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BugSplit enables genome-resolved metagenomics through highly accurate taxonomic binning of metagenomic assemblies
A large gap remains between sequencing a microbial community and characterizing all of the organisms inside of it. Here we develop a novel method to taxonomically bin metagenomic assemblies through alignment of contigs against a reference database. We show that this workflow, BugSplit, bins metageno...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8864044/ https://www.ncbi.nlm.nih.gov/pubmed/35194141 http://dx.doi.org/10.1038/s42003-022-03114-4 |
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author | Chandrakumar, Induja Gauthier, Nick P. G. Nelson, Cassidy Bonsall, Michael B. Locher, Kerstin Charles, Marthe MacDonald, Clayton Krajden, Mel Manges, Amee R. Chorlton, Samuel D. |
author_facet | Chandrakumar, Induja Gauthier, Nick P. G. Nelson, Cassidy Bonsall, Michael B. Locher, Kerstin Charles, Marthe MacDonald, Clayton Krajden, Mel Manges, Amee R. Chorlton, Samuel D. |
author_sort | Chandrakumar, Induja |
collection | PubMed |
description | A large gap remains between sequencing a microbial community and characterizing all of the organisms inside of it. Here we develop a novel method to taxonomically bin metagenomic assemblies through alignment of contigs against a reference database. We show that this workflow, BugSplit, bins metagenome-assembled contigs to species with a 33% absolute improvement in F1-score when compared to alternative tools. We perform nanopore mNGS on patients with COVID-19, and using a reference database predating COVID-19, demonstrate that BugSplit’s taxonomic binning enables sensitive and specific detection of a novel coronavirus not possible with other approaches. When applied to nanopore mNGS data from cases of Klebsiella pneumoniae and Neisseria gonorrhoeae infection, BugSplit’s taxonomic binning accurately separates pathogen sequences from those of the host and microbiota, and unlocks the possibility of sequence typing, in silico serotyping, and antimicrobial resistance prediction of each organism within a sample. BugSplit is available at https://bugseq.com/academic. |
format | Online Article Text |
id | pubmed-8864044 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-88640442022-03-17 BugSplit enables genome-resolved metagenomics through highly accurate taxonomic binning of metagenomic assemblies Chandrakumar, Induja Gauthier, Nick P. G. Nelson, Cassidy Bonsall, Michael B. Locher, Kerstin Charles, Marthe MacDonald, Clayton Krajden, Mel Manges, Amee R. Chorlton, Samuel D. Commun Biol Article A large gap remains between sequencing a microbial community and characterizing all of the organisms inside of it. Here we develop a novel method to taxonomically bin metagenomic assemblies through alignment of contigs against a reference database. We show that this workflow, BugSplit, bins metagenome-assembled contigs to species with a 33% absolute improvement in F1-score when compared to alternative tools. We perform nanopore mNGS on patients with COVID-19, and using a reference database predating COVID-19, demonstrate that BugSplit’s taxonomic binning enables sensitive and specific detection of a novel coronavirus not possible with other approaches. When applied to nanopore mNGS data from cases of Klebsiella pneumoniae and Neisseria gonorrhoeae infection, BugSplit’s taxonomic binning accurately separates pathogen sequences from those of the host and microbiota, and unlocks the possibility of sequence typing, in silico serotyping, and antimicrobial resistance prediction of each organism within a sample. BugSplit is available at https://bugseq.com/academic. Nature Publishing Group UK 2022-02-22 /pmc/articles/PMC8864044/ /pubmed/35194141 http://dx.doi.org/10.1038/s42003-022-03114-4 Text en © The Author(s) 2022 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Chandrakumar, Induja Gauthier, Nick P. G. Nelson, Cassidy Bonsall, Michael B. Locher, Kerstin Charles, Marthe MacDonald, Clayton Krajden, Mel Manges, Amee R. Chorlton, Samuel D. BugSplit enables genome-resolved metagenomics through highly accurate taxonomic binning of metagenomic assemblies |
title | BugSplit enables genome-resolved metagenomics through highly accurate taxonomic binning of metagenomic assemblies |
title_full | BugSplit enables genome-resolved metagenomics through highly accurate taxonomic binning of metagenomic assemblies |
title_fullStr | BugSplit enables genome-resolved metagenomics through highly accurate taxonomic binning of metagenomic assemblies |
title_full_unstemmed | BugSplit enables genome-resolved metagenomics through highly accurate taxonomic binning of metagenomic assemblies |
title_short | BugSplit enables genome-resolved metagenomics through highly accurate taxonomic binning of metagenomic assemblies |
title_sort | bugsplit enables genome-resolved metagenomics through highly accurate taxonomic binning of metagenomic assemblies |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8864044/ https://www.ncbi.nlm.nih.gov/pubmed/35194141 http://dx.doi.org/10.1038/s42003-022-03114-4 |
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