ArticleGenes2024
coiTAD: Detection of Topologically Associating Domains Based on Clustering of Circular Influence Features from Hi-C Data.
Article in Genes, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- From 2D to 4D: a containerized workflow and browser to explore dynamic chromatin architecture.BMC bioinformatics · 2026Article
- From 2D to 4D: a Containerized Workflow and Browser to Explore Dynamic Chromatin Architecture.bioRxiv : the preprint server for biology · 2025Article
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3 authors.
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Abstract
BACKGROUND/
objectivesTopologically associating domains (TADs) are key structural units of the genome, playing a crucial role in gene regulation. TAD boundaries are enriched with specific biological markers and have been linked to genetic diseases, making consistent TAD detection essential. However, accurately identifying TADs remains challenging due to the lack of a definitive validation method. This study aims to develop a novel algorithm, termed coiTAD, which introduces an innovative approach for preprocessing Hi-C data to improve TAD prediction. This method employs a proposed "circle of influence" (COI) approach derived from Hi-C contact matrices.
methodsThe coiTAD algorithm is based on the creation of novel features derived from the circle of influence in input contact matrices, which are subsequently clustered using the HDBSCAN clustering algorithm. The TADs are extracted from the clustered features based on intra-cluster interactions, thereby providing a more accurate method for identifying TADs.
resultsRigorous tests were conducted using both simulated and real Hi-C datasets. The algorithm's validation included analysis of boundary proteins such as H3K4me1, RNAPII, and CTCF. coiTAD consistently matched other TAD prediction methods.
conclusionsThe coiTAD algorithm represents a novel approach for detecting TADs. At its core, the circle-of-influence methodology introduces an innovative strategy for preparing Hi-C data, enabling the assessment of interaction strengths between genomic regions. This approach facilitates a nuanced analysis that effectively captures structural variations within chromatin. Ultimately, the coiTAD algorithm enhances our understanding of chromatin organization and offers a robust tool for genomic research. The source code for coiTAD is publicly available, and the URL can be found in the Data Availability Statement section.
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