SACSANN: identifying sequence-based determinants of chromosomal compartments

Genomic organization is critical for proper gene regulation and based on a hierarchical model, where chromosomes are segmented into megabase-sized, cell-type-specific transcriptionally active (A) and inactive (B) compartments. Here, we describe SACSANN, a machine learning pipeline consisting of stacked artificial neural networks that predicts compartment annotation solely from genomic sequence-based features such as predicted transcription factor binding sites and transposable elements. SACSANN provides accurate and cell-type specific compartment predictions, while identifying key genomic sequence determinants that associate with A/B compartments. Models are shown to be largely transferable across analogous human and mouse cell types. By enabling the study of chromosome compartmentalization in species for which no Hi-C data is available, SACSANN paves the way toward the study of 3D genome evolution. SACSANN is publicly available on GitHub: https://github.com/BlanchetteLab/SACSANN