Machine Learning Reveals the Diversity of Human 3D Chromatin Contact Patterns.

ABSTRACT: Understanding variation in chromatin contact patterns across diverse humans is critical for interpreting noncoding variants and their effects on gene expression and phenotypes. However, experimental determination of chromatin contact patterns across large samples is prohibitively expensive. To overcome this challenge, we develop and validate a machine learning method to quantify the variation in 3D chromatin contacts at 2 kilobase resolution from genome sequence alone. We apply this approach to thousands of human genomes from the 1000 Genomes Project and the inferred hominin ancestral genome. While patterns of 3D contact divergence genome wide are qualitatively similar to patterns of sequence divergence, we find substantial differences in 3D divergence and sequence divergence in local 1 me

SUBMITTER: Gilbertson EN 

PROVIDER: S-EPMC11523124 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature