metaMIC: reference-free misassembly identification and correction of de novo metagenomic assemblies

Evaluating the quality of metagenomic assemblies is important for constructing reliable metagenome-assembled genomes and downstream analyses. Here, we present metaMIC (https://github.com/ZhaoXM-Lab/metaMIC), a machine learning-based tool for identifying and correcting misassemblies in metagenomic as...

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Autores principales: Lai, Senying, Pan, Shaojun, Sun, Chuqing, Coelho, Luis Pedro, Chen, Wei-Hua, Zhao, Xing-Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9661791/
https://www.ncbi.nlm.nih.gov/pubmed/36376928
http://dx.doi.org/10.1186/s13059-022-02810-y
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author Lai, Senying
Pan, Shaojun
Sun, Chuqing
Coelho, Luis Pedro
Chen, Wei-Hua
Zhao, Xing-Ming
author_facet Lai, Senying
Pan, Shaojun
Sun, Chuqing
Coelho, Luis Pedro
Chen, Wei-Hua
Zhao, Xing-Ming
author_sort Lai, Senying
collection PubMed
description Evaluating the quality of metagenomic assemblies is important for constructing reliable metagenome-assembled genomes and downstream analyses. Here, we present metaMIC (https://github.com/ZhaoXM-Lab/metaMIC), a machine learning-based tool for identifying and correcting misassemblies in metagenomic assemblies. Benchmarking results on both simulated and real datasets demonstrate that metaMIC outperforms existing tools when identifying misassembled contigs. Furthermore, metaMIC is able to localize the misassembly breakpoints, and the correction of misassemblies by splitting at misassembly breakpoints can improve downstream scaffolding and binning results. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-022-02810-y.
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spelling pubmed-96617912022-11-15 metaMIC: reference-free misassembly identification and correction of de novo metagenomic assemblies Lai, Senying Pan, Shaojun Sun, Chuqing Coelho, Luis Pedro Chen, Wei-Hua Zhao, Xing-Ming Genome Biol Method Evaluating the quality of metagenomic assemblies is important for constructing reliable metagenome-assembled genomes and downstream analyses. Here, we present metaMIC (https://github.com/ZhaoXM-Lab/metaMIC), a machine learning-based tool for identifying and correcting misassemblies in metagenomic assemblies. Benchmarking results on both simulated and real datasets demonstrate that metaMIC outperforms existing tools when identifying misassembled contigs. Furthermore, metaMIC is able to localize the misassembly breakpoints, and the correction of misassemblies by splitting at misassembly breakpoints can improve downstream scaffolding and binning results. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-022-02810-y. BioMed Central 2022-11-14 /pmc/articles/PMC9661791/ /pubmed/36376928 http://dx.doi.org/10.1186/s13059-022-02810-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Method
Lai, Senying
Pan, Shaojun
Sun, Chuqing
Coelho, Luis Pedro
Chen, Wei-Hua
Zhao, Xing-Ming
metaMIC: reference-free misassembly identification and correction of de novo metagenomic assemblies
title metaMIC: reference-free misassembly identification and correction of de novo metagenomic assemblies
title_full metaMIC: reference-free misassembly identification and correction of de novo metagenomic assemblies
title_fullStr metaMIC: reference-free misassembly identification and correction of de novo metagenomic assemblies
title_full_unstemmed metaMIC: reference-free misassembly identification and correction of de novo metagenomic assemblies
title_short metaMIC: reference-free misassembly identification and correction of de novo metagenomic assemblies
title_sort metamic: reference-free misassembly identification and correction of de novo metagenomic assemblies
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9661791/
https://www.ncbi.nlm.nih.gov/pubmed/36376928
http://dx.doi.org/10.1186/s13059-022-02810-y
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