Epiphany: predicting Hi-C contact maps from 1D epigenomic signals

Recent deep learning models that predict the Hi-C contact map from DNA sequence achieve promising accuracy but cannot generalize to new cell types and or even capture differences among training cell types. We propose Epiphany, a neural network to predict cell-type-specific Hi-C contact maps from wid...

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Bibliographic Details
Published inGenome Biology Vol. 24; no. 1; p. 134
Main Authors Yang, Rui, Das, Arnav, Gao, Vianne R, Karbalayghareh, Alireza, Noble, William S, Bilmes, Jeffrey A, Leslie, Christina S
Format Journal Article
LanguageEnglish
Published England BioMed Central 06.06.2023
BMC
Subjects
3D genome
Chromatin
Chromosomes
Deep learning
Epigenomics
Gene mapping
Genomes
Long short-term memory
Method
Neural networks
Neural Networks, Computer
Nucleotide sequence
Deep learning
3D genome
Epigenomics
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Summary:Recent deep learning models that predict the Hi-C contact map from DNA sequence achieve promising accuracy but cannot generalize to new cell types and or even capture differences among training cell types. We propose Epiphany, a neural network to predict cell-type-specific Hi-C contact maps from widely available epigenomic tracks. Epiphany uses bidirectional long short-term memory layers to capture long-range dependencies and optionally a generative adversarial network architecture to encourage contact map realism. Epiphany shows excellent generalization to held-out chromosomes within and across cell types, yields accurate TAD and interaction calls, and predicts structural changes caused by perturbations of epigenomic signals.
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ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-023-02934-9