<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Churcheward B</submitter><funding>Centre National de la Recherche Scientifique</funding><funding>European Commission</funding><pagination>e0043222</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9426564</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>7(4)</volume><pubmed_abstract>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 si</pubmed_abstract><journal>mSystems</journal><pubmed_title>MAGNETO: An Automated Workflow for Genome-Resolved Metagenomics.</pubmed_title><pmcid>PMC9426564</pmcid><funding_grant_id>862923 (AtlantECO)</funding_grant_id><funding_grant_id>GOBITMAP</funding_grant_id><pubmed_authors>Chaffron S</pubmed_authors><pubmed_authors>Fertin G</pubmed_authors><pubmed_authors>Bihouee A</pubmed_authors><pubmed_authors>Millet M</pubmed_authors><pubmed_authors>Churcheward B</pubmed_authors></additional><is_claimable>false</is_claimable><name>MAGNETO: An Automated Workflow for Genome-Resolved Metagenomics.</name><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 si</description><dates><release>2022-01-01T00:00:00Z</release><publication>2022 Aug</publication><modification>2026-06-12T10:02:00.499Z</modification><creation>2025-02-19T03:28:58.031Z</creation></dates><accession>S-EPMC9426564</accession><cross_references><pubmed>35703559</pubmed><doi>10.1128/msystems.00432-22</doi></cross_references></HashMap>