Species abundance information improves sequence taxonomy classification accuracy.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
11 10 2019
11 10 2019
Historique:
received:
27
02
2019
accepted:
19
09
2019
entrez:
13
10
2019
pubmed:
13
10
2019
medline:
6
2
2020
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 31604942
doi: 10.1038/s41467-019-12669-6
pii: 10.1038/s41467-019-12669-6
pmc: PMC6789115
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
4643Subventions
Organisme : NIMH NIH HHS
ID : P30 MH062512
Pays : United States
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