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PLoS One. 2015 Feb 6;10(2):e0117617. doi: 10.1371/journal.pone.0117617. eCollection 2015.

Use of 16S rRNA gene for identification of a broad range of clinically relevant bacterial pathogens.

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University of California San Francisco, Department of Medicine, Gastroenterology Division, 513 Parnassus Ave, San Francisco, CA 94143-0538, United States of America.
Lawrence Berkeley National Laboratory, Earth Sciences Division, 1 Cyclotron Rd., MS70A-3317, Berkeley, CA 94720, United States of America.
University of California, Berkeley, CA 94720, United States of America.
School of Biological Sciences, Victoria University of Wellington, 34 Kenepuru Drive, Porirua, Wellington, New Zealand.
San Francisco General Hospital, Department of Medicine, Bldg 100, San Francisco, CA 94110, United States of America.
University of California San Francisco, Clinical Microbiology Laboratory, 185 Berry Street, Suite 290, San Francisco, CA 94107, United States of America.


According to World Health Organization statistics of 2011, infectious diseases remain in the top five causes of mortality worldwide. However, despite sophisticated research tools for microbial detection, rapid and accurate molecular diagnostics for identification of infection in humans have not been extensively adopted. Time-consuming culture-based methods remain to the forefront of clinical microbial detection. The 16S rRNA gene, a molecular marker for identification of bacterial species, is ubiquitous to members of this domain and, thanks to ever-expanding databases of sequence information, a useful tool for bacterial identification. In this study, we assembled an extensive repository of clinical isolates (n = 617), representing 30 medically important pathogenic species and originally identified using traditional culture-based or non-16S molecular methods. This strain repository was used to systematically evaluate the ability of 16S rRNA for species level identification. To enable the most accurate species level classification based on the paucity of sequence data accumulated in public databases, we built a Naïve Bayes classifier representing a diverse set of high-quality sequences from medically important bacterial organisms. We show that for species identification, a model-based approach is superior to an alignment based method. Overall, between 16S gene based and clinical identities, our study shows a genus-level concordance rate of 96% and a species-level concordance rate of 87.5%. We point to multiple cases of probable clinical misidentification with traditional culture based identification across a wide range of gram-negative rods and gram-positive cocci as well as common gram-negative cocci.

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