Deciphering Multi-way Interactions in the Human Genome

Chromatin architecture, a key regulator of gene expression, is inferred through chromatin contacts. However, classical analyses of chromosome conformation data do not preserve multi-way relationships. Here we use long sequencing reads to map genome-wide multi-way contacts and investigate higher order chromatin organization of the human genome. We use the theory of hypergraphs for data representation and analysis, and quantify higher order structures in primary human fibroblasts and B lymphocytes. Through integration of multi-way contact data with chromatin accessibility, gene expression, and transcription factor binding data, we introduce a data-driven method to extract transcriptional clusters.