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  • steven
    replied
    Originally posted by idonaldson View Post
    The thing about choosing random bits of the genome is that not all of it is mappable (~70%). So random coordinates should probably come from the mappable sequence? What I have not worked out is exactly which sequence is mappable or not. A possible proxy for this is choosing sequence from regions that have any input DNA tags......

    Any thoughts about this?

    Ian
    There is a "Mapability" track at the UCSC genome browser. It should be possible to exclude these regions from the random picks.

    Leave a comment:


  • idonaldson
    replied
    The thing about choosing random bits of the genome is that not all of it is mappable (~70%). So random coordinates should probably come from the mappable sequence? What I have not worked out is exactly which sequence is mappable or not. A possible proxy for this is choosing sequence from regions that have any input DNA tags......

    Any thoughts about this?

    Ian

    Leave a comment:


  • xuer
    replied
    i also have same questions, have they been solved?

    Leave a comment:


  • Maxim
    started a topic a question about "regions"

    a question about "regions"

    Hi,

    I guess my question would better fit to a statistics board but it is hard to explain genomics to non biologists (at least for me).

    I use the nice bedtools package to find overlapping intervalls of ChIP-seq regions from different factors/chromatin modifications.

    1st question:
    How can one calculate whether a given observation is significantly different than random overlap, i.e. what is the probability that 1000 regions (let's say from 500bp to 1000bp) of factor A show overlap with factor B (2000 regions) in 800 cases.

    Of course in this case is there is significantly more overlap than random. I did this via binning the genome into 1kb bins. Then I assigned to each bind whether it was bound by A and/or B. This enabled me to easily assign distinct probabilities for each bin to be associated with a single factor or with both factors (as it is easy to determine the "universe"). Then I perform Chi-Square or similar tests.

    But this approach appears to be too complicated too me. There must be a way to directly calculate the probability for an overlap of two (or more factors).

    Does anyone have a suggestion how to accomplish this?

    2. question is related to above question. Is there a script (or can someone explain what I have to think about to write one on my own -> Perl/R/Python) that creates intersection (overlap) between n factors' regions and looks for all different possible outcomes, i.e. sites with all factors, all but one factor, all but to factors etc.?

    I'd be glad if someone could point me at a direction how to approach these two aspects!

    Maxim

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