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      Temporal dynamics of the human vaginal microbiota.

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          Abstract

          Elucidating the factors that impinge on the stability of bacterial communities in the vagina may help in predicting the risk of diseases that affect women's health. Here, we describe the temporal dynamics of the composition of vaginal bacterial communities in 32 reproductive-age women over a 16-week period. The analysis revealed the dynamics of five major classes of bacterial communities and showed that some communities change markedly over short time periods, whereas others are relatively stable. Modeling community stability using new quantitative measures indicates that deviation from stability correlates with time in the menstrual cycle, bacterial community composition, and sexual activity. The women studied are healthy; thus, it appears that neither variation in community composition per se nor higher levels of observed diversity (co-dominance) are necessarily indicative of dysbiosis.

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          QIIME allows analysis of high-throughput community sequencing data.

          J. Gregory Caporaso, Justin Kuczynski, Jesse Stombaugh … (2010)
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            Search and clustering orders of magnitude faster than BLAST.

            Robert Edgar (2010)
            Biological sequence data is accumulating rapidly, motivating the development of improved high-throughput methods for sequence classification. UBLAST and USEARCH are new algorithms enabling sensitive local and global search of large sequence databases at exceptionally high speeds. They are often orders of magnitude faster than BLAST in practical applications, though sensitivity to distant protein relationships is lower. UCLUST is a new clustering method that exploits USEARCH to assign sequences to clusters. UCLUST offers several advantages over the widely used program CD-HIT, including higher speed, lower memory use, improved sensitivity, clustering at lower identities and classification of much larger datasets. Binaries are available at no charge for non-commercial use at http://www.drive5.com/usearch.
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              Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy.

              Qiong Wang, George Garrity, James Tiedje … (2007)
              The Ribosomal Database Project (RDP) Classifier, a naïve Bayesian classifier, can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes (2nd ed., release 5.0, Springer-Verlag, New York, NY, 2004). It provides taxonomic assignments from domain to genus, with confidence estimates for each assignment. The majority of classifications (98%) were of high estimated confidence (> or = 95%) and high accuracy (98%). In addition to being tested with the corpus of 5,014 type strain sequences from Bergey's outline, the RDP Classifier was tested with a corpus of 23,095 rRNA sequences as assigned by the NCBI into their alternative higher-order taxonomy. The results from leave-one-out testing on both corpora show that the overall accuracies at all levels of confidence for near-full-length and 400-base segments were 89% or above down to the genus level, and the majority of the classification errors appear to be due to anomalies in the current taxonomies. For shorter rRNA segments, such as those that might be generated by pyrosequencing, the error rate varied greatly over the length of the 16S rRNA gene, with segments around the V2 and V4 variable regions giving the lowest error rates. The RDP Classifier is suitable both for the analysis of single rRNA sequences and for the analysis of libraries of thousands of sequences. Another related tool, RDP Library Compare, was developed to facilitate microbial-community comparison based on 16S rRNA gene sequence libraries. It combines the RDP Classifier with a statistical test to flag taxa differentially represented between samples. The RDP Classifier and RDP Library Compare are available online at http://rdp.cme.msu.edu/.
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                Author and article information

                Journal
                Journal ID (iso-abbrev): Sci Transl Med
                Title: Science translational medicine
                Publisher: American Association for the Advancement of Science (AAAS)
                ISSN (Electronic): 1946-6242
                ISSN (Print): 1946-6234
                Publication date (Electronic): May 02 2012
                Volume: 4
                Issue: 132
                Affiliations
                [1 ] Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
                Article
                Publisher Item ID: 4/132/132ra52 Mid ID: NIHMS428591
                DOI: 10.1126/scitranslmed.3003605
                PMC ID: 3722878
                PubMed ID: 22553250
                SO-VID: a1e244fd-066a-4f84-b2ea-867283865693
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