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      Kraken: ultrafast metagenomic sequence classification using exact alignments

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          Abstract

          Kraken is an ultrafast and highly accurate program for assigning taxonomic labels to metagenomic DNA sequences. Previous programs designed for this task have been relatively slow and computationally expensive, forcing researchers to use faster abundance estimation programs, which only classify small subsets of metagenomic data. Using exact alignment of k-mers, Kraken achieves classification accuracy comparable to the fastest BLAST program. In its fastest mode, Kraken classifies 100 base pair reads at a rate of over 4.1 million reads per minute, 909 times faster than Megablast and 11 times faster than the abundance estimation program MetaPhlAn. Kraken is available at http://ccb.jhu.edu/software/kraken/.

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          Community structure and metabolism through reconstruction of microbial genomes from the environment.

          Gene W. Tyson, Jarrod Chapman, Philip Hugenholtz … (2004)
          Microbial communities are vital in the functioning of all ecosystems; however, most microorganisms are uncultivated, and their roles in natural systems are unclear. Here, using random shotgun sequencing of DNA from a natural acidophilic biofilm, we report reconstruction of near-complete genomes of Leptospirillum group II and Ferroplasma type II, and partial recovery of three other genomes. This was possible because the biofilm was dominated by a small number of species populations and the frequency of genomic rearrangements and gene insertions or deletions was relatively low. Because each sequence read came from a different individual, we could determine that single-nucleotide polymorphisms are the predominant form of heterogeneity at the strain level. The Leptospirillum group II genome had remarkably few nucleotide polymorphisms, despite the existence of low-abundance variants. The Ferroplasma type II genome seems to be a composite from three ancestral strains that have undergone homologous recombination to form a large population of mosaic genomes. Analysis of the gene complement for each organism revealed the pathways for carbon and nitrogen fixation and energy generation, and provided insights into survival strategies in an extreme environment.
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            NCBI Reference Sequences (RefSeq): current status, new features and genome annotation policy

            Kim D. Pruitt, Tatiana Tatusova, Garth R. Brown … (2011)
            The National Center for Biotechnology Information (NCBI) Reference Sequence (RefSeq) database is a collection of genomic, transcript and protein sequence records. These records are selected and curated from public sequence archives and represent a significant reduction in redundancy compared to the volume of data archived by the International Nucleotide Sequence Database Collaboration. The database includes over 16 000 organisms, 2.4 × 106 genomic records, 13 × 106 proteins and 2 × 106 RNA records spanning prokaryotes, eukaryotes and viruses (RefSeq release 49, September 2011). The RefSeq database is maintained by a combined approach of automated analyses, collaboration and manual curation to generate an up-to-date representation of the sequence, its features, names and cross-links to related sources of information. We report here on recent growth, the status of curating the human RefSeq data set, more extensive feature annotation and current policy for eukaryotic genome annotation via the NCBI annotation pipeline. More information about the resource is available online (see http://www.ncbi.nlm.nih.gov/RefSeq/).
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              Phymm and PhymmBL: Metagenomic Phylogenetic Classification with Interpolated Markov Models

              Arthur Brady, Steven Salzberg (2009)
              Metagenomics projects collect DNA from uncharacterized environments that may contain thousands of species per sample. One main challenge facing metagenomic analysis is phylogenetic classification of raw sequence reads into groups representing the same or similar taxa, a prerequisite for genome assembly and for analyzing the biological diversity of a sample. New sequencing technologies have made metagenomics easier, by making sequencing faster, and more difficult, by producing shorter reads than previous technologies. Classifying sequences from reads as short as 100 base pairs has until now been relatively inaccurate, requiring researchers to use older, long-read technologies. We present Phymm, a classifier for metagenomic data, that has been trained on 539 complete, curated genomes and can accurately classify reads as short as 100 bp, representing a substantial leap forward over previous composition-based classification methods. We also describe how combining Phymm with sequence alignment algorithms, further improves accuracy.
              Article 0 comments Cited 208 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Derrick E Wood
                Steven L Salzberg
                Journal
                Journal ID (nlm-ta): Genome Biol
                Journal ID (iso-abbrev): Genome Biol
                Title: Genome Biology
                Publisher: BioMed Central
                ISSN (Print): 1465-6906
                ISSN (Electronic): 1465-6914
                Publication date (Print): 2014
                Publication date (Electronic): 3 March 2014
                Volume: 15
                Issue: 3
                Page: R46
                Affiliations
                [1 ]Department of Computer Science and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA
                [2 ]Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
                [3 ]Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
                Article
                Publisher ID: gb-2014-15-3-r46
                DOI: 10.1186/gb-2014-15-3-r46
                PMC ID: 4053813
                PubMed ID: 24580807
                SO-VID: a9e259ec-a344-48cb-91f3-e417e9f23886
                Copyright © Copyright © 2014 Wood and Salzberg; licensee BioMed Central Ltd.
                License:

                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.

                History
                Date received : 17 November 2013
                Date accepted : 3 March 2014
                Categories
                Subject: Method

                ScienceOpen disciplines: Genetics
                Keywords: metagenomics,sequence classification,sequence alignment,next-generation sequencing,microbiome
                Data availability:
                ScienceOpen disciplines: Genetics
                Keywords: metagenomics, sequence classification, sequence alignment, next-generation sequencing, microbiome

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