<div dir="ltr"><div class="gmail_extra"><div class="gmail_quote"><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><br><div>Following up on my previous question - I ran a few different assemblies exploring the effect of using khmer digital normalization and FLASH to merge short reads. I compared the results of (1) running diginorm only, (2) running diginorm than attempting to merge still-paired reads with FLASH, and (3) first attempting to merge paired reads with FLASH followed by diginorm. In all cases, I used trimmed-and-filtered reads and performed assembly using velvet-oases with a kmer of 21. Below are some assembly statistics. </div>
</div></blockquote><div><br></div><div>I can confirm a similar result with a massive 200M read mouse-ish RNA-Seq data set.</div><div><br></div><div>Using FLASH to overlap first giving ~280 bp SE reads, then doing Diginorm (k=??) then using Oases (k=61) gave the 'best' txome assembly compared to feeding read pairs. (and Trinity did 'worse' on everything, no matter what we did, and took 1 week for each run).</div>
</div><div class="gmail_extra"><br></div>It will eventually get published.<br clear="all"><div><br></div>-- <br><div dir="ltr"><font face="tahoma, sans-serif" size="1"><i>--Dr Torsten Seemann<br>--Scientific Director : Victorian Bioinformatics Consortium, Monash University, AUSTRALIA</i></font><div>
<font face="tahoma, sans-serif"><i><font size="1">--Acting Head : Life Sciences Computation Centre, VLSCI, Parkville, AUSTRALIA<br>--<a href="http://www.bioinformatics.net.au/" target="_blank">http://www.bioinformatics.net.au/</a></font></i><br>
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