Accelerating metagenomic read classification on CUDA-enabled GPUs
Identifieur interne : 000935 ( Pmc/Checkpoint ); précédent : 000934; suivant : 000936Accelerating metagenomic read classification on CUDA-enabled GPUs
Auteurs : Robin Kobus [Allemagne] ; Christian Hundt [Allemagne] ; André Müller [Allemagne] ; Bertil Schmidt [Allemagne]Source :
- BMC Bioinformatics [ 1471-2105 ] ; 2017.
Abstract
Metagenomic sequencing studies are becoming increasingly popular with prominent examples including the sequencing of human microbiomes and diverse environments. A fundamental computational problem in this context is read classification; i.e. the assignment of each read to a taxonomic label. Due to the large number of reads produced by modern high-throughput sequencing technologies and the rapidly increasing number of available reference genomes software tools for fast and accurate metagenomic read classification are urgently needed.
We present cuCLARK, a read-level classifier for CUDA-enabled GPUs, based on the fast and accurate classification of metagenomic sequences using reduced
cuCLARK can perform metagenomic read classification at superior speeds on CUDA-enabled GPUs. It is free software licensed under GPL and can be downloaded at
Url:
DOI: 10.1186/s12859-016-1434-6
PubMed: 28049411
PubMed Central: 5209836
Affiliations:
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>Metagenomic sequencing studies are becoming increasingly popular with prominent examples including the sequencing of human microbiomes and diverse environments. A fundamental computational problem in this context is read classification; i.e. the assignment of each read to a taxonomic label. Due to the large number of reads produced by modern high-throughput sequencing technologies and the rapidly increasing number of available reference genomes software tools for fast and accurate metagenomic read classification are urgently needed.</p>
</sec>
<sec><title>Results</title>
<p>We present cuCLARK, a read-level classifier for CUDA-enabled GPUs, based on the fast and accurate classification of metagenomic sequences using reduced <italic>k</italic>
-mers (CLARK) method. Using the processing power of a single Titan X GPU, cuCLARK can reach classification speeds of up to 50 million reads per minute. Corresponding speedups for species- (genus-)level classification range between 3.2 and 6.6 (3.7 and 6.4) compared to multi-threaded CLARK executed on a 16-core Xeon CPU workstation.</p>
</sec>
<sec><title>Conclusion</title>
<p>cuCLARK can perform metagenomic read classification at superior speeds on CUDA-enabled GPUs. It is free software licensed under GPL and can be downloaded at <ext-link ext-link-type="uri" xlink:href="https://github.com/funatiq/cuclark">https://github.com/funatiq/cuclark</ext-link>
free of charge.</p>
</sec>
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<contrib contrib-type="author"><name><surname>Hundt</surname>
<given-names>Christian</given-names>
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<address><email>hundt@uni-mainz.de</email>
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<contrib contrib-type="author"><name><surname>Müller</surname>
<given-names>André</given-names>
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<address><email>muellan@uni-mainz.de</email>
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<xref ref-type="aff" rid="Aff1"></xref>
</contrib>
<contrib contrib-type="author"><name><surname>Schmidt</surname>
<given-names>Bertil</given-names>
</name>
<address><email>bertil.schmidt@uni-mainz.de</email>
</address>
<xref ref-type="aff" rid="Aff1"></xref>
</contrib>
<aff id="Aff1">Institute of Computer Science, Johannes Gutenberg University Mainz, Staudingerweg 9, Mainz, 55435 Germany</aff>
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<volume>18</volume>
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<history><date date-type="received"><day>9</day>
<month>9</month>
<year>2016</year>
</date>
<date date-type="accepted"><day>16</day>
<month>12</month>
<year>2016</year>
</date>
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<permissions><copyright-statement>© The Author(s) 2017</copyright-statement>
<license license-type="OpenAccess"><license-p><bold>Open Access</bold>
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>
), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/publicdomain/zero/1.0/">http://creativecommons.org/publicdomain/zero/1.0/</ext-link>
) applies to the data made available in this article, unless otherwise stated.</license-p>
</license>
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<abstract id="Abs1"><sec><title>Background</title>
<p>Metagenomic sequencing studies are becoming increasingly popular with prominent examples including the sequencing of human microbiomes and diverse environments. A fundamental computational problem in this context is read classification; i.e. the assignment of each read to a taxonomic label. Due to the large number of reads produced by modern high-throughput sequencing technologies and the rapidly increasing number of available reference genomes software tools for fast and accurate metagenomic read classification are urgently needed.</p>
</sec>
<sec><title>Results</title>
<p>We present cuCLARK, a read-level classifier for CUDA-enabled GPUs, based on the fast and accurate classification of metagenomic sequences using reduced <italic>k</italic>
-mers (CLARK) method. Using the processing power of a single Titan X GPU, cuCLARK can reach classification speeds of up to 50 million reads per minute. Corresponding speedups for species- (genus-)level classification range between 3.2 and 6.6 (3.7 and 6.4) compared to multi-threaded CLARK executed on a 16-core Xeon CPU workstation.</p>
</sec>
<sec><title>Conclusion</title>
<p>cuCLARK can perform metagenomic read classification at superior speeds on CUDA-enabled GPUs. It is free software licensed under GPL and can be downloaded at <ext-link ext-link-type="uri" xlink:href="https://github.com/funatiq/cuclark">https://github.com/funatiq/cuclark</ext-link>
free of charge.</p>
</sec>
</abstract>
<kwd-group xml:lang="en"><title>Keywords</title>
<kwd>Metagenomics</kwd>
<kwd>Taxonomic assignment</kwd>
<kwd>Exact <italic>k</italic>
-mer matching</kwd>
<kwd>CUDA</kwd>
<kwd>GPUs</kwd>
</kwd-group>
<custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name>
<meta-value>© The Author(s) 2017</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
</pmc>
<affiliations><list><country><li>Allemagne</li>
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<region><li>Rhénanie-Palatinat</li>
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<settlement><li>Mayence</li>
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<name sortKey="Hundt, Christian" sort="Hundt, Christian" uniqKey="Hundt C" first="Christian" last="Hundt">Christian Hundt</name>
<name sortKey="Muller, Andre" sort="Muller, Andre" uniqKey="Muller A" first="André" last="Müller">André Müller</name>
<name sortKey="Schmidt, Bertil" sort="Schmidt, Bertil" uniqKey="Schmidt B" first="Bertil" last="Schmidt">Bertil Schmidt</name>
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