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                <text>Coronavirus</text>
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            <description>An account of the resource</description>
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                <text>Dominio científico: Coronavirus</text>
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    <description>A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text.</description>
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      <name>Dublin Core</name>
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          <name>Title</name>
          <description>A name given to the resource</description>
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              <text>MegaGTA: a sensitive and accurate metagenomic gene-targeted assembler using iterative de Bruijn graphs</text>
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          <name>Creator</name>
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              <text>Dinghua Li, Yukun Huang, Chi-Ming Leung, Ruibang Luo, Hing-Fung Ting, Tak-Wah Lam</text>
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          <description>An account of the resource</description>
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              <text>Abstract Background The recent release of the gene-targeted metagenomics assembler Xander has demonstrated that using the trained Hidden Markov Model (HMM) to guide the traversal of de Bruijn graph gives obvious advantage over other assembly methods. Xander, as a pilot study, indeed has a lot of room for improvement. Apart from its slow speed, Xander uses only 1 k-mer size for graph construction and whatever choice of k will compromise either sensitivity or accuracy. Xander uses a Bloom-filter representation of de Bruijn graph to achieve a lower memory footprint. Bloom filters bring in false positives, and it is not clear how this would impact the quality of assembly. Xander does not keep track of the multiplicity of k-mers, which would have been an effective way to differentiate between erroneous k-mers and correct k-mers. Results In this paper, we present a new gene-targeted assembler MegaGTA, which attempts to improve Xander in different aspects. Quality-wise, it utilizes iterative de Bruijn graphs to take full advantage of multiple k-mer sizes to make the best of both sensitivity and accuracy. Computation-wise, it employs succinct de Bruijn graphs (SdBG) to achieve low memory footprint and high speed (the latter is benefited from a highly efficient parallel algorithm for constructing SdBG). Unlike Bloom filters, an SdBG is an exact representation of a de Bruijn graph. It enables MegaGTA to avoid false-positive contigs and to easily incorporate the multiplicity of k-mers for building better HMM model. We have compared MegaGTA and Xander on an HMP-defined mock metagenomic dataset, and showed that MegaGTA excelled in both sensitivity and accuracy. On a large rhizosphere soil metagenomic sample (327Gbp), MegaGTA produced 9.7–19.3% more contigs than Xander, and these contigs were assigned to 10–25% more gene references. In our experiments, MegaGTA, depending on the number of k-mers used, is two to ten times faster than Xander. Conclusion MegaGTA improves on the algorithm of Xander and achieves higher sensitivity, accuracy and speed. Moreover, it is capable of assembling gene sequences from ultra-large metagenomic datasets. Its source code is freely available at https://github.com/HKU-BAL/megagta .</text>
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              <text>2017</text>
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          <name>Subject</name>
          <description>The topic of the resource</description>
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              <text>metagenomics, assembly, de Bruijn Graph, Targeted gene</text>
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          <name>Identifier</name>
          <description>An unambiguous reference to the resource within a given context</description>
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            <elementText elementTextId="22240">
              <text>DOI: 10.1186/s12859-017-1825-3</text>
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          <name>Source</name>
          <description>A related resource from which the described resource is derived</description>
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            <elementText elementTextId="22241">
              <text>BMC Bioinformatics</text>
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          <name>Publisher</name>
          <description>An entity responsible for making the resource available</description>
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              <text>BMC</text>
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          <name>Coverage</name>
          <description>The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant</description>
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              <text>Biology (General), Computer applications to medicine. Medical informatics</text>
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          <name>Language</name>
          <description>A language of the resource</description>
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            <elementText elementTextId="22244">
              <text>EN</text>
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