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
>
> >
> > Best, Mark
> >
> >
> > On 08.11.2012, at 08:37, Cittaro Davide wrote:
> >
> >> Hi there, I'm writing to the list to have your comment about the
> possibility of using edgeR or DESeq for the analysis of ChIP-seq samples.
> >> Standard approaches to ChIP-seq analysis (relying on external software
> such as MACS) do not make analysis of replicates easy. I've seen people
> looking for peaks and then compare the common/differential intervals
> between replicates in case/control design. I wonder if a more general
> approach may work (and I'm going to test this anyway...).
> >> Since the negative binomial model stands for ChIP-seq analysis, both
> edgeR and DESeq should work well. One can use external software to identify
> regions and compute the union of all regions as it was a "gene list". From
> that point on, the pipeline should not differ from standard gene expression
> analysis.
> >> What do you think?
> >>
> >> d
> >> /*
> >> Davide Cittaro, PhD
> >>
> >> Coordinator of Bioinformatics Core
> >> Center for Translational Genomics and Bioinformatics
> >> Ospedale San Raffaele
> >> Via Olgettina 58
> >> 20132 Milano
> >> Italy
> >>
> >> Office: +39 02 26439140
> >> Mail: cittaro.davide@hsr.it
> >> Skype: daweonline
> >> */
> >>
> >> _______________________________________________
> >> 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
> >
> > ----------
> > Prof. Dr. Mark Robinson
> > Bioinformatics
> > Institute of Molecular Life Sciences
> > University of Zurich
> > Winterthurerstrasse 190
> > 8057 Zurich
> > Switzerland
> >
> > v: +41 44 635 4848
> > f: +41 44 635 6898
> > e: mark.robinson@imls.uzh.ch
> > o: Y11-J-16
> > w: http://tiny.cc/mrobin
> >
> > ----------
> > http://www.fgcz.ch/Bioconductor2012
> >
> >
>
> /*
> Davide Cittaro, PhD
>
> Coordinator of Bioinformatics Core
> Center for Translational Genomics and Bioinformatics
> Ospedale San Raffaele
> Via Olgettina 58
> 20132 Milano
> Italy
>
> Office: +39 02 26439140
> Mail: cittaro.davide@hsr.it
> Skype: daweonline
> */
>
> _______________________________________________
> 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
>

	[[alternative HTML version deleted]]

