ArticleCommunications biology2022
TADfit is a multivariate linear regression model for profiling hierarchical chromatin domains on replicate Hi-C data.
Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- TADShop: systematic benchmarking and identification of topologically associating domains.Nature methods · 2026Article
- BINDER achieves accurate identification of hierarchical TADs by comprehensively characterizing consensus TAD boundaries.Genome research · 2025Article
- Identifying TAD-like domains on single-cell Hi-C data by graph embedding and changepoint detection.Bioinformatics (Oxford, England) · 2024Article
- Efficient Hi-C inversion facilitates chromatin folding mechanism discovery and structure prediction.Biophysical journal · 2023Article
- Efficient Hi-C inversion facilitates chromatin folding mechanism discovery and structure prediction.bioRxiv : the preprint server for biology · 2023Article
- TADfit is a multivariate linear regression model for profiling hierarchical chromatin domains on replicate Hi-C data.Communications biology · 2022Article
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6 authors.
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Abstract
Topologically associating domains (TADs) are fundamental building blocks of three dimensional genome, and organized into complex hierarchies. Identifying hierarchical TADs on Hi-C data helps to understand the relationship between genome architectures and gene regulation. Herein we propose TADfit, a multivariate linear regression model for profiling hierarchical chromatin domains, which tries to fit the interaction frequencies in Hi-C contact matrix with and without replicates using all-possible hierarchical TADs, and the significant ones can be determined by the regression coefficients obtained with the help of an online learning solver called Follow-The-Regularized-Leader (FTRL). Beyond the existing methods, TADfit has an ability to handle multiple contact matrix replicates and find partially overlapping TADs on them, which helps to find the comprehensive underlying TADs across replicates from different experiments. The comparative results tell that TADfit has better accuracy and reproducibility, and the hierarchical TADs called by it exhibit a reasonable biological relevance.
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