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Background <p>Metagenomics combined with High-throughput Chromosome Conformation Capture (Hi-C) provides a powerful approach to study microbial communities by linking genomic content with spatial interactions. Hi-C complements shotgun sequencing by revealing taxonomic composition, functional interactions, and genomic organization within a single sample. However, aligning Hi-C reads to metagenomic contigs is challenging due to variable insert sizes of Hi-C paired-end reads, multi-species complexity, and gaps in assemblies. Although several benchmark studies have evaluated general alignment tools and Hi-C data alignment, none have specifically focused on metagenomic Hi-C data.</p> Results <p>We evaluated seven alignment strategies commonly used in Hi-C analyses: BWA MEM -5SP, BWA MEM default, BWA aln default, Bowtie2 default, Bowtie2 –very-sensitive-local, Minimap2 default, and Chromap Hi-C default. We benchmarked these tools on one synthetic dataset and seven real-world environments. Performance was assessed based on the number of inter-contig Hi-C read pairs and their impact on downstream tasks, such as binning quality.</p> Conclusions <p>We show that BWA MEM -5SP generally outperformed all other tools across most&#xa0;environments in terms of inter-contig read pairs and binning quality, followed by BWA MEM default. Chromap and Minimap2, while less effective in these metrics, demonstrated the highest computational efficiency.</p>

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Benchmarking alignment strategies for Hi-C reads in metagenomic Hi-C data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月30日

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  • Yuqiu Wang,
  • Wenxuan Zuo,
  • Jiawei Huang,
  • Fengzhu Sun,
  • Yuxuan Du

摘要

Background

Metagenomics combined with High-throughput Chromosome Conformation Capture (Hi-C) provides a powerful approach to study microbial communities by linking genomic content with spatial interactions. Hi-C complements shotgun sequencing by revealing taxonomic composition, functional interactions, and genomic organization within a single sample. However, aligning Hi-C reads to metagenomic contigs is challenging due to variable insert sizes of Hi-C paired-end reads, multi-species complexity, and gaps in assemblies. Although several benchmark studies have evaluated general alignment tools and Hi-C data alignment, none have specifically focused on metagenomic Hi-C data.

Results

We evaluated seven alignment strategies commonly used in Hi-C analyses: BWA MEM -5SP, BWA MEM default, BWA aln default, Bowtie2 default, Bowtie2 –very-sensitive-local, Minimap2 default, and Chromap Hi-C default. We benchmarked these tools on one synthetic dataset and seven real-world environments. Performance was assessed based on the number of inter-contig Hi-C read pairs and their impact on downstream tasks, such as binning quality.

Conclusions

We show that BWA MEM -5SP generally outperformed all other tools across most environments in terms of inter-contig read pairs and binning quality, followed by BWA MEM default. Chromap and Minimap2, while less effective in these metrics, demonstrated the highest computational efficiency.