SuperTAD-Fast : Accelerating Topologically Associating Domains Detection Through Discretization
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 784-796 |
Journal / Publication | Journal of Computational Biology |
Volume | 31 |
Issue number | 9 |
Online published | 4 Sept 2024 |
Publication status | Published - Sept 2024 |
Link(s)
Abstract
High-throughput chromosome conformation capture (Hi-C) technology captures spatial interactions of DNA sequences into matrices, and software tools are developed to identify topologically associating domains (TADs) from the Hi-C matrices. With structural information theory, SuperTAD adopted a dynamic programming approach to find the TAD hierarchy with minimal structural entropy. However, the algorithm suffers from high time complexity. To accelerate this algorithm, we design and implement an approximation algorithm with a theoretical performance guarantee. We implemented a package, SuperTAD-Fast. Using Hi-C matrices and simulated data, we demonstrated that SuperTAD-Fast achieved great runtime improvement compared with SuperTAD. SuperTAD-Fast shows high consistency and significant enrichment of structural proteins from Hi-C data of human cell lines in comparison with the existing six hierarchical TADs detecting methods. © Mary Ann Liebert, Inc.
Research Area(s)
- discretization, dynamic programming, Hi-C, structural information theory, topologically associating domains
Citation Format(s)
SuperTAD-Fast: Accelerating Topologically Associating Domains Detection Through Discretization. / LING, Zhao; ZHANG, Yu Wei; LI, Shuai Cheng.
In: Journal of Computational Biology, Vol. 31, No. 9, 09.2024, p. 784-796.
In: Journal of Computational Biology, Vol. 31, No. 9, 09.2024, p. 784-796.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review