ePeak: from replicated chromatin profiling data to epigenomic dynamics.

Area of Science:

• Epigenomics
• Computational Biology
• Bioinformatics

Background:

• ChIP-seq, CUT&RUN, and CUT&Tag are key epigenomic profiling techniques.
• Analyzing reproducible peaks and differential binding is crucial for understanding gene regulation.
• Existing pipelines may lack comprehensive statistical modules for differential analysis.

Purpose of the Study:

• To introduce ePeak, a Snakemake-based pipeline for robust identification and quantification of reproducible peaks from epigenomic data.
• To provide a statistical module for tailored differential marking and binding analysis using state-of-the-art methods.
• To streamline quality assessment, spike-in calibration, and reproducible peak selection for both narrow and broad peaks.

Main Methods:

• ePeak utilizes Snakemake for pipeline automation.
• It incorporates modules for immunoprecipitation quality assessment and spike-in calibration.
• Statistical methods include linear and nonlinear normalization, and conservative/stringent models for variance estimation and significance testing.

Main Results:

• ePeak successfully identifies and quantifies reproducible peaks from raw ChIP-seq, CUT&RUN, and CUT&Tag data.
• Differential analysis revealed distinct populations of differentially marked/bound peaks in a published ChIP-seq dataset.
• Analysis of peak dynamics (read coverage, summit position) and neighboring gene expression was performed.

Conclusions:

• ePeak offers a streamlined approach for epigenomic data analysis, enhancing reproducibility and interpretability.
• The pipeline facilitates advanced differential analysis accounting for chromatin factor biological dynamics.
• ePeak can quantify the epigenomic landscape's richness by identifying diverse regulatory regimes.