Hi Christian,<div><br></div><div>could you clarify: do you have a reference genome for the virus in question?</div><div><br></div><div>Regards</div><div><br></div><div>philipp<br><br><div id="cm_footer"><div id="cm_sent_from">Sent using <a href="https://cloudmagic.com/k/d/mailapp?ct=ti&cv=5.2.12&pv=8.0.2">CloudMagic</a></div></div><br><div id="cm_replymail_content_wrap"><div class="cm_replymail_content_1413321969_wrapper" style="color: rgb(0, 0, 0);">On Di., Okt. 14, 2014 at 10:46 nachm., Christian Frech <frech.christian@gmail.com> wrote: <div id="cm_replymail_content_1413321969" style="overflow: visible;"><blockquote style="color: #303B40;"><div dir="ltr"><div><div><div><div>Hi all,<br><br></div>we have an RNA-seq data set (Illumina HiSeq 2000, 50 bp, single end reads) from a human cell line where 8.2 million reads (~25% of the total) do not map against the human reference genome. I figured out that almost all these unmapped reads are of viral origin. Including the identified viral genome into the reference and inspecting the re-mapped reads in IGV shows that this viral genome is almost completely covered by several thousands-fold (!). However, I can also see 2-3 gaps in coverage with no mapped reads.<br><br></div>To figure out exactly how the viral transcript looks like, I tried Minia for de novo assembly but failed, i.e. no contigs from the viral genome assembled. Minia works in principle, because the same Minia output contains an almost perfect assembly of the PhiX genome, which I know is also in the data. <br><br>After corresponding with Rayan Chikhi (author of Minia) we came to the conclusion that the problem is that I have too HIGH sequence coverage for the viral genome and that this confuses the de bruin graph assembler. The reason is that with so many reads covering each position in the genome, inevitably you get quite a few reads (> 20) with sequencing errors at every position, which obviously wreaks havoc on the de bruin graph.<br><br>He suggested to use digital normalization as possible remedy, so I gave it a try. I followed the single-pass mRNA-seq pipeline at <a href="http://ged.msu.edu/angus/diginorm-2012/tutorial.html">http://ged.msu.edu/angus/diginorm-2012/tutorial.html</a>, with currently no success. I can still not see any major contigs from my viral genome in the Minia output. From the diginorm output, I see that about 80% of my 8.2 million unmapped reads were removed, which I guess is good.<br><br>Any suggestions how to proceed from here? Given my particular problem, is there anything to tweak with diginorm or should I just try different RNA-seq assemblers (Oasis, Trinity)?<br><br></div>Christian<br></div></div>
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