I have 2 conditions, under which I want to find histone modifications that are different. I have 4 samples in total, Condition1 and its control, and condition2 with its control.
SICER does it just fine.
I was wondering if there is any other tool anybody knows of. In fact, It is easy to find peaks using for a condition and its control (or background distribution), is there something available that can take already identified peaks (bed, GFF whatever) and reports differential reads counts with statistical significance. I looked at few, but nothing that works to my satisfaction:
ChIPDiff Apparently does exactly what I am looking for, however uses only single genomic position as against chr, start and end. Seems apt for TF but not histones because of variable lengths. In addition asks for orientation which my peak finder does not report. Just not a good fit for my data I guess.
DESeq finds differential expression using counts, but relies on multiple samples to report significance. And needs gene-expression like matrix which I will have to figure out how to get from the peak data.
I know this has been already asked here, and I apologize for creating a new thread. But it seems the discussion is still to reach climax (as if it can ever ) and new tools and papers are coming every day so hoping there is something by new out there.
Thanks for replying.
SICER does it just fine.
I was wondering if there is any other tool anybody knows of. In fact, It is easy to find peaks using for a condition and its control (or background distribution), is there something available that can take already identified peaks (bed, GFF whatever) and reports differential reads counts with statistical significance. I looked at few, but nothing that works to my satisfaction:
ChIPDiff Apparently does exactly what I am looking for, however uses only single genomic position as against chr, start and end. Seems apt for TF but not histones because of variable lengths. In addition asks for orientation which my peak finder does not report. Just not a good fit for my data I guess.
DESeq finds differential expression using counts, but relies on multiple samples to report significance. And needs gene-expression like matrix which I will have to figure out how to get from the peak data.
I know this has been already asked here, and I apologize for creating a new thread. But it seems the discussion is still to reach climax (as if it can ever ) and new tools and papers are coming every day so hoping there is something by new out there.
Thanks for replying.
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