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 datasets. Given this shift, there is a demand 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 characterization experiments, including whole-genome, ChIP-seq, ATAC-seq, and Hi-C, thereby eliminating the need for multiple analytics pipelines. To demonstrate SLUR(M)-py's 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 sequenced 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 and reliable data analytics for the epigenomics community.