Machine learning reveals the diversity of human 3D chromatin contact patterns.

ABSTRACT: Understanding variation in chromatin contact patterns across human populations is critical for interpreting non-coding variants and their ultimate effects on gene expression and phenotypes. However, experimental determination of chromatin contacts at a population-scale is prohibitively expensive. To overcome this challenge, we develop and validate a machine learning method to quantify the diversity 3D chromatin contacts at 2 kilobase resolution from genome sequence alone. We then apply this approach to thousands of diverse modern humans and the inferred human-archaic hominin ancestral genome. While patterns of 3D contact divergence genome-wide are qualitatively similar to patterns of sequence divergence, we find that 3D divergence in local 1-megabase genomic windows does not follow sequenc

SUBMITTER: Gilbertson EN 

PROVIDER: S-EPMC10769343 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature