ArticleBriefings in bioinformatics2021
Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 45 citations in OpenAlex.
- THC-net: an attention-based deep learning model for chromatin compartment prediction from histone modifications.BMC bioinformatics · 2026Article
- Accurate prediction of cohesin and RNA Polymerase II-associated chromatin interactions using convolutional neural networks.Nucleic acids research · 2026Article
- EPIFBMC: A New Model for Enhancer-Promoter Interaction Prediction.International journal of molecular sciences · 2025Article
- The challenge of chromatin model comparison and validation: A project from the first international 4D Nucleome Hackathon.PLoS computational biology · 2025Article
- Techniques to detect physical interactions between genomic regions.Epigenomics · 2025Review
- SEgene identifies links between super enhancers and gene expression across cell types.NPJ systems biology and applications · 2025Article
- Machine and Deep Learning Methods for Predicting 3D Genome Organization.Methods in molecular biology (Clifton, N.J.) · 2025Review
- DNA sequence analysis landscape: a comprehensive review of DNA sequence analysis task types, databases, datasets, word embedding methods, and language models.Frontiers in medicine · 2025Review
- GATv2EPI: Predicting Enhancer-Promoter Interactions with a Dynamic Graph Attention Network.Genes · 2024Article
- A review of deep learning models for the prediction of chromatin interactions with DNA and epigenomic profiles.Briefings in bioinformatics · 2024Review
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- ChIPr: accurate prediction of cohesin-mediated 3D genome organization from 2D chromatin features.Genome biology · 2024Article
- Integrative approaches based on genomic techniques in the functional studies on enhancers.Briefings in bioinformatics · 2023Review
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- 3D genomic organization in cancers.Quantitative biology (Beijing, China) · 2023Review
- Computational methods for identifying enhancer-promoter interactions.Quantitative biology (Beijing, China) · 2023Review
- InferLoop: leveraging single-cell chromatin accessibility for the signal of chromatin loop.Briefings in bioinformatics · 2023Article
- The effects of metabolic indicators and immune biomarkers on pregnancy outcomes in women with recurrent spontaneous abortion: a retrospective study.Frontiers in endocrinology · 2023Article
- Integrating extrusion complex-associated pattern to predict cell type-specific long-range chromatin loops.iScience · 2022Article
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Authors and funding
14 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The exploration of three-dimensional chromatin interaction and organization provides insight into mechanisms underlying gene regulation, cell differentiation and disease development. Advances in chromosome conformation capture technologies, such as high-throughput chromosome conformation capture (Hi-C) and chromatin interaction analysis by paired-end tag (ChIA-PET), have enabled the exploration of chromatin interaction and organization. However, high-resolution Hi-C and ChIA-PET data are only available for a limited number of cell lines, and their acquisition is costly, time consuming, laborious and affected by theoretical limitations. Increasing evidence shows that DNA sequence and epigenomic features are informative predictors of regulatory interaction and chromatin architecture. Based on these features, numerous computational methods have been developed for the prediction of chromatin interaction and organization, whereas they are not extensively applied in biomedical study. A systematical study to summarize and evaluate such methods is still needed to facilitate their application. Here, we summarize 48 computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles, categorize them and compare their performance. Besides, we provide a comprehensive guideline for the selection of suitable methods to predict chromatin interaction and organization based on available data and biological question of interest.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.