Learning representations of chromatin contacts using a recurrent neural network identifies genomic drivers of conformation

Kevin B. Dsouza (), Alexandra Maslova, Ediem Al-Jibury, Matthias Merkenschlager, Vijay K. Bhargava and Maxwell W. Libbrecht ()
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Kevin B. Dsouza: University of British Columbia
Alexandra Maslova: Simon Fraser University
Ediem Al-Jibury: Imperial College London
Matthias Merkenschlager: Imperial College London
Vijay K. Bhargava: University of British Columbia
Maxwell W. Libbrecht: Simon Fraser University

Nature Communications, 2022, vol. 13, issue 1, 1-19

Abstract: Abstract Despite the availability of chromatin conformation capture experiments, discerning the relationship between the 1D genome and 3D conformation remains a challenge, which limits our understanding of their affect on gene expression and disease. We propose Hi-C-LSTM, a method that produces low-dimensional latent representations that summarize intra-chromosomal Hi-C contacts via a recurrent long short-term memory neural network model. We find that these representations contain all the information needed to recreate the observed Hi-C matrix with high accuracy, outperforming existing methods. These representations enable the identification of a variety of conformation-defining genomic elements, including nuclear compartments and conformation-related transcription factors. They furthermore enable in-silico perturbation experiments that measure the influence of cis-regulatory elements on conformation.

Date: 2022
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DOI: 10.1038/s41467-022-31337-w

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