دورية أكاديمية
أعرض في EDS

Species abundance information improves sequence taxonomy classification accuracy.

التفاصيل البيبلوغرافية
العنوان: Species abundance information improves sequence taxonomy classification accuracy.
المؤلفون: Kaehler BD; Research School of Biology, Australian National University, Canberra, Australia. b.kaehler@adfa.edu.au.; School of Science, University of New South Wales, Canberra, Australia. b.kaehler@adfa.edu.au., Bokulich NA; Center for Applied Microbiome Science, The Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA. nicholas.bokulich@nau.edu.; Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA. nicholas.bokulich@nau.edu., McDonald D; Department of Pediatrics, University of California San Diego, La Jolla, CA, USA., Knight R; Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.; Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA.; Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA., Caporaso JG; Center for Applied Microbiome Science, The Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA. gregcaporaso@gmail.com.; Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA. gregcaporaso@gmail.com., Huttley GA; Research School of Biology, Australian National University, Canberra, Australia. gavin.huttley@anu.edu.au.
المصدر: Nature communications [Nat Commun] 2019 Oct 11; Vol. 10 (1), pp. 4643. Date of Electronic Publication: 2019 Oct 11.
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.
اللغة: English
بيانات الدورية: Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101528555 Publication Model: Electronic Cited Medium: Internet ISSN: 2041-1723 (Electronic) Linking ISSN: 20411723 NLM ISO Abbreviation: Nat Commun Subsets: MEDLINE
أسماء مطبوعة: Original Publication: [London] : Nature Pub. Group
مواضيع طبية MeSH: Phylogeny*, Microbiota/*genetics, Bacteria/genetics ; Classification/methods ; Computational Biology ; Metagenomics/methods ; Population Density ; Software
مستخلص: 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.
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معلومات مُعتمدة: P30 MH062512 United States MH NIMH NIH HHS
تواريخ الأحداث: Date Created: 20191013 Date Completed: 20200204 Latest Revision: 20210110
رمز التحديث: 20231215
مُعرف محوري في PubMed: PMC6789115
DOI: 10.1038/s41467-019-12669-6
PMID: 31604942
قاعدة البيانات: MEDLINE
الوصف
تدمد:2041-1723
DOI:10.1038/s41467-019-12669-6