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ImputeCC Enhances Integrative Hi-C-Based Metagenomic Binning Through Constrained Random-Walk-Based Imputation

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Research in Computational Molecular Biology (RECOMB 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14758))

Abstract

Metagenomic Hi-C (metaHi-C) enables the recognition of relationships between contigs in terms of their physical proximity within the same cell, facilitating the reconstruction of high-quality metagenome-assembled genomes (MAGs) from complex microbial communities. However, current Hi-C-based contig binning methods solely depend on Hi-C interactions between contigs to group them, ignoring invaluable biological information, including the presence of single-copy marker genes. Here, we introduce ImputeCC, an integrative contig binning tool tailored for metaHi-C datasets. ImputeCC integrates Hi-C interactions with the inherent discriminative power of single-copy marker genes, initially clustering them as preliminary bins, and develops a new constrained random walk with restart (CRWR) algorithm to improve Hi-C connectivity among these contigs. Extensive evaluations on mock and real metaHi-C datasets from diverse environments, including the human gut, wastewater, cow rumen, and sheep gut, demonstrate that ImputeCC consistently outperforms other Hi-C-based contig binning tools. ImputeCC’s genus-level analysis of the sheep gut microbiota further reveals its ability and potential to recover essential species from dominant genera such as Bacteroides, detect previously unrecognized genera, and shed light on the characteristics and functional roles of genera such as Alistipes within the sheep gut ecosystem.

Availability: ImputeCC is implemented in Python and available at https://github.com/dyxstat/ImputeCC. The Supplementary Information is available at https://doi.org/10.5281/zenodo.10776604.

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Acknowledgments

Y.D. and F.S. conceived the ideas and designed the study. Y.D. implemented the methods, carried out the computational analyses, and drafted the manuscript. Y.D. and W.Z. developed the software. All authors modified and finalized the paper. The research is partially funded by NSF grant EF-2125142. The authors declare no competing interests.

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Authors and Affiliations

  1. Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, USA

    Yuxuan Du, Wenxuan Zuo & Fengzhu Sun

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  1. Yuxuan Du
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  2. Wenxuan Zuo
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Correspondence to Fengzhu Sun .

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  1. Carnegie Mellon University, Pittsburgh, PA, USA

    Jian Ma

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Du, Y., Zuo, W., Sun, F. (2024). ImputeCC Enhances Integrative Hi-C-Based Metagenomic Binning Through Constrained Random-Walk-Based Imputation. In: Ma, J. (eds) Research in Computational Molecular Biology. RECOMB 2024. Lecture Notes in Computer Science, vol 14758. Springer, Cham. https://doi.org/10.1007/978-1-0716-3989-4_7

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  • DOI: https://doi.org/10.1007/978-1-0716-3989-4_7

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