Question: Assessing significance of protein binding data inside defined genomic intervals
1
gravatar for Sakti
4.8 years ago by
Sakti370
United States
Sakti370 wrote:

Dear friends,

Once again I am back to consult your wisdom. Very recently I obtained a list of regions inside mouse chromosome 7 which are contacting a specific nuclear body (sorry, cannot give more details about it). Several proteins overlap these regions (i.e. cohesin). However, I would like to know how significant these overlap ratios are compared to a randomly chosen region set (which has the same length characteristics as my original nuclear body dataset).

Does anyone know any tool one could use to perform this analysis? I found the R package named coocur but this analyzes protein binding sites co-occurrence, which I think is a little different from what I'm trying to do.

Also, in case such program does not exist, what would be the best way to proceed in terms of statistical tests? I was thinking on writing a script that chooses regions randomly with the same length as my nuclear dataset, calculating overlaps, and then comparing such ratios with my nuclear body ratios. But then I think maybe boostrapping is also necessary, but I'm not sure what statistical test should I use in that case.

I'd appreciate any insight you may provide.

Thanks!!

Sakti

ADD COMMENTlink modified 4.2 years ago by Biostar ♦♦ 20 • written 4.8 years ago by Sakti370

nuclear body = sparse term ? can you be a bit more specific ? 
transcription factor, enhancer etc. ? 

ADD REPLYlink written 4.8 years ago by Khader Shameer18k

I think the answers for this question is what you are looking for: Сalculating fold-enrichment of ChIP-seq peaks intersecting with promoters (vs. genome average)

ADD REPLYlink written 4.2 years ago by Fidel1.9k
1
gravatar for Michael Dondrup
4.7 years ago by
Bergen, Norway
Michael Dondrup46k wrote:

An experiment of drawing random genomic positions with two outcomes - overlap with a gene (success) or no overlap (fail) -  is a Bernoulli trial with success probability C/G (C= #of bases in genes, vs. total # of bases in the Genome). Therefore the Binomial distribution is suitable to calculate the cummulative distribution function for a certain number of N or more successes in M trials. This doesn't depend on how your genomic location is selected. 

ADD COMMENTlink modified 4.7 years ago • written 4.7 years ago by Michael Dondrup46k
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