ArticleNature communications2022
Learning representations of chromatin contacts using a recurrent neural network identifies genomic drivers of conformation.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Operando state monitoring of diversified lithium-ion batteries via laser-excited ultrasonic sensing with transformer networks.Science advances · 2026Article
- NanoLoop: A Deep Learning Framework Leveraging Nanopore Sequencing for Chromatin Loop Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Genetically regulated eRNA expression predicts chromatin contact frequency and reveals genetic mechanisms at GWAS loci.Nature communications · 2025Article
- HiC4D: forecasting spatiotemporal Hi-C data with residual ConvLSTM.Briefings in bioinformatics · 2023Article
- Integrative chromatin domain annotation through graph embedding of Hi-C data.Bioinformatics (Oxford, England) · 2023Article
- Reference panel guided topological structure annotation of Hi-C data.Nature communications · 2022Article
- Learning representations of chromatin contacts using a recurrent neural network identifies genomic drivers of conformation.Nature communications · 2022Article
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Authors and funding
6 authors.
Funding
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.
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Registered trials
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