Hello,
I want to ask a very general question. I am conducting ChIP-seq analysis. I know that every dataset is unique and has its own properties. Parameters during ChIP-seq is needed to adjusted to the datasets. However, I have spent a long time to understand and optimize the method of optimization . Now when I conduct the analysis on a dataset published in nature, the biological raplicates almost does not overlap at all. This is not a difference slight but it is like I am applying a wrong analysis pipeline for it. I am not sure where am I missing. Is there anyone facing the same problem and maybe advice me. Thank you.
1 answer
There are some excellent tools in deeptools (available on cmd line or within galaxy) which are excellent for finding the correlation and divergence between datasets (eg bigwig, bedg, bam etc).
Try those on for example, the raw mapped bams for all replicates and maybe report back for more opinions ?
In general though, the raw data behind many published papers are very flaky in my experience.
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Can you clarify:
Hello, I am doing the second one. I have taken the dataset from the analysis. And my results do not overlap with the results in the article. Thus, normally we expect biological replicates of a condition in chip-seq samples overlap for a larger fraction (compared to my venn diagram shown here). However, my results look like this.
It looks like I took samples treated with distinct conditions and expect them to overlap. However, these are biological replicates.
I hope I clearly explained my situation. Thank you.