Profiling the Epigenetic Landscape of the Spermatogonial Stem Cell: Part 2—Computational Analysis of Epigenomics Data

The final data-generation step of genome-wide profiling of any epigenetic parameter typically involves DNA deep sequencing which yields large datasets that must then be computationally analyzed both individually and collectively to comprehensively describe the epigenetic programming that dictates cell fate and function. Here, we describe computational pipelines for analysis of bulk mepigenomic profiling data, including whole-genome bisulfite sequencing (WGBS) to detect DNA methylation patterns, chromatin immunoprecipitation-sequencing (ChIP-seq) to detect genomic patterns of either specific histone modifications or bound transcription factors, the assay for transposase-accessible chromatin-sequencing (ATAC-seq) to detect genomic patterns of chromatin accessibility, and high-throughput chromosome conformation capture-sequencing (Hi-C-seq) to detect 3-dimensional interactions among distant genomic regions. In addition, we describe Chromatin State Discovery and Characterization (ChromHMM) methodology to integrate data from these individual analyses, plus that from RNA-seq analysis of gene expression, to obtain the most comprehensive overall assessment of epigenetic programming associated with gene expression.