(Michael Love and I had some discussion on this Friday)

I also think it would be a very convenient class/method.  A lot of data
these days are naturally represented (and are available from say GEO) as
bigWig files (essentially coverage tracks), for example ChIP-seq.  This
would be much more efficient than converting BAM to coverage on the fly.

It seems to me that bigWig ought to be efficient for this, but I am not
very familiar with its performance.  What we want is really to be able to
chunk multiple coverage profiles over the genome, and do computations on
each of the chunks.  Any idea on efficiency?  I am happy to contribute a
bit, at least with design.

Best,
Kasper


On Mon, Nov 18, 2013 at 6:11 PM, Michael Lawrence <lawrence.michael@gene.com
> wrote:

> Aggregating coverage over multiple samples is a popular request recently.
> I'm happy to support this effort, but I thinks someone in Seattle is going
> to have to take the lead on it.
>
>
> On Mon, Nov 18, 2013 at 2:36 PM, Michael Love
> <michaelisaiahlove@gmail.com>wrote:
>
> > a discussion came up on devel last year about looking at a genomic range
> > over multiple samples and multiple experiments (
> >
> >
> https://stat.ethz.ch/pipermail/bioc-devel/attachments/20120920/93a4fb61/attachment.pl
> >  )
> >
> > stepping aside the multiple experiment part, I'm interested in
> > BigWigViews() with fixed ranges across samples. Has there been any more
> > thoughts in this direction?
> >
> > BigWigViews would be incredibly useful for genomics applications where we
> > want to scan along the genome looking at lots of samples. BigWig offers a
> > concise representation of the information compared to BAM files.
> >
> > What I am trying now is using import(BigWigFile, which=gr) on files one
> by
> > one, and then binding the coverage together.
> >
> > best,
> >
> > Mike
> >
> >         [[alternative HTML version deleted]]
> >
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> >
>
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