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Metagenomic profiling pipelines improve taxonomic classification for 16S amplicon sequencing data
Most experiments studying bacterial microbiomes rely on the PCR amplification of all or part of the gene for the 16S rRNA subunit, which serves as a biomarker for identifying and quantifying the various taxa present in a microbiome sample. Several computational methods exist for analyzing 16S amplic...
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/PMC10460424/ https://www.ncbi.nlm.nih.gov/pubmed/37633998 http://dx.doi.org/10.1038/s41598-023-40799-x |
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author | Odom, Aubrey R. Faits, Tyler Castro-Nallar, Eduardo Crandall, Keith A. Johnson, W. Evan |
author_facet | Odom, Aubrey R. Faits, Tyler Castro-Nallar, Eduardo Crandall, Keith A. Johnson, W. Evan |
author_sort | Odom, Aubrey R. |
collection | PubMed |
description | Most experiments studying bacterial microbiomes rely on the PCR amplification of all or part of the gene for the 16S rRNA subunit, which serves as a biomarker for identifying and quantifying the various taxa present in a microbiome sample. Several computational methods exist for analyzing 16S amplicon sequencing. However, the most-used bioinformatics tools cannot produce high quality genus-level or species-level taxonomic calls and may underestimate the potential accuracy of these calls. We used 16S sequencing data from mock bacterial communities to evaluate the sensitivity and specificity of several bioinformatics pipelines and genomic reference libraries used for microbiome analyses, concentrating on measuring the accuracy of species-level taxonomic assignments of 16S amplicon reads. We evaluated the tools DADA2, QIIME 2, Mothur, PathoScope 2, and Kraken 2 in conjunction with reference libraries from Greengenes, SILVA, Kraken 2, and RefSeq. Profiling tools were compared using publicly available mock community data from several sources, comprising 136 samples with varied species richness and evenness, several different amplified regions within the 16S rRNA gene, and both DNA spike-ins and cDNA from collections of plated cells. PathoScope 2 and Kraken 2, both tools designed for whole-genome metagenomics, outperformed DADA2, QIIME 2 using the DADA2 plugin, and Mothur, which are theoretically specialized for 16S analyses. Evaluations of reference libraries identified the SILVA and RefSeq/Kraken 2 Standard libraries as superior in accuracy compared to Greengenes. These findings support PathoScope and Kraken 2 as fully capable, competitive options for genus- and species-level 16S amplicon sequencing data analysis, whole genome sequencing, and metagenomics data tools. |
format | Online Article Text |
id | pubmed-10460424 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-104604242023-08-28 Metagenomic profiling pipelines improve taxonomic classification for 16S amplicon sequencing data Odom, Aubrey R. Faits, Tyler Castro-Nallar, Eduardo Crandall, Keith A. Johnson, W. Evan Sci Rep Article Most experiments studying bacterial microbiomes rely on the PCR amplification of all or part of the gene for the 16S rRNA subunit, which serves as a biomarker for identifying and quantifying the various taxa present in a microbiome sample. Several computational methods exist for analyzing 16S amplicon sequencing. However, the most-used bioinformatics tools cannot produce high quality genus-level or species-level taxonomic calls and may underestimate the potential accuracy of these calls. We used 16S sequencing data from mock bacterial communities to evaluate the sensitivity and specificity of several bioinformatics pipelines and genomic reference libraries used for microbiome analyses, concentrating on measuring the accuracy of species-level taxonomic assignments of 16S amplicon reads. We evaluated the tools DADA2, QIIME 2, Mothur, PathoScope 2, and Kraken 2 in conjunction with reference libraries from Greengenes, SILVA, Kraken 2, and RefSeq. Profiling tools were compared using publicly available mock community data from several sources, comprising 136 samples with varied species richness and evenness, several different amplified regions within the 16S rRNA gene, and both DNA spike-ins and cDNA from collections of plated cells. PathoScope 2 and Kraken 2, both tools designed for whole-genome metagenomics, outperformed DADA2, QIIME 2 using the DADA2 plugin, and Mothur, which are theoretically specialized for 16S analyses. Evaluations of reference libraries identified the SILVA and RefSeq/Kraken 2 Standard libraries as superior in accuracy compared to Greengenes. These findings support PathoScope and Kraken 2 as fully capable, competitive options for genus- and species-level 16S amplicon sequencing data analysis, whole genome sequencing, and metagenomics data tools. Nature Publishing Group UK 2023-08-26 /pmc/articles/PMC10460424/ /pubmed/37633998 http://dx.doi.org/10.1038/s41598-023-40799-x 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 Odom, Aubrey R. Faits, Tyler Castro-Nallar, Eduardo Crandall, Keith A. Johnson, W. Evan Metagenomic profiling pipelines improve taxonomic classification for 16S amplicon sequencing data |
title | Metagenomic profiling pipelines improve taxonomic classification for 16S amplicon sequencing data |
title_full | Metagenomic profiling pipelines improve taxonomic classification for 16S amplicon sequencing data |
title_fullStr | Metagenomic profiling pipelines improve taxonomic classification for 16S amplicon sequencing data |
title_full_unstemmed | Metagenomic profiling pipelines improve taxonomic classification for 16S amplicon sequencing data |
title_short | Metagenomic profiling pipelines improve taxonomic classification for 16S amplicon sequencing data |
title_sort | metagenomic profiling pipelines improve taxonomic classification for 16s amplicon sequencing data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10460424/ https://www.ncbi.nlm.nih.gov/pubmed/37633998 http://dx.doi.org/10.1038/s41598-023-40799-x |
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