
As Mark said, DiffBind provides a straightforward workflow for this type of ChIP-seq analysis, with all the statistical heavy lifting done by edgeR and/or DESeq.

Regarding histone marks, we have had success using DiffBind to analyze wider regions of enrichment. For example, in Chandra et al (Mol Cell 2012 47:2), we found biologically meaningful differences in five histone marks over enrichment regions as wide as 500Kb (cf Figure 3d). The tricky part with this type of enrichment is in peak calling, as the most popular peak callers (esp. ones that rely on strand information) assume that the enriched area (peak) is shorter than the sequenced fragment length. There are a number of peak callers that are designed to find wider areas of enrichment. We have used these peak callers, or avoided peak calling all together using general windowing schemes or genomic annotations (eg windows oriented around transcription start sites to capture binding profiles in promoter regions).

Cheers-
Rory

----------------------------------------------------------------------------
Dr. Rory Stark

Principal Bioinformatics  Analyst

Cancer Research UK
Cambridge Research Institute
Robinson Way
Cambridge CB2 0RE
United Kingdom
 +44 1223 404 311

rory.stark@cancer.org.uk
----------------------------------------------------------------------------

> Hi,


> I want to continue this discussion.

> I saw in some papers, people used edgeR and DESeq to analysis
> differentially bound between different sample groups following ChIP-seq.

> But most of them are studying transcription factors.

> Is it the case for histone modifications ChIP-seq (H3K4me1, H3K4me2 or> H3k9me3)?

> Regards,
> Sheng


On Thu, Nov 8, 2012 at 8:59 AM, Cittaro Davide <cittaro.davide@hsr.it>wrote:

> Dear Mark
>
> On Nov 8, 2012, at 8:53 AM, Mark Robinson <mark.robinson@imls.uzh.ch>
> wrote:
>
> > Dear Davide,
> >
> > Indeed, edgeR and DESeq can be (and have been) used in this mode.  We
> published something recently on this:
> >
> > http://www.ncbi.nlm.nih.gov/pubmed/22879430
> > http://imlspenticton.uzh.ch/robinson_lab/ABCD-DNA/ABCD-DNA.pdf
> >
>
> I've missed that :-( Thanks for the paper
>
> > You can apply that approach regardless of copy number being a factor ...
> basically, we counted tiled bins of the genome, but yes, you could focus in
> on regions of interest.  The function abcdDNA() is really just a wrapper
> for the edgeR GLM.  As usual, "normalization" can be delicate, depending on
> the type of data.
> >
> > Also note that the DiffBind package already does something similar, but
> has a lot more machinery to collect and organize the sets of enriched
> regions.
>
> I wonder why I've never used DiffBind before :-)
>
> >
> > Hope that helps.
>
> It does, thanks!
>
> d

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