ArticleBMC bioinformatics2017
ClusterTAD: an unsupervised machine learning approach to detecting topologically associated domains of chromosomes from Hi-C data.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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34 citing papers in PubMed.
- TADShop: systematic benchmarking and identification of topologically associating domains.Nature methods · 2026Article
- Persistent homology-based multiscale Hi-C analysis uncovers TADs-mediated gene regulation.Scientific reports · 2026Article
- A comparative analysis of topological domain callers over RNA-associated interactome.BMC bioinformatics · 2026Article
- A wavelet-based approach generates quantitative, scale-free and hierarchical descriptions of 3D genome structures and new biological insights.PLoS computational biology · 2026Article
- EmbedTAD Using Graph Embedding and Unsupervised Learning to Identify TADs from High-Resolution Hi-C Data.Communications biology · 2025Article
- Review
- Association Between Activated Loci of HML-2 Primate-Specific Endogenous Retrovirus and Newly Formed Chromatin Contacts in Human Primordial Germ Cell-like Cells.International journal of molecular sciences · 2024Article
- HTAD: a human-in-the-loop framework for supervised chromatin domain detection.Genome biology · 2024Article
- Article
- Genome structural dynamics: insights from Gaussian network analysis of Hi-C data.Briefings in functional genomics · 2024Article
- [An identification method of chromatin topological associated domains based on spatial density clustering].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Identifying TAD-like domains on single-cell Hi-C data by graph embedding and changepoint detection.Bioinformatics (Oxford, England) · 2024Article
- EVRC: reconstruction of chromosome 3D structure models using error-vector resultant algorithm with clustering coefficient.Bioinformatics (Oxford, England) · 2023Article
- The shape of chromatin: insights from computational recognition of geometric patterns in Hi-C data.Briefings in bioinformatics · 2023Review
- DNA double-strand break end synapsis by DNA loop extrusion.Nature communications · 2023Article
- The 3D genome and its impacts on human health and disease.Life medicine · 2023Review
- TOAST: A novel method for identifying topologically associated domains based on graph auto-encoders and clustering.Computational and structural biotechnology journal · 2023Article
- HPTAD: A computational method to identify topologically associating domains from HiChIP and PLAC-seq datasets.Computational and structural biotechnology journal · 2023Article
- Pattern recognition of topologically associating domains using deep learning.BMC bioinformatics · 2022Article
- TADfit is a multivariate linear regression model for profiling hierarchical chromatin domains on replicate Hi-C data.Communications biology · 2022Article
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Abstract
backgroundWith the development of chromosomal conformation capturing techniques, particularly, the Hi-C technique, the study of the spatial conformation of a genome is becoming an important topic in bioinformatics and computational biology. The Hi-C technique can generate genome-wide chromosomal interaction (contact) data, which can be used to investigate the higher-level organization of chromosomes, such as Topologically Associated Domains (TAD), i.e., locally packed chromosome regions bounded together by intra chromosomal contacts. The identification of the TADs for a genome is useful for studying gene regulation, genomic interaction, and genome function.
resultsHere, we formulate the TAD identification problem as an unsupervised machine learning (clustering) problem, and develop a new TAD identification method called ClusterTAD. We introduce a novel method to represent chromosomal contacts as features to be used by the clustering algorithm. Our results show that ClusterTAD can accurately predict the TADs on a simulated Hi-C data. Our method is also largely complementary and consistent with existing methods on the real Hi-C datasets of two mouse cells. The validation with the chromatin immunoprecipitation (ChIP) sequencing (ChIP-Seq) data shows that the domain boundaries identified by ClusterTAD have a high enrichment of CTCF binding sites, promoter-related marks, and enhancer-related histone modifications.
conclusionsAs ClusterTAD is based on a proven clustering approach, it opens a new avenue to apply a large array of clustering methods developed in the machine learning field to the TAD identification problem. The source code, the results, and the TADs generated for the simulated and real Hi-C datasets are available here: https://github.com/BDM-Lab/ClusterTAD .
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