ArticleThe ISME journal2012
Impact of training sets on classification of high-throughput bacterial 16s rRNA gene surveys.
Article in The ISME journal, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 268 papers, 1 of them a synthesis that pooled it.
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Who cites it
268 citing papers in PubMed, 1 synthesis or guideline pooled it, 620 citations in OpenAlex.
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- SILVA in 2026: a global core biodata resource for rRNA within the DSMZ digital diversity.Nucleic acids research · 2026Article
- Dietary allyl-isothiocyanate affects male triglyceride levels inFrontiers in microbiology · 2026Article
- Bioinformatic tools for microbiome analysis: from raw sequences to biological insights.Frontiers in microbiology · 2026Review
- Inferred functional potential of microbial communities in traditional African fermented dairy products using 16S rRNA.Access microbiology · 2026Article
- Historical mosquito colonization dynamics are associated with patterns of microbial community assembly in aboveground aquatic habitats.Environmental microbiome · 2025Article
208 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.