Efficient Hi-C inversion facilitates chromatin folding mechanism discovery and structure prediction

Genome-wide chromosome conformation capture (Hi-C) experiments have revealed many structural features of chromatin across multiple length scales. Further understanding genome organization requires relating these discoveries to the mechanisms that establish chromatin structures and reconstructing these structures in three dimensions, but both objectives are difficult to achieve with existing algorithms that are often computationally expensive. To alleviate this challenge, we present an algorithm that efficiently converts Hi-C data into contact energies, which measure the interaction strength between genomic loci brought into proximity. Contact energies are local quantities unaffected by the topological constraints that correlate Hi-C contact probabilities. Thus, extracting contact energies from Hi-C contact probabilities distills the biologically unique information contained in the data. We show that contact energies reveal the location of chromatin loop anchors, support a phase separation mechanism for genome compartmentalization, and parameterize polymer simulations that predict three-dimensional chromatin structures. Therefore, we anticipate that contact energy extraction will unleash the full potential of Hi-C data and that our inversion algorithm will facilitate the widespread adoption of contact energy analysis. Significance StatementThe three-dimensional organization of the genome is essential to many DNA-templated processes, and numerous experimental techniques have been introduced to characterize its features. High-throughput chromosome conformation capture experiments, or Hi-C, have proven particularly useful, reporting the interaction frequency between pairs of DNA segments in vivo and genome-wide. However, the polymer topology of chromosomes complicates Hi-C data analysis, which often employs sophisticated algorithms without explicitly accounting for the disparate processes affecting each interaction frequency. In contrast, we introduce a computational framework based on polymer physics arguments that efficiently removes the correlation between Hi-C interaction frequencies and quantifies how each local interaction influences genome folding globally. This framework facilitates the identification of mechanistically important interactions and the prediction of three-dimensional genome structures.