Add output files so they show up on GH pages

.gitignore

@@ -2,8 +2,3 @@
 .doit*
 # Snakemake cache
 .snakemake
-
-# Auto-generated files
-index.html
-/ryan_thompson_resume.pdf
-/ryan_thompson_resume.html

examples/Salomon/450k/index.html

@@ -0,0 +1,7 @@
+<p>This is a series of diagnostic plots that were used to evaluate how well a particular statistical model fits the data and explains the sources of variation in an Illumina 450k dataset.</p>
+<ul>
+<li><a href="mean-var-model.pdf"><code>mean-var-model.pdf</code></a> shows the variance trend modeling performed by voom, a method originally designed for mean-variance modeling in RNA-seq data. In this case, it models the mean-variance dependency induced by the logistic transform used for converting beta values (i.e. percent methylation) to M-values (i.e. ratio of methylated to unmethylated signal) in methylation data. Page 2 shows the mean-variance trend after fitting the model with the voom weights to cancel out the trend.</li>
+<li><a href="sample-weights.pdf"><code>sample-weights.pdf</code></a> Shows the results of limma's <code>arrayWeights</code> method, which detects and down-weights outlier samples, plotted against all known clinical covariates for those samples. Diabetes status had a significant association with the sample weights, indicating that the Type I diabetes samples were overall more consistent and had fewer outlier observations that Type II diabetes samples.</li>
+<li><a href="pcoa.pdf"><code>pcoa.pdf</code></a> shows a Principle Coordinate Plot (similar to a PCA plot) of all the samples after subtracting out the effects of known covariates. Points are sized by their sample weight, and a crosshair shows the center of mass of each group.</li>
+<li><a href="pval-histograms.pdf"><code>pval-histograms.pdf</code></a> and <a href="pval-cdf.pdf"><code>pval-cdf.pdf</code></a> show the p-value distributions for each contrast of interest, presented as a histogram and as an empirical cumulative distribution function. Each is annotated with asymptotes indicating the estimated fraction of probes affected by that contrast.</li>
+</ul>

examples/Salomon/CD4/index.html

@@ -0,0 +1,20 @@
+<p>This is a series of example plots and tables from a combined RNA-seq/ChIP-seq study on differences between naive and memory T-cell activation. You can view the (old and messy) code for these plots <a href="https://github.com/DarwinAwardWinner/cd4-histone-paper-code">here</a>.</p>
+<ul>
+<li><a href="p-value%20distributions.pdf"><code>p-value distributions.pdf</code></a> is a series of p-value histograms for each of the contrasts tested. A contrast with no significant differential expression would exhibit a uniform distribution, while differential expression would be reflected by an excess of small p-values.</li>
+<li><a href="FPKM%20by%20Peak%20Status%20H3K4.pdf"><code>FPKM by Peak Status H3K4.pdf</code></a> shows the variation in gene expression based on the presence or absence of two histone marks in the gene promoters.</li>
+<li><a href="promoter-edger-topgenes3-ql.xlsx"><code>promoter-edger-topgenes3-ql.xlsx</code></a> is a spreadsheet of all promoters with differential histone modification in their promoters based on the ChIP-seq read counts.</li>
+<li><a href="Promoter%20Peak%20Distance%20Profile.pdf"><code>Promoter Peak Distance Profile.pdf</code></a> shows the distribution of distances from transcription start sites to the nearest peak for the three histone modifications studied. This was used to determine the &quot;promoter radius&quot; for read counting. Notably, the three histone marks do not all have the same promoter radius.</li>
+<li><a href="rnaseq-MDSPlots.pdf"><code>rnaseq-MDSPlots.pdf</code></a> shows a series of MDS plots (similar to PCA plots) before and after correction of a known batch effect. Note the implausible zigzag-shaped progression over time before correction, compared to the more plausible cyclic time progression after.</li>
+<li><a href="rnaseq-edgeR-vs-limma.pdf"><code>rnaseq-edgeR-vs-limma.pdf</code></a> and <a href="rnaseq-limma-weighted-vs-uw.pdf"><code>rnaseq-limma-weighted-vs-uw.pdf</code></a> show comparisons of p-values for all genes in each contrast of the RNA-seq data, comparing edgeR and limma-voom with/without sample quality weights. The final choice of method was limma-voom with sample quality weights.</li>
+<li><a href="rnaseq-maplots-limma-sampleweights.pdf"><code>rnaseq-maplots-limma-sampleweights.pdf</code></a> shows the MA plot for each contrast of the RNA-seq data</li>
+</ul>
+<p>There are also some plots from an in-progress analysis of the same data based on sliding windows, rather than just analyzing promoter regions. You can view the code for generating these plots <a href="https://github.com/DarwinAwardWinner/CD4-csaw">here</a>, and you can view some presentation slides based on this analysis <a href="./ChIP-Seq%20presentation.pdf">here</a>.</p>
+<ul>
+<li><a href="CCF-plots.pdf"><code>CCF-plots.pdf</code></a> shows the cross-correlation functions of the ChIP-Seq data for 3 different histone marks, at several different levels of smoothing. This plot is used to determine the fragment size. You can also observe from the periodic wave-like pattern, indicating that multiple adjacent histones tend to share the same histone modification.</li>
+<li><a href="CCF-plots-noBL.pdf"><code>CCF-plots-noBL.pdf</code></a> shows the same plots as above, but without removing reads in so-called &quot;blacklist&quot; regions that typically contain high-coverage artifact signals. The result is a much messier plot, with many samples having an artifactual peak at the read length (dotted line) rather than the actual width of a histone (solid line).</li>
+<li><a href="site-profile-plots.pdf"><code>site-profile-plots.pdf</code></a> shows plots of the relative coverage depth profiles around local coverage maxima in the ChIP-Seq data. This plot is used to determine the footprint size of the protein being imunoprecipitated. Since this is histone mark data, the footprint size should match the size of a nucleosome, about 147 bp.</li>
+<li><a href="D4659vsD5053_idrplots.pdf"><code>D4659vsD5053_idrplots.pdf</code></a> shows an example plot from the <a href="https://sites.google.com/site/anshulkundaje/projects/idr">Irreproducible Discovery Rate</a> analysis used to identify biologically reproducible peaks in the ChIP-Seq data. The plot shows the degree of consistency in the scores for overlapping peaks in two biological replicates. Peaks with consistently high-ranking scores in both replicates are considered reproducible.</li>
+<li>The following reports show QC and exploratory analysis for 3 histone marks and RNA-seq: <a href="reports/ChIP-seq/H3K4me3-exploration.html">H3K4me3</a>, <a href="reports/ChIP-seq/H3K4me2-exploration.html">H3K4me2</a>, <a href="reports/ChIP-seq/H3K27me3-exploration.html">H3K27me3</a>, <a href="reports/RNA-seq/salmon_hg38.analysisSet_ensembl.85-exploration.html">RNA-seq</a>. The purpose of these reports is to ensure that the modelling assumptions and strategies are appropriate for the data. Sometimes several strategies are tested against each other, and the best performer is chosen for the subsequent differential expression/modification analysis.</li>
+<li>The following reports show the differential expression/modification analyses and p-value histograms for the 3 histone marks and RNA-seq: <a href="reports/ChIP-seq/H3K4me3-diffmod.html">H3K4me3</a>, <a href="reports/ChIP-seq/H3K4me2-diffmod.html">H3K4me2</a>, <a href="reports/ChIP-seq/H3K27me3-diffmod.html">H3K27me3</a>, <a href="reports/RNA-seq/salmon_hg38.analysisSet_ensembl.85-diffexp.html">RNA-seq</a></li>
+<li>The RNA-seq data were processed using 10 different combinations of quantification pipeline and transcriptome reference. <a href="reports/RNA-seq/rnaseq-compare.html"><code>rnaseq-compare.html</code></a> shows a series of comparisons designed to investigate the differences between these pipelines and references.</li>
+</ul>

examples/Salomon/fRMA/index.html

@@ -0,0 +1,12 @@
+<h1 id="results">Results</h1>
+<p>The scripts below were used to evaluate the consistency of the fRMA normalization vectors by repeating the training process with 5 different random samples and then comparing a random selection of arrays normalized by all five trained vectors as well as by ordinary RMA. <a href="fRMA_consistency_results">This folder</a> shows the results.</p>
+<h1 id="scripts">Scripts</h1>
+<p>There are two pairs of scripts. The first pair, <code>train.R</code> and <code>test.R</code>, handle the tasks of (respectively) generating/training the main fRMA vectors and ensuring that they work by normalizing all the data with them. The second pair, <code>consistency-train.R</code> and <code>consistency-evaluate.R</code>, handle (respectively) training five separate fRMA vector sets and testing their consistency.</p>
+<h2 id="train.r-creating-the-frma-vectors"><a href="train.R"><code>train.R</code></a>: Creating the fRMA vectors</h2>
+<p>This script reads the sample metadata tables, assembles the full file lists for BX and PAX tissues, and trains a set of fRMA vectors for each tissue. It exports each of these vector sets to an installable R package.</p>
+<h2 id="test.r-testing-the-frma-vectors"><a href="test.R"><code>test.R</code></a>: Testing the fRMA vectors</h2>
+<p>This script simply loads all the arrays and normalizes them using the appropriate fRMA vectors that were generated by <code>train.R</code>. It should be run after installing the packages produced by <code>train.R</code>. It is simply used for testing to make sure the fRMA vectors work.</p>
+<h2 id="consistency-train.r-train-several-vector-sets-for-each-tissue"><a href="consistency-train.R"><code>consistency-train.R</code></a>: Train several vector sets for each tissue</h2>
+<p>This script essentially does the same thing as <code>train.R</code>, only it does it five times with five different subsamplings of the arrays to generate five different fRMA vector sets and saves them all in an R data file.</p>
+<h2 id="consistency-evaluate.r-verify-consistency-of-frma-vectors"><a href="consistency-evaluate.R"><code>consistency-evaluate.R</code></a>: Verify consistency of fRMA vectors</h2>
+<p>This script loads the data file from <code>consistency-train.R</code>, then loads 20 random arrays from each tissue and normalizes them with all five fRMA vector sets, and also by ordinary RMA. It then produces plots of M vs A for every pair of normalizations. Unlike regular MA plots, these are <em>not</em> plotting arrays against each other, but rather arrays against themselves, but normalized using two different methods. So if two normalizations were perfectly consistent, the MA plot would be a flat horizontal line at M=0. It also produces boxplots and violin plots showing the M distribution for each of the pairwise comparisons.</p>

examples/Salomon/globin/index.html

@@ -0,0 +1,22 @@
+<h1 id="human-globin-protocol-stats">Human globin protocol stats</h1>
+<p>This is a pair of spreadsheets summarizing the globin-reducing properties of an experimental RNA-seq protocol.</p>
+<ul>
+<li><a href="method-select.xlsx"><code>method-select.xlsx</code></a> shows the results for several different methods of globin reduction. The most important column is &quot;Non-HB&quot;, which represents the fraction of total reads that map to non-globin genes.</li>
+<li><a href="concentration-select.xlsx"><code>concentration-select.xlsx</code></a> shows the results of selecting the best globin blocking method and optimizing the concentration of blockers as well as the number of hybridizations.</li>
+</ul>
+<h1 id="cyno-globin-plots">Cyno globin plots</h1>
+<p>This is a series of example plots for evaluation of a similar globin reduction protocol that was designed for cynomolgus monkeys.</p>
+<ul>
+<li><a href="BCVplots.pdf"><code>BCVplots.pdf</code></a> and <a href="corrplot.pdf"><code>corrplot.pdf</code></a> show, respectively, that the biological coefficient of variation is not increased, and the sample-to-sample correlation is not decreased, by the globin reduction protocol.</li>
+<li><a href="cyno-vs-hg19.pdf"><code>cyno-vs-hg19.pdf</code></a> shows excellent correlation between total read counts on the cyno and human genomes, indicating that the cyno annotation is reasonably complete.</li>
+<li><a href="pval-comparisons.pdf"><code>pval-comparisons.pdf</code></a> shows the comparison between p-values from edgeR, limma-voom, and DESeq2 on the exact same differential expression test. Surprisingly, despite the significant algorithmic differences, edgeR and limma are in quite close agreement. DESeq2 id overly liberal because it does not account for the negative estimation bias in negative-binomial dispersions.</li>
+</ul>
+<h1 id="publication-plots">Publication plots</h1>
+<p>These are the figures from an upcoming publication on the developed globin blocking method.</p>
+<ul>
+<li><a href="figure1%20-%20globin-fractions.pdf">Figure 1</a>: Globin blocking (GB) substantially increases yield and consistency of non-globin reads.</li>
+<li><a href="figure2%20-%20aveLogCPM-colored.pdf">Figure 2</a>: GB lowers the noise floor and increases the distance between noise floor and signal.</li>
+<li><a href="figure3%20-%20detection.pdf">Figrue 3</a>: GB allows detection of more genes at any abundance threshold.</li>
+<li><a href="figure4%20-%20maplot-colored.pdf">Figure 4</a>: GB has small but systematic effects on other genes' measured expression levels.</li>
+<li><a href="figure5%20-%20corrplot.pdf">Figure 5</a>: GB Significantly increases correlation between libraries.</li>
+</ul>

index.html

@@ -0,0 +1 @@
+ryan_thompson_resume.html

ryan_thompson_resume.html

@@ -0,0 +1,289 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.1 plus MathML 2.0//EN" "http://www.w3.org/Math/DTD/mathml2/xhtml-math11-f.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+<meta name="GENERATOR" content="LyX 2.2.2" />
+<meta http-equiv="Content-type" content="text/html;charset=UTF-8" />
+<title>LyX Document</title>
+<style type='text/css'>
+/* LyX Provided Styles */
+div.bibtexentry { margin-left: 2em; text-indent: -2em; }
+span.bibtexlabel:before{ content: "["; }
+span.bibtexlabel:after{ content: "] "; }
+
+/* Layout-provided Styles */
+div.standard {
+text-align: left;
+
+}
+div.plain_layout {
+text-align: left;
+
+}
+div.cv_name {
+font-size: x-large;
+margin-bottom: 1ex;
+text-align: center;
+
+}
+div.contact_address {
+margin-left: 3ex;
+margin-right: 3ex;
+text-align: left;
+
+}
+span.contact_address_label {
+font-weight: bold;
+
+}
+div.phone_number {
+margin-left: 3ex;
+margin-right: 3ex;
+text-align: left;
+
+}
+span.phone_number_label {
+font-weight: bold;
+
+}
+div.email {
+margin-left: 3ex;
+margin-right: 3ex;
+text-align: left;
+
+}
+span.email_label {
+font-weight: bold;
+
+}
+div.website {
+margin-left: 3ex;
+margin-right: 3ex;
+text-align: left;
+
+}
+span.website_label {
+font-weight: bold;
+
+}
+div.section {
+font-family: sans-serif;
+font-weight: bold;
+font-size: large;
+margin-top: 1.3ex;
+margin-bottom: 0.7ex;
+text-align: left;
+
+}
+ul.itemize {
+margin-top: 0.7ex;
+margin-bottom: 0.7ex;
+margin-left: 3ex;
+text-align: left;
+
+}
+dl.description dt { font-weight: bold; }
+div.subsection {
+font-family: sans-serif;
+font-weight: bold;
+font-size: small;
+margin-top: 0.9ex;
+margin-bottom: 0.5ex;
+text-align: left;
+
+}
+
+
+</style>
+</head>
+<body dir="auto">
+<div class="standard"><a id='magicparlabel-1' /><span class="flex_cv_image"><img src="headshot-crop.jpg" width="100"></span></div>
+<div class="cv_name"><a id='magicparlabel-6' />Ryan C. Thompson</div>
+<div class="contact_address"><a id='magicparlabel-7' /><span class="contact_address_label">Contact Address:</span> 8656 Via Mallorca
+<br />
+
+Unit G
+<br />
+
+La Jolla, CA 92037</div>
+<div class="phone_number"><a id='magicparlabel-8' /><span class="phone_number_label">Phone:</span> (908)&thinsp;922-7470</div>
+<div class="email"><a id='magicparlabel-9' /><span class="email_label">Email:</span> rct@thompsonclan.org</div>
+<div class="website"><a id='magicparlabel-10' /><span class="website_label">Website:</span> https://github.com/DarwinAwardWinner</div>
+<div class="section"><a id='magicparlabel-11' /><span class="section_label"></span> Summary</div>
+
+<ul class="itemize"><a id='magicparlabel-12' /><li class="itemize_item">Well-rounded computational biologist with deep knowledge of biology, mathematics, and programming</li>
+<li class="itemize_item">9 years of bioinformatics experience answering a wide range of complex biological questions through analysis of large NGS- and microarray-based whole transcriptome and epigenetic data sets </li>
+<li class="itemize_item">Strong focus on sound statistical analysis and effective data visualization and presentation</li>
+<li class="itemize_item">Supportive team member with a strong ability to foster cross-disciplinary literacy by explaining concepts at the right level of abstraction</li>
+<li class="itemize_item">Focus on packaging expert knowledge and successful analysis methods into re-usable software tools to share best practices and improve team efficiency over time</li>
+</ul>
+<div class="section"><a id='magicparlabel-17' /><span class="section_label"></span> Education</div>
+
+<dl class='description'><a id='magicparlabel-18' /><dt class="description_label">The Scripps Research Institute</dt>
+<dd class="description_item">  
+<br />
+
+Ph. D. in Bioinformatics, expected June 2017</dd>
+<dt class="description_label">University of Virginia</dt>
+<dd class="description_item">  
+<br />
+
+B.S. in Biology, B.A. in Mathematics, 2009
+<br />
+
+Echols Scholar</dd>
+</dl>
+<div class="section"><a id='magicparlabel-20' /><span class="section_label"></span> Research Experience</div>
+<div class="subsection"><a id='magicparlabel-21' /><span class="subsection_label"></span> Salomon Lab, The Scripps Research Institute  La Jolla, CA&emsp;&emsp; 2012 - 2017</div>
+
+<dl class='description'><a id='magicparlabel-22' /><dt class="description_label">Contact:</dt>
+<dd class="description_item"> Dr. Andrew Su, <a href="asu@scripps.edu">asu@scripps.edu</a></dd>
+</dl>
+
+<ul class="itemize"><a id='magicparlabel-23' /><li class="itemize_item">Created an open source, reproducible workflow to analyze a large multi-omics next-gen sequencing dataset of 220 RNA-seq and ChIP-seq samples to reveal interactions between differential histone methylation and differential gene expression during T-cell activation, as well as key differences in activation between naïve and memory cells [<a href='#LyXCite-lamere2016'><span class="bib-label">2</span></a>, <a href='#LyXCite-lamere2016_JMJD3'>#<span class="bib-key">lamere2016-JMJD3</span></a>].
+<br />
+
+Links: <a href="https://github.com/DarwinAwardWinner/CD4-csaw#re-analysis-of-a-combined-chip-seq--rna-seq-data-set">Reproducible workflow</a>, <a href="http://mneme.homenet.org/~ryan/resume/examples/Salomon/CD4/ChIP-Seq%20presentation.pdf">Slides</a>, <a href="http://mneme.homenet.org/~ryan/resume/examples/Salomon/CD4/">Example results and visualizations</a></li>
+<li class="itemize_item">Investigated effects of life-span-extending drug on worm gene expression over time revealing that the drug retards age-related &ldquo;transcriptional drift&rdquo;, preserving a youthful phenotype at the molecular level. [<a href='#LyXCite-Rangarajue08833'><span class="bib-label">3</span></a>] 
+<br />
+
+Link: <a href="http://mneme.homenet.org/~ryan/resume/examples/Salomon/mdsplots-multidim.pdf">PCoA Plot</a> </li>
+<li class="itemize_item">Significantly improved performce of machine learning classifier for identifying transplant rejection by developing appropriate single-sample microarray normalization procedures[<a href='#LyXCite-kurian2014molecular'><span class="bib-label">1</span></a>], including training a custom set of frozen RMA normalization vectors. Classifier is currently being developed into a clinical test for transplant dysfunction.
+<br />
+
+Links: <a href="http://mneme.homenet.org/~ryan/resume/examples/Salomon/fRMA/">fRMA example code &amp; plots</a>, <a href="http://mneme.homenet.org/~ryan/resume/examples/Salomon/Classifier%20Math%20Write-up.pdf">Classifier Method Write-up</a></li>
+<li class="itemize_item">Implemented a systems biology tool to analyze and efficiently present and summarize differential expression for multiple gene set &amp; pathway methods run on multiple pathway databases, as well as differential expression of individual genes within each pathway.
+<br />
+
+Links: <a href="http://mneme.homenet.org/~ryan/resume/examples/Salomon/Pathways/Pathway%20Presentation.pdf">Presentation</a>, <a href="http://mneme.homenet.org/~ryan/resume/examples/Salomon/Pathways/Pathway%20Analysis%20Example.html">Example Results</a>, <a href="http://mneme.homenet.org/~ryan/resume/examples/Salomon/Pathways/Cyno%20Pathway%20Summary.pdf">Summary</a></li>
+<li class="itemize_item">Performed comparative analysis of multiple differential expression statistical models to define best practice for optimal sensitivity while maintaining false positive control. Presented on theoretical and practical similarities and differences between methods.
+<br />
+
+Links: <a href="http://mneme.homenet.org/~ryan/resume/examples/Salomon/DGE%20Presentation.pdf">RNA-seq Presentation</a>, <a href="http://mneme.homenet.org/~ryan/resume/examples/Salomon/Advanced%20RNA-seq%20Analysis.pdf">Advanced RNA-seq Presentation</a>, <a href="http://mneme.homenet.org/~ryan/resume/examples/Salomon/globin/pval-comparisons.pdf">Example plot </a></li>
+<li class="itemize_item">Taught basic RNA-seq theory and practical analysis for the graduate-level introductory bioinformatics course.
+<br />
+
+Links: <a href="http://mneme.homenet.org/~ryan/resume/examples/Salomon/Teaching/RNA-Seq Lecture.pdf">Lecture Slides</a>, <a href="http://mneme.homenet.org/~ryan/resume/examples/Salomon/Teaching/RNA-Seq Lab.html">Hands-on lab section</a></li>
+<li class="itemize_item">Evaluated and optimized cost and performance of custom protocol for RNA-seq of human and primate blood samples while minimizing nuisance globin reads. Increased yield of useful reads nearly 2-fold. [<a href='#LyXCite-globin_reduction'><span class="bib-label">6</span></a>]
+<br />
+
+Link: <a href="http://mneme.homenet.org/~ryan/resume/examples/Salomon/globin/">Example results</a></li>
+<li class="itemize_item">Adapted common normalization methods from RNA-seq to improve performance in analysis of RASL-seq experiments. [<a href='#LyXCite-Scott036061'><span class="bib-label">4</span></a>]</li>
+<li class="itemize_item">Performed a comprehensive comparative evaluation of over 20 subtly different statistical models for differential methylation in Illumina 450k arrays, selecting a model that best explained the observed sources and trends of variation in the data, including cross-domain application of a method originally designed for RNA-seq data.
+<br />
+
+Link: <a href="http://mneme.homenet.org/~ryan/resume/examples/Salomon/450k/">Example diagnostic plots</a></li>
+<li class="itemize_item">Active member of the Bioconductor community and contributing developer for several Bioconductor packages. 
+<br />
+
+Links: <a href="http://bioconductor.org/packages/release/bioc/html/BiocParallel.html">BiocParallel</a>, <a href="http://bioconductor.org/packages/release/bioc/html/ChIPpeakAnno.html">ChIPPeakAnno</a>, <a href="http://bioconductor.org/packages/release/bioc/html/mygene.html">MyGene</a>, <a href="https://support.bioconductor.org/u/5618/">Support Profile</a></li>
+<li class="itemize_item">Mentored 2 coworkers in learning bioinformatics.</li>
+</ul>
+<div class="subsection"><a id='magicparlabel-34' /><span class="subsection_label"></span> Summer Internship, Informatics IT, Merck &amp; Co. Boston, MA&emsp;&emsp; 2011</div>
+
+<dl class='description'><a id='magicparlabel-35' /><dt class="description_label">Contact:</dt>
+<dd class="description_item"> Adnan Derti, <a href="adnan.derti@gmail.com">adnan.derti@gmail.com</a> </dd>
+</dl>
+
+<ul class="itemize"><a id='magicparlabel-36' /><li class="itemize_item">Built a transcriptome assembly and quantification pipeline using Cufflinks, including fully-automated cluster job control for high-throughput reproducible analysis, and presented a conceptual overview of Cufflinks' assembly and quantification algorithms to help the team understand Cufflinks.
+<br />
+
+Link: <a href="http://mneme.homenet.org/~ryan/resume/examples/cufflinks-presentation.pdf">Presentation Slides</a></li>
+<li class="itemize_item">Assisted in a molecular genetics study to evaluate peformance of two variant calling algorithms in detection of causal mutations in antibiotic-resistant bacterial genomes.</li>
+</ul>
+<div class="subsection"><a id='magicparlabel-38' /><span class="subsection_label"></span> Gaasterland Lab, UCSD Bioinformatcs La Jolla, CA&emsp;&emsp; 2010 - 2012</div>
+
+<dl class='description'><a id='magicparlabel-39' /><dt class="description_label">Contact:</dt>
+<dd class="description_item"> Terry Gaasterland, <a href="gaasterland@ucsd.edu">gaasterland@ucsd.edu</a> </dd>
+</dl>
+
+<ul class="itemize"><a id='magicparlabel-40' /><li class="itemize_item">Designed and implemented Deloxer, a critical software step in a new Illumina mate-pair sequencing protocol using Cre recombination. Deloxer is published[<a href='#LyXCite-van2011illumina'><span class="bib-label">7</span></a>] and now in use in several labs around the world. 
+<br />
+
+Links: <a href="http://genomes.sdsc.edu/downloads/deloxer/index.html">Documentation</a>; <a href="http://genomes.sdsc.edu/downloads/deloxer/delox.R">Code</a></li>
+<li class="itemize_item">Performed a molecular genetics study to find potential causal mutations for <a href="https://en.wikipedia.org/wiki/Black_rhinoceros#Threats">fatal iron overload disease</a> in critically endangered black rhinoceros by <em>de novo</em> assembly of black rhino transcriptome using Trinity and comparison to closely-related white rhino transcriptome. Developed a custom pipeline to match up ortholog gene pairs, discover single-nucleotide differences between them, and functionally annotate these differences, and delivered a list of potential causal variants to collaborators for follow-up. 
+<br />
+
+Link: <a href="http://mneme.homenet.org/~ryan/resume/examples/Gaasterland/ipi-results-small.txt">Example results</a></li>
+<li class="itemize_item">Helped design &amp; implement a large-scale high-throughput exome sequencing pipeline for SNP discovery and functional annotation, including QC and validation of on-target coverage depth and reproducibility of coverage. 
+<br />
+
+Links: <a href="http://mneme.homenet.org/~ryan/resume/examples/Gaasterland/neartarget.pdf">Example 1</a>; <a href="http://mneme.homenet.org/~ryan/resume/examples/Gaasterland/on-off-coverage.pdf">Example 2</a>; <a href="http://mneme.homenet.org/~ryan/resume/examples/Gaasterland/depth-consistency.pdf">Example 3</a></li>
+<li class="itemize_item">Created a fully-automated pipeline to produce quality-control metrics and plots for Illumina high-throughput sequencing data for early identification of failed runs or samples. 
+<br />
+
+Link: <a href="http://mneme.homenet.org/~ryan/resume/examples/Gaasterland/illumina-qc.html">Example results</a></li>
+<li class="itemize_item">Investigated the binding motif specificity of ZASC1 transcription factor in mouse T-cells using Affymetrix expression microarrays in ZASC1 siRNA knockdown experiment.</li>
+<li class="itemize_item">Analyzed miRNA target predictions using GO &amp; KEGG grouping to identify target pathways of autophagy-related miRNAs for biological validation. 
+<br />
+
+Link: <a href="http://mneme.homenet.org/~ryan/resume/examples/Gaasterland/mirna-results.html">Example results</a></li>
+</ul>
+<div class="subsection"><a id='magicparlabel-46' /><span class="subsection_label"></span> Timko Lab, U. of Virginia BiologyCharlottesville, VA&emsp;&emsp; 2007 - 2009</div>
+
+<dl class='description'><a id='magicparlabel-47' /><dt class="description_label">Contact:</dt>
+<dd class="description_item"> Paul J. Rushton, <a href="Paul.Rushton@sdstate.edu">Paul.Rushton@sdstate.edu</a></dd>
+</dl>
+
+<ul class="itemize"><a id='magicparlabel-48' /><li class="itemize_item">Undergraduate thesis: Designed and implemented Contig Farmer, an algorithm for efficient selective contig assembly starting from “seed” sequences of interest, and used Contig Farmer to accelerate transcription factor gene discovery in cowpea and tobacco shotgun genomic sequence data[<a href='#LyXCite-cfarmer'><span class="bib-label">5</span></a>].</li>
+<li class="itemize_item">Investigated transcription factors mediating plant stress response using expression microarray time-course, and refined the custom microarray design using data from previous runs to identify and eliminate uninformative probes, yielding an improved design for future studies. 
+<br />
+
+Link: <a href="http://mneme.homenet.org/~ryan/resume/examples/UVa/probe-selection.R">Code</a></li>
+<li class="itemize_item">Acted as interpreter to explain complex biological concepts to programmers and explain complex computational problems to biologists.
+<br />
+
+Link: <a href="http://mneme.homenet.org/~ryan/resume/examples/UVa/blast-slides.pdf">Presentation Slides</a> </li>
+</ul>
+<div class="section"><a id='magicparlabel-51' /><span class="section_label"></span> Skills</div>
+
+<dl class='description'><a id='magicparlabel-52' /><dt class="description_label">Computing&nbsp;Skills</dt>
+<dd class="description_item"> R/BioConductor, Python, Perl, Lisp, Java, C#, C++; Git version control; Remote UNIX system administration &amp; software compilation, computing cluster job management &amp; parallel computation
+<br />
+
+Public code: <a href="https://github.com/DarwinAwardWinner">https://github.com/DarwinAwardWinner</a> 
+<br />
+
+StackOverflow Profile: <a href="http://stackoverflow.com/users/125921">http://stackoverflow.com/users/125921</a> </dd>
+<dt class="description_label">Statistics&nbsp;&amp;&nbsp;Data&nbsp;Analysis</dt>
+<dd class="description_item"> Multi-omics NGS &amp; microarray analysis, multi-factor linear and generalized linear regression, experimental design and parametrization, empirical Bayesian methods, predictive modeling of clinical outcomes, machine learning classifier training &amp; validation, survival analysis, data visualization &amp; presentation, reproducible research practices</dd>
+<dt class="description_label">Wet&nbsp;Lab:</dt>
+<dd class="description_item"> PCR, molecular cloning, recombinant protein purification, epitope mapping, site-directed mutagenesis, 2D gels, real-time PCR, and associated data analysis </dd>
+<dt class="description_label">Languages:</dt>
+<dd class="description_item"> English: native; German: written and spoken</dd>
+</dl>
+<div class="section"><a id='magicparlabel-56' /><span class="section_label"></span> Other Work Experience</div>
+<div class="subsection"><a id='magicparlabel-57' /><span class="subsection_label"></span> Computing Advisor &amp; Help Desk, U.&thinsp;Va. IT Dept.  Charlottesville, VA &emsp;&emsp; 2005 - 2007 </div>
+
+<ul class="itemize"><a id='magicparlabel-58' /><li class="itemize_item">Provided support via phone and in person for students, faculty, and staff having problems with computers, phone system, network access, malware, hardware setup, and university web services</li>
+<li class="itemize_item">Tasks included support for university-provided software, virus removal, iPod recovery, printer setup and repair, diagnosis of hardware malfunctions, and data recovery from failing hard disks</li>
+</ul>
+<div class="subsection"><a id='magicparlabel-60' /><span class="subsection_label"></span> Summer Sailing Instructor, Raritan Yacht Club  Perth Amboy, NJ &emsp;&emsp; 2006 - 2009 </div>
+
+<ul class="itemize"><a id='magicparlabel-61' /><li class="itemize_item">Instructed children ages 8-18 in sailing skills, safety, seamanship, knots, rigging &amp; de-rigging boats, navigation, and racing strategy and technique, with an emphasis on building character and self-reliance</li>
+<li class="itemize_item">Ensured safety of students and staff by maintaining boats and equipment in good repair, by being vigilant to traffic and hazards on a busy waterway, and by communicating and coordinating efficiently with other staff</li>
+<li class="itemize_item">Helped organize, run, and referee several regattas per season for students from RYC and neighboring yacht clubs</li>
+</ul>
+
+
+
+
+<div class="standard"><a id='magicparlabel-74' /><h2 class='bibtex'>References</h2><div class='bibtex'><div class='bibtexentry' id='LyXCite-cfarmer'><span class='bibtexinfo'><span class="bib-author">Ryan C. Thompson and Rushton, Paul J. and Laudeman, Tom W. and Timko, Michael P.</span>, "<span class="bib-title">http://mneme.homenet.org/~ryan/resume/examples/UVa/contigfarmer.pdfContig Farmer: a tool for extracting maximal-le…</span>" (<span class="bib-year">2009</span>).</span></div>
+<div class='bibtexentry' id='LyXCite-eiv_2017'><span class='bibtexinfo'><span class="bib-author">Kurian, Sunil and Velazquez, Enrique and Ryan Thompson and Whisenant, Thomas and Rose, Stanley and Riley, Nicole and H…</span>, "<span class="bib-title">Orthogonal Comparison of Molecular Signatures of Kidney Transplants with Subclinical and Clinical Acute Rejection -- Equivalent…</span>", <i><span class="bib-journal">American Journal of Transplantation</span></i> (<span class="bib-year">2017</span>).</span></div>
+<div class='bibtexentry' id='LyXCite-globin_reduction'><span class='bibtexinfo'><span class="bib-author">Ryan C. Thompson and Gelbart, Terri and Head, Steven R. and Ordoukhanian, Phillip and Mullen, Courtney and Han, Dong…</span>, "<span class="bib-title">Optimizing yield of deep RNA sequencing for gene expression profiling of peripheral blood samples from cynomolgus monkeys (…</span>", <i><span class="bib-journal">Journal of Biological Methods (in review)</span></i> (<span class="bib-year">2016</span>).</span></div>
+<div class='bibtexentry' id='LyXCite-kurian2014molecular'><span class='bibtexinfo'><span class="bib-author">Kurian, SM and Williams, AN and Gelbart, T and Campbell, D and Mondala, TS and Head, SR and Horvath, S and Gaber, L and …</span>, "<span class="bib-title">Molecular Classifiers for Acute Kidney Transplant Rejection in Peripheral Blood by Whole Genome Gene Expression Profiling</span>", <i><span class="bib-journal">American Journal of Transplantation</span></i> (<span class="bib-year">2014</span>), <span class="bib-pages">1164--1172</span>.</span></div>
+<div class='bibtexentry' id='LyXCite-lamere2016'><span class='bibtexinfo'><span class="bib-author">LaMere, Sarah and Ryan C. Thompson and Komori, H. Kiyomi and Mark, Adam and Salomon, Daniel R.</span>, "<span class="bib-title">Promoter H3K4 methylation dynamically reinforces activation-induced pathways in human CD4 T cells</span>", <i><span class="bib-journal">Genes &amp; Immunity</span></i> (<span class="bib-year">2016</span>).</span></div>
+<div class='bibtexentry' id='LyXCite-lamere2017_JMJD3'><span class='bibtexinfo'><span class="bib-author">LaMere, Sarah and Ryan C. Thompson and Meng, Xiangzhi and Komori, H. Kiyomi and Mark, A. and Salomon, Daniel R.</span>, "<span class="bib-title">H3K27 methylation dynamics during CD4 T cell activation: regulation of JAK/STAT and IL12RB2 expression by JMJD3</span>", <i><span class="bib-journal">Journal of Immunology (in review)</span></i> (<span class="bib-year">2017</span>).</span></div>
+<div class='bibtexentry' id='LyXCite-math_history_paper'><span class='bibtexinfo'><span class="bib-author">Ryan C. Thompson</span>, "<span class="bib-title">http://mneme.homenet.org/~ryan/resume/examples/UVa/math-history-paper.pdfThe sources and limits of geometric rigor fro…</span>" (<span class="bib-year">2008</span>).</span></div>
+<div class='bibtexentry' id='LyXCite-Rangarajue08833'><span class='bibtexinfo'><span class="bib-author">Rangaraju, Sunitha and Solis, Gregory M and Ryan C Thompson and Gomez-Amaro, Rafael L and Kurian, Leo and Encalada, …</span>, "<span class="bib-title">Suppression of transcriptional drift extends C. elegans lifespan by postponing the onset of mortality</span>", <i><span class="bib-journal">eLife</span></i> (<span class="bib-year">2015</span>).</span></div>
+<div class='bibtexentry' id='LyXCite-Scott036061'><span class='bibtexinfo'><span class="bib-author">Scott, Erick R. and Larman, H. Benjamin and Torkamani, Ali and Schork, Nicholas J. and Wineinger, Nathan and Nanis, Max and ͡…</span>, "<span class="bib-title">RASLseqTools: open-source methods for designing and analyzing RNA-mediated oligonucleotide Annealing, Selection, and, Liga…</span>", <i><span class="bib-journal">Nucleic Acids Research (in review)</span></i> (<span class="bib-year">2016</span>).</span></div>
+<div class='bibtexentry' id='LyXCite-van2011illumina'><span class='bibtexinfo'><span class="bib-author">Van Nieuwerburgh, Filip and Ryan C. Thompson and Ledesma, Jessica and Deforce, Dieter and Gaasterland, Terry and O…</span>, "<span class="bib-title">Illumina Mate-Paired DNA Sequencing Library Preparation Using Cre-Lox Recombination</span>", <i><span class="bib-journal">Nucleic Acids Research</span></i> (<span class="bib-year">2011</span>), <span class="bib-pages">gkr1000</span>.</span></div>
+</div></div>
+
+
+
+<div class="standard"><a id='magicparlabel-80' /><div style='height:1em'></div></div>
+
+<div class="standard"><a id='magicparlabel-81' />Online version (with links): <a href="http://mneme.homenet.org/~ryan/resume/ryan_thompson_resume.pdf">http://mneme.homenet.org/~ryan/resume/ryan_thompson_resume.pdf</a></div>
+</body>
+</html>