Thanks very much for the suggestions
I will likely have more questions as I start the analysis

Lucia


On Tue, Oct 4, 2011 at 2:57 PM, Martin Morgan <mtmorgan@fhcrc.org> wrote:

> On 10/04/2011 07:33 AM, Ivan Gregoretti wrote:
> > Hello Lucia,
> >
> > A proper response to your post would take a lecture rather than an
> > email. I can't do that but I can bullet the main points. I think that
> > it will help you if you are indeed a newcomer to ChIP-seq.
> >
> > 1) Expect 10 million reads per sample for a genome the size of human.
>
> I'd run some basic QA on your lanes, via ShortRead::qa on the fastq files
> (or bam if fastq are not available); use FastqSampler if memory is tight
> (but in general if memory is tight the solution will be to find a larger
> computer).
>
> See http://bioconductor.org/help/**workflows/high-throughput-**sequencing/<http://bioconductor.org/help/workflows/high-throughput-sequencing/>for qa and perhaps other operations common to RNAseq / ChIPseq work flows
>
>
> >
> > 2) Stick to SAM/BAM formats so that you can use well known, publicly
> > available tools. Your best friend is called Picard.
>
> People can and do use R / Bioconductor for Picard-like tasks.
>
>
> > 3) Remove duplicates. Again, Picard is your best friend.
>
> > 4) Create WIG files for all samples, treatments and controls so that
> > you can display them simultaneously on any genome browser.
>
> here for interactive use I would rather use basic R plotting commands,
> avoiding the round-trip and allowing programmatic interaction.
>
>
> > 5) Find peaks with a well documented peak finder.
>
> probably a good suggestion for a one-off or common ChIP; the chipseq
> vignette
>
>  http://bioconductor.org/**packages/release/bioc/html/**chipseq.html<http://bioconductor.org/packages/release/bioc/html/chipseq.html>
>
> provides inspiration for more flexible analysis; packages under the ChIPseq
> biocViews term (Software --> AssayTechnologies -> HighThroughputSequencing->
> **ChIPSeq) might offer a solution tailored to your ChIP.
>
>
> > 6) Compute enrichment for all treatments relative to their controls.
>
> again the chipseq vignette is an alternative source.
>
>
> >
> > So, points 4 and 6 are your quality controls at this stage. Once you
> > know what a good immunoprecipitation looks like compared to a bad one,
> > you can start diving into the details. You can invent your own quality
>
> especially at getting a sense for good versus bad results the interactivity
> of R / Bioconductor seem essential.
>
> Martin
>
>
> > indicators. For instance, I compute the proportion of tags inside the
> > 1000 strongest peaks. I do that for BOTH treatment and controls.
> >
> > In my workflow, Bioconductor does not get involved until I reach point 6.
> >
> > Happy ChIPing.
> >
> > Ivan
> >
> >
> >
> >
> >
> > On Mon, Oct 3, 2011 at 5:17 PM, Lucia Peixoto<luciap@iscb.org>  wrote:
> >> Hi,
> >> I am new to Chip-seq, my experiment's sequencing has finished, and the
> read
> >> alignment is currently running
> >> The experiment  was done for histone acetylation, and I have two types
> of
> >> controls: input DNA and unmodified histone.
> >> I have two conditions and 6 biological replicates of each condition
> >> I wanted some advice on how to perform basic quality control on Chip-seq
> >> data using Bioconductor
> >> and also some ideas of which kinds of biases people usually observe and
> I
> >> should keep my eyes open for
> >> any advice will be greatly appreciated!
> >> thanks
> >>
> >> Lucia
> >>
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