Abstract

We present ChIPflow, a Snakemake-based pipeline for epigenomic data from the raw fastq files to the differential analysis. It can be applied to any chromatin factor, e.g. histone modification or transcription factor, which can be profiled with ChIP-seq. ChIPflow streamlines critical steps like the quality assessment of the immunoprecipitation using cross-correlation and the replicate comparison for both narrow and broad peaks. For the differential analysis ChIPflow provides linear and nonlinear methods for normalisation between samples as well as conservative and stringent models for estimating the variance and testing the significance of the observed binding/marking differences. ChIPflow can process in parallel multiple chromatin factors with different experimental designs, number of biological replicates and/or conditions. It also facilitates the specific parametrisation of each dataset allowing both narrow or broad peak calling, as well as comparisons between the conditions using multiple statistical settings. Finally, complete reports are produced at the end of the bioinformatic and the statistical part of the analysis, which facilitate the data quality control and the interpretation of the results. We explored the discriminative power of the statistical settings for the differential analysis, using a published dataset of three histone marks (H3K4me3, H3K27ac and H3K4me1) and two transcription factors (Oct4 and Klf4) profiled with ChIP-seq in two biological conditions (shControl and shUbc9). We show that distinct results are obtained depending on the sources of ChIP-seq variability and the dynamics of the chromatin factor under study. We propose that ChIPflow can be used to measure the richness of the epigenomic landscape underlying a biological process by identifying diverse regulatory regimes and the associated genes sets.