Taxonomic Classification of Bacterial 16S rRNA Genes Using Short Sequencing Reads: Evaluation of Effective Study Designs

By Orna Mizrahi-Man, Emily R. Davenport, Yoav Gilad. PLoS ONE, 2013

312 citations recorded by the source corpus.

Massively parallel high throughput sequencing technologies allow us to interrogate the microbial composition of biological samples at unprecedented resolution. The typical approach is to perform high-throughout sequencing of 16S rRNA genes, which are then taxonomically classified based on similarity to known sequences in existing databases. Current technologies cause a predicament though, because although they enable deep coverage of samples, they are limited in the length of sequence they can produce. As a result, high-throughout studies of microbial communities often do not sequence the entire 16S rRNA gene. The challenge is to obtain reliable representation of bacterial communities through taxonomic classification of short 16S rRNA gene sequences. In this study we explored properties of different study designs and developed specific recommendations for effective use of short-read sequencing technologies for the purpose of interrogating bacterial communities, with a focus on classification using naïve Bayesian classifiers. To assess precision and coverage of each design, we used a collection of ∼8,500 manually curated 16S rRNA gene sequences from cultured bacteria and a set of over one million bacterial 16S rRNA gene sequences retrieved from environmental samples, respectively. We also tested different configurations of taxonomic classification approaches using short read sequencing data, and provide recommendations for optimal choice of the relevant parameters. We conclude that with a judicious selection of the sequenced region and the corresponding choice of a suitable training set for taxonomic classification, it is possible to explore bacterial communities at great depth using current technologies, with only a minimal loss of taxonomic resolution.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e1f2cba98596b0d66fba86529ea947bc5cf506e0

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Citations in the corpus

metaxa2: improved identification and taxonomic classification of small and large subunit rRNA in metagenomic data (2015, result): “For the SSU, previous studies have suggested that the V3 and V4 regions would give more accurate results compared to the V5 or V9 region for short-read data (Mizrahi-Man et al. 2013).”
What is the Healthy Gut Microbiota Composition? A Changing Ecosystem across Age, Environment, Diet, and Diseases (2019, background): “The majority of these techniques are based on the extraction of DNA and the amplification of the 16S ribosomal RNA gene (rRNA) [5,6].”
Insights from 20 years of bacterial genome sequencing (2015, methodology): “For many years, ribosomal RNA (rRNA) operons, specifically the 16S rRNA genes, were used as the primary tool for taxonomic assignment and phylogenetic trees (Mizrahi-Man et al. 2013).”