SuperTAD: robust detection of hierarchical topologically associated domains with optimized structural information

Topologically associating domains (TADs) are the organizational units of chromosome structures. TADs can contain TADs, thus forming a hierarchy. TAD hierarchies can be inferred from Hi-C data through coding trees. However, the current method for computing coding trees is not optimal. In this paper,...

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Bibliographic Details
Published in:Genome Biology 2021-01, Vol.22 (1), p.45-45, Article 45
Main Authors: Zhang, Yu Wei, Wang, Meng Bo, Li, Shuai Cheng
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Animals
Asymmetry
Binding sites
Cell Line
Chromatin
chromosomes
data collection
Dynamic programming
Epigenomics
Gene Expression Regulation
genome
Genomes
Hi-C
Histone Code
Histones
Humans
Information theory
Method
Noise
Software
Structural proteins
Structure information theory
Topologically associating domain
Trees
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Summary:Topologically associating domains (TADs) are the organizational units of chromosome structures. TADs can contain TADs, thus forming a hierarchy. TAD hierarchies can be inferred from Hi-C data through coding trees. However, the current method for computing coding trees is not optimal. In this paper, we propose optimal algorithms for this computation. In comparison with seven state-of-art methods using two public datasets, from GM12878 and IMR90 cells, SuperTAD shows a significant enrichment of structural proteins around detected boundaries and histone modifications within TADs and displays a high consistency between various resolutions of identical Hi-C matrices.
ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-020-02234-6