Journal ArticleMetaCC allows scalable and integrative analyses of both long-read and short-read metagenomic Hi-C dataDu, Yuxuan; Sun, Fengzhu<title>Abstract</title> <p>Metagenomic Hi-C (metaHi-C) can identify contig-to-contig relationships with respect to their proximity within the same physical cell. Shotgun libraries in metaHi-C experiments can be constructed by next-generation sequencing (short-read metaHi-C) or more recent third-generation sequencing (long-read metaHi-C). However, all existing metaHi-C analysis methods are developed and benchmarked on short-read metaHi-C datasets and there exists much room for improvement in terms of more scalable and stable analyses, especially for long-read metaHi-C data. Here we report MetaCC, an efficient and integrative framework for analyzing both short-read and long-read metaHi-C datasets. MetaCC outperforms existing methods on normalization and binning. In particular, the MetaCC normalization module, named NormCC, is more than 3000 times faster than the current state-of-the-art method HiCzin on a complex wastewater dataset. When applied to one sheep gut long-read metaHi-C dataset, MetaCC binning module can retrieve 709 high-quality genomes with the largest species diversity using one single sample, including an expansion of five uncultured members from the order<italic>Erysipelotrichales</italic>, and is the only binner that can recover the genome of one important species<italic>Bacteroides vulgatus</italic>. Further plasmid analyses reveal that MetaCC binning is able to capture multi-copy plasmids.</p>Nature Communications2023-12-0110478145Nature Communications1412041-1723https://doi.org/10.1038/s41467-023-41209-62125142National Science Foundation