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  • experimental versus control enrichemnt for chip seq data

    Hi all
    I have done few chips using antibody against a transcription factor in honeybee. I also used pre immune IgG (from the animal where antibody was generated) and did chip in identical way. I have now used input as control and have found peaks in both the experimental chip data and the control (chip using pre immune IgG ) data.
    Some of the peaks found in experimental dataset are also turning up in control dataset. However, the fold enrichment differs- say if the experimental dataset shows an enrichment of 100 fold, the control dataset shows enrichment of 10 fold.
    To get the final data- should i subtract the control dataset from experimental in a quantitative manner or just qualitative way. I mean should I consider the fold enrichment while subtraction or should I just ignore it

    Thanks
    naveen

  • #2
    I don't think there's a perfectly agreed upon way to subtract input or IgG control from the experimental sample, I've tried looking for the same thing. If you just need to identify peaks, you can use various peak calling programs that take input into consideration using various algorithms when identifying them. Some peak calling programs. I don't know if they all consider input, I've only used SISSRS, which does.

    These are some previous threads discussing normalization to input:

    Any non-primary sequence heritable modification of genetic material. ChIP-SEQ, DNA methylation (Bisulfite-SEQ), chromatin modifications (methylation, acetylation, etc), non coding RNA.

    Discussion of next-gen sequencing related bioinformatics: resources, algorithms, open source efforts, etc

    Discussion of next-gen sequencing related bioinformatics: resources, algorithms, open source efforts, etc

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