AbstractEukaryotic gene transcription is regulated by a large cohort of chromatin associated proteins, and inferring their differential binding sites between cellular contexts requires a rigorous comparison of the corresponding ChIP-seq data. We present MAnorm2, a new computational tool for quantitatively comparing groups of ChIP-seq samples. MAnorm2 uses a hierarchical strategy to normalize ChIP-seq data and then performs differential analysis by assessing within-group variability of ChIP-seq signals under an empirical Bayes framework. In this framework, MAnorm2 considers the abundance of differential ChIP-seq signals between groups of samples and the possibility of different within-group variability between groups. When samples in each group are biological replicates, MAnorm2 can reliably identify differential binding events even between highly similar cellular contexts. Using a number of real ChIP-seq data sets, we observed that MAnorm2 clearly outperformed existing tools for differential ChIP-seq analysis, with the improvement in performance being most dramatic when the groups of samples being compared had distinct global within-group variability.
Specificity in the regulation of eukaryotic gene transcription
