<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Graph Genomes on Dr. Eric T. Dawson</title><link>http://www.erictdawson.com/series/graph-genomes/</link><description>Recent content in Graph Genomes on Dr. Eric T. Dawson</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sat, 01 Apr 2017 00:00:00 +0000</lastBuildDate><atom:link href="http://www.erictdawson.com/series/graph-genomes/index.xml" rel="self" type="application/rss+xml"/><item><title>Structural variant genotyping with variation graphs</title><link>http://www.erictdawson.com/posts/sv-genotyping-vg-v1/</link><pubDate>Sat, 01 Apr 2017 00:00:00 +0000</pubDate><guid>http://www.erictdawson.com/posts/sv-genotyping-vg-v1/</guid><description>&lt;p>This past year I&amp;rsquo;ve been working with the &lt;a href="https://github.com/vgteam" class="external-link" target="_blank" rel="noopener">vgteam&lt;/a> on the variation graph toolkit, or vg. vg has grown nicely into a very functional software package for graph construction, read mapping, SNP/MNP calling, and visualization.&lt;/p>
&lt;p>I&amp;rsquo;ve been working on methods for detecting structural variants using the graph, both novel SVs in the sample being analyzed and shared SVs known to exist in a population. I&amp;rsquo;m certainly not the only one, and at least two other variant callers exist in vg already. Currently I&amp;rsquo;m testing a method for genotyping known indels of any size, but I&amp;rsquo;d like to extend this to other, more complex forms of structural variation as well as novel variant detection.&lt;/p></description></item></channel></rss>