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Species abundance information improves sequence taxonomy classification accuracy.

ABSTRACT: Popular naive Bayes taxonomic classifiers for amplicon sequences assume that all species in the reference database are equally likely to be observed. We demonstrate that classification accuracy degrades linearly with the degree to which that assumption is violated, and in practice it is always violated. By incorporating environment-specific taxonomic abundance information, we demonstrate a significant increase in the species-level classification accuracy across common sample types. At the species level, overall average error rates decline from 25% to 14%, which is favourably comparable to the error rates that existing classifiers achieve at the genus level (16%). Our findings indicate that for most practical purposes, the assumption that reference species are equally likely to be observed is untenable. q2-clawback provides a straightforward alternative for samples from common environments.

SUBMITTER: Kaehler BD

PROVIDER: S-EPMC6789115 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Species abundance information improves sequence taxonomy classification accuracy.

Kaehler Benjamin D BD Bokulich Nicholas A NA McDonald Daniel D Knight Rob R Caporaso J Gregory JG Huttley Gavin A GA

Nature communications 20191011 1

Popular naive Bayes taxonomic classifiers for amplicon sequences assume that all species in the reference database are equally likely to be observed. We demonstrate that classification accuracy degrades linearly with the degree to which that assumption is violated, and in practice it is always violated. By incorporating environment-specific taxonomic abundance information, we demonstrate a significant increase in the species-level classification accuracy across common sample types. At the specie ...[more]

PMID: 31604942

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Dataset Information

Species abundance information improves sequence taxonomy classification accuracy.

Publications

Species abundance information improves sequence taxonomy classification accuracy.

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