On Tue, Dec 15, 2009 at 2:21 PM, Sean Davis <seandavi@gmail.com> wrote:

> On Tue, Dec 15, 2009 at 5:01 PM, Johannes Waage <johannes.waage@bric.dk>
> wrote:
> > Hi all,
> >
> > I have a small but important challenge set before me, that I've been
> unable
> > to solve. I need to aggregate all phastCon scores for 75-100 nt around
> all *
> > mus* exon splicesites. I've tried different approaches, such as
> downloading
> > the entire mulitz30way phastCon dataset from UCSC (too big to work with
> > smoothly), download using intersect with UCSC table browser and Galaxy
> > (limits me to 10 million data points, unfortunately), and fetching data
> > trough rtracklayer (too slow). Can anyone point me towards an elegant and
> > fast way to fetch datapoints for many genomic intervals? With around 22k
> > genes, with an average exon count of 8 times 100 nt, it seems I need to
> be
> > able to fetch around 20m data points.
> >
> > I need to use the data as background in comparison to select upregulated
> > exons in a RNA-seq splice study.
>
> Could you do this chromosome-by-chromosome by loading the per-base
> data one chromosome at a time from the files into an R vector and then
> using normal vector subsetting to get the regions of interest?
>
> Alternatively, with a little work, you could probably also build a
> little index file and then use random access to get the data from the
> files.
>
> Finally, there are probably some tools in the UCSC browser tool chain
> that you could download to deal with conservation data fairly quickly.
>
>
This may be a decent use case for bigWig support in Bioconductor. The data
is stored in a binary, indexed form, so it should be easy and efficient to
bring subsets into memory/R.

The mappability tracks are another example. Looks like rtracklayer may be
the place for this, at least initially.  The mythical common IO package
would be helpful though.

Michael

Sean
>
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