1) single end sequencing, 36 cycles on an Illumina Genome Analyzer (not sure
what kind)

2) Probably will be interested in junction reads later but don't know what
to do with them yet.  I want exons to fall into nice little mail-slots so
that I can model their counts and the within/between correlations with other
genomic features, even though I am becoming aware that this is not how it
actually works.

3) These are pooled samples so I think I have to allow for indels.  Unpooled
data is coming, but I won't have to align that.

4) Mostly interested in known transcripts, but I realized that I don't see
any poly-A selection here, so maybe ncRNA too?  Although I imagine that 36bp
reads are too short to effectively do that?  (Triplicates of similar data
are being generated; really I just want a starting point for comparing some
affected samples to, at high enough resolution to match their data)


On Sun, Oct 9, 2011 at 5:33 PM, Sean Davis <sdavis2@mail.nih.gov> wrote:

> Hi, Tim.
>
> For aligning RNA-seq reads, the major questions include:
>
> 1)  Paired-end or not?
> 2)  Interested in junction reads?
> 3)  Allow indels or not?
> 4)  Interested in known transcripts/junctions or also interested in
> novel transcripts?
>
> If you answer those questions, you can usually limit your choices
> significantly.
>
> As for stats, consider following the DEseq or edgeR workflows.  Both
> rely on raw counts and can perform exon usage statistics as well as
> potentially using junction reads for similar calculations.
>
> Sean
>
> On Sun, Oct 9, 2011 at 8:07 PM, Tim Triche, Jr. <tim.triche@gmail.com>
> wrote:
> > A professor sent me a bunch of raw RNA-seq reads (as FASTQ files) and I
> want
> > to align them, and I couldn't really make heads or tails of the options,
> so
> > I listened to what Phil Green told me at a conference and looked around
> for
> > a sensible word-nucleated aligner like he described.  It seems that
> Rsubread
> > works this way?
> >
> > http://sourceforge.net/projects/subread/
> >
> > I would like BAM files as intermediate output, but my real interest is
> > differential exon usage in differentiating cells.  Given that the reads I
> > have to align are relatively short (36bp, SE), is there an advantage or
> > disadvantage in using subread compared to other options?  And when I'm
> done
> > trimming and aligning, I could choose raw counts, conditional quantile
> > normalized counts, or something like RPKM to summarize how often a given
> > exon seems to have been transcribed.  I read this:
> >
> > http://seqanswers.com/forums/showthread.php?t=586
> >
> > and I see that packages using a Gamma prior for the dispersion of a
> Poisson
> > count model benefit from having raw counts.  If I am after correlated
> > changes in exon usage depending on other sequence features, is it
> reasonable
> > to use (say) 'cqn' on the raw counts, then log-transform and work with
> those
> > normalized counts?
> >
> > Thanks for any suggestions,
> >
> > --
> > Tim Triche, Jr.
> > USC Biostatistics
> >
> >        [[alternative HTML version deleted]]
> >
> > _______________________________________________
> > Bioconductor mailing list
> > Bioconductor@r-project.org
> > https://stat.ethz.ch/mailman/listinfo/bioconductor
> > Search the archives:
> http://news.gmane.org/gmane.science.biology.informatics.conductor
> >
>



-- 
If people do not believe that mathematics is simple,
it is only because they do not realize how complicated life is. John von
Neumann<http://www-groups.dcs.st-and.ac.uk/~history/Biographies/Von_Neumann.html>

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