SuperTAD-Fast:通过离散化加速拓扑关联域检测

IF 1.4 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS
Journal of Computational Biology Pub Date : 2024-09-01 Epub Date: 2024-07-24 DOI:10.1089/cmb.2024.0490
Zhao Ling, Yu Wei Zhang, Shuai Cheng Li
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引用次数: 0

摘要

高通量染色体构象捕获(Hi-C)技术将DNA序列的空间相互作用捕获到矩阵中,并开发了软件工具从Hi-C矩阵中识别拓扑关联结构域(TAD)。根据结构信息理论,SuperTAD 采用动态编程方法,以最小的结构熵找到 TAD 层次。然而,该算法的时间复杂度较高。为了加速该算法,我们设计并实现了一种具有理论性能保证的近似算法。我们实现了一个软件包--SuperTAD-Fast。我们使用 Hi-C 矩阵和模拟数据证明,与 SuperTAD 相比,SuperTAD-Fast 在运行时间上取得了很大的改进。与现有的六种分层 TADs 检测方法相比,SuperTAD-Fast 从人类细胞系的 Hi-C 数据中发现的结构蛋白具有很高的一致性和显著的富集性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SuperTAD-Fast: Accelerating Topologically Associating Domains Detection Through Discretization.

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.

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来源期刊
Journal of Computational Biology
Journal of Computational Biology 生物-计算机:跨学科应用
CiteScore
3.60
自引率
5.90%
发文量
113
审稿时长
6-12 weeks
期刊介绍: Journal of Computational Biology is the leading peer-reviewed journal in computational biology and bioinformatics, publishing in-depth statistical, mathematical, and computational analysis of methods, as well as their practical impact. Available only online, this is an essential journal for scientists and students who want to keep abreast of developments in bioinformatics. Journal of Computational Biology coverage includes: -Genomics -Mathematical modeling and simulation -Distributed and parallel biological computing -Designing biological databases -Pattern matching and pattern detection -Linking disparate databases and data -New tools for computational biology -Relational and object-oriented database technology for bioinformatics -Biological expert system design and use -Reasoning by analogy, hypothesis formation, and testing by machine -Management of biological databases
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