SLUR(M)-py: A SLURM Powered Pythonic Pipeline for Parallel Processing of 3D (Epi)genomic Profiles

Epigenomics has become multi-faceted, with researchers exploring chromatin structure, nucleosome states, and epigenetic modifications, producing large, complex multi-omic data sets. Given this shift, there is de-mand for bioinformatics that leverage high performance computing (HPC) and parallelization to quickly process data. As such, we developed SLUR(M)-py: a pythonic computational platform that leverages the Simple Linux Utility for Resource Management system (SLURM) to process sequencing data. SLUR(M)-py is multi-omic and automates calls to SLURM for processing paired-end sequences from chromatin charac-terization experiments, including whole-genome, ChIP-seq, ATAC-seq, and Hi-C, thereby eliminating the need for multiple analytics pipelines. To demonstrate SLUR(M)-pys utility, we employ ATAC-seq and Hi-C data from viral infection experiments and the ENCODE project, and illustrate its processing speed and completeness, which outpaces current HPC pipelines. We explore the effect of dropping duplicate se-quenced reads in ATAC-seq, demonstrate how SLUR(M)-py can be used for quality control, and how to detect artifacts in Hi-C from viral infection experiments. Finally, we show how features in SLUR(M)-py, like inter-chromosomal analysis, can be used to explore the dynamics of chromosomal contacts in mammalian cells. This multi-omic, system agnostic platform eases the computational burden for researchers and quickly produces accurate, reliable data analytics for the epigenomics community.