On Wed, May 6, 2009 at 12:40 PM, Ivan Gregoretti <ivangreg@gmail.com> wrote:

> Hello Bioc-sig-seq,
>
> Say you run your ChIP-seq and find binding positions like this
>
> chr1  3660781  3662707
> chr1  4481742  4482656
> chr1  4482813  4484003
> chr1  4561320  4562262
> chr1  4774887  4776304
> chr1  4797291  4798822
> chr1     4847807  4848846
> chr1  5008093  5009386
> chr1  5009514  5010046
> chr1  5010095  5010583
> ...[many more loci and chromosomes]...
>
> Then you want to compare it to published data like this
>
> chr1  3659579  3662079
> chr1  4773791  4776291
> chr1  4797473  4799973
> chr1  4847394  4849894
> chr1  5007460  5009960
> chr1  5072753  5075253
> chr1  6204242  6206742
> chr1  7078730  7081230
> chr1  9282452  9284952
> chr1  9683423  9685923
> ...[many more loci and chromosomes]...
>
> What method would you use to test whether these two lists are
> significantly different?
>

This is a tough statistical question that probably needs to be a bit more
specific, but as far as technical tools, in addition to genomeIntervals
there is the IRanges package and its efficient "overlap" function. IRanges
is well integrated with the rest of sequence analysis infrastructure in
Bioconductor.


>
> Any pointer would be appreciated.
>
> Ivan
>
> Ivan Gregoretti, PhD
> National Institute of Diabetes and Digestive and Kidney Diseases
> National Institutes of Health
> 5 Memorial Dr, Building 5, Room 205.
> Bethesda, MD 20892. USA.
> Phone: 1-301-496-1592
> Fax: 1-301-496-9878
>
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>

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