ArticleBMC genomics2019
A computational method to predict topologically associating domain boundaries combining histone Marks and sequence information.
Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Systematic evaluation of the heterogeneity of topologically associating domain boundaries in a large genomic context in humans.NAR genomics and bioinformatics · 2026Article
- Integrating heterogeneity into topologically associating domain boundary prediction in large genomic context in human.NAR genomics and bioinformatics · 2026Article
- GraphLooper: predicting chromatin loops based on hierarchical multi-view graph pooling method.Briefings in bioinformatics · 2026Article
- 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
- A review of deep learning models for the prediction of chromatin interactions with DNA and epigenomic profiles.Briefings in bioinformatics · 2024Review
- Article
- preciseTAD: a transfer learning framework for 3D domain boundary prediction at base-pair resolution.Bioinformatics (Oxford, England) · 2022Article
- Review
- Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles.Briefings in bioinformatics · 2021Article
- PredTAD: A machine learning framework that models 3D chromatin organization alterations leading to oncogene dysregulation in breast cancer cell lines.Computational and structural biotechnology journal · 2021Article
- A machine learning framework for the prediction of chromatin folding inPeerJ. Computer science · 2020Article
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6 authors.
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Abstract
backgroundThe three-dimensional (3D) structure of chromatins plays significant roles during cell differentiation and development. Hi-C and other 3C-based technologies allow us to look deep into the chromatin architectures. Many studies have suggested that topologically associating domains (TAD), as the structure and functional unit, are conserved across different organs. However, our understanding about the underlying mechanism of the TAD boundary formation is still limited.
resultsWe developed a computational method, TAD-Lactuca, to infer this structure by taking the contextual information of the epigenetic modification signals and the primary DNA sequence information on the genome. TAD-Lactuca is found stable in the case of multi-resolutions and different datasets. It could achieve high accuracy and even outperforms the state-of-art methods when the sequence patterns were incorporated. Moreover, several transcript factor binding motifs, besides the well-known CCCTC-binding factor (CTCF) motif, were found significantly enriched on the boundaries.
conclusionsWe provided a low cost, effective method to predict TAD boundaries. Above results suggested the incorporation of sequence features could significantly improve the performance. The sequence motif enrichment analysis indicates several gene regulation motifs around the boundaries, which is consistent with TADs may serve as the functional units of gene regulation and implies the sequence patterns would be important in chromatin folding.
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