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The Chaperonin-60 Universal Target Is a Barcode for Bacteria That Enables De Novo Assembly of Metagenomic Sequence Data

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      Abstract

      Barcoding with molecular sequences is widely used to catalogue eukaryotic biodiversity. Studies investigating the community dynamics of microbes have relied heavily on gene-centric metagenomic profiling using two genes (16S rRNA and cpn60) to identify and track Bacteria. While there have been criteria formalized for barcoding of eukaryotes, these criteria have not been used to evaluate gene targets for other domains of life. Using the framework of the International Barcode of Life we evaluated DNA barcodes for Bacteria. Candidates from the 16S rRNA gene and the protein coding cpn60 gene were evaluated. Within complete bacterial genomes in the public domain representing 983 species from 21 phyla, the largest difference between median pairwise inter- and intra-specific distances (“barcode gap”) was found from cpn60. Distribution of sequence diversity along the ∼555 bp cpn60 target region was remarkably uniform. The barcode gap of the cpn60 universal target facilitated the faithful de novo assembly of full-length operational taxonomic units from pyrosequencing data from a synthetic microbial community. Analysis supported the recognition of both 16S rRNA and cpn60 as DNA barcodes for Bacteria. The cpn60 universal target was found to have a much larger barcode gap than 16S rRNA suggesting cpn60 as a preferred barcode for Bacteria. A large barcode gap for cpn60 provided a robust target for species-level characterization of data. The assembly of consensus sequences for barcodes was shown to be a reliable method for the identification and tracking of novel microbes in metagenomic studies.

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      Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities.

      mothur aims to be a comprehensive software package that allows users to use a single piece of software to analyze community sequence data. It builds upon previous tools to provide a flexible and powerful software package for analyzing sequencing data. As a case study, we used mothur to trim, screen, and align sequences; calculate distances; assign sequences to operational taxonomic units; and describe the alpha and beta diversity of eight marine samples previously characterized by pyrosequencing of 16S rRNA gene fragments. This analysis of more than 222,000 sequences was completed in less than 2 h with a laptop computer.
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        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/.
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          Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences.

          In 2001 and 2002, we published two papers (Bioinformatics, 17, 282-283, Bioinformatics, 18, 77-82) describing an ultrafast protein sequence clustering program called cd-hit. This program can efficiently cluster a huge protein database with millions of sequences. However, the applications of the underlying algorithm are not limited to only protein sequences clustering, here we present several new programs using the same algorithm including cd-hit-2d, cd-hit-est and cd-hit-est-2d. Cd-hit-2d compares two protein datasets and reports similar matches between them; cd-hit-est clusters a DNA/RNA sequence database and cd-hit-est-2d compares two nucleotide datasets. All these programs can handle huge datasets with millions of sequences and can be hundreds of times faster than methods based on the popular sequence comparison and database search tools, such as BLAST.
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            Author and article information

            Affiliations
            [1 ]Agriculture and AgriFood Canada, Saskatoon, Saskatchewan, Canada
            [2 ]Department of Veterinary Microbiology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
            [3 ]National Research Council Canada, Saskatoon, Saskatchewan, Canada
            [4 ]Department of Microbiology and Immunology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
            University of Waterloo, Canada
            Author notes

            Competing Interests: The authors have declared that no competing interests exist.

            Conceived and designed the experiments: MGL TJD SMH JEH. Performed the experiments: MGL JEH. Analyzed the data: MGL JEH. Contributed reagents/materials/analysis tools: MGL TJD. Wrote the paper: MGL TJD SMH JEH.

            Contributors
            Role: Editor
            Journal
            PLoS One
            PLoS ONE
            plos
            plosone
            PLoS ONE
            Public Library of Science (San Francisco, USA )
            1932-6203
            2012
            26 November 2012
            : 7
            : 11
            23189159
            3506640
            PONE-D-12-26347
            10.1371/journal.pone.0049755
            (Editor)

            This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

            Counts
            Pages: 10
            Funding
            This work was supported by funding from Agriculture and AgriFood Canada, the Natural Sciences and Engineering Research Council of Canada, and the Canadian Institutes for Health Research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
            Categories
            Research Article
            Biology
            Computational Biology
            Genomics
            Metagenomics
            Sequence Analysis
            Evolutionary Biology
            Evolutionary Systematics
            Phylogenetics
            Taxonomy
            Genomics
            Metagenomics
            Microbiology
            Bacteriology
            Bacterial Taxonomy
            Applied Microbiology
            Microbial Ecology

            Uncategorized

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