Impact of training sets on classification of high-throughput bacterial 16s rRNA gene surveys

Werner, Jeffrey J., Koren, Omry, Hugenholtz, Philip, DeSantis, Todd Z., Walters, William A., Caporaso, J. Gregory, Angenent, Largus T., Knight, Rob and Ley, Ruth E (2012) Impact of training sets on classification of high-throughput bacterial 16s rRNA gene surveys. ISME Journal, 6 1: 94-103. doi:10.1038/ismej.2011.82

Author Werner, Jeffrey J.
Koren, Omry
Hugenholtz, Philip
DeSantis, Todd Z.
Walters, William A.
Caporaso, J. Gregory
Angenent, Largus T.
Knight, Rob
Ley, Ruth E
Title Impact of training sets on classification of high-throughput bacterial 16s rRNA gene surveys
Journal name ISME Journal   Check publisher's open access policy
ISSN 1751-7362
Publication date 2012-01
Year available 2011
Sub-type Article (original research)
DOI 10.1038/ismej.2011.82
Volume 6
Issue 1
Start page 94
End page 103
Total pages 10
Place of publication London, United Kingdom
Publisher Nature Publishing Group
Collection year 2012
Language eng
Formatted abstract
Taxonomic classification of the thousands–millions of 16S rRNA gene sequences generated in microbiome studies is often achieved using a naïve Bayesian classifier (for example, the Ribosomal Database Project II (RDP) classifier), due to favorable trade-offs among automation, speed and accuracy. The resulting classification depends on the reference sequences and taxonomic hierarchy used to train the model; although the influence of primer sets and classification algorithms have been explored in detail, the influence of training set has not been characterized. We compared classification results obtained using three different publicly available databases as training sets, applied to five different bacterial 16S rRNA gene pyrosequencing data sets generated (from human body, mouse gut, python gut, soil and anaerobic digester samples). We observed numerous advantages to using the largest, most diverse training set available, that we constructed from the Greengenes (GG) bacterial/archaeal 16S rRNA gene sequence database and the latest GG taxonomy. Phylogenetic clusters of previously unclassified experimental sequences were identified with notable improvements (for example, 50% reduction in reads unclassified at the phylum level in mouse gut, soil and anaerobic digester samples), especially for phylotypes belonging to specific phyla (Tenericutes, Chloroflexi, Synergistetes and Candidate phyla TM6, TM7). Trimming the reference sequences to the primer region resulted in systematic improvements in classification depth, and greatest gains at higher confidence thresholds. Phylotypes unclassified at the genus level represented a greater proportion of the total community variation than classified operational taxonomic units in mouse gut and anaerobic digester samples, underscoring the need for greater diversity in existing reference databases.
Keyword Greengenes
Naïve Bayesian classifier
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online 30 June 2011

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2012 Collection
School of Chemistry and Molecular Biosciences
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 111 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 115 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Tue, 24 Jan 2012, 08:48:55 EST by Lucy O'Brien on behalf of School of Chemistry & Molecular Biosciences