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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Publication Details

Journal
Journal of Computational Biology
Published
2024-09-09
DOI
https://doi.org/10.1089/cmb.2024.0663
Citations
2
Primary Topic
Genomics and Phylogenetic Studies
Type
article
Field-Weighted Citation Impact
0.47
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article

Imputing Metagenomic Hi-C Contacts Facilitates the Integrative Contig Binning Through Constrained Random Walk with Restart

Yuxuan Du, Fengzhu Sun, Wenxuan Zuo
2 citations
Journal of Computational Biology
Genomics and Phylogenetic Studies
0.47
article

Imputing Metagenomic Hi-C Contacts Facilitates the Integrative Contig Binning Through Constrained Random Walk with Restart

Yuxuan Du, Fengzhu Sun, Wenxuan Zuo
article en
2 citations

Abstract

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.

Journal of Computational BiologyVol. 31(10)
University of Southern California (US)
Openalex Percentile: Top 35%
Genomics and Phylogenetic Studies
0.47
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Imputing Metagenomic Hi-C Contacts Facilitates the Integrative Contig Binning Through Constrained Random Walk with Restart — Yuxuan Du, Fengzhu Sun, et al. · Journal of Computational Biology (2024) | TGRS Research Map | TGRS