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      Fast and sensitive taxonomic classification for metagenomics with Kaiju

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      Nature Communications

      Nature Publishing Group

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          Abstract

          Metagenomics emerged as an important field of research not only in microbial ecology but also for human health and disease, and metagenomic studies are performed on increasingly larger scales. While recent taxonomic classification programs achieve high speed by comparing genomic k-mers, they often lack sensitivity for overcoming evolutionary divergence, so that large fractions of the metagenomic reads remain unclassified. Here we present the novel metagenome classifier Kaiju, which finds maximum (in-)exact matches on the protein-level using the Burrows–Wheeler transform. We show in a genome exclusion benchmark that Kaiju classifies reads with higher sensitivity and similar precision compared with current k-mer-based classifiers, especially in genera that are underrepresented in reference databases. We also demonstrate that Kaiju classifies up to 10 times more reads in real metagenomes. Kaiju can process millions of reads per minute and can run on a standard PC. Source code and web server are available at http://kaiju.binf.ku.dk.

          Abstract

          Here, Anders Krogh and colleagues describe Kaiju, a metagenome taxonomic classification program that uses maximum (in-)exact matches on the protein-level to account for evolutionary divergence. The authors show that Kaiju performs faster and is more sensitive compared with existing algorithms and can be used on a standard computer.

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          Most cited references 11

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          ART: a next-generation sequencing read simulator.

          ART is a set of simulation tools that generate synthetic next-generation sequencing reads. This functionality is essential for testing and benchmarking tools for next-generation sequencing data analysis including read alignment, de novo assembly and genetic variation discovery. ART generates simulated sequencing reads by emulating the sequencing process with built-in, technology-specific read error models and base quality value profiles parameterized empirically in large sequencing datasets. We currently support all three major commercial next-generation sequencing platforms: Roche's 454, Illumina's Solexa and Applied Biosystems' SOLiD. ART also allows the flexibility to use customized read error model parameters and quality profiles. Both source and binary software packages are available at http://www.niehs.nih.gov/research/resources/software/art.
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            Metagenomics: genomic analysis of microbial communities.

            Uncultured microorganisms comprise the majority of the planet's biological diversity. Microorganisms represent two of the three domains of life and contain vast diversity that is the product of an estimated 3.8 billion years of evolution. In many environments, as many as 99% of the microorganisms cannot be cultured by standard techniques, and the uncultured fraction includes diverse organisms that are only distantly related to the cultured ones. Therefore, culture-independent methods are essential to understand the genetic diversity, population structure, and ecological roles of the majority of microorganisms. Metagenomics, or the culture-independent genomic analysis of an assemblage of microorganisms, has potential to answer fundamental questions in microbial ecology. This review describes progress toward understanding the biology of uncultured Bacteria, Archaea, and viruses through metagenomic analyses.
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              Comparative genomic structure of prokaryotes.

              Recent advances in DNA-sequencing technologies have made available an enormous resource of data for the study of bacterial genomes. The broad sample of complete genomes currently available allows us to look at variation in the gross features and characteristics of genomes while the detail of the sequences reveal some of the mechanisms by which these genomes evolve. This review aims to describe bacterial genome structures according to current knowledge and proposed hypotheses. We also describe examples where mechanisms of genome evolution have acted in the adaptation of bacterial species to particular niches.
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                Author and article information

                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group
                2041-1723
                13 April 2016
                2016
                : 7
                Affiliations
                [1 ]Department of Biology, University of Copenhagen , Copenhagen 2200, Denmark
                Author notes
                Article
                ncomms11257
                10.1038/ncomms11257
                4833860
                27071849
                Copyright © 2016, Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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