ArticleBioinformatics (Oxford, England)2022
preciseTAD: a transfer learning framework for 3D domain boundary prediction at base-pair resolution.
Article in Bioinformatics (Oxford, England), 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.
- Integrating heterogeneity into topologically associating domain boundary prediction in large genomic context in human.NAR genomics and bioinformatics · 2026Article
- Systematic evaluation of the heterogeneity of topologically associating domain boundaries in a large genomic context in humans.NAR genomics and bioinformatics · 2026Article
- THC-net: an attention-based deep learning model for chromatin compartment prediction from histone modifications.BMC bioinformatics · 2026Article
- CT-TADB predicts TAD boundaries without Hi-C by integrating DNA sequences and epigenomic features.NPJ systems biology and applications · 2026Article
- Review
- deepTAD: an approach for identifying topologically associated domains based on convolutional neural network and transformer model.Briefings in bioinformatics · 2025Article
- Machine and Deep Learning Methods for Predicting 3D Genome Organization.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Article
- Article
- Integrative computational analyses implicate regulatory genomic elements contributing to spina bifida.Genetics in medicine open · 2024Article
- CREaTor: zero-shot cis-regulatory pattern modeling with attention mechanisms.Genome biology · 2023Article
- Article
- HPTAD: A computational method to identify topologically associating domains from HiChIP and PLAC-seq datasets.Computational and structural biotechnology journal · 2023Article
- i2APP: A Two-Step Machine Learning Framework For Antiparasitic Peptides Identification.Frontiers in genetics · 2022Article
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Authors and funding
3 authors.
Funding
Abstract
motivationChromosome conformation capture technologies (Hi-C) revealed extensive DNA folding into discrete 3D domains, such as Topologically Associating Domains and chromatin loops. The correct binding of CTCF and cohesin at domain boundaries is integral in maintaining the proper structure and function of these 3D domains. 3D domains have been mapped at the resolutions of 1 kilobase and above. However, it has not been possible to define their boundaries at the resolution of boundary-forming proteins.
resultsTo predict domain boundaries at base-pair resolution, we developed preciseTAD, an optimized transfer learning framework trained on high-resolution genome annotation data. In contrast to current TAD/loop callers, preciseTAD-predicted boundaries are strongly supported by experimental evidence. Importantly, this approach can accurately delineate boundaries in cells without Hi-C data. preciseTAD provides a powerful framework to improve our understanding of how genomic regulators are shaping the 3D structure of the genome at base-pair resolution. AVAILABILITY AND IMPLEMENTATION: preciseTAD is an R/Bioconductor package available at https://bioconductor.org/packages/preciseTAD/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Registered trials
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.