ReviewComputational and structural biotechnology journal2022
Predicting 3D chromatin interactions from DNA sequence using Deep Learning.
Review in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Power-law penalties correct distance bias in single-cell co-accessibility and deep-learning chromatin interaction predictions.NAR genomics and bioinformatics · 2026Article
- Accurate prediction of cohesin and RNA Polymerase II-associated chromatin interactions using convolutional neural networks.Nucleic acids research · 2026Article
- Gaining insights into epigenetic memories through artificial intelligence and omics science in plants.Journal of integrative plant biology · 2025Review
- Polymer-derived distance penalties improve chromatin interaction predictions from single-cell data across crop genomes.bioRxiv : the preprint server for biology · 2025Article
- Polymer model integrates imaging and sequencing to reveal how nanoscale heterochromatin domains influence gene expression.Nature communications · 2025Article
- Overview and Prospects of DNA Sequence Visualization.International journal of molecular sciences · 2025Review
- Perspective on recent developments and challenges in regulatory and systems genomics.Bioinformatics advances · 2025Review
- A review of deep learning models for the prediction of chromatin interactions with DNA and epigenomic profiles.Briefings in bioinformatics · 2024Review
- GenomicLinks: deep learning predictions of 3D chromatin interactions in the maize genome.NAR genomics and bioinformatics · 2024Article
- Mechanistic drivers of chromatin organization into compartments.Current opinion in genetics & development · 2024Review
- A comprehensive benchmarking with interpretation and operational guidance for the hierarchy of topologically associating domains.Nature communications · 2024Article
- ChIPr: accurate prediction of cohesin-mediated 3D genome organization from 2D chromatin features.Genome biology · 2024Article
- Topology regulatory elements: From shaping genome architecture to gene regulation.Current opinion in structural biology · 2023Review
- 3D genome organization links non-coding disease-associated variants to genes.Frontiers in cell and developmental biology · 2022Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gene regulation in eukaryotes is profoundly shaped by the 3D organization of chromatin within the cell nucleus. Distal regulatory interactions between enhancers and their target genes are widespread and many causal loci underlying heritable agricultural or clinical traits have been mapped to distal cis-regulatory elements. Dissecting the sequence features that mediate such distal interactions is key to understanding their underlying biology. Deep Learning (DL) models coupled with genome-wide 3C-based sequencing data have emerged as powerful tools to infer the DNA sequence grammar underlying such distal interactions. In this review we show that most DL models have remarkably high prediction accuracy, which indicates that DNA sequence features are important determinants of chromatin looping. However, DL model training has so far been limited to a small set of human cell lines, raising questions about the generalization of these predictions to other tissue-types and species. Furthermore, we find that the model architecture seems less relevant for model performance than the training strategy and the data preparation step. Transfer learning, coupled with functionally curated interactions, appear to be the most promising approach to learn cell-type specific and possibly species- specific sequence features in future applications.
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Registered trials
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