BiSeq is designed to address some of these issues, although I'm not certain
whether the "single CpG" issue is one of them.  Obviously it would be a
good idea to be 100% certain when looking at individual DMPs that the CpG
is not a SNP with varying frequencies between the groups being compared :-)


--t


On Wed, Jan 22, 2014 at 2:15 AM, Alex Gutteridge <alexg@ruggedtextile.com>wrote:

> On 21.01.2014 20:48, Gilgi Friedlander wrote:
>
>> Hi Alex,
>>
>> Sorry to bother you, but I have a question regarding RRBS analysis.
>>
>> I saw your post from last year, and also was wondering if bsmooth can
>> work well for RRBS data, as going to work on such data.
>>
>> I wanted to reply to the post on the mailing list, but didn't see
>> such an option.
>>
>> If you already have results, and can share your experience if one can
>> use bsmooth for RRBS, it will be great. And if not, if you have
>> recommendations for other tools.
>>
>> Thanks a lot,
>>
>> Gilgi
>>
>
> cc'ing the list for future reference and in case anyone else has
> additional insights on RRBS analysis with Bioconductor:
>
> Hi Gilgi,
>
> I had mixed results with bsmooth and rrbs data when I tried it. Certainly
> the various smoothing parameters required tweaking before it would run
> successfully. I can let you know what worked for me, but I'd be lying if I
> said I arrived at them through careful experimentation. One also has to be
> cautious I think about the overall approach of whether smoothing makes
> sense when the data from rrbs is by definition non-contiguous. That said,
> bsmooth did run and did detect differential methylated regions for us which
> looked correct on deeper inspection. What bsmooth (by design I guess) does
> not detect are the single CpGs that seem to change between experimental
> groups, but sit within larger regions that clearly do not change. It's
> still an open question (in my mind at least) whether such sites are likely
> to be biologically significant or not.
>
> --
> Alex Gutteridge
>
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