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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta charset="utf-8">
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="pandoc" />
<meta name="author" content="L Collado-Torres" />
<meta name="date" content="2016-03-21" />
<title>derfinder software paper Supplementary Website</title>
<style type="text/css">code{white-space: pre;}</style>
<link href="data:text/css,pre%20%2Eoperator%2C%0Apre%20%2Eparen%20%7B%0A%20color%3A%20rgb%28104%2C%20118%2C%20135%29%0A%7D%0A%0Apre%20%2Eliteral%20%7B%0A%20color%3A%20%23990073%0A%7D%0A%0Apre%20%2Enumber%20%7B%0A%20color%3A%20%23099%3B%0A%7D%0A%0Apre%20%2Ecomment%20%7B%0A%20color%3A%20%23998%3B%0A%20font%2Dstyle%3A%20italic%0A%7D%0A%0Apre%20%2Ekeyword%20%7B%0A%20color%3A%20%23900%3B%0A%20font%2Dweight%3A%20bold%0A%7D%0A%0Apre%20%2Eidentifier%20%7B%0A%20color%3A%20rgb%280%2C%200%2C%200%29%3B%0A%7D%0A%0Apre%20%2Estring%20%7B%0A%20color%3A%20%23d14%3B%0A%7D%0A" rel="stylesheet" type="text/css" />
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<div id="header">
<h1 class="title">derfinder software paper Supplementary Website</h1>
<h4 class="author"><em>L Collado-Torres</em></h4>
<h4 class="date"><em>21 March 2016</em></h4>
</div>
<h1>Contents</h1>
<div id="TOC">
<ul>
<li><a href="#brainspan-data-set"><span class="toc-section-number">1</span> BrainSpan data set</a><ul>
<li><a href="#code-to-reproduce-analyses"><span class="toc-section-number">1.1</span> Code to reproduce analyses</a></li>
<li><a href="#single-base-level"><span class="toc-section-number">1.2</span> Single base-level</a><ul>
<li><a href="#quick-overview-html-report"><span class="toc-section-number">1.2.1</span> Quick overview HTML report</a></li>
<li><a href="#csv-files-and-annotation-comparison"><span class="toc-section-number">1.2.2</span> CSV files and annotation comparison</a></li>
</ul></li>
<li><a href="#timing-and-memory-information"><span class="toc-section-number">1.3</span> Timing and memory information</a></li>
</ul></li>
<li><a href="#gtex-analysis"><span class="toc-section-number">2</span> GTEx analysis</a></li>
<li><a href="#simulation"><span class="toc-section-number">3</span> Simulation</a><ul>
<li><a href="#generating-reads"><span class="toc-section-number">3.1</span> Generating reads</a></li>
<li><a href="#processing-reads"><span class="toc-section-number">3.2</span> Processing reads</a></li>
<li><a href="#evaluating-results"><span class="toc-section-number">3.3</span> Evaluating results</a></li>
</ul></li>
<li><a href="#miscellaneous"><span class="toc-section-number">4</span> Miscellaneous</a><ul>
<li><a href="#expressed-regions-level-overview-figure"><span class="toc-section-number">4.1</span> Expressed regions-level overview figure</a></li>
<li><a href="#single-base-level-overview-figure"><span class="toc-section-number">4.2</span> Single base-level overview figure</a></li>
<li><a href="#additional-analyses"><span class="toc-section-number">4.3</span> Additional analyses</a></li>
</ul></li>
<li><a href="#reproducibility"><span class="toc-section-number">5</span> Reproducibility</a></li>
<li><a href="#bibliography"><span class="toc-section-number">6</span> Bibliography</a></li>
</ul>
</div>
<p>This page describes the supplementary material for the <code>derfinder</code> software paper. All the <code>bash</code>, <code>R</code> and <code>R Markdown</code> source files used to analyze the data for this project as well as generate the HTML reports are available in this website. However, it is easier to view them at <a href="https://github.com/leekgroup/derSupplement">github.com/leekgroup/derSupplement</a>.</p>
<div id="brainspan-data-set" class="section level1">
<h1><span class="header-section-number">1</span> BrainSpan data set</h1>
<p>This section of the website describes the code and reports associated with the BrainSpan data set that are referred to in the paper and Supplementary Methods and Results.</p>
<div id="code-to-reproduce-analyses" class="section level2">
<h2><span class="header-section-number">1.1</span> Code to reproduce analyses</h2>
<p>There are 9 main <code>bash</code> scripts named _step1-*_ through _step9-*_ for running the expressed regions-level and single base-level approaches.</p>
<ol style="list-style-type: decimal">
<li><em>fullCoverage</em> loads the data from the raw files. See <a href="step1-fullCoverage.sh" class="uri">step1-fullCoverage.sh</a> and <a href="step1-fullCoverage.R" class="uri">step1-fullCoverage.R</a>.</li>
<li><em>makeModels</em> creates the models used for the single-level base analysis. See <a href="step2-makeModels.sh" class="uri">step2-makeModels.sh</a> and <a href="step2-makeModels.R" class="uri">step2-makeModels.R</a>.</li>
<li><em>analyzeChr</em> runs the single base-level analysis by chromosome. See <a href="step3-analyzeChr.sh" class="uri">step3-analyzeChr.sh</a> and <a href="step3-analyzeChr.R" class="uri">step3-analyzeChr.R</a>.</li>
<li><em>mergeResults</em> merges the single base-level analysis results for all the chromosomes. See <a href="step4-mergeResults.sh" class="uri">step4-mergeResults.sh</a>.</li>
<li><em>derfinderReport</em> generates a HTML report for the single base-level DERs. See <a href="step5-derfinderReport.sh" class="uri">step5-derfinderReport.sh</a>.</li>
<li><em>regionMatrix</em> identifies the expressed regions for the expressed-regions level approach. See <a href="step6-regionMatrix.sh" class="uri">step6-regionMatrix.sh</a>.</li>
<li><em>regMatVsDERs</em> creates a simple HTML report comparing the single base-level and expressed regions-level approaches. See <a href="step7-regMatVsDERs.sh" class="uri">step7-regMatVsDERs.sh</a> and <a href="step7-regMatVsDERs.Rmd" class="uri">step7-regMatVsDERs.Rmd</a>.</li>
<li><em>coverageToExon</em> creates an exon count table using known annotation information. See <a href="step8-coverageToExon.sh" class="uri">step8-coverageToExon.sh</a> and <a href="step8-coverageToExon.R" class="uri">step8-coverageToExon.R</a>.</li>
<li><em>summaryInfo</em> creates a HTML report with brief summary information for the given experiment. See <a href="step9-summaryInfo.sh" class="uri">step9-summaryInfo.sh</a>, <a href="step9-summaryInfo.R" class="uri">step9-summaryInfo.R</a>, and <a href="step9-summaryInfo.Rmd" class="uri">step9-summaryInfo.Rmd</a>.</li>
</ol>
<p>A final <code>bash</code> script, <a href="run-all.sh" class="uri">run-all.sh</a>, can be used to run the main 9 steps (or a subset of them).</p>
<p>All scripts show at the beginning the way they were used. Some of them generate intermediate small <code>bash</code> scripts, for example one script per chromosome for the <em>analyzeChr</em> step. For some steps, there is a companion <code>R</code> or <code>R Markdown</code> code file when the code is more involved or an HTML file is generated in the particular step.</p>
<p>The <a href="check-analysis-time.R" class="uri">check-analysis-time.R</a> script was useful for checking the progress of the <em>step3-analyzeChr</em> jobs and detect whenever a node in the cluster was presenting problems.</p>
<p>We expect that these scripts will be useful to <code>derfinder</code> users who want to automate the single base-level and/or expressed regions-level analyses for several data sets and/or have the jobs run automatically without having to check if each step has finished running.</p>
<p>Note that all <code>bash</code> scripts are tailored for the cluster we have access to which administer job queues with Sun Grid Engine (SGE).</p>
</div>
<div id="single-base-level" class="section level2">
<h2><span class="header-section-number">1.2</span> Single base-level</h2>
<div id="quick-overview-html-report" class="section level3">
<h3><span class="header-section-number">1.2.1</span> Quick overview HTML report</h3>
<p>This HTML report contains basic information on the <code>derfinder</code> <a id="cite-derfinder"></a>(<a href="http://www.biorxiv.org/content/early/2015/02/19/015370.abstract">Collado-Torres, Frazee, Love, Irizarry, et al., 2015</a>) results from the <em>BrainSpan</em> data set. The report answers basic questions such as:</p>
<ul>
<li>What is the number of filtered bases?</li>
<li>What is the number of candidate regions?</li>
<li>How many candidate regions are significant?</li>
</ul>
<p>It also illustrates three clusters of candidate differentially expressed regions (DERs) from the single base-level analysis. You can view the report by following this link:</p>
<ul>
<li><a href="brainspan/summaryInfo/run4-v1.0.10/summaryInfo.html">BrainSpan</a></li>
</ul>
</div>
<div id="csv-files-and-annotation-comparison" class="section level3">
<h3><span class="header-section-number">1.2.2</span> CSV files and annotation comparison</h3>
<p>This HTML report has the code for loading the R data files and generating the CSV files. The report also has Venn diagrams showing the number of candidate DERs from the single base-level analysis that overlap known exons, introns and intergenic regions using the UCSC hg19 annotation. It also includes a detailed description of the columns in the CSV file.</p>
<p>View the <a href="venn/venn.html">venn</a> report or its <code>R Markdown</code> source file <a href="venn/venn.Rmd">venn.Rmd</a>.</p>
</div>
</div>
<div id="timing-and-memory-information" class="section level2">
<h2><span class="header-section-number">1.3</span> Timing and memory information</h2>
<p>This HTML report has code for reading and processing the time and memory information for each job extracted with <a href="https://github.com/alyssafrazee/efficiency_analytics">efficiency_analytics</a> <a id="cite-eff"></a>(<a href="http://dx.doi.org/10.6084/m9.figshare.878000">Frazee, 2014</a>). The report contains a detailed description of the analysis steps and tables summarizing the maximum memory and time for each analysis step if all the jobs for that particular step were running simultaneously. Finally, there is an interactive table with the timing results.</p>
<p>View the <a href="timing/timing.html">timing</a> report or check the <code>R Markdown</code> file <a href="timing/timing.Rmd">timing.Rmd</a>.</p>
</div>
</div>
<div id="gtex-analysis" class="section level1">
<h1><span class="header-section-number">2</span> GTEx analysis</h1>
<p>The script <a href="gtex/mergeInfo.R">mergeInfo.R</a> takes several phenotype tables and merges them into a single one. This information is then used by the <a href="gtex/select_samples.R">select_samples.R</a> script for choosing the 24 samples to analyze. These samples have a RIN greater than 7 and are from subjects that have samples from the heart (left ventricle), testis and liver. The script <a href="gtex/create_meanCov.R">create_meanCov.R</a> creates a mean coverage BigWig file just as you would get from using <code>Rail-RNA</code> <a id="cite-rail"></a>(<a href="#bib-rail">Nellore, Collado-Torres, Jaffe, Alquicira-Hernández, et al., 2015</a>) on only these 24 samples. The actual script for running <code>Rail-RNA</code> on the GTEx data are described at the <a href="https://github.com/nellore/runs">nellore/runs</a> GitHub repository. The scripts <a href="gtex/run-railMatrix.sh">run-railMatrix.sh</a> and <a href="gtex/railMatrix.R">railMatrix.R</a> then run <code>railMatrix()</code> using derfinder version 1.5.19 to identify the expressed regions. The resulting set of regions is then analyzed with the <a href="gtex/analyze_gtex.R">analyze_gtex.R</a> script.</p>
</div>
<div id="simulation" class="section level1">
<h1><span class="header-section-number">3</span> Simulation</h1>
<div id="generating-reads" class="section level2">
<h2><span class="header-section-number">3.1</span> Generating reads</h2>
<p>The code for generating the simulated RNA-seq reads and the chosen setup is described in the <a href="simulation/generateReads/generateReads.html">generateReads</a> report. This report is generated by the <code>R Markdown</code> <a href="simulation/generateReads/generateReads.Rmd">generateReads.Rmd</a> file.</p>
</div>
<div id="processing-reads" class="section level2">
<h2><span class="header-section-number">3.2</span> Processing reads</h2>
<p>We analyzed the simulation reads with the following pipelines:</p>
<ul>
<li>Align with <code>HISAT</code> <a id="cite-hisat"></a>(<a href="#bib-hisat">Kim, Langmead, and Salzberg, 2015</a>), summarize with <code>Rsubread::featureCounts()</code> at the exon-level with and without the complete annotation, identify differentially expressed exons with <code>DESeq2</code> <a id="cite-DESeq2"></a>(<a href="http://dx.doi.org/10.1186/s13059-014-0550-8">Love, Huber, and Anders, 2014</a>) or <code>edgeR</code>-robust <a id="cite-edgeR"></a>(<a href="#bib-edgeR">Zhou, Lindsay, and Robinson, 2014</a>).</li>
<li>Align with <code>HISAT</code>, summarize transcripts with <code>StringTie</code> <a id="cite-stringtie"></a>(<a href="#bib-stringtie">Pertea, Pertea, Antonescu, Chang, et al., 2015</a>), and test at the transcript and exon levels with <code>ballgown</code>.</li>
<li>Align with <code>HISAT</code>, summarize with <code>derfinder::regionMatrix()</code>, and test with <code>DESeq2</code> or <code>edgeR</code>-robust.</li>
<li>Align with <code>Rail-RNA</code> (<a href="#bib-rail">Nellore, Collado-Torres, Jaffe, Alquicira-Hernández, et al., 2015</a>), summarize with <code>derfinder::railMatrix()</code>, and test with <code>DESeq2</code> or <code>edgeR</code>-robust.</li>
</ul>
<p>Here we list the role of different scripts.</p>
<ul>
<li>The code for aligning the reads to the genome with <code>HISAT</code> is in the <a href="simulation/hisat/run-paired-hisat.sh">run-paired-hisat.sh</a> script while the code for aligning with <code>Rail-RNA</code> is in <a href="simulation/rail/prep-manifest.R">prep-manifest.R</a> and <a href="simulation/rail/run-rail.sh">run-rail.sh</a> scripts.</li>
<li><a href="simulation/gtf/createGTF.html">createGTF</a> creates GTF file with the complete and incomplete annotation (source <a href="simulation/gtf/createGTF.Rmd">createGTF.Rmd</a>).</li>
<li>The scripts <a href="simulation/featurecounts/run-featCounts.sh">run-featCounts.sh</a>, <a href="simulation/featurecounts/featureCounts.R">featureCounts.R</a> and <a href="simulation/featurecounts-inc/run-featCounts-inc.sh">run-featCounts-inc.sh</a>, <a href="simulation/featurecounts-inc/featureCounts-inc.R">featureCounts-inc.R</a> run <code>Rsubread::featureCounts()</code> at the exon level with the complete and incomplete annotation respectively.</li>
<li>Similarly, the scripts <a href="simulation/stringtie/run-stringtie.sh">run-stringtie.sh</a> and <a href="simulation/stringtie-inc/run-stringtie-inc.sh">run-stringtie-inc.sh</a> run <code>StringTie</code> with the complete and incomplete annotation creating the input needed to run <code>ballgown</code>.</li>
<li>The scripts <a href="simulation/ballgown/run_ballgown_analysis.sh">run_ballgown_analysis.sh</a> and <a href="simulation/ballgown/ballgown_analysis.R">ballgown_analysis.R</a> then perform the <code>ballgown</code> <a id="cite-ballgown"></a>(<a href="#bib-ballgown">Frazee, Pertea, Jaffe, Langmead, et al., 2015</a>) analyses at the transcript and exon levels.</li>
<li>The scripts <a href="simulation/regionMatrix/run_regionMat.sh">run_regionMat.sh</a> and <a href="simulation/regionMatrix/regionMat.R">regionMat.R</a> run <code>derfinder::regionMatrix()</code> with the <code>HISAT</code> output.</li>
<li>Similarly, the scripts <a href="simulation/railMatrix/run_railMat.sh">run_railMat.sh</a> and <a href="simulation/railMatrix/railMat.R">railMat.R</a> run <code>derfinder::railMatrix()</code> with the <code>Rail-RNA</code> output.</li>
<li>The scripts <a href="simulation/deseq2-edger/run_calc_stats.sh">run_calc_stats.sh</a> and <a href="simulation/deseq2-edge/calc_stats.R">calc_stats.R</a> use the count matrices created by <code>regionMatrix()</code>, <code>railMatrix()</code> and <code>featureCounts()</code> to perform differential expression tests using <code>DESeq2</code> and <code>edgeR</code>-robust.</li>
</ul>
</div>
<div id="evaluating-results" class="section level2">
<h2><span class="header-section-number">3.3</span> Evaluating results</h2>
<p>The report <a href="simulation/evaluate/evaluate.html">evaluate</a> (source <a href="simulation/evaluate/evaluate.Rmd">evaluate.Rmd</a>) defines different reference sets one could consider. It then takes the results from all the different pipelines and compares them against these reference sets. The report includes summary tables from these results showing the minimum and maximum empirical power, false positive rate and false discovery rate. The main results are highlighted in the paper. Finally <a href="simulation/timing/timing.html">timing</a> (source <a href="simulation/timing/timing.Rmd">timing.Rmd</a>) shows information about the timing and computer resources used by the different pipelines for the simulation analysis.</p>
</div>
</div>
<div id="miscellaneous" class="section level1">
<h1><span class="header-section-number">4</span> Miscellaneous</h1>
<div id="expressed-regions-level-overview-figure" class="section level2">
<h2><span class="header-section-number">4.1</span> Expressed regions-level overview figure</h2>
<p>The code used for generating the panels using in figure showing the expressed regions-level approach is available in the <a href="figure-expressed-regions/figure-expressed-regions.R">figure-expressed-regions.R</a> file.</p>
</div>
<div id="single-base-level-overview-figure" class="section level2">
<h2><span class="header-section-number">4.2</span> Single base-level overview figure</h2>
<p>The code used for generating the panels using in the figure showing the single base-level approach is available in the <a href="figure-single-base/figure-single-base.R">figure-single-base.R</a> file.</p>
</div>
<div id="additional-analyses" class="section level2">
<h2><span class="header-section-number">4.3</span> Additional analyses</h2>
<p>The following <code>R</code> source files have the code for reproducing additional analyses described in the paper</p>
<ul>
<li><a href="additional-analyses/analyze_brainspan.R">analyze_brainspan.R</a> and <a href="additional-analyses/brainspan_regionLevel.R">brainspan_regionLevel.R</a> are the scripts containing the analysis of BrainSpan expressed regions-level DERs.</li>
<li><a href="additional-analyses/characterize_brainspan_DERs.R">characterize_brainspan_DERs.R</a> Analysis of BrainSpan single base-level DERs.</li>
</ul>
<p>These scripts also include other exploratory code.</p>
</div>
</div>
<div id="reproducibility" class="section level1">
<h1><span class="header-section-number">5</span> Reproducibility</h1>
<p>Date this page was generated.</p>
<pre><code>## [1] "2016-03-21 10:08:47 EDT"</code></pre>
<p>Wallclock time spent generating the report.</p>
<pre><code>## Time difference of 1.351 secs</code></pre>
<p><code>R</code> session information.</p>
<pre><code>## Session info -----------------------------------------------------------------------------------------------------------</code></pre>
<pre><code>## setting value
## version R version 3.2.2 (2015-08-14)
## system x86_64, darwin13.4.0
## ui X11
## language (EN)
## collate en_US.UTF-8
## tz America/New_York
## date 2016-03-21</code></pre>
<pre><code>## Packages ---------------------------------------------------------------------------------------------------------------</code></pre>
<pre><code>## package * version date source
## bibtex 0.4.0 2014-12-31 CRAN (R 3.2.0)
## BiocStyle * 1.8.0 2015-10-14 Bioconductor
## bitops 1.0-6 2013-08-17 CRAN (R 3.2.0)
## devtools 1.10.0 2016-01-23 CRAN (R 3.2.3)
## digest 0.6.9 2016-01-08 CRAN (R 3.2.3)
## evaluate 0.8 2015-09-18 CRAN (R 3.2.0)
## formatR 1.2.1 2015-09-18 CRAN (R 3.2.0)
## htmltools 0.3 2015-12-29 CRAN (R 3.2.3)
## httr 1.1.0 2016-01-28 CRAN (R 3.2.3)
## knitcitations * 1.0.7 2015-10-28 CRAN (R 3.2.0)
## knitr 1.12.3 2016-01-22 CRAN (R 3.2.3)
## lubridate 1.5.0 2015-12-03 CRAN (R 3.2.3)
## magrittr 1.5 2014-11-22 CRAN (R 3.2.0)
## memoise 1.0.0 2016-01-29 CRAN (R 3.2.3)
## plyr 1.8.3 2015-06-12 CRAN (R 3.2.1)
## R6 2.1.2 2016-01-26 CRAN (R 3.2.3)
## Rcpp 0.12.3 2016-01-10 CRAN (R 3.2.3)
## RCurl 1.95-4.7 2015-06-30 CRAN (R 3.2.1)
## RefManageR 0.10.6 2016-02-15 CRAN (R 3.2.3)
## RJSONIO 1.3-0 2014-07-28 CRAN (R 3.2.0)
## rmarkdown * 0.9.2 2016-01-01 CRAN (R 3.2.3)
## stringi 1.0-1 2015-10-22 CRAN (R 3.2.0)
## stringr 1.0.0 2015-04-30 CRAN (R 3.2.0)
## XML 3.98-1.3 2015-06-30 CRAN (R 3.2.0)
## yaml 2.1.13 2014-06-12 CRAN (R 3.2.0)</code></pre>
<p>You can view the source <code>R Markdown</code> file for this page at <a href="index.Rmd" class="uri">index.Rmd</a>.</p>
</div>
<div id="bibliography" class="section level1">
<h1><span class="header-section-number">6</span> Bibliography</h1>
<p>This report was generated using <code>BiocStyle</code> <a id="cite-BiocStyle"></a>(<a href="https://github.com/Bioconductor/BiocStyle">Morgan, Oleś, and Huber, 2016</a>) with <code>knitr</code> <a id="cite-knitr"></a>(<a href="http://www.crcpress.com/product/isbn/9781466561595">Xie, 2014</a>) and <code>rmarkdown</code> <a id="cite-rmarkdown"></a>(<a href="http://CRAN.R-project.org/package=rmarkdown">Allaire, Cheng, Xie, McPherson, et al., 2016</a>) running behind the scenes.</p>
<p>Citations were made with <code>knitcitations</code> <a id="cite-knitcitations"></a>(<a href="http://CRAN.R-project.org/package=knitcitations">Boettiger, 2015</a>). Citation file: <a href="index.bib" class="uri">index.bib</a>.</p>
<p>
<a id="bib-rmarkdown"></a><a href="#cite-rmarkdown">[1]</a><cite> J. Allaire, J. Cheng, Y. Xie, J. McPherson, et al. <em>rmarkdown: Dynamic Documents for R</em>. R package version 0.9.2. 2016. URL: <a href="http://CRAN.R-project.org/package=rmarkdown">http://CRAN.R-project.org/package=rmarkdown</a>.</cite>
</p>
<p>
<a id="bib-knitcitations"></a><a href="#cite-knitcitations">[2]</a><cite> C. Boettiger. <em>knitcitations: Citations for ‘Knitr’ Markdown Files</em>. R package version 1.0.7. 2015. URL: <a href="http://CRAN.R-project.org/package=knitcitations">http://CRAN.R-project.org/package=knitcitations</a>.</cite>
</p>
<p>
<a id="bib-derfinder"></a><a href="#cite-derfinder">[3]</a><cite> L. Collado-Torres, A. C. Frazee, M. I. Love, R. A. Irizarry, et al. “derfinder: Software for annotation-agnostic RNA-seq differential expression analysis”. In: <em>bioRxiv</em> (2015). DOI: <a href="http://dx.doi.org/10.1101/015370">10.1101/015370</a>. URL: <a href="http://www.biorxiv.org/content/early/2015/02/19/015370.abstract">http://www.biorxiv.org/content/early/2015/02/19/015370.abstract</a>.</cite>
</p>
<p>
<a id="bib-eff"></a><a href="#cite-eff">[4]</a><cite> A. Frazee. <em>Efficiency analysis of Sun Grid Engine batch jobs</em>. 2014. URL: <a href="http://dx.doi.org/10.6084/m9.figshare.878000">http://dx.doi.org/10.6084/m9.figshare.878000</a>.</cite>
</p>
<p>
<a id="bib-ballgown"></a><a href="#cite-ballgown">[5]</a><cite> A. C. Frazee, G. Pertea, A. E. Jaffe, B. Langmead, et al. “Ballgown bridges the gap between transcriptome assembly and expression analysis”. In: <em>Nature Biotechnology</em> (2015).</cite>
</p>
<p>
<a id="bib-hisat"></a><a href="#cite-hisat">[6]</a><cite> D. Kim, B. Langmead and S. L. Salzberg. “HISAT: a fast spliced aligner with low memory requirements”. In: <em>Nature Methods</em> (2015).</cite>
</p>
<p>
<a id="bib-DESeq2"></a><a href="#cite-DESeq2">[7]</a><cite> M. I. Love, W. Huber and S. Anders. “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2”. In: <em>Genome Biology</em> 15 (12 2014), p. 550. DOI: <a href="http://dx.doi.org/10.1186/s13059-014-0550-8">10.1186/s13059-014-0550-8</a>.</cite>
</p>
<p>
<a id="bib-BiocStyle"></a><a href="#cite-BiocStyle">[8]</a><cite> M. Morgan, A. Oleś and W. Huber. <em>BiocStyle: Standard styles for vignettes and other Bioconductor documents</em>. R package version 1.8.0. 2016. URL: <a href="https://github.com/Bioconductor/BiocStyle">https://github.com/Bioconductor/BiocStyle</a>.</cite>
</p>
<p>
<a id="bib-rail"></a><a href="#cite-rail">[9]</a><cite> A. Nellore, L. Collado-Torres, A. E. Jaffe, J. Alquicira-Hernández, et al. “Rail-RNA: Scalable analysis of RNA-seq splicing and coverage”. In: <em>bioRxiv</em> (2015).</cite>
</p>
<p>
<a id="bib-stringtie"></a><a href="#cite-stringtie">[10]</a><cite> M. Pertea, G. M. Pertea, C. M. Antonescu, T. Chang, et al. “StringTie enables improved reconstruction of a transcriptome from RNA-seq reads”. In: <em>Nature Biotechnology</em> (2015).</cite>
</p>
<p>
<a id="bib-knitr"></a><a href="#cite-knitr">[11]</a><cite> Y. Xie. “knitr: A Comprehensive Tool for Reproducible Research in R”. In: <em>Implementing Reproducible Computational Research</em>. Ed. by V. Stodden, F. Leisch and R. D. Peng. ISBN 978-1466561595. Chapman and Hall/CRC, 2014. URL: <a href="http://www.crcpress.com/product/isbn/9781466561595">http://www.crcpress.com/product/isbn/9781466561595</a>.</cite>
</p>
<p>
<a id="bib-edgeR"></a><a href="#cite-edgeR">[12]</a><cite> X. Zhou, H. Lindsay and M. D. Robinson. “Robustly detecting differential expression in RNA sequencing data using observation weights”. In: <em>Nucleic Acids Research</em> 42 (2014), p. e91.</cite>
</p>
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