Deciphering multi-way interactions in the human genome

Gabrielle A. Dotson, Can Chen, Stephen Lindsly, Anthony Cicalo, Sam Dilworth, Charles Ryan, Sivakumar Jeyarajan, Walter Meixner, Cooper Stansbury, Joshua Pickard, Nicholas Beckloff, Amit Surana, Max Wicha, Lindsey A. Muir and Indika Rajapakse ()
Additional contact information
Gabrielle A. Dotson: University of Michigan
Can Chen: University of Michigan
Stephen Lindsly: University of Michigan
Anthony Cicalo: University of Michigan
Sam Dilworth: iReprogram
Charles Ryan: University of Michigan
Sivakumar Jeyarajan: University of Michigan
Walter Meixner: University of Michigan
Cooper Stansbury: University of Michigan
Joshua Pickard: University of Michigan
Nicholas Beckloff: Oxford Nanopore Technologies
Amit Surana: Raytheon Technologies Research Center
Max Wicha: University of Michigan
Lindsey A. Muir: University of Michigan
Indika Rajapakse: University of Michigan

Nature Communications, 2022, vol. 13, issue 1, 1-15

Abstract: Abstract Chromatin architecture, a key regulator of gene expression, can be inferred using chromatin contact data from chromosome conformation capture, or Hi-C. However, classical Hi-C does not preserve multi-way contacts. Here we use long sequencing reads to map genome-wide multi-way contacts and investigate higher order chromatin organization in the human genome. We use hypergraph theory for data representation and analysis, and quantify higher order structures in neonatal fibroblasts, biopsied adult fibroblasts, and B lymphocytes. By integrating multi-way contacts with chromatin accessibility, gene expression, and transcription factor binding, we introduce a data-driven method to identify cell type-specific transcription clusters. We provide transcription factor-mediated functional building blocks for cell identity that serve as a global signature for cell types.

Date: 2022
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DOI: 10.1038/s41467-022-32980-z

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