Dataset Information

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 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/TaxAssIMPORTANCE 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.

SUBMITTER: Rohwer RR

PROVIDER: S-EPMC6126143 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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

Rohwer Robin R RR Hamilton Joshua J JJ Newton Ryan J RJ McMahon Katherine D KD

mSphere 20180905 5

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 ...[more]

PMID: 30185512

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Project description:Bacteria have been inferred to exhibit relatively weak biogeographic patterns. To what extent such findings reflect true biological phenomena or methodological artifacts remains unclear. Here, we addressed this question by analyzing the turnover of soil bacterial communities from three data sets. We applied three methodological innovations: (i) design of a hierarchical sampling scheme to disentangle environmental from spatial factors driving turnover; (ii) resolution of 16S rRNA gene amplicon sequence variants to enable higher-resolution community profiling; and (iii) application of the new metric zeta diversity to analyze multisite turnover and drivers. At fine taxonomic resolution, rapid compositional turnover was observed across multiple spatial scales. Turnover was overwhelmingly driven by deterministic processes and influenced by the rare biosphere. The communities also exhibited strong distance decay patterns and taxon-area relationships, with z values within the interquartile range reported for macroorganisms. These biogeographical patterns were weakened upon applying two standard approaches to process community sequencing data: clustering sequences at 97% identity threshold and/or filtering the rare biosphere (sequences lower than 0.05% relative abundance). Comparable findings were made across local, regional, and global data sets and when using shotgun metagenomic markers. Altogether, these findings suggest that bacteria exhibit strong biogeographic patterns, but these signals can be obscured by methodological limitations. We advocate various innovations, including using zeta diversity, to advance the study of microbial biogeography.IMPORTANCE It is commonly thought that bacterial distributions show lower spatial variation than for multicellular organisms. In this article, we present evidence that these inferences are artifacts caused by methodological limitations. Through leveraging innovations in sampling design, sequence processing, and diversity analysis, we provide multifaceted evidence that bacterial communities in fact exhibit strong distribution patterns. This is driven by selection due to factors such as local soil characteristics. Altogether, these findings suggest that the processes underpinning diversity patterns are more unified across all domains of life than previously thought, which has broad implications for the understanding and management of soil biodiversity.

Dataset Information

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

Publications

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

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