Copy number normalization distinguishes differential signals driven by copy number differences in ATAC-seq and ChIP-seq
A common objective across ATAC-seq and ChIP-seq analyses is to identify differential signals across contrasted conditions. However, in differential analyses, the impact of copy number variation is often overlooked. Here, we demonstrated copy number differences among samples could drive, if not dominate, differential signals. To address this, we propose a pipeline featuring copy number normalization. By comparing the averaged signal per gene copy, it effectively segregates differential signals driven by copy number differences from other factors. Further applying it to Down syndrome, we unveiled distinct dosage-dependent and -independent changes on chromosome 21. Thus, we recommend normalization as a general approach. Overall design: To characterize the endogenously formed G-quadruplex structures in wildtype and BLM-/-  cells, we performed G4 ChIPseq with a specific antibody against the secondary DNA structures. During the process of characterizing the differential G-quadruplex forming sites, we noticed that copy number differences at local regions between the samples skew the differential analysis towards the sample with a higher copy number. We explored the impacts of copy number variation on the differential analysis and solutions to segregate the differential signals driven by copy number variation.