Question: normalize ChIP-seq data by copy number variation
gravatar for Ming Tang
5.5 years ago by
Ming Tang2.6k
Houston/MD Anderson Cancer Center
Ming Tang2.6k wrote:

Hi there,

I did some ChIP-seq in cancer cells. cancer genomes have copy number variations. Do you know any tools for normalizing ChIP-seq data for copy number variations and differentia binding?



cnv chip-seq • 2.6k views
ADD COMMENTlink modified 14 months ago by wszheng10100 • written 5.5 years ago by Ming Tang2.6k
gravatar for Ryan Dale
5.5 years ago by
Ryan Dale4.9k
Bethesda, MD
Ryan Dale4.9k wrote:

I don't know of any tools, but as long as you have matched input controls for each, you could try doing CNV detection to remove those regions from analysis.

Alternatively, if you're interested in bound regions within CNV regions, whatever peak-caller you use might handle it via the IP vs input normalization. After peak-calling, and If you're doing ChIP-seq on proteins that have punctate binding sites, true peaks should have the characteristic strand bias, while CNVs would be larger and presumably not have this bias.

Differential binding will get tricky. You might need something like csaw, which uses the full edgeR GLM functionality, to explicitly model and control for the differences in input.

ADD COMMENTlink written 5.5 years ago by Ryan Dale4.9k

Thank you for your comment. Yes, I will use input as a control. Just curious, does IgG control also work? I am doing chromatin marks. some are broad, I am planning to use diffReps


ADD REPLYlink written 5.5 years ago by Ming Tang2.6k

The issue with IgG is that you're essentially pulling down noise, so there's a higher potential for things like sequencing bias and PCR bias to swamp any signal.  Input seems to be a better control for open chromatin since you can sequence it better. For histone mods, you also might want something like a pan-H3 control.

Differential peak-callers seem to be at the same state peak-callers were 5 yrs ago. Sometimes you just have to try them all . . . 

ADD REPLYlink written 5.5 years ago by Ryan Dale4.9k
gravatar for wszheng1010
14 months ago by
wszheng10100 wrote:

You can try HMCan(enter link description here) and HMCan-diff(enter link description here). Both are designed to handle copy number variation on ChIP-seq data in cancer samples.

HMCan is a peak caller, which can adjust copy number variation, GC bias and background noises. HMCan-diff is comparison tool based on HMCan, which can compare two ChIPed samples with copy numver variation, eg. Histone modification ChIP-seq from cancer sample .vs. normal sample.

ADD COMMENTlink written 14 months ago by wszheng10100
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