Abstract / Summary
Nucleosomes regulate DNA accessibility and thereby influence gene expression, DNA repair, and mutagenesis. Although DNA sequence strongly determines nucleosome positioning, the higher-order sequence grammar underlying nucleosome organization and its relationship to cancer-associated mutational and epigenetic processes remain poorly understood. We developed DeepND, an interpretable deep learning framework that distinguishes nucleosomal from inter-nucleosomal DNA and identifies sequence features associated with nucleosome architecture. Our interpretable framework enabled analysis of sequence patterns and identified 210 sequence motifs of 2-16 bp length that were significantly enriched or depleted in nucleosomal DNA, substantially extending the previously characterized nucleosomal motifs. These motifs revealed distinct sequence grammars within nucleosomal footprints and linker regions, including patterns associated with inward- and outward-facing minor grooves. Forty-four of these motifs showed cancer-type-specific enrichment or depletion of somatic mutations, and six motifs were associated with differential DNA methylation levels. In lung and endometrial cancers, mutations exhibited a significant ~10-bp periodic enrichment pattern across distinct minor-groove orientations, consistent with differential sensitivity of mutational and DNA repair processes to nucleosomal rotational positioning. We further identified sequence-specific methylation patterns consistent with reduced accessibility of nucleosomal DNA to DNA methyltransferases, including differential methylation at inward- and outward-facing CpG motifs. Together, these findings provide a sequence-resolved characterization of nucleosome-level organization and its link to cancer-specific mutational processes and epigenetic landscapes.