4
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      VALENCIA: a nearest centroid classification method for vaginal microbial communities based on composition.

      Read this article at

          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Taxonomic profiles of vaginal microbial communities can be sorted into a discrete number of categories termed community state types (CSTs). This approach is advantageous because collapsing a hyper-dimensional taxonomic profile into a single categorical variable enables efforts such as data exploration, epidemiological studies, and statistical modeling. Vaginal communities are typically assigned to CSTs based on the results of hierarchical clustering of the pairwise distances between samples. However, this approach is problematic because it complicates between-study comparisons and because the results are entirely dependent on the particular set of samples that were analyzed. We sought to standardize and advance the assignment of samples to CSTs.

          Related collections

          Most cited references52

          • Record: found
          • Abstract: found
          • Article: not found

          DADA2: High resolution sample inference from Illumina amplicon data

          We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

            SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy.

              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/.
                Bookmark

                Author and article information

                Journal
                Microbiome
                Microbiome
                Springer Science and Business Media LLC
                2049-2618
                2049-2618
                November 23 2020
                : 8
                : 1
                Affiliations
                [1 ] Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
                [2 ] Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, USA.
                [3 ] Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, USA.
                [4 ] Department of Obstetrics and Gynecology, University of California Davis School of Medicine, Sacramento, USA.
                [5 ] Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA. jravel@som.umaryland.edu.
                [6 ] Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, USA. jravel@som.umaryland.edu.
                Article
                10.1186/s40168-020-00934-6
                10.1186/s40168-020-00934-6
                7684964
                33228810
                d84b6bb1-75e5-41c4-996b-95721ea21f81
                History

                Comments

                Comment on this article