通过重新开始的受限随机游走,推算元基因组 Hi-C 联系促进了整合式 Contig 分选。

IF 1.4 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS
Journal of Computational Biology Pub Date : 2024-10-01 Epub Date: 2024-09-09 DOI:10.1089/cmb.2024.0663
Yuxuan Du, Wenxuan Zuo, Fengzhu Sun
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引用次数: 0

摘要

元基因组Hi-C(metaaHi-C)在从复杂微生物群落中检索高质量元基因组组装基因组方面显示出了巨大的潜力。然而,现有的基于元 Hi-C 的等位基因分选方法仅依赖于等位基因之间的 Hi-C 相互作用,而忽略了关键的生物信息,如单拷贝标记基因的存在。为了克服这一局限性,我们引入了 ImputeCC,这是一种针对元 Hi-C 数据集优化的整合式等位基因分选工具。ImputeCC 综合了 Hi-C 相互作用和单拷贝标记基因的鉴别力,将含有标记基因的等位基因归入初步的分选组。它还引入了一种新颖的带重启的受限随机行走算法,以增强等位基因之间的 Hi-C 连接性。使用来自不同环境的模拟和真实元 Hi-C 数据集进行的综合评估表明,ImputeCC 始终优于其他基于 Hi-C 的等位基因分选工具。对 ImputeCC 重建的绵羊肠道微生物群进行的种属级分析强调了它从优势种属中恢复关键物种和识别以前未知种属的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Imputing Metagenomic Hi-C Contacts Facilitates the Integrative Contig Binning Through Constrained Random Walk with Restart.

Metagenomic Hi-C (metaHi-C) has shown remarkable potential for retrieving high-quality metagenome-assembled genomes from complex microbial communities. Nevertheless, existing metaHi-C-based contig binning methods solely rely on Hi-C interactions between contigs, disregarding crucial biological information such as the presence of single-copy marker genes. To overcome this limitation, we introduce ImputeCC, an integrative contig binning tool optimized for metaHi-C datasets. ImputeCC integrates both Hi-C interactions and the discriminative power of single-copy marker genes to group marker-gene-containing contigs into preliminary bins. It also introduces a novel constrained random walk with restart algorithm to enhance Hi-C connectivity among contigs. Comprehensive assessments using both mock and real metaHi-C datasets from diverse environments demonstrate that ImputeCC consistently outperforms other Hi-C-based contig binning tools. A genus-level analysis of the sheep gut microbiota reconstructed by ImputeCC underlines its capability to recover key species from dominant genera and identify previously unknown genera.

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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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