Species abundance information improves sequence taxonomy classification accuracy

Benjamin D. Kaehler, Nicholas A. Bokulich, Daniel McDonald, Rob Knight, J. Gregory Caporaso, Gavin A. Huttley

Research output: Contribution to journalArticlepeer-review

65 Scopus citations


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.

Original languageEnglish (US)
Article number4643
JournalNature Communications
Issue number1
StatePublished - Dec 1 2019

ASJC Scopus subject areas

  • General Physics and Astronomy
  • General Chemistry
  • General Biochemistry, Genetics and Molecular Biology


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