Abstract Nucleosomes represent elementary building units of eukaryotic chromosomes and consist of DNA wrapped around a histone octamer flanked by linker DNA segments. Nucleosomes are central in epigenetic pathways and their genomic positioning is associated with regulation of gene expression, DNA replication, DNA methylation and DNA repair, among other functions. Building on prior discoveries, that DNA sequences noticeably affect nucleosome positioning, our objective is to identify nucleosome positions and related features across entire genome. Here we introduce an interpretable framework based on the concepts of deep residual networks (NuPose). Trained on high-coverage human experimental MNase-seq data, NuPose is able to learn sequence and structural patterns and their dependencies associated with nucleosome organization in human genome. NuPoSe can be used to identify nucleosomal regions, not covered by experiments, and be applied to unseen data from different organisms and cell types. Our findings point to 43 informative DNA sequence features, most of them constitute tri-nucleotides, di-nucleotides and one tetra-nucleotide. Most features are significantly associated with the structural characteristics, namely, periodicity of nucleosomal DNA and its location with respect to a histone octamer. Importantly, we show that linker DNA features contribute ∼10% to the quality of the prediction model, which together with comprehensive training sets, deep-learning architecture and feature selection may explain the advanced performance of NuPose of 80-89% accuracy.

Genome-wide Nucleosome Positioning and Associated Features uncovered with Interpretable Deep Residual Networks