Epiphany: Predicting Hi-C contact maps from 1D epigenomic signals Journal Article
| Authors: | Yang, R.; Das, A.; Gao, V. R.; Karbalayghareh, A.; Noble, W. S.; Bilmes, J. A.; Leslie, C. S. |
| Article Title: | Epiphany: Predicting Hi-C contact maps from 1D epigenomic signals |
| Abstract: | 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. © 2023, The Author(s). |
| Keywords: | chromosome; epigenetics; dna sequence; epigenomics; short term memory; article; deep learning; 3d genome |
| Journal Title: | Genome Biology |
| Volume: | 24 |
| ISSN: | 1465-6906 |
| Publisher: | Biomed Central Ltd |
| Date Published: | 2023-06-06 |
| Start Page: | 134 |
| Language: | English |
| DOI: | 10.1186/s13059-023-02934-9 |
| PUBMED: | 37280678 |
| PROVIDER: | scopus |
| PMCID: | PMC10242996 |
| DOI/URL: | |
| Notes: | Article -- MSK corresponding author is Christina Leslie -- Erratum issued, see DOI: 10.1186/s13059-024-03281-z -- Source: Scopus |
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