TaxAss: Leveraging a Custom Freshwater Database Achieves Fine-Scale Taxonomic Resolution

ABSTRACT Taxonomy assignment of freshwater microbial communities is limited by the minimally curated phylogenies used for large taxonomy databases. Here we introduce TaxAss, a taxonomy assignment workflow that classifies 16S rRNA gene amplicon data using two taxonomy reference databases: a large com...

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Autores principales:	Robin R. Rohwer, Joshua J. Hamilton, Ryan J. Newton, Katherine D. McMahon
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2018
Materias:	16S rRNA gene amplicon sequencing freshwater limnology microbial ecology taxonomy Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/2a8da9d167d2420f916099567fa13277
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spelling	oai:doaj.org-article:2a8da9d167d2420f916099567fa132772021-11-15T15:22:26ZTaxAss: Leveraging a Custom Freshwater Database Achieves Fine-Scale Taxonomic Resolution10.1128/mSphere.00327-182379-5042https://doaj.org/article/2a8da9d167d2420f916099567fa132772018-10-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSphere.00327-18https://doaj.org/toc/2379-5042ABSTRACT Taxonomy assignment of freshwater microbial communities is limited by the minimally curated phylogenies used for large taxonomy databases. Here we introduce TaxAss, a taxonomy assignment workflow that classifies 16S rRNA gene amplicon data using two taxonomy reference databases: a large comprehensive database and a small ecosystem-specific database rigorously curated by scientists within a field. We applied TaxAss to five different freshwater data sets using the comprehensive SILVA database and the freshwater-specific FreshTrain database. TaxAss increased the percentage of the data set classified compared to using only SILVA, especially at fine-resolution family to species taxon levels, while across the freshwater test data sets classifications increased by as much as 11 to 40% of total reads. A similar increase in classifications was not observed in a control mouse gut data set, which was not expected to contain freshwater bacteria. TaxAss also maintained taxonomic richness compared to using only the FreshTrain across all taxon levels from phylum to species. Without TaxAss, most organisms not represented in the FreshTrain were unclassified, but at fine taxon levels, incorrect classifications became significant. We validated TaxAss using simulated amplicon data derived from full-length clone libraries and found that 96 to 99% of test sequences were correctly classified at fine resolution. TaxAss splits a data set’s sequences into two groups based on their percent identity to reference sequences in the ecosystem-specific database. Sequences with high similarity to sequences in the ecosystem-specific database are classified using that database, and the others are classified using the comprehensive database. TaxAss is free and open source and is available at https://www.github.com/McMahonLab/TaxAss. IMPORTANCE Microbial communities drive ecosystem processes, but microbial community composition analyses using 16S rRNA gene amplicon data sets are limited by the lack of fine-resolution taxonomy classifications. Coarse taxonomic groupings at the phylum, class, and order levels lump ecologically distinct organisms together. To avoid this, many researchers define operational taxonomic units (OTUs) based on clustered sequences, sequence variants, or unique sequences. These fine-resolution groupings are more ecologically relevant, but OTU definitions are data set dependent and cannot be compared between data sets. Microbial ecologists studying freshwater have curated a small, ecosystem-specific taxonomy database to provide consistent and up-to-date terminology. We created TaxAss, a workflow that leverages this database to assign taxonomy. We found that TaxAss improves fine-resolution taxonomic classifications (family, genus, and species). Fine taxonomic groupings are more ecologically relevant, so they provide an alternative to OTU-based analyses that is consistent and comparable between data sets.Robin R. RohwerJoshua J. HamiltonRyan J. NewtonKatherine D. McMahonAmerican Society for Microbiologyarticle16S rRNA geneamplicon sequencingfreshwaterlimnologymicrobial ecologytaxonomyMicrobiologyQR1-502ENmSphere, Vol 3, Iss 5 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	16S rRNA gene amplicon sequencing freshwater limnology microbial ecology taxonomy Microbiology QR1-502
spellingShingle	16S rRNA gene amplicon sequencing freshwater limnology microbial ecology taxonomy Microbiology QR1-502 Robin R. Rohwer Joshua J. Hamilton Ryan J. Newton Katherine D. McMahon TaxAss: Leveraging a Custom Freshwater Database Achieves Fine-Scale Taxonomic Resolution
description	ABSTRACT Taxonomy assignment of freshwater microbial communities is limited by the minimally curated phylogenies used for large taxonomy databases. Here we introduce TaxAss, a taxonomy assignment workflow that classifies 16S rRNA gene amplicon data using two taxonomy reference databases: a large comprehensive database and a small ecosystem-specific database rigorously curated by scientists within a field. We applied TaxAss to five different freshwater data sets using the comprehensive SILVA database and the freshwater-specific FreshTrain database. TaxAss increased the percentage of the data set classified compared to using only SILVA, especially at fine-resolution family to species taxon levels, while across the freshwater test data sets classifications increased by as much as 11 to 40% of total reads. A similar increase in classifications was not observed in a control mouse gut data set, which was not expected to contain freshwater bacteria. TaxAss also maintained taxonomic richness compared to using only the FreshTrain across all taxon levels from phylum to species. Without TaxAss, most organisms not represented in the FreshTrain were unclassified, but at fine taxon levels, incorrect classifications became significant. We validated TaxAss using simulated amplicon data derived from full-length clone libraries and found that 96 to 99% of test sequences were correctly classified at fine resolution. TaxAss splits a data set’s sequences into two groups based on their percent identity to reference sequences in the ecosystem-specific database. Sequences with high similarity to sequences in the ecosystem-specific database are classified using that database, and the others are classified using the comprehensive database. TaxAss is free and open source and is available at https://www.github.com/McMahonLab/TaxAss. IMPORTANCE Microbial communities drive ecosystem processes, but microbial community composition analyses using 16S rRNA gene amplicon data sets are limited by the lack of fine-resolution taxonomy classifications. Coarse taxonomic groupings at the phylum, class, and order levels lump ecologically distinct organisms together. To avoid this, many researchers define operational taxonomic units (OTUs) based on clustered sequences, sequence variants, or unique sequences. These fine-resolution groupings are more ecologically relevant, but OTU definitions are data set dependent and cannot be compared between data sets. Microbial ecologists studying freshwater have curated a small, ecosystem-specific taxonomy database to provide consistent and up-to-date terminology. We created TaxAss, a workflow that leverages this database to assign taxonomy. We found that TaxAss improves fine-resolution taxonomic classifications (family, genus, and species). Fine taxonomic groupings are more ecologically relevant, so they provide an alternative to OTU-based analyses that is consistent and comparable between data sets.
format	article
author	Robin R. Rohwer Joshua J. Hamilton Ryan J. Newton Katherine D. McMahon
author_facet	Robin R. Rohwer Joshua J. Hamilton Ryan J. Newton Katherine D. McMahon
author_sort	Robin R. Rohwer
title	TaxAss: Leveraging a Custom Freshwater Database Achieves Fine-Scale Taxonomic Resolution
title_short	TaxAss: Leveraging a Custom Freshwater Database Achieves Fine-Scale Taxonomic Resolution
title_full	TaxAss: Leveraging a Custom Freshwater Database Achieves Fine-Scale Taxonomic Resolution
title_fullStr	TaxAss: Leveraging a Custom Freshwater Database Achieves Fine-Scale Taxonomic Resolution
title_full_unstemmed	TaxAss: Leveraging a Custom Freshwater Database Achieves Fine-Scale Taxonomic Resolution
title_sort	taxass: leveraging a custom freshwater database achieves fine-scale taxonomic resolution
publisher	American Society for Microbiology
publishDate	2018
url	https://doaj.org/article/2a8da9d167d2420f916099567fa13277
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TaxAss: Leveraging a Custom Freshwater Database Achieves Fine-Scale Taxonomic Resolution

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