Uncovering Human Genome Structure Through Data-driven Approaches

Abstract

Data-driven genomic analysis uses genome-wide data to explore novel hypotheses without relying on prior biological knowledge. Its benefits are maximized when the data are large and multi-omics, known as large-scale multi-omics analysis, which has the potential to reveal unexpected regulatory mechanisms. This review focuses on our recent work using multi-omics data with next-generation sequencing assays to understand transcriptional regulation via chromatin folding in a data-driven manner. The results highlight the variation in genomic physical interactions regulated by the context-specific combination of cohesin and its associated factors, including an insulator factor CTCF.