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Learning Representations of Chromatin Contacts Using a Recurrent Neural Network Identifies Genomic Drivers of Conformation by Kevin B. Dsouza; Alexandra Maslova; Ediem Al-Jibury; Matthias Merkenschlager; Vijay K. Bhargava; Maxwell W. Libbrecht is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Learning Representations of Chromatin Contacts Using a Recurrent Neural Network Identifies Genomic Drivers of Conformation about?

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.

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Author

Kevin B. Dsouza; Alexandra Maslova; Ediem Al-Jibury; Matthias Merkenschlager; Vijay K. Bhargava; Maxwell W. Libbrecht

Publisher

Springer Science and Business Media LLC

Published

2022

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)