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The application of next-generation sequencing to the study of the vaginal microbiome is revealing the spectrum of microbial communities that inhabit the human vagina. High-resolution identification of bacterial taxa, minimally to the species level, is necessary to fully understand the association of the vaginal microbiome with bacterial vaginosis, sexually transmitted infections, pregnancy complications, menopause, and other physiological and infectious conditions. However, most current taxonomic assignment strategies based on metagenomic 16S rDNA sequence analysis provide at best a genus-level resolution. While surveys of 16S rRNA gene sequences are common in microbiome studies, few well-curated, body-site-specific reference databases of 16S rRNA gene sequences are available, and no such resource is available for vaginal microbiome studies.
We constructed the Vaginal 16S rDNA Reference Database, a comprehensive and non-redundant database of 16S rDNA reference sequences for bacterial taxa likely to be associated with vaginal health, and we developed STIRRUPS, a new method that employs the USEARCH algorithm with a curated reference database for rapid species-level classification of 16S rDNA partial sequences. The method was applied to two datasets of V1-V3 16S rDNA reads: one generated from a mock community containing DNA from six bacterial strains associated with vaginal health, and a second generated from over 1,000 mid-vaginal samples collected as part of the Vaginal Human Microbiome Project at Virginia Commonwealth University. In both datasets, STIRRUPS, used in conjunction with the Vaginal 16S rDNA Reference Database, classified more than 95% of processed reads to a species-level taxon using a 97% global identity threshold for assignment.
This database and method provide accurate species-level classifications of metagenomic 16S rDNA sequence reads that will be useful for analysis and comparison of microbiome profiles from vaginal samples. STIRRUPS can be used to classify 16S rDNA sequence reads from other ecological niches if an appropriate reference database of 16S rDNA sequences is available.
© 2012 Fettweis et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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VCU Microbiology and Immunology Faculty Publications
Multiple sequence alignment of V1-V3 16S rDNA sequences from Lactobacillus species. The MUSCLE algorithm was used to align the V1-V3 region of the 16S rDNA reference sequences from 144 species of Lactobacillus.
1471-2164-13-s8-s17-s2.pdf (102 kB)
Multiple sequence alignment of V1-V3 16S rDNA sequences from Prevotella species. The MUSCLE algorithm was used to align the V1-V3 region of the 16S rDNA reference sequences from 43 species of Prevotella.
1471-2164-13-s8-s17-s3.pdf (87 kB)
Multiple sequence alignment of V1-V3 16S rDNA sequences from Staphylococcus species. The MUSCLE algorithm was used to align the V1-V3 region of the 16S rDNA reference sequences from 37 species of Staphylococcus.
1471-2164-13-s8-s17-s4.xls (168 kB)
(Excel Spreadsheet) Vaginal 16D rDNA Reference Database. The database includes the names of all reference sequences. GenBank accession numbers and gene identifiers are provided for named species. Species-level taxon assignments are based on clustering of V1-V3 16S rDNA reference sequences as described in the Methods.