Hi all,
I've been looking around and there doesn't seem to be much information on the development of RNA-Seq pipelines for differential expression analysis. I am about to start work on setting up a basic skeleton pipeline.
As a very high level overview, how do the following steps look? Can anyone comment on/add/remove steps? I have also added some questions regarding the steps to aid in my own understanding.
1) Align reads to genome using Tophat/Bowtie
(perhaps use the new Tophat-fusion to find fusion transcripts? I also guess it is important to ensure that the genome we use will match our preferred annotation source downstream e.g. if we prefer Ensembl, we should use NCBIv37 rather than hg19 to ensure consistency in chromosome names?)
2) Mark/remove duplicate reads.
3) Use Cufflinks to assemble transcripts.
4) Run Cuffdiff to assess differential expression.
5) Annotate transcripts
(unsure on how exactly this is done - can anyone comment? For example, what program might be used and what happens when we attempt to annotate novel-spliced or fully novel transcripts? Will these be recognised somehow?)
6) At this point I guess we have an annotated matrix that could be used in next gen or classical visualisation programs? Any suggestions on how to view?
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written
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Travis
3 answers
Some comments on your steps:
1) TopHat is fine, but Bowtie (or BWA) only makes sense if you are mapping directly against the transcriptome (IMO). Mapping against the transcriptome may be a good idea for many applications, although mapping against the genome is much more common and I haven't seen any in-depth comparison of which one is more sensitive/specific. Apart from TopHat, there are other good spliced mapping methods such as MapSplice, SpliceMap and (especially) RUM.
Yes, you should take care to keep your reference genome and annotation "in sync".
2) Yes, at least for paired-end it's a good idea to remove duplicates.
3) In my opinion, assembling the transcripts with Cufflinks only makes sense if you don't have a good annotation. If you are sequencing human RNA, I would just run Cufflinks with a GTF file to quantify the expression of annotated transcripts. If you want to run DESeq or other count-based methods for differential expression later, you would use HTSeq or something similar here instead of Cufflinks.
4) Cuffdiff is probably OK, or you could use e g DESeq, which uses counts.
5)-6) Not sure I understand the questions.
The genome has gaps between the exons and bowtie and bwa cannot map a read that crosses those gaps.
@Travis: BWA does gapped alignment, but the gaps are on the order of 1-10 bp; BWA does not handle gaps the size of introns. You need to use a splice-aware aligner when aligning to the genome. See my answer and Mikael's above for some aligner suggestions.
yes, but Solexa reads used to be shorter so the junction spanners were not common when ~30bp. software is always fighting the last war.
See this blog post for a quick start. See this question and consider alternative alignment algorithms. Consider reading some of these review articles.
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Sean Davis
can you walk us through your GSNAP-based pipeline?
GSNAP is used for the alignment step only. After that, the workflow can be similar to those used for tophat or any other aligner and could include the cufflinks suite, DESeq, etc.
how do you divvy up hits to different overlapping transcripts?
SEQanswers.com have published an interesting post for a basic pipeline than you can refine later, I forgot the link to the post but found the guide on RNA seq blog
Hope that help
Radhouane
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