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MAGNETO: An Automated Workflow for Genome-Resolved Metagenomics

Metagenome-assembled genomes (MAGs) represent individual genomes recovered from metagenomic data. MAGs are extremely useful to analyze uncultured microbial genomic diversity, as well as to characterize associated functional and metabolic potential in natural environments. Recent computational develo...

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Autores principales: Churcheward, Benjamin, Millet, Maxime, Bihouée, Audrey, Fertin, Guillaume, Chaffron, Samuel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society for Microbiology 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9426564/
https://www.ncbi.nlm.nih.gov/pubmed/35703559
http://dx.doi.org/10.1128/msystems.00432-22
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author Churcheward, Benjamin
Millet, Maxime
Bihouée, Audrey
Fertin, Guillaume
Chaffron, Samuel
author_facet Churcheward, Benjamin
Millet, Maxime
Bihouée, Audrey
Fertin, Guillaume
Chaffron, Samuel
author_sort Churcheward, Benjamin
collection PubMed
description Metagenome-assembled genomes (MAGs) represent individual genomes recovered from metagenomic data. MAGs are extremely useful to analyze uncultured microbial genomic diversity, as well as to characterize associated functional and metabolic potential in natural environments. Recent computational developments have considerably improved MAG reconstruction but also emphasized several limitations, such as the nonbinning of sequence regions with repetitions or distinct nucleotidic composition. Different assembly and binning strategies are often used; however, it still remains unclear which assembly strategy, in combination with which binning approach, offers the best performance for MAG recovery. Several workflows have been proposed in order to reconstruct MAGs, but users are usually limited to single-metagenome assembly or need to manually define sets of metagenomes to coassemble prior to genome binning. Here, we present MAGNETO, an automated workflow dedicated to MAG reconstruction, which includes a fully-automated coassembly step informed by optimal clustering of metagenomic distances, and implements complementary genome binning strategies, for improving MAG recovery. MAGNETO is implemented as a Snakemake workflow and is available at: https://gitlab.univ-nantes.fr/bird_pipeline_registry/magneto. IMPORTANCE Genome-resolved metagenomics has led to the discovery of previously untapped biodiversity within the microbial world. As the development of computational methods for the recovery of genomes from metagenomes continues, existing strategies need to be evaluated and compared to eventually lead to standardized computational workflows. In this study, we compared commonly used assembly and binning strategies and assessed their performance using both simulated and real metagenomic data sets. We propose a novel approach to automate coassembly, avoiding the requirement for a priori knowledge to combine metagenomic information. The comparison against a previous coassembly approach demonstrates a strong impact of this step on genome binning results, but also the benefits of informing coassembly for improving the quality of recovered genomes. MAGNETO integrates complementary assembly-binning strategies to optimize genome reconstruction and provides a complete reads-to-genomes workflow for the growing microbiome research community.
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spelling pubmed-94265642022-08-31 MAGNETO: An Automated Workflow for Genome-Resolved Metagenomics Churcheward, Benjamin Millet, Maxime Bihouée, Audrey Fertin, Guillaume Chaffron, Samuel mSystems Methods and Protocols Metagenome-assembled genomes (MAGs) represent individual genomes recovered from metagenomic data. MAGs are extremely useful to analyze uncultured microbial genomic diversity, as well as to characterize associated functional and metabolic potential in natural environments. Recent computational developments have considerably improved MAG reconstruction but also emphasized several limitations, such as the nonbinning of sequence regions with repetitions or distinct nucleotidic composition. Different assembly and binning strategies are often used; however, it still remains unclear which assembly strategy, in combination with which binning approach, offers the best performance for MAG recovery. Several workflows have been proposed in order to reconstruct MAGs, but users are usually limited to single-metagenome assembly or need to manually define sets of metagenomes to coassemble prior to genome binning. Here, we present MAGNETO, an automated workflow dedicated to MAG reconstruction, which includes a fully-automated coassembly step informed by optimal clustering of metagenomic distances, and implements complementary genome binning strategies, for improving MAG recovery. MAGNETO is implemented as a Snakemake workflow and is available at: https://gitlab.univ-nantes.fr/bird_pipeline_registry/magneto. IMPORTANCE Genome-resolved metagenomics has led to the discovery of previously untapped biodiversity within the microbial world. As the development of computational methods for the recovery of genomes from metagenomes continues, existing strategies need to be evaluated and compared to eventually lead to standardized computational workflows. In this study, we compared commonly used assembly and binning strategies and assessed their performance using both simulated and real metagenomic data sets. We propose a novel approach to automate coassembly, avoiding the requirement for a priori knowledge to combine metagenomic information. The comparison against a previous coassembly approach demonstrates a strong impact of this step on genome binning results, but also the benefits of informing coassembly for improving the quality of recovered genomes. MAGNETO integrates complementary assembly-binning strategies to optimize genome reconstruction and provides a complete reads-to-genomes workflow for the growing microbiome research community. American Society for Microbiology 2022-06-15 /pmc/articles/PMC9426564/ /pubmed/35703559 http://dx.doi.org/10.1128/msystems.00432-22 Text en Copyright © 2022 Churcheward et al. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Methods and Protocols
Churcheward, Benjamin
Millet, Maxime
Bihouée, Audrey
Fertin, Guillaume
Chaffron, Samuel
MAGNETO: An Automated Workflow for Genome-Resolved Metagenomics
title MAGNETO: An Automated Workflow for Genome-Resolved Metagenomics
title_full MAGNETO: An Automated Workflow for Genome-Resolved Metagenomics
title_fullStr MAGNETO: An Automated Workflow for Genome-Resolved Metagenomics
title_full_unstemmed MAGNETO: An Automated Workflow for Genome-Resolved Metagenomics
title_short MAGNETO: An Automated Workflow for Genome-Resolved Metagenomics
title_sort magneto: an automated workflow for genome-resolved metagenomics
topic Methods and Protocols
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9426564/
https://www.ncbi.nlm.nih.gov/pubmed/35703559
http://dx.doi.org/10.1128/msystems.00432-22
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