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MetaCC allows scalable and integrative analyses of both long-read and short-read metagenomic Hi-C data
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-rea...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10558524/ https://www.ncbi.nlm.nih.gov/pubmed/37802989 http://dx.doi.org/10.1038/s41467-023-41209-6 |
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author | Du, Yuxuan Sun, Fengzhu |
author_facet | Du, Yuxuan Sun, Fengzhu |
author_sort | Du, Yuxuan |
collection | PubMed |
description | 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 Erysipelotrichales, and is the only binner that can recover the genome of one important species Bacteroides vulgatus. Further plasmid analyses reveal that MetaCC binning is able to capture multi-copy plasmids. |
format | Online Article Text |
id | pubmed-10558524 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-105585242023-10-08 MetaCC allows scalable and integrative analyses of both long-read and short-read metagenomic Hi-C data Du, Yuxuan Sun, Fengzhu Nat Commun Article 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 Erysipelotrichales, and is the only binner that can recover the genome of one important species Bacteroides vulgatus. Further plasmid analyses reveal that MetaCC binning is able to capture multi-copy plasmids. Nature Publishing Group UK 2023-10-06 /pmc/articles/PMC10558524/ /pubmed/37802989 http://dx.doi.org/10.1038/s41467-023-41209-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Du, Yuxuan Sun, Fengzhu MetaCC allows scalable and integrative analyses of both long-read and short-read metagenomic Hi-C data |
title | MetaCC allows scalable and integrative analyses of both long-read and short-read metagenomic Hi-C data |
title_full | MetaCC allows scalable and integrative analyses of both long-read and short-read metagenomic Hi-C data |
title_fullStr | MetaCC allows scalable and integrative analyses of both long-read and short-read metagenomic Hi-C data |
title_full_unstemmed | MetaCC allows scalable and integrative analyses of both long-read and short-read metagenomic Hi-C data |
title_short | MetaCC allows scalable and integrative analyses of both long-read and short-read metagenomic Hi-C data |
title_sort | metacc allows scalable and integrative analyses of both long-read and short-read metagenomic hi-c data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10558524/ https://www.ncbi.nlm.nih.gov/pubmed/37802989 http://dx.doi.org/10.1038/s41467-023-41209-6 |
work_keys_str_mv | AT duyuxuan metaccallowsscalableandintegrativeanalysesofbothlongreadandshortreadmetagenomichicdata AT sunfengzhu metaccallowsscalableandintegrativeanalysesofbothlongreadandshortreadmetagenomichicdata |