Characterizing batch effects and binding site-specific variability in ChIP-seq data.

ABSTRACT: Multiple sources of variability can bias ChIP-seq data toward inferring transcription factor (TF) binding profiles. As ChIP-seq datasets increase in public repositories, it is now possible and necessary to account for complex sources of variability in ChIP-seq data analysis. We find that two types of variability, the batch effects by sequencing laboratories and differences between biological replicates, not associated with changes in condition or state, vary across genomic sites. This implies that observed differences between samples from different conditions or states, such as cell-type, must be assessed statistically, with an understanding of the distribution of obscuring noise. We present a statistical approach that characterizes both differences of interests and these source of variabi

SUBMITTER: Teng M 

PROVIDER: S-EPMC8515842 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature