\nBZpmYP/+JwEA4+MfIJdbefnz2tqG0Nm5Hl/96n8HIDA0dA/6+m5HV9dmvPDC/8TTTz8OVTXw8MNf\nkvQ3IRl27QI6OmRXQVEmPM/zZBdB0XbmDPDtbwN8phHRXNasAe6/X3YVFHWc0qKmW7EC2MHZCSKa\nQ6EA3HPP/J9HtFwMPNQS27b5uy+IiC7RdX/djsbFFdQCDDzUEkL4uy9yOdmVEFEQCOGfwZfPy66E\n4oKBh1rGNIGPf5zdU4kIuPtuv30FUasw8FBLZTJ+6EkkZFdCRLLcfjuwcaPsKihuGHio5QoF4OBB\nf/6eiOJl82Z2UiY5GHhIio4OYP9+QFVlV0JErbJ2LbBzp+wqKK4YeEiaFSv83htsI08UfatW+Wfs\n8XonWRh4SKrBQfbgIIq63l6/6zrDDsnEwEPSrVsH3Hmn7CqIqBna24EDBzh9TfIx8FAgbN3qNyck\noujI5/1dmdygQEHAwEOBcccdwIYNsqsgokZIp9mCgoKFgYcCZfduYGhIdhVEtByJhB920mnZlRBd\nwcBDgXLpCIq+PtmVENFS6LrfZ4tHRlDQCM/zPNlFEF3PtoEXXwROnZJdCREtVCIBPPQQ0NkpuxKi\nj2LgocByXeB73wOOHZNdCRHNJ5Xyp7EKBdmVEM2NgYcCzfOAH/4QeOcd2ZUQ0Y3kcn7YyWRkV0J0\nYww8FAo/+Yn/QUTBUiz6YceyZFdCdHMMPBQa77wDvPqq7CqI6JLubn/NjmHIroRofgw8FCrHjvnr\nelxXdiVE8bZqlX8WnqbJroRoYRh4KHTOnQO+8x2gWpVdCVE8bdoE3HUXz8aicGHgoVAaHwdeeAGY\nnpZdCVF8CAHs3Als3iy7EqLFY+Ch0CqX/dAzPCy7EqLo0zS/KejgoOxKiJaGgYdCzbaB734XOHlS\ndiVE0WVZ/onnbChIYcbAQ6HnecBrrwGHD8uuhCh62tqA/fuBbFZ2JUTLw8BDkXHypL+Di4uZiRpj\n40Z/cbKqyq6EaPkYeChSZmb8Ka4LF2RXQhRehgHs2QOsXi27EqLGYeChyHFd4I03gJ/9THYlROHT\n3g488ACnsCh6GHgosk6fBv7zP4FKRXYlROGwaRNw552cwqJoYuChSJudBV56yW9WSERzM03gnnu4\n5ZyijYGHIs/z/INH33zT/z0RXdHZCezbx5POKfoYeCg2zpzxp7hKJdmVEAXDli3AHXcAiiK7EqLm\nY+ChWCmX/SmuM2dkV0IkTyIB7N0L9PfLroSodRh4KHY8z9/B9ZOf8NR1ip+eHv+U81RKdiVErcXA\nQ7E1Pg688goXNFM8JBL+Dqy1a3nKOcUTAw/F3tGjwI9+5E93EUXR+vX+Wp1EQnYlRPIw8BDBP47i\nxz8Gfv5z7uSi6CgWgd27ga4u2ZUQycfAQ3SV4WHg+98HRkZkV0K0dLoO7NjhNxLk9BWRj4GH6Dqe\nBxw54o/41GqyqyFanNWrgZ07uSiZ6HoMPEQ3UC77a3uOHpVdCdH8cjng7ruBvj7ZlRAFEwMP0TzO\nnvV3c01MyK6E6KNUFbjtNmDrVp6BRXQzDDxEC+C6wOHDwE9/Cti27GqIfP39wK5dPNmcaCEYeIgW\noVwG3n4bePddoF6XXQ3FVV8fsH070N0tuxKi8GDgIVqCSgV45x3/gwubqVX6+/2g09kpuxKi8GHg\nIVqGavVK8KlWZVdDUbVqlb9Op6NDdiVE4cXAQ9QAtZo/zfX22/7oD1EjDA76IzrFouxKiMKPgYeo\ngep1v4fP4cM8qoKWRghgaMgf0Wlrk10NUXQw8BA1gW37x1S89RZQKsmuhsJACGDNGj/o5POyqyGK\nHgYeoiZyHD/4vP02MD0tuxoKIlX1g862bX7zQCJqDgYeohbwPODcOeAXvwBOnGAvH/IXIK9b5x8F\nYZqyqyGKPgYeohar1YBjx/zwMzwsuxpqpUQCuOUWP+hwfQ5RazHwEEk0NuYHn6NHubsrqoQAVq70\nQ05/P49/IJKFgYcoAFwXOHnSDz+nT/tTYBRuuZwfcm65hSeXEwUBAw9RwMzO+iM+v/gFMDkpuxpa\nDE3z1+SsXQv09MiuhoiuxsBDFGAXLvgjPx9+CIyMyK6G5mJZ/tlWK1cCAwOArsuuiIjmwsBDFBKl\nkh98Tp/2f+VRFnIIAXR1+QGnrw9ob/f/jIiCjYGHKIQ8z9/hdfq0/zE8zHU/zZRK+QFn5Uqgt5fb\nyInCiIGHKAIqFeDMmSsBiMdaLI+i+GtwLk1VcQs5Ufgx8BBFjOf5290vrfsZHfUXP/NKv7FEwj+g\ns1j0g05vL9fiEEUNAw9RDNi2H4JGR698jI35h53GiRD+dvFi0R+1aW/3f+W2caLoY+AhiinPA6am\nroSfkRH/15kZ2ZU1hqZdGbW59NHW5v85EcUPAw8RXaNa9UPQ9LS/Fqhc9neIXf2r7B1iqgokk/6W\n8Eu/Xvp9MgkUCkA2y91TRHQFAw8RLZrj3DgMlcv+ImrX9T8876O/9zx/YbCi+KHk+t/r+o3DjGUB\nhiH7X4CIwoaBh4iIiCJPkV0AERERUbMx8BAREVHkMfAQERFR5DHwEBERUeQx8BAREVHkMfAQERFR\n5DHwEBERUeQx8BAREVHkMfAQERFR5DHwEBERUeQx8BAREVHkMfAQERFR5DHwEBERUeQx8BAREVHk\nMfAQERFR5DHwEBERUeQx8BAREVHkMfAQ0YLU63Xs3r0bv/3bvy27FCKiRWPgIaIF+Y//+A+sX78e\nR44cwfvvvy+7HCKiRRGe53myiyCi4Dt06BB+5Vd+Be+99x7q9Tr+9E//FK+99hr+6q/+Cv39/Th6\n9ChqtRq+8IUv4M4778Qf/dEfIZ1O47333sP58+cxNDSEv/zLv0QymcQbb7yBP//zP0e5XIau6/iD\nP/gD7NmzB5/61KfwW7/1Wzhw4AAA4C/+4i8AAL/7u7+LP/mTP8HJkycxMTGBVCqFL33pSxgcHMSh\nQ4eQz+dx/PhxPP744/j0pz8t85+JiAKKIzxENK9jx47hrbfewsGDB/Hoo4/iueeew8TEBADg7bff\nxhNPPIFnnnkGv/7rv46//du/vfx1R44cwd/93d/h29/+Ni5evIgXXngB4+Pj+P3f/318/vOfx3PP\nPYc/+7M/w+c+9zl8+OGHeOyxx/DMM88AABzHwfPPP4/HHnsML7/8MnK5HL7+9a/j3//937F582Y8\n9dRTlx8nl8vhW9/6FsMOEd0QAw8RzetrX/sa9u7di2w2i82bN6Ovrw9f//rXIYRAb28v1q9fDwDY\nsGHD5SAkhMCePXug6zo0TcPatWsxOTmJw4cPo7+/H1u2bAEArFmzBtu3b8frr7+OgwcP4s0338TI\nyAheeeUVDAwMoL+/HwcOHMCjjz6Kf/zHf8STTz6J119/HaVS6XJ9O3bsaP0/ChGFiia7ACIKtlKp\nhGeffRaWZeH+++8HAMzOzuKpp57C5s2bkUgkLn+uEOKarzVN85r/5nke5ppFd10XjuPAsiw89NBD\n+Nd//Ve8+eabeOyxxwAAX/3qV/GNb3wDn/nMZ/DII48gn8/jzJkzl78+mUw29O9MRNHDER4iuqnn\nn38exWIR3//+9/HSSy/hpZdewosvvohSqYTR0dEbft1cwUYIga1bt+LEiRM4fPgwAODo0aN44403\ncMcddwAAHnvsMXzzm9/Ez372M+zfvx8A8IMf/ACf+MQn8MlPfhKrVq3CSy+9BNd1b/pYRERX4wgP\nEd3U008/jd/8zd+8ZvQmk8ng0KFD+Id/+Icbfp0Q4iMjPgBQKBTwN3/zN3jyySdRLpehKAq++MUv\nYmBgAABw6623Qtd17N+/H4ZhAACeeOIJfOELX8Czzz6LQqGABx54AC+//PI1j0VEdDPcpUVERESR\nxyktIiIiijwGHiIiIoo8Bh4iIiKKPAYeIiIiijwGHiIiIoo8Bh4iIiKKPAYeIiIiijwGHiIiIoo8\nBh4iIiKKPAYeIiIiijwGHiIiIoo8Bh4iIiKKPAYeIiIiijwGHiIiIoo8Bh4iIiKKPAYeIiIiijwG\nHiIiIoo8Bh4iIiKKPAYeIiIiijwGHiIiIoq8/w+MsfgWmOTVygAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x13e69a390>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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K2pM5tPIICiqjP9ePZ/c9C7bHqwwGnko4fhx47jnVVZDD9c2OIC94ZIRfCCmx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\nwCMiIiKR9/8Bl2bOI/pVmusAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x13e58bad0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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TO857lAbld/mxYvIKVIeqjS6FhsDAY3Y9Pdn1Ol1dRldCJpZdnJzg0uQ8iPr9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\n8BAREVHo/X/wpjzfetML/AAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x13d92e590>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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w4xaqrmNJVoUKdtml0tbtX4fd8d2iy/AEBp5KM01g7VogHhddCdlYZlwQ/SGG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\nSiM6siALi0y9wPyxAtX68p/7lkxeWh8z6U4KPBHWrrloD8M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5YadQCLoSkfNT4JGu8POf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\nREREJPIUeERERCTy/j/r4BZFWN3aAQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x13d4ea290>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AtoP5+2jN5jHb4SJmGp1pm3hsz2PcuTVJGHgmQ09POfAQVTAwOwrTCXa/nXg0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\nO9zbQxuWdmgFyZlxKHuaipBLlEjwpqWLliA1u2B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6mEl3UuCJsHbNRXsY3qjmeIMcw6kq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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x140a5e6d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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qXhx6kcdP1AB3aZ2t3/yGzQWpKjMZwrEuK9BTWSdIKbHt3+/DnsOVNvr/3wff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\nIiKh9/8BczJ5hTCLtU4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x13c870510>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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snLaSocdCGHisZN++3AJlnolFeRgeGd3TeCaWaIYsY2ulD51g6KT8Zk+ajXNb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\nEZHY+//++NXpQopXsQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x13bf69390>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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dxka7K5Ec1lETpLUoPiw7JdutPRRknS9BTBsWSh98bh8njjuR6VXT7S5FhoAC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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x13bd69850>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AA0/Niy8CLu/caXG+ITCXYUdlCpZktYpNbEZISzh49iB8yZ5hDDyuWws8RHUU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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x13bbffa50>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sampletables = '<h1>Other algo\\'s agree, but...</h1>'\n",
      "for effect in master_df[\"normalized_so_snpeff\"].unique():\n",
      "    sampletables += \"<h2> Annovar doesn't match for <em>\" + str(effect) + \"</em></h2>\"\n",
      "    query = master_df.loc[(master_df[\"normalized_so_annovar\"]!=effect) & \n",
      "                                  (master_df[\"normalized_so_snpeff\"]==effect) & \n",
      "                                  (master_df[\"normalized_so_vep\"]==effect)]\n",
      "    num_rows = query.count()[0]\n",
      "    if num_rows > 0:\n",
      "        sampletables += query.head(5).to_html()\n",
      "    sampletables += \"<p>\" + str(num_rows) + \" rows</p>\"\n",
      "HTML(sampletables)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<h1>Other algo's agree, but...</h1><h2> Annovar doesn't match for <em>intergenic_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> </td>\n",
        "      <td> 117105737</td>\n",
        "      <td> C</td>\n",
        "      <td>    A</td>\n",
        "      <td> .</td>\n",
        "      <td> INTERGENIC</td>\n",
        "      <td> </td>\n",
        "      <td> </td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> .</td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> </td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> </td>\n",
        "      <td> 117105737</td>\n",
        "      <td> C</td>\n",
        "      <td>   CA</td>\n",
        "      <td> .</td>\n",
        "      <td> INTERGENIC</td>\n",
        "      <td> </td>\n",
        "      <td> </td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> .</td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> </td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> </td>\n",
        "      <td> 117105737</td>\n",
        "      <td> C</td>\n",
        "      <td>  CAG</td>\n",
        "      <td> .</td>\n",
        "      <td> INTERGENIC</td>\n",
        "      <td> </td>\n",
        "      <td> </td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> .</td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> </td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> </td>\n",
        "      <td> 117105737</td>\n",
        "      <td> C</td>\n",
        "      <td>   CC</td>\n",
        "      <td> .</td>\n",
        "      <td> INTERGENIC</td>\n",
        "      <td> </td>\n",
        "      <td> </td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> .</td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> </td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> </td>\n",
        "      <td> 117105737</td>\n",
        "      <td> C</td>\n",
        "      <td> CCTG</td>\n",
        "      <td> .</td>\n",
        "      <td> INTERGENIC</td>\n",
        "      <td> </td>\n",
        "      <td> </td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> .</td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> </td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>9198 rows</p><h2> Annovar doesn't match for <em>upstream_gene_variant</em></h2><p>0 rows</p><h2> Annovar doesn't match for <em>5_prime_UTR_variant</em></h2><p>0 rows</p><h2> Annovar doesn't match for <em>inframe_variant</em></h2><p>0 rows</p><h2> Annovar doesn't match for <em>frameshift_variant</em></h2><p>0 rows</p><h2> Annovar doesn't match for <em>nonsynonymous_variant</em></h2><p>0 rows</p><h2> Annovar doesn't match for <em>stop_gained</em></h2><p>0 rows</p><h2> Annovar doesn't match for <em>synonymous_variant</em></h2><p>0 rows</p><h2> Annovar doesn't match for <em>splicing_variant</em></h2><p>0 rows</p><h2> Annovar doesn't match for <em>intron_variant</em></h2><p>0 rows</p><h2> Annovar doesn't match for <em>stop_lost</em></h2><p>0 rows</p><h2> Annovar doesn't match for <em>3_prime_UTR_variant</em></h2><p>0 rows</p><h2> Annovar doesn't match for <em>downstream_gene_variant</em></h2><p>0 rows</p><h2> Annovar doesn't match for <em>nan</em></h2><p>0 rows</p>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "<IPython.core.display.HTML at 0x11dc81f50>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sampletables = '<h1>At least 1 column doesn\\'t match</h1>'\n",
      "for effect in master_df[\"normalized_so_snpeff\"].unique():\n",
      "    sampletables += \"<h2> Annovar doesn't match for <em>\" + str(effect) + \"</em></h2>\"\n",
      "    query = master_df.loc[(master_df[\"normalized_so_annovar\"]!=effect) & \n",
      "                                  ((master_df[\"normalized_so_snpeff\"]==effect) | (master_df[\"normalized_so_vep\"]==effect))]\n",
      "    num_rows = query.count()[0]\n",
      "    if num_rows > 0:\n",
      "        sampletables += query.head(5).to_html()\n",
      "    sampletables += \"<p>\" + str(num_rows) + \" rows</p>\"\n",
      "HTML(sampletables)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<h1>At least 1 column doesn't match</h1><h2> Annovar doesn't match for <em>intergenic_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> </td>\n",
        "      <td> 117105737</td>\n",
        "      <td> C</td>\n",
        "      <td>    A</td>\n",
        "      <td> .</td>\n",
        "      <td> INTERGENIC</td>\n",
        "      <td> </td>\n",
        "      <td> </td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> .</td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> </td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> </td>\n",
        "      <td> 117105737</td>\n",
        "      <td> C</td>\n",
        "      <td>   CA</td>\n",
        "      <td> .</td>\n",
        "      <td> INTERGENIC</td>\n",
        "      <td> </td>\n",
        "      <td> </td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> .</td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> </td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> </td>\n",
        "      <td> 117105737</td>\n",
        "      <td> C</td>\n",
        "      <td>  CAG</td>\n",
        "      <td> .</td>\n",
        "      <td> INTERGENIC</td>\n",
        "      <td> </td>\n",
        "      <td> </td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> .</td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> </td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> </td>\n",
        "      <td> 117105737</td>\n",
        "      <td> C</td>\n",
        "      <td>   CC</td>\n",
        "      <td> .</td>\n",
        "      <td> INTERGENIC</td>\n",
        "      <td> </td>\n",
        "      <td> </td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> .</td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> </td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> </td>\n",
        "      <td> 117105737</td>\n",
        "      <td> C</td>\n",
        "      <td> CCTG</td>\n",
        "      <td> .</td>\n",
        "      <td> INTERGENIC</td>\n",
        "      <td> </td>\n",
        "      <td> </td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> .</td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> </td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>32500 rows</p><h2> Annovar doesn't match for <em>upstream_gene_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>32500</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117115644</td>\n",
        "      <td> G</td>\n",
        "      <td>    A</td>\n",
        "      <td> .</td>\n",
        "      <td> UPSTREAM</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> upstream_gene_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32501</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117115644</td>\n",
        "      <td> G</td>\n",
        "      <td>    C</td>\n",
        "      <td> .</td>\n",
        "      <td> UPSTREAM</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> upstream_gene_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32502</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117115644</td>\n",
        "      <td> G</td>\n",
        "      <td>   GA</td>\n",
        "      <td> .</td>\n",
        "      <td> UPSTREAM</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> upstream_gene_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32503</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117115644</td>\n",
        "      <td> G</td>\n",
        "      <td>  GAC</td>\n",
        "      <td> .</td>\n",
        "      <td> UPSTREAM</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> upstream_gene_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32504</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117115644</td>\n",
        "      <td> G</td>\n",
        "      <td> GACG</td>\n",
        "      <td> .</td>\n",
        "      <td> UPSTREAM</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> upstream_gene_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>83012 rows</p><h2> Annovar doesn't match for <em>5_prime_UTR_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>46611</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120014</td>\n",
        "      <td> GGGA</td>\n",
        "      <td>    G</td>\n",
        "      <td> .</td>\n",
        "      <td> UTR_5_PRIME</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> 5_prime_UTR_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>46624</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120015</td>\n",
        "      <td>  GGA</td>\n",
        "      <td>    G</td>\n",
        "      <td> .</td>\n",
        "      <td> UTR_5_PRIME</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> 5_prime_UTR_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>46625</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120015</td>\n",
        "      <td> GGAA</td>\n",
        "      <td>    G</td>\n",
        "      <td> .</td>\n",
        "      <td> UTR_5_PRIME</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> 5_prime_UTR_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>46628</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120016</td>\n",
        "      <td>    G</td>\n",
        "      <td>   GA</td>\n",
        "      <td> .</td>\n",
        "      <td> UTR_5_PRIME</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> 5_prime_UTR_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>46629</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120016</td>\n",
        "      <td>    G</td>\n",
        "      <td> GAGC</td>\n",
        "      <td> .</td>\n",
        "      <td> UTR_5_PRIME</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> 5_prime_UTR_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>9940 rows</p><h2> Annovar doesn't match for <em>inframe_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>48459</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120146</td>\n",
        "      <td> ACCA</td>\n",
        "      <td>    A</td>\n",
        "      <td> .</td>\n",
        "      <td>  CODON_DELETION</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.Met1X/c.1*&gt;-CCA</td>\n",
        "      <td> inframe_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48473</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120147</td>\n",
        "      <td> CCAT</td>\n",
        "      <td>    C</td>\n",
        "      <td> .</td>\n",
        "      <td>  CODON_DELETION</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.Met1X/c.1*&gt;-CAT</td>\n",
        "      <td> inframe_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48476</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120148</td>\n",
        "      <td>    C</td>\n",
        "      <td> CACT</td>\n",
        "      <td> .</td>\n",
        "      <td> CODON_INSERTION</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.Met1X/c.1*&gt;+ACT</td>\n",
        "      <td> inframe_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48482</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120148</td>\n",
        "      <td>    C</td>\n",
        "      <td> CTGC</td>\n",
        "      <td> .</td>\n",
        "      <td> CODON_INSERTION</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.Met1X/c.1*&gt;+TGC</td>\n",
        "      <td> inframe_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48487</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120148</td>\n",
        "      <td> CATG</td>\n",
        "      <td>    C</td>\n",
        "      <td> .</td>\n",
        "      <td>  CODON_DELETION</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.Met1X/c.1*&gt;-ATG</td>\n",
        "      <td> inframe_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>46483 rows</p><h2> Annovar doesn't match for <em>frameshift_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>48472</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120147</td>\n",
        "      <td> CCA</td>\n",
        "      <td>   C</td>\n",
        "      <td> .</td>\n",
        "      <td> FRAME_SHIFT</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>   p.X1X/c.1*&gt;-CA</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48475</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120148</td>\n",
        "      <td>   C</td>\n",
        "      <td>  CA</td>\n",
        "      <td> .</td>\n",
        "      <td> FRAME_SHIFT</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>  p.Met1X/c.1*&gt;+A</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48477</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120148</td>\n",
        "      <td>   C</td>\n",
        "      <td>  CC</td>\n",
        "      <td> .</td>\n",
        "      <td> FRAME_SHIFT</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>  p.Met1X/c.1*&gt;+C</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48478</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120148</td>\n",
        "      <td>   C</td>\n",
        "      <td>  CG</td>\n",
        "      <td> .</td>\n",
        "      <td> FRAME_SHIFT</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>  p.Met1X/c.1*&gt;+G</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48479</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120148</td>\n",
        "      <td>   C</td>\n",
        "      <td> CGC</td>\n",
        "      <td> .</td>\n",
        "      <td> FRAME_SHIFT</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.Met1X/c.1*&gt;+GC</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>132305 rows</p><h2> Annovar doesn't match for <em>nonsynonymous_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>48496</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120149</td>\n",
        "      <td> A</td>\n",
        "      <td> C</td>\n",
        "      <td> .</td>\n",
        "      <td> NON_SYNONYMOUS_START</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.Met1?/c.1A&gt;C</td>\n",
        "      <td> nonsynonymous_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48497</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120149</td>\n",
        "      <td> A</td>\n",
        "      <td> G</td>\n",
        "      <td> .</td>\n",
        "      <td>           START_LOST</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.Met1?/c.1A&gt;G</td>\n",
        "      <td> nonsynonymous_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48498</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120149</td>\n",
        "      <td> A</td>\n",
        "      <td> T</td>\n",
        "      <td> .</td>\n",
        "      <td> NON_SYNONYMOUS_START</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.Met1?/c.1A&gt;T</td>\n",
        "      <td> nonsynonymous_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48502</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120150</td>\n",
        "      <td> T</td>\n",
        "      <td> A</td>\n",
        "      <td> .</td>\n",
        "      <td>           START_LOST</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.Met1?/c.2T&gt;A</td>\n",
        "      <td> nonsynonymous_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48503</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120150</td>\n",
        "      <td> T</td>\n",
        "      <td> C</td>\n",
        "      <td> .</td>\n",
        "      <td>           START_LOST</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.Met1?/c.2T&gt;C</td>\n",
        "      <td> nonsynonymous_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>36437 rows</p><h2> Annovar doesn't match for <em>stop_gained</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>48525</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120151</td>\n",
        "      <td> G</td>\n",
        "      <td> GTGA</td>\n",
        "      <td> .</td>\n",
        "      <td> STOP_GAINED</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>  p.Gln2*/c.4*&gt;+TGA</td>\n",
        "      <td> stop_gained</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48540</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120152</td>\n",
        "      <td> C</td>\n",
        "      <td>    T</td>\n",
        "      <td> .</td>\n",
        "      <td> STOP_GAINED</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>     p.Gln2*/c.4C&gt;T</td>\n",
        "      <td> stop_gained</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48564</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120154</td>\n",
        "      <td> G</td>\n",
        "      <td>   GT</td>\n",
        "      <td> .</td>\n",
        "      <td> STOP_GAINED</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>    p.Arg3*/c.7*&gt;+T</td>\n",
        "      <td> stop_gained</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48565</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120154</td>\n",
        "      <td> G</td>\n",
        "      <td>  GTA</td>\n",
        "      <td> .</td>\n",
        "      <td> STOP_GAINED</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>   p.Arg3*/c.7*&gt;+TA</td>\n",
        "      <td> stop_gained</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48609</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120157</td>\n",
        "      <td> G</td>\n",
        "      <td> GTAG</td>\n",
        "      <td> .</td>\n",
        "      <td> STOP_GAINED</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.Ser4*/c.10*&gt;+TAG</td>\n",
        "      <td> stop_gained</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>6778 rows</p><h2> Annovar doesn't match for <em>synonymous_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>48558</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120154</td>\n",
        "      <td> G</td>\n",
        "      <td> A</td>\n",
        "      <td> .</td>\n",
        "      <td> SYNONYMOUS_CODING</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>  p.Gln2Gln/c.6G&gt;A</td>\n",
        "      <td> synonymous_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48580</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120155</td>\n",
        "      <td> A</td>\n",
        "      <td> C</td>\n",
        "      <td> .</td>\n",
        "      <td> SYNONYMOUS_CODING</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>  p.Arg3Arg/c.7A&gt;C</td>\n",
        "      <td> synonymous_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48600</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120157</td>\n",
        "      <td> G</td>\n",
        "      <td> A</td>\n",
        "      <td> .</td>\n",
        "      <td> SYNONYMOUS_CODING</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>  p.Arg3Arg/c.9G&gt;A</td>\n",
        "      <td> synonymous_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48642</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120160</td>\n",
        "      <td> G</td>\n",
        "      <td> A</td>\n",
        "      <td> .</td>\n",
        "      <td> SYNONYMOUS_CODING</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.Ser4Ser/c.12G&gt;A</td>\n",
        "      <td> synonymous_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48643</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120160</td>\n",
        "      <td> G</td>\n",
        "      <td> C</td>\n",
        "      <td> .</td>\n",
        "      <td> SYNONYMOUS_CODING</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.Ser4Ser/c.12G&gt;C</td>\n",
        "      <td> synonymous_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>11010 rows</p><h2> Annovar doesn't match for <em>splicing_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>49201</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120199</td>\n",
        "      <td> CAGG</td>\n",
        "      <td>    C</td>\n",
        "      <td> .</td>\n",
        "      <td> SPLICE_SITE_DONOR</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49214</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120200</td>\n",
        "      <td>  AGG</td>\n",
        "      <td>    A</td>\n",
        "      <td> .</td>\n",
        "      <td> SPLICE_SITE_DONOR</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49215</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120200</td>\n",
        "      <td> AGGT</td>\n",
        "      <td>    A</td>\n",
        "      <td> .</td>\n",
        "      <td> SPLICE_SITE_DONOR</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49218</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120201</td>\n",
        "      <td>    G</td>\n",
        "      <td>   GA</td>\n",
        "      <td> .</td>\n",
        "      <td> SPLICE_SITE_DONOR</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49219</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120201</td>\n",
        "      <td>    G</td>\n",
        "      <td> GACT</td>\n",
        "      <td> .</td>\n",
        "      <td> SPLICE_SITE_DONOR</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>5016 rows</p><h2> Annovar doesn't match for <em>intron_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>49247</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120203</td>\n",
        "      <td> T</td>\n",
        "      <td>   TA</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>   c.53+3*&gt;+A</td>\n",
        "      <td> intron_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49248</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120203</td>\n",
        "      <td> T</td>\n",
        "      <td>  TAC</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>  c.53+3*&gt;+AC</td>\n",
        "      <td> intron_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49249</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120203</td>\n",
        "      <td> T</td>\n",
        "      <td> TAGC</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> c.53+3*&gt;+AGC</td>\n",
        "      <td> intron_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49250</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120203</td>\n",
        "      <td> T</td>\n",
        "      <td>   TC</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>   c.53+3*&gt;+C</td>\n",
        "      <td> intron_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49251</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120203</td>\n",
        "      <td> T</td>\n",
        "      <td>   TG</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>   c.53+3*&gt;+G</td>\n",
        "      <td> intron_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>321983 rows</p><h2> Annovar doesn't match for <em>stop_lost</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>187885</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117307157</td>\n",
        "      <td> CTTT</td>\n",
        "      <td>  C</td>\n",
        "      <td> .</td>\n",
        "      <td> STOP_LOST</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.X1480Glnext*?/c.4439*&gt;-TTT</td>\n",
        "      <td> stop_lost</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>187899</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117307158</td>\n",
        "      <td> TTTA</td>\n",
        "      <td>  T</td>\n",
        "      <td> .</td>\n",
        "      <td> STOP_LOST</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.X1480Leuext*?/c.4440*&gt;-TTA</td>\n",
        "      <td> stop_lost</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>187903</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117307159</td>\n",
        "      <td>    T</td>\n",
        "      <td> TA</td>\n",
        "      <td> .</td>\n",
        "      <td> STOP_LOST</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>     p.*1481Xext*?/c.4441*&gt;+A</td>\n",
        "      <td> stop_lost</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>187905</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117307159</td>\n",
        "      <td>    T</td>\n",
        "      <td> TC</td>\n",
        "      <td> .</td>\n",
        "      <td> STOP_LOST</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>     p.*1481Xext*?/c.4441*&gt;+C</td>\n",
        "      <td> stop_lost</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>187907</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117307159</td>\n",
        "      <td>    T</td>\n",
        "      <td> TG</td>\n",
        "      <td> .</td>\n",
        "      <td> STOP_LOST</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>     p.*1481Xext*?/c.4441*&gt;+G</td>\n",
        "      <td> stop_lost</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>160 rows</p><h2> Annovar doesn't match for <em>3_prime_UTR_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>187944</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117307162</td>\n",
        "      <td> G</td>\n",
        "      <td>   GA</td>\n",
        "      <td> .</td>\n",
        "      <td> UTR_3_PRIME</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> 3_prime_UTR_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>187945</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117307162</td>\n",
        "      <td> G</td>\n",
        "      <td>   GC</td>\n",
        "      <td> .</td>\n",
        "      <td> UTR_3_PRIME</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> 3_prime_UTR_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>187946</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117307162</td>\n",
        "      <td> G</td>\n",
        "      <td>  GCA</td>\n",
        "      <td> .</td>\n",
        "      <td> UTR_3_PRIME</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> 3_prime_UTR_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>187947</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117307162</td>\n",
        "      <td> G</td>\n",
        "      <td> GCAG</td>\n",
        "      <td> .</td>\n",
        "      <td> UTR_3_PRIME</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> 3_prime_UTR_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>187948</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117307162</td>\n",
        "      <td> G</td>\n",
        "      <td>   GG</td>\n",
        "      <td> .</td>\n",
        "      <td> UTR_3_PRIME</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> 3_prime_UTR_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>7970 rows</p><h2> Annovar doesn't match for <em>downstream_gene_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>188824</th>\n",
        "      <td> CTTNBP2</td>\n",
        "      <td> 117349970</td>\n",
        "      <td> C</td>\n",
        "      <td>   A</td>\n",
        "      <td> .</td>\n",
        "      <td> DOWNSTREAM</td>\n",
        "      <td> NM_033427.2</td>\n",
        "      <td> </td>\n",
        "      <td> downstream_gene_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>188825</th>\n",
        "      <td> CTTNBP2</td>\n",
        "      <td> 117349970</td>\n",
        "      <td> C</td>\n",
        "      <td>  CA</td>\n",
        "      <td> .</td>\n",
        "      <td> DOWNSTREAM</td>\n",
        "      <td> NM_033427.2</td>\n",
        "      <td> </td>\n",
        "      <td> downstream_gene_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>188826</th>\n",
        "      <td> CTTNBP2</td>\n",
        "      <td> 117349970</td>\n",
        "      <td> C</td>\n",
        "      <td> CAG</td>\n",
        "      <td> .</td>\n",
        "      <td> DOWNSTREAM</td>\n",
        "      <td> NM_033427.2</td>\n",
        "      <td> </td>\n",
        "      <td> downstream_gene_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>188827</th>\n",
        "      <td> CTTNBP2</td>\n",
        "      <td> 117349970</td>\n",
        "      <td> C</td>\n",
        "      <td> CAT</td>\n",
        "      <td> .</td>\n",
        "      <td> DOWNSTREAM</td>\n",
        "      <td> NM_033427.2</td>\n",
        "      <td> </td>\n",
        "      <td> downstream_gene_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>188828</th>\n",
        "      <td> CTTNBP2</td>\n",
        "      <td> 117349970</td>\n",
        "      <td> C</td>\n",
        "      <td>  CC</td>\n",
        "      <td> .</td>\n",
        "      <td> DOWNSTREAM</td>\n",
        "      <td> NM_033427.2</td>\n",
        "      <td> </td>\n",
        "      <td> downstream_gene_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>75432 rows</p><h2> Annovar doesn't match for <em>nan</em></h2><p>0 rows</p>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<IPython.core.display.HTML at 0x11dc81810>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sampletables =''\n",
      "for effect in master_df[\"normalized_so_snpeff\"].unique():\n",
      "    sampletables += \"<h2> Snpeff doesn't match for <em>\" + str(effect) + \"</em></h2>\"\n",
      "    query = master_df.loc[(master_df[\"normalized_so_annovar\"]==effect) & \n",
      "                                  (master_df[\"normalized_so_snpeff\"]!=effect) & \n",
      "                                  (master_df[\"normalized_so_vep\"]==effect)]\n",
      "    num_rows = query.count()[0]\n",
      "    if num_rows > 0:\n",
      "        sampletables += query.head(5).to_html()\n",
      "    sampletables += \"<p>\" + str(num_rows) + \" rows</p>\"\n",
      "HTML(sampletables)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<h2> Snpeff doesn't match for <em>intergenic_variant</em></h2><p>0 rows</p><h2> Snpeff doesn't match for <em>upstream_gene_variant</em></h2><p>0 rows</p><h2> Snpeff doesn't match for <em>5_prime_UTR_variant</em></h2><p>0 rows</p><h2> Snpeff doesn't match for <em>inframe_variant</em></h2><p>0 rows</p><h2> Snpeff doesn't match for <em>frameshift_variant</em></h2><p>0 rows</p><h2> Snpeff doesn't match for <em>nonsynonymous_variant</em></h2><p>0 rows</p><h2> Snpeff doesn't match for <em>stop_gained</em></h2><p>0 rows</p><h2> Snpeff doesn't match for <em>synonymous_variant</em></h2><p>0 rows</p><h2> Snpeff doesn't match for <em>splicing_variant</em></h2><p>0 rows</p><h2> Snpeff doesn't match for <em>intron_variant</em></h2><p>0 rows</p><h2> Snpeff doesn't match for <em>stop_lost</em></h2><p>0 rows</p><h2> Snpeff doesn't match for <em>3_prime_UTR_variant</em></h2><p>0 rows</p><h2> Snpeff doesn't match for <em>downstream_gene_variant</em></h2><p>0 rows</p><h2> Snpeff doesn't match for <em>nan</em></h2><p>0 rows</p>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<IPython.core.display.HTML at 0x11dcb9990>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sampletables = '<h1>Other algo\\'s agree, but...</h1>'\n",
      "for effect in master_df[\"normalized_so_snpeff\"].unique():\n",
      "    sampletables += \"<h2> Snpeff doesn't match for <em>\" + str(effect) + \"</em></h2>\"\n",
      "    query = master_df.loc[(master_df[\"normalized_so_annovar\"]==effect) & \n",
      "                                  (master_df[\"normalized_so_snpeff\"]!=effect) & \n",
      "                                  (master_df[\"normalized_so_vep\"]==effect)]\n",
      "    num_rows = query.count()[0]\n",
      "    if num_rows > 0:\n",
      "        sampletables += query.head(5).to_html()\n",
      "    sampletables += \"<p>\" + str(num_rows) + \" rows</p>\"\n",
      "HTML(sampletables)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<h1>Other algo's agree, but...</h1><h2> Snpeff doesn't match for <em>intergenic_variant</em></h2><p>0 rows</p><h2> Snpeff doesn't match for <em>upstream_gene_variant</em></h2><p>0 rows</p><h2> Snpeff doesn't match for <em>5_prime_UTR_variant</em></h2><p>0 rows</p><h2> Snpeff doesn't match for <em>inframe_variant</em></h2><p>0 rows</p><h2> Snpeff doesn't match for <em>frameshift_variant</em></h2><p>0 rows</p><h2> Snpeff doesn't match for <em>nonsynonymous_variant</em></h2><p>0 rows</p><h2> Snpeff doesn't match for <em>stop_gained</em></h2><p>0 rows</p><h2> Snpeff doesn't match for <em>synonymous_variant</em></h2><p>0 rows</p><h2> Snpeff doesn't match for <em>splicing_variant</em></h2><p>0 rows</p><h2> Snpeff doesn't match for <em>intron_variant</em></h2><p>0 rows</p><h2> Snpeff doesn't match for <em>stop_lost</em></h2><p>0 rows</p><h2> Snpeff doesn't match for <em>3_prime_UTR_variant</em></h2><p>0 rows</p><h2> Snpeff doesn't match for <em>downstream_gene_variant</em></h2><p>0 rows</p><h2> Snpeff doesn't match for <em>nan</em></h2><p>0 rows</p>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<IPython.core.display.HTML at 0x11dcb9650>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sampletables = '<h1>At least 1 column doesn\\'t match</h1>'\n",
      "for effect in master_df[\"normalized_so_snpeff\"].unique():\n",
      "    sampletables += \"<h2> Snpeff doesn't match for <em>\" + str(effect) + \"</em></h2>\"\n",
      "    query = num_rows = master_df.loc[(master_df[\"normalized_so_snpeff\"]!=effect) & \n",
      "                                     ((master_df[\"normalized_so_annovar\"]==effect) | (master_df[\"normalized_so_vep\"]==effect))]\n",
      "    num_rows = query.count()[0]\n",
      "    if num_rows > 0:\n",
      "        sampletables += query.head(5).to_html()\n",
      "    sampletables += \"<p>\" + str(num_rows) + \" rows</p>\"\n",
      "HTML(sampletables)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<h1>At least 1 column doesn't match</h1><h2> Snpeff doesn't match for <em>intergenic_variant</em></h2><p>0 rows</p><h2> Snpeff doesn't match for <em>upstream_gene_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>224357</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117115644</td>\n",
        "      <td> G</td>\n",
        "      <td>    A</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> upstream_gene_variant</td>\n",
        "      <td> </td>\n",
        "      <td> upstream_gene_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>224358</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117115644</td>\n",
        "      <td> G</td>\n",
        "      <td>    C</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> upstream_gene_variant</td>\n",
        "      <td> </td>\n",
        "      <td> upstream_gene_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>224359</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117115644</td>\n",
        "      <td> G</td>\n",
        "      <td>   GA</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> upstream_gene_variant</td>\n",
        "      <td> </td>\n",
        "      <td> upstream_gene_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>224360</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117115644</td>\n",
        "      <td> G</td>\n",
        "      <td>  GAC</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> upstream_gene_variant</td>\n",
        "      <td> </td>\n",
        "      <td> upstream_gene_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>224361</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117115644</td>\n",
        "      <td> G</td>\n",
        "      <td> GACG</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> upstream_gene_variant</td>\n",
        "      <td> </td>\n",
        "      <td> upstream_gene_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>70294 rows</p><h2> Snpeff doesn't match for <em>5_prime_UTR_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>238468</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120014</td>\n",
        "      <td> GGGA</td>\n",
        "      <td> G</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> 5_prime_UTR_variant</td>\n",
        "      <td>                             </td>\n",
        "      <td> 5_prime_UTR_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>238481</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120015</td>\n",
        "      <td>  GGA</td>\n",
        "      <td> G</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> 5_prime_UTR_variant</td>\n",
        "      <td>                             </td>\n",
        "      <td> 5_prime_UTR_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>238482</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120015</td>\n",
        "      <td> GGAA</td>\n",
        "      <td> G</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> 5_prime_UTR_variant</td>\n",
        "      <td>                             </td>\n",
        "      <td> 5_prime_UTR_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>238494</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120016</td>\n",
        "      <td>   GA</td>\n",
        "      <td> G</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> 5_prime_UTR_variant</td>\n",
        "      <td>       NM_000492.3:c.-132delA</td>\n",
        "      <td> 5_prime_UTR_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>238495</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120016</td>\n",
        "      <td>  GAA</td>\n",
        "      <td> G</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> 5_prime_UTR_variant</td>\n",
        "      <td> NM_000492.3:c.-132_-131delAA</td>\n",
        "      <td> 5_prime_UTR_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>13796 rows</p><h2> Snpeff doesn't match for <em>inframe_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>240344</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120148</td>\n",
        "      <td> CATG</td>\n",
        "      <td>    C</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td>  inframe_deletion</td>\n",
        "      <td>        NP_000483.3:p.Met1?NM_000492.3:c.1_3delATG</td>\n",
        "      <td> inframe_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>240349</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120149</td>\n",
        "      <td>    A</td>\n",
        "      <td> AGAC</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> inframe_insertion</td>\n",
        "      <td>        NP_000483.3:p.Met1?NM_000492.3:c.1_2insGAC</td>\n",
        "      <td> inframe_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>240350</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120149</td>\n",
        "      <td>    A</td>\n",
        "      <td> AGAT</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> inframe_insertion</td>\n",
        "      <td>        NP_000483.3:p.Met1?NM_000492.3:c.1_2insGAT</td>\n",
        "      <td> inframe_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>240358</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120149</td>\n",
        "      <td> ATGC</td>\n",
        "      <td>    A</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td>  inframe_deletion</td>\n",
        "      <td>   NP_000483.3:p.MetGln1_?2NM_000492.3:c.2_4delTGC</td>\n",
        "      <td> inframe_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>240366</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120150</td>\n",
        "      <td>    T</td>\n",
        "      <td> TGCA</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> inframe_insertion</td>\n",
        "      <td> NP_000483.3:p.Met1_Gln2insGlnNM_000492.3:c.2_3...</td>\n",
        "      <td> inframe_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>46597 rows</p><h2> Snpeff doesn't match for <em>frameshift_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>240342</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120148</td>\n",
        "      <td>  CA</td>\n",
        "      <td>  C</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td>    NP_000483.3:p.Met1CysfsTer?NM_000492.3:c.1delA</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>240343</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120148</td>\n",
        "      <td> CAT</td>\n",
        "      <td>  C</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td> NP_000483.3:p.Met1AlafsTer?NM_000492.3:c.1_2delAT</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>240345</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120149</td>\n",
        "      <td>   A</td>\n",
        "      <td> AA</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td>    NP_000483.3:p.Met1AsnfsTer?NM_000492.3:c.1dupA</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>240346</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120149</td>\n",
        "      <td>   A</td>\n",
        "      <td> AC</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td>  NP_000483.3:p.Met1ThrfsTer?NM_000492.3:c.1_2insC</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>240347</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120149</td>\n",
        "      <td>   A</td>\n",
        "      <td> AG</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td>  NP_000483.3:p.Met1SerfsTer?NM_000492.3:c.1_2insG</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>132883 rows</p><h2> Snpeff doesn't match for <em>nonsynonymous_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>240353</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120149</td>\n",
        "      <td> A</td>\n",
        "      <td> C</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> initiator_codon_variant</td>\n",
        "      <td> NP_000483.3:p.Met1?NM_000492.3:c.1A&gt;C</td>\n",
        "      <td> nonsynonymous_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>240354</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120149</td>\n",
        "      <td> A</td>\n",
        "      <td> G</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> initiator_codon_variant</td>\n",
        "      <td> NP_000483.3:p.Met1?NM_000492.3:c.1A&gt;G</td>\n",
        "      <td> nonsynonymous_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>240355</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120149</td>\n",
        "      <td> A</td>\n",
        "      <td> T</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> initiator_codon_variant</td>\n",
        "      <td> NP_000483.3:p.Met1?NM_000492.3:c.1A&gt;T</td>\n",
        "      <td> nonsynonymous_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>240359</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120150</td>\n",
        "      <td> T</td>\n",
        "      <td> A</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> initiator_codon_variant</td>\n",
        "      <td> NP_000483.3:p.Met1?NM_000492.3:c.2T&gt;A</td>\n",
        "      <td> nonsynonymous_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>240360</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120150</td>\n",
        "      <td> T</td>\n",
        "      <td> C</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> initiator_codon_variant</td>\n",
        "      <td> NP_000483.3:p.Met1?NM_000492.3:c.2T&gt;C</td>\n",
        "      <td> nonsynonymous_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>36437 rows</p><h2> Snpeff doesn't match for <em>stop_gained</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>240382</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120151</td>\n",
        "      <td> G</td>\n",
        "      <td> GTGA</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> stop_gained</td>\n",
        "      <td> NP_000483.3:p.Met1_Gln2insTerNM_000492.3:c.3_4...</td>\n",
        "      <td> stop_gained</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>240397</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120152</td>\n",
        "      <td> C</td>\n",
        "      <td>    T</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> stop_gained</td>\n",
        "      <td>           NP_000483.3:p.Gln2TerNM_000492.3:c.4C&gt;T</td>\n",
        "      <td> stop_gained</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>240466</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120157</td>\n",
        "      <td> G</td>\n",
        "      <td> GTAG</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> stop_gained</td>\n",
        "      <td> NP_000483.3:p.Arg3_Ser4insTerNM_000492.3:c.9_1...</td>\n",
        "      <td> stop_gained</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>240475</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120158</td>\n",
        "      <td> T</td>\n",
        "      <td> TAGT</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> stop_gained</td>\n",
        "      <td> NP_000483.3:p.Arg3_Ser4insTerNM_000492.3:c.10_...</td>\n",
        "      <td> stop_gained</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>240485</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120159</td>\n",
        "      <td> C</td>\n",
        "      <td>    A</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> stop_gained</td>\n",
        "      <td>          NP_000483.3:p.Ser4TerNM_000492.3:c.11C&gt;A</td>\n",
        "      <td> stop_gained</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>6252 rows</p><h2> Snpeff doesn't match for <em>synonymous_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>240415</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120154</td>\n",
        "      <td> G</td>\n",
        "      <td> A</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> synonymous_variant</td>\n",
        "      <td>   NM_000492.3:c.6G&gt;A(p.%3D)NM_000492.3:c.6G&gt;A</td>\n",
        "      <td> synonymous_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>240437</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120155</td>\n",
        "      <td> A</td>\n",
        "      <td> C</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> synonymous_variant</td>\n",
        "      <td>   NM_000492.3:c.7A&gt;C(p.%3D)NM_000492.3:c.7A&gt;C</td>\n",
        "      <td> synonymous_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>240457</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120157</td>\n",
        "      <td> G</td>\n",
        "      <td> A</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> synonymous_variant</td>\n",
        "      <td>   NM_000492.3:c.9G&gt;A(p.%3D)NM_000492.3:c.9G&gt;A</td>\n",
        "      <td> synonymous_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>240499</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120160</td>\n",
        "      <td> G</td>\n",
        "      <td> A</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> synonymous_variant</td>\n",
        "      <td> NM_000492.3:c.12G&gt;A(p.%3D)NM_000492.3:c.12G&gt;A</td>\n",
        "      <td> synonymous_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>240500</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120160</td>\n",
        "      <td> G</td>\n",
        "      <td> C</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> synonymous_variant</td>\n",
        "      <td> NM_000492.3:c.12G&gt;C(p.%3D)NM_000492.3:c.12G&gt;C</td>\n",
        "      <td> synonymous_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>11010 rows</p><h2> Snpeff doesn't match for <em>splicing_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>241058</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120199</td>\n",
        "      <td> CAGG</td>\n",
        "      <td> C</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> splice_donor_variant</td>\n",
        "      <td>  NM_000492.3:c.52_53+1delAGG</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>241071</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120200</td>\n",
        "      <td>  AGG</td>\n",
        "      <td> A</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> splice_donor_variant</td>\n",
        "      <td>   NM_000492.3:c.53_53+1delGG</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>241072</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120200</td>\n",
        "      <td> AGGT</td>\n",
        "      <td> A</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> splice_donor_variant</td>\n",
        "      <td>  NM_000492.3:c.53_53+2delGGT</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>241084</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120201</td>\n",
        "      <td>   GG</td>\n",
        "      <td> G</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> splice_donor_variant</td>\n",
        "      <td>       NM_000492.3:c.53+1delG</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>241085</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120201</td>\n",
        "      <td>  GGT</td>\n",
        "      <td> G</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> splice_donor_variant</td>\n",
        "      <td> NM_000492.3:c.53+1_53+2delGT</td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>5044 rows</p><h2> Snpeff doesn't match for <em>intron_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>241104</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120203</td>\n",
        "      <td> T</td>\n",
        "      <td>   TA</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> intron_variant</td>\n",
        "      <td>   NM_000492.3:c.53+2_53+3insA</td>\n",
        "      <td> intron_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>241105</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120203</td>\n",
        "      <td> T</td>\n",
        "      <td>  TAC</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> intron_variant</td>\n",
        "      <td>  NM_000492.3:c.53+2_53+3insAC</td>\n",
        "      <td> intron_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>241106</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120203</td>\n",
        "      <td> T</td>\n",
        "      <td> TAGC</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> intron_variant</td>\n",
        "      <td> NM_000492.3:c.53+2_53+3insAGC</td>\n",
        "      <td> intron_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>241107</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120203</td>\n",
        "      <td> T</td>\n",
        "      <td>   TC</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> intron_variant</td>\n",
        "      <td>   NM_000492.3:c.53+2_53+3insC</td>\n",
        "      <td> intron_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>241108</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117120203</td>\n",
        "      <td> T</td>\n",
        "      <td>   TG</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> intron_variant</td>\n",
        "      <td>   NM_000492.3:c.53+2_53+3insG</td>\n",
        "      <td> intron_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>320838 rows</p><h2> Snpeff doesn't match for <em>stop_lost</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>379742</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117307157</td>\n",
        "      <td> CTTT</td>\n",
        "      <td> C</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> stop_lost</td>\n",
        "      <td> NP_000483.3:p.LeuTer1480GlnNM_000492.3:c.4439_...</td>\n",
        "      <td> stop_lost</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>379755</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117307158</td>\n",
        "      <td>  TTT</td>\n",
        "      <td> T</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> stop_lost</td>\n",
        "      <td> NP_000483.3:p.Ter1481GluNM_000492.3:c.4440_444...</td>\n",
        "      <td> stop_lost</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>379756</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117307158</td>\n",
        "      <td> TTTA</td>\n",
        "      <td> T</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> stop_lost</td>\n",
        "      <td> NP_000483.3:p.Ter1481delextTer3NM_000492.3:c.4...</td>\n",
        "      <td> stop_lost</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>379768</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117307159</td>\n",
        "      <td>   TT</td>\n",
        "      <td> T</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> stop_lost</td>\n",
        "      <td>    NP_000483.3:p.Ter1481ArgNM_000492.3:c.4441delT</td>\n",
        "      <td> stop_lost</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>379769</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117307159</td>\n",
        "      <td>  TTA</td>\n",
        "      <td> T</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> stop_lost</td>\n",
        "      <td> NP_000483.3:p.Ter1481GluNM_000492.3:c.4441_444...</td>\n",
        "      <td> stop_lost</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>150 rows</p><h2> Snpeff doesn't match for <em>3_prime_UTR_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>379784</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117307160</td>\n",
        "      <td> TAGA</td>\n",
        "      <td>  T</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> 3_prime_UTR_variant</td>\n",
        "      <td> NM_000492.3:c.*1_4442delAGA</td>\n",
        "      <td> 3_prime_UTR_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>379797</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117307161</td>\n",
        "      <td>  AGA</td>\n",
        "      <td>  A</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> 3_prime_UTR_variant</td>\n",
        "      <td>  NM_000492.3:c.*1_4443delGA</td>\n",
        "      <td> 3_prime_UTR_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>379798</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117307161</td>\n",
        "      <td> AGAG</td>\n",
        "      <td>  A</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> 3_prime_UTR_variant</td>\n",
        "      <td> NM_000492.3:c.*2_4443delGAG</td>\n",
        "      <td> 3_prime_UTR_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>379801</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117307162</td>\n",
        "      <td>    G</td>\n",
        "      <td> GA</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> 3_prime_UTR_variant</td>\n",
        "      <td>   NM_000492.3:c.*1_4443insA</td>\n",
        "      <td> 3_prime_UTR_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>379802</th>\n",
        "      <td> 1080</td>\n",
        "      <td> 117307162</td>\n",
        "      <td>    G</td>\n",
        "      <td> GC</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> 3_prime_UTR_variant</td>\n",
        "      <td>   NM_000492.3:c.*1_4443insC</td>\n",
        "      <td> 3_prime_UTR_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>8909 rows</p><h2> Snpeff doesn't match for <em>downstream_gene_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>380681</th>\n",
        "      <td> 83992</td>\n",
        "      <td> 117349970</td>\n",
        "      <td> C</td>\n",
        "      <td>   A</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> downstream_gene_variant</td>\n",
        "      <td> </td>\n",
        "      <td> downstream_gene_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>380682</th>\n",
        "      <td> 83992</td>\n",
        "      <td> 117349970</td>\n",
        "      <td> C</td>\n",
        "      <td>  CA</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> downstream_gene_variant</td>\n",
        "      <td> </td>\n",
        "      <td> downstream_gene_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>380683</th>\n",
        "      <td> 83992</td>\n",
        "      <td> 117349970</td>\n",
        "      <td> C</td>\n",
        "      <td> CAG</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> downstream_gene_variant</td>\n",
        "      <td> </td>\n",
        "      <td> downstream_gene_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>380684</th>\n",
        "      <td> 83992</td>\n",
        "      <td> 117349970</td>\n",
        "      <td> C</td>\n",
        "      <td> CAT</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> downstream_gene_variant</td>\n",
        "      <td> </td>\n",
        "      <td> downstream_gene_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>380685</th>\n",
        "      <td> 83992</td>\n",
        "      <td> 117349970</td>\n",
        "      <td> C</td>\n",
        "      <td>  CC</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> .</td>\n",
        "      <td> downstream_gene_variant</td>\n",
        "      <td> </td>\n",
        "      <td> downstream_gene_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>66262 rows</p><h2> Snpeff doesn't match for <em>nan</em></h2><p>0 rows</p>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<IPython.core.display.HTML at 0x13f4a6310>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sampletables = '<h1>Other algo\\'s agree, but...</h1>'\n",
      "for effect in master_df[\"normalized_so_snpeff\"].unique():\n",
      "    sampletables += \"<h2> VEP doesn't match for <em>\" + str(effect) + \"</em></h2>\"\n",
      "    query = master_df.loc[(master_df[\"normalized_so_annovar\"]==effect) & \n",
      "                                  (master_df[\"normalized_so_snpeff\"]==effect) & \n",
      "                                  (master_df[\"normalized_so_vep\"]!=effect)]\n",
      "    num_rows = query.count()[0]\n",
      "    if num_rows > 0:\n",
      "        sampletables += query.head(5).to_html()\n",
      "    sampletables += \"<p>\" + str(num_rows) + \" rows</p>\"\n",
      "HTML(sampletables)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<h1>Other algo's agree, but...</h1><h2> VEP doesn't match for <em>intergenic_variant</em></h2><p>0 rows</p><h2> VEP doesn't match for <em>upstream_gene_variant</em></h2><p>0 rows</p><h2> VEP doesn't match for <em>5_prime_UTR_variant</em></h2><p>0 rows</p><h2> VEP doesn't match for <em>inframe_variant</em></h2><p>0 rows</p><h2> VEP doesn't match for <em>frameshift_variant</em></h2><p>0 rows</p><h2> VEP doesn't match for <em>nonsynonymous_variant</em></h2><p>0 rows</p><h2> VEP doesn't match for <em>stop_gained</em></h2><p>0 rows</p><h2> VEP doesn't match for <em>synonymous_variant</em></h2><p>0 rows</p><h2> VEP doesn't match for <em>splicing_variant</em></h2><p>0 rows</p><h2> VEP doesn't match for <em>intron_variant</em></h2><p>0 rows</p><h2> VEP doesn't match for <em>stop_lost</em></h2><p>0 rows</p><h2> VEP doesn't match for <em>3_prime_UTR_variant</em></h2><p>0 rows</p><h2> VEP doesn't match for <em>downstream_gene_variant</em></h2><p>0 rows</p><h2> VEP doesn't match for <em>nan</em></h2><p>0 rows</p>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "<IPython.core.display.HTML at 0x110c49dd0>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sampletables = '<h1>At least 1 column doesn\\'t match</h1>'\n",
      "for effect in master_df[\"normalized_so_snpeff\"].unique():\n",
      "    sampletables += \"<h2> VEP doesn't match for <em>\" + str(effect) + \"</em></h2>\"\n",
      "    query = master_df.loc[(master_df[\"normalized_so_vep\"] != effect) &\n",
      "                          ((master_df[\"normalized_so_annovar\"]==effect) | (master_df[\"normalized_so_snpeff\"]==effect))]\n",
      "    num_rows = query.count()[0]\n",
      "    if num_rows > 0:\n",
      "        sampletables += query.head(5).to_html()\n",
      "    sampletables += \"<p>\" + str(num_rows) + \" rows</p>\"\n",
      "HTML(sampletables)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<h1>At least 1 column doesn't match</h1><h2> VEP doesn't match for <em>intergenic_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>3472</th>\n",
        "      <td> </td>\n",
        "      <td> 117115644</td>\n",
        "      <td> G</td>\n",
        "      <td>    A</td>\n",
        "      <td> .</td>\n",
        "      <td> INTERGENIC</td>\n",
        "      <td> </td>\n",
        "      <td> </td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3473</th>\n",
        "      <td> </td>\n",
        "      <td> 117115644</td>\n",
        "      <td> G</td>\n",
        "      <td>    C</td>\n",
        "      <td> .</td>\n",
        "      <td> INTERGENIC</td>\n",
        "      <td> </td>\n",
        "      <td> </td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3474</th>\n",
        "      <td> </td>\n",
        "      <td> 117115644</td>\n",
        "      <td> G</td>\n",
        "      <td>   GA</td>\n",
        "      <td> .</td>\n",
        "      <td> INTERGENIC</td>\n",
        "      <td> </td>\n",
        "      <td> </td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3475</th>\n",
        "      <td> </td>\n",
        "      <td> 117115644</td>\n",
        "      <td> G</td>\n",
        "      <td>  GAC</td>\n",
        "      <td> .</td>\n",
        "      <td> INTERGENIC</td>\n",
        "      <td> </td>\n",
        "      <td> </td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3476</th>\n",
        "      <td> </td>\n",
        "      <td> 117115644</td>\n",
        "      <td> G</td>\n",
        "      <td> GACG</td>\n",
        "      <td> .</td>\n",
        "      <td> INTERGENIC</td>\n",
        "      <td> </td>\n",
        "      <td> </td>\n",
        "      <td> intergenic_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>23302 rows</p><h2> VEP doesn't match for <em>upstream_gene_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>32500</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117115644</td>\n",
        "      <td> G</td>\n",
        "      <td>    A</td>\n",
        "      <td> .</td>\n",
        "      <td> UPSTREAM</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> upstream_gene_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32501</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117115644</td>\n",
        "      <td> G</td>\n",
        "      <td>    C</td>\n",
        "      <td> .</td>\n",
        "      <td> UPSTREAM</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> upstream_gene_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32502</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117115644</td>\n",
        "      <td> G</td>\n",
        "      <td>   GA</td>\n",
        "      <td> .</td>\n",
        "      <td> UPSTREAM</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> upstream_gene_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32503</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117115644</td>\n",
        "      <td> G</td>\n",
        "      <td>  GAC</td>\n",
        "      <td> .</td>\n",
        "      <td> UPSTREAM</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> upstream_gene_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32504</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117115644</td>\n",
        "      <td> G</td>\n",
        "      <td> GACG</td>\n",
        "      <td> .</td>\n",
        "      <td> UPSTREAM</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> upstream_gene_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>15534 rows</p><h2> VEP doesn't match for <em>5_prime_UTR_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>46611</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120014</td>\n",
        "      <td> GGGA</td>\n",
        "      <td>    G</td>\n",
        "      <td> .</td>\n",
        "      <td> UTR_5_PRIME</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> 5_prime_UTR_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>46624</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120015</td>\n",
        "      <td>  GGA</td>\n",
        "      <td>    G</td>\n",
        "      <td> .</td>\n",
        "      <td> UTR_5_PRIME</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> 5_prime_UTR_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>46625</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120015</td>\n",
        "      <td> GGAA</td>\n",
        "      <td>    G</td>\n",
        "      <td> .</td>\n",
        "      <td> UTR_5_PRIME</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> 5_prime_UTR_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>46628</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120016</td>\n",
        "      <td>    G</td>\n",
        "      <td>   GA</td>\n",
        "      <td> .</td>\n",
        "      <td> UTR_5_PRIME</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> 5_prime_UTR_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>46629</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120016</td>\n",
        "      <td>    G</td>\n",
        "      <td> GAGC</td>\n",
        "      <td> .</td>\n",
        "      <td> UTR_5_PRIME</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> 5_prime_UTR_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>7552 rows</p><h2> VEP doesn't match for <em>inframe_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>48459</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120146</td>\n",
        "      <td> ACCA</td>\n",
        "      <td>    A</td>\n",
        "      <td> .</td>\n",
        "      <td>  CODON_DELETION</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.Met1X/c.1*&gt;-CCA</td>\n",
        "      <td> inframe_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48473</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120147</td>\n",
        "      <td> CCAT</td>\n",
        "      <td>    C</td>\n",
        "      <td> .</td>\n",
        "      <td>  CODON_DELETION</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.Met1X/c.1*&gt;-CAT</td>\n",
        "      <td> inframe_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48476</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120148</td>\n",
        "      <td>    C</td>\n",
        "      <td> CACT</td>\n",
        "      <td> .</td>\n",
        "      <td> CODON_INSERTION</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.Met1X/c.1*&gt;+ACT</td>\n",
        "      <td> inframe_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48482</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120148</td>\n",
        "      <td>    C</td>\n",
        "      <td> CTGC</td>\n",
        "      <td> .</td>\n",
        "      <td> CODON_INSERTION</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.Met1X/c.1*&gt;+TGC</td>\n",
        "      <td> inframe_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48487</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120148</td>\n",
        "      <td> CATG</td>\n",
        "      <td>    C</td>\n",
        "      <td> .</td>\n",
        "      <td>  CODON_DELETION</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.Met1X/c.1*&gt;-ATG</td>\n",
        "      <td> inframe_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>24882 rows</p><h2> VEP doesn't match for <em>frameshift_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>48472</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120147</td>\n",
        "      <td> CCA</td>\n",
        "      <td>   C</td>\n",
        "      <td> .</td>\n",
        "      <td> FRAME_SHIFT</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>   p.X1X/c.1*&gt;-CA</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48475</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120148</td>\n",
        "      <td>   C</td>\n",
        "      <td>  CA</td>\n",
        "      <td> .</td>\n",
        "      <td> FRAME_SHIFT</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>  p.Met1X/c.1*&gt;+A</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48477</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120148</td>\n",
        "      <td>   C</td>\n",
        "      <td>  CC</td>\n",
        "      <td> .</td>\n",
        "      <td> FRAME_SHIFT</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>  p.Met1X/c.1*&gt;+C</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48478</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120148</td>\n",
        "      <td>   C</td>\n",
        "      <td>  CG</td>\n",
        "      <td> .</td>\n",
        "      <td> FRAME_SHIFT</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>  p.Met1X/c.1*&gt;+G</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48479</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120148</td>\n",
        "      <td>   C</td>\n",
        "      <td> CGC</td>\n",
        "      <td> .</td>\n",
        "      <td> FRAME_SHIFT</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.Met1X/c.1*&gt;+GC</td>\n",
        "      <td> frameshift_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>69548 rows</p><h2> VEP doesn't match for <em>nonsynonymous_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>48496</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120149</td>\n",
        "      <td> A</td>\n",
        "      <td> C</td>\n",
        "      <td> .</td>\n",
        "      <td> NON_SYNONYMOUS_START</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.Met1?/c.1A&gt;C</td>\n",
        "      <td> nonsynonymous_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48497</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120149</td>\n",
        "      <td> A</td>\n",
        "      <td> G</td>\n",
        "      <td> .</td>\n",
        "      <td>           START_LOST</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.Met1?/c.1A&gt;G</td>\n",
        "      <td> nonsynonymous_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48498</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120149</td>\n",
        "      <td> A</td>\n",
        "      <td> T</td>\n",
        "      <td> .</td>\n",
        "      <td> NON_SYNONYMOUS_START</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.Met1?/c.1A&gt;T</td>\n",
        "      <td> nonsynonymous_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48502</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120150</td>\n",
        "      <td> T</td>\n",
        "      <td> A</td>\n",
        "      <td> .</td>\n",
        "      <td>           START_LOST</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.Met1?/c.2T&gt;A</td>\n",
        "      <td> nonsynonymous_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48503</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120150</td>\n",
        "      <td> T</td>\n",
        "      <td> C</td>\n",
        "      <td> .</td>\n",
        "      <td>           START_LOST</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.Met1?/c.2T&gt;C</td>\n",
        "      <td> nonsynonymous_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>19460 rows</p><h2> VEP doesn't match for <em>stop_gained</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>48525</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120151</td>\n",
        "      <td> G</td>\n",
        "      <td> GTGA</td>\n",
        "      <td> .</td>\n",
        "      <td> STOP_GAINED</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>  p.Gln2*/c.4*&gt;+TGA</td>\n",
        "      <td> stop_gained</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48540</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120152</td>\n",
        "      <td> C</td>\n",
        "      <td>    T</td>\n",
        "      <td> .</td>\n",
        "      <td> STOP_GAINED</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>     p.Gln2*/c.4C&gt;T</td>\n",
        "      <td> stop_gained</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48564</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120154</td>\n",
        "      <td> G</td>\n",
        "      <td>   GT</td>\n",
        "      <td> .</td>\n",
        "      <td> STOP_GAINED</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>    p.Arg3*/c.7*&gt;+T</td>\n",
        "      <td> stop_gained</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48565</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120154</td>\n",
        "      <td> G</td>\n",
        "      <td>  GTA</td>\n",
        "      <td> .</td>\n",
        "      <td> STOP_GAINED</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>   p.Arg3*/c.7*&gt;+TA</td>\n",
        "      <td> stop_gained</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48609</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120157</td>\n",
        "      <td> G</td>\n",
        "      <td> GTAG</td>\n",
        "      <td> .</td>\n",
        "      <td> STOP_GAINED</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.Ser4*/c.10*&gt;+TAG</td>\n",
        "      <td> stop_gained</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>4588 rows</p><h2> VEP doesn't match for <em>synonymous_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>48558</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120154</td>\n",
        "      <td> G</td>\n",
        "      <td> A</td>\n",
        "      <td> .</td>\n",
        "      <td> SYNONYMOUS_CODING</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>  p.Gln2Gln/c.6G&gt;A</td>\n",
        "      <td> synonymous_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48580</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120155</td>\n",
        "      <td> A</td>\n",
        "      <td> C</td>\n",
        "      <td> .</td>\n",
        "      <td> SYNONYMOUS_CODING</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>  p.Arg3Arg/c.7A&gt;C</td>\n",
        "      <td> synonymous_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48600</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120157</td>\n",
        "      <td> G</td>\n",
        "      <td> A</td>\n",
        "      <td> .</td>\n",
        "      <td> SYNONYMOUS_CODING</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>  p.Arg3Arg/c.9G&gt;A</td>\n",
        "      <td> synonymous_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48642</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120160</td>\n",
        "      <td> G</td>\n",
        "      <td> A</td>\n",
        "      <td> .</td>\n",
        "      <td> SYNONYMOUS_CODING</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.Ser4Ser/c.12G&gt;A</td>\n",
        "      <td> synonymous_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48643</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120160</td>\n",
        "      <td> G</td>\n",
        "      <td> C</td>\n",
        "      <td> .</td>\n",
        "      <td> SYNONYMOUS_CODING</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.Ser4Ser/c.12G&gt;C</td>\n",
        "      <td> synonymous_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>5882 rows</p><h2> VEP doesn't match for <em>splicing_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>49201</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120199</td>\n",
        "      <td> CAGG</td>\n",
        "      <td>    C</td>\n",
        "      <td> .</td>\n",
        "      <td> SPLICE_SITE_DONOR</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49214</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120200</td>\n",
        "      <td>  AGG</td>\n",
        "      <td>    A</td>\n",
        "      <td> .</td>\n",
        "      <td> SPLICE_SITE_DONOR</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49215</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120200</td>\n",
        "      <td> AGGT</td>\n",
        "      <td>    A</td>\n",
        "      <td> .</td>\n",
        "      <td> SPLICE_SITE_DONOR</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49218</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120201</td>\n",
        "      <td>    G</td>\n",
        "      <td>   GA</td>\n",
        "      <td> .</td>\n",
        "      <td> SPLICE_SITE_DONOR</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49219</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120201</td>\n",
        "      <td>    G</td>\n",
        "      <td> GACT</td>\n",
        "      <td> .</td>\n",
        "      <td> SPLICE_SITE_DONOR</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> splicing_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>3252 rows</p><h2> VEP doesn't match for <em>intron_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>49247</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120203</td>\n",
        "      <td> T</td>\n",
        "      <td>   TA</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>   c.53+3*&gt;+A</td>\n",
        "      <td> intron_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49248</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120203</td>\n",
        "      <td> T</td>\n",
        "      <td>  TAC</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>  c.53+3*&gt;+AC</td>\n",
        "      <td> intron_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49249</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120203</td>\n",
        "      <td> T</td>\n",
        "      <td> TAGC</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> c.53+3*&gt;+AGC</td>\n",
        "      <td> intron_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49250</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120203</td>\n",
        "      <td> T</td>\n",
        "      <td>   TC</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>   c.53+3*&gt;+C</td>\n",
        "      <td> intron_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49251</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117120203</td>\n",
        "      <td> T</td>\n",
        "      <td>   TG</td>\n",
        "      <td> .</td>\n",
        "      <td> INTRON</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>   c.53+3*&gt;+G</td>\n",
        "      <td> intron_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>173531 rows</p><h2> VEP doesn't match for <em>stop_lost</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>187885</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117307157</td>\n",
        "      <td> CTTT</td>\n",
        "      <td>  C</td>\n",
        "      <td> .</td>\n",
        "      <td> STOP_LOST</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.X1480Glnext*?/c.4439*&gt;-TTT</td>\n",
        "      <td> stop_lost</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>187899</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117307158</td>\n",
        "      <td> TTTA</td>\n",
        "      <td>  T</td>\n",
        "      <td> .</td>\n",
        "      <td> STOP_LOST</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> p.X1480Leuext*?/c.4440*&gt;-TTA</td>\n",
        "      <td> stop_lost</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>187903</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117307159</td>\n",
        "      <td>    T</td>\n",
        "      <td> TA</td>\n",
        "      <td> .</td>\n",
        "      <td> STOP_LOST</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>     p.*1481Xext*?/c.4441*&gt;+A</td>\n",
        "      <td> stop_lost</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>187905</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117307159</td>\n",
        "      <td>    T</td>\n",
        "      <td> TC</td>\n",
        "      <td> .</td>\n",
        "      <td> STOP_LOST</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>     p.*1481Xext*?/c.4441*&gt;+C</td>\n",
        "      <td> stop_lost</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>187907</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117307159</td>\n",
        "      <td>    T</td>\n",
        "      <td> TG</td>\n",
        "      <td> .</td>\n",
        "      <td> STOP_LOST</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td>     p.*1481Xext*?/c.4441*&gt;+G</td>\n",
        "      <td> stop_lost</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>50 rows</p><h2> VEP doesn't match for <em>3_prime_UTR_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>187944</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117307162</td>\n",
        "      <td> G</td>\n",
        "      <td>   GA</td>\n",
        "      <td> .</td>\n",
        "      <td> UTR_3_PRIME</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> 3_prime_UTR_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>187945</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117307162</td>\n",
        "      <td> G</td>\n",
        "      <td>   GC</td>\n",
        "      <td> .</td>\n",
        "      <td> UTR_3_PRIME</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> 3_prime_UTR_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>187946</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117307162</td>\n",
        "      <td> G</td>\n",
        "      <td>  GCA</td>\n",
        "      <td> .</td>\n",
        "      <td> UTR_3_PRIME</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> 3_prime_UTR_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>187947</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117307162</td>\n",
        "      <td> G</td>\n",
        "      <td> GCAG</td>\n",
        "      <td> .</td>\n",
        "      <td> UTR_3_PRIME</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> 3_prime_UTR_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>187948</th>\n",
        "      <td> CFTR</td>\n",
        "      <td> 117307162</td>\n",
        "      <td> G</td>\n",
        "      <td>   GG</td>\n",
        "      <td> .</td>\n",
        "      <td> UTR_3_PRIME</td>\n",
        "      <td> NM_000492.3</td>\n",
        "      <td> </td>\n",
        "      <td> 3_prime_UTR_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>6897 rows</p><h2> VEP doesn't match for <em>downstream_gene_variant</em></h2><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Gene</th>\n",
        "      <th>POS</th>\n",
        "      <th>REF</th>\n",
        "      <th>ALT</th>\n",
        "      <th>ID_x</th>\n",
        "      <th>Effect</th>\n",
        "      <th>Transcript_ID</th>\n",
        "      <th>hgvs_snpeff</th>\n",
        "      <th>normalized_so_snpeff</th>\n",
        "      <th>ID_y</th>\n",
        "      <th>Consequence</th>\n",
        "      <th>hgvs_vep</th>\n",
        "      <th>normalized_so_vep</th>\n",
        "      <th>normalized_so_annovar</th>\n",
        "      <th>combined_effect</th>\n",
        "      <th>hgvs</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>188824</th>\n",
        "      <td> CTTNBP2</td>\n",
        "      <td> 117349970</td>\n",
        "      <td> C</td>\n",
        "      <td>   A</td>\n",
        "      <td> .</td>\n",
        "      <td> DOWNSTREAM</td>\n",
        "      <td> NM_033427.2</td>\n",
        "      <td> </td>\n",
        "      <td> downstream_gene_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>188825</th>\n",
        "      <td> CTTNBP2</td>\n",
        "      <td> 117349970</td>\n",
        "      <td> C</td>\n",
        "      <td>  CA</td>\n",
        "      <td> .</td>\n",
        "      <td> DOWNSTREAM</td>\n",
        "      <td> NM_033427.2</td>\n",
        "      <td> </td>\n",
        "      <td> downstream_gene_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>188826</th>\n",
        "      <td> CTTNBP2</td>\n",
        "      <td> 117349970</td>\n",
        "      <td> C</td>\n",
        "      <td> CAG</td>\n",
        "      <td> .</td>\n",
        "      <td> DOWNSTREAM</td>\n",
        "      <td> NM_033427.2</td>\n",
        "      <td> </td>\n",
        "      <td> downstream_gene_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>188827</th>\n",
        "      <td> CTTNBP2</td>\n",
        "      <td> 117349970</td>\n",
        "      <td> C</td>\n",
        "      <td> CAT</td>\n",
        "      <td> .</td>\n",
        "      <td> DOWNSTREAM</td>\n",
        "      <td> NM_033427.2</td>\n",
        "      <td> </td>\n",
        "      <td> downstream_gene_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>188828</th>\n",
        "      <td> CTTNBP2</td>\n",
        "      <td> 117349970</td>\n",
        "      <td> C</td>\n",
        "      <td>  CC</td>\n",
        "      <td> .</td>\n",
        "      <td> DOWNSTREAM</td>\n",
        "      <td> NM_033427.2</td>\n",
        "      <td> </td>\n",
        "      <td> downstream_gene_variant</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>9170 rows</p><h2> VEP doesn't match for <em>nan</em></h2><p>0 rows</p>"
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