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Accelerating metagenomic read classification on CUDA-enabled GPUs

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Accelerating 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.

RBID : PMC:5209836

Abstract

Background

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.

Results

We present cuCLARK, a read-level classifier for CUDA-enabled GPUs, based on the fast and accurate classification of metagenomic sequences using reduced k-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.

Conclusion

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 https://github.com/funatiq/cuclark free of charge.

Url:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5209836

DOI: 10.1186/s12859-016-1434-6
PubMed: 28049411
PubMed Central: 5209836

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<author><name sortKey="Kobus, Robin" sort="Kobus, Robin" uniqKey="Kobus R" first="Robin" last="Kobus">Robin Kobus</name>
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<author><name sortKey="Hundt, Christian" sort="Hundt, Christian" uniqKey="Hundt C" first="Christian" last="Hundt">Christian Hundt</name>
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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>
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  <front><journal-meta><journal-id journal-id-type="nlm-ta">BMC Bioinformatics</journal-id>
<journal-id journal-id-type="iso-abbrev">BMC Bioinformatics</journal-id>
<journal-title-group><journal-title>BMC Bioinformatics</journal-title>
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<article-categories><subj-group subj-group-type="heading"><subject>Software</subject>
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<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>
</name>
<address><email>muellan@uni-mainz.de</email>
</address>
<xref ref-type="aff" rid="Aff1"></xref>
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<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>
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<aff id="Aff1">Institute of Computer Science, Johannes Gutenberg University Mainz, Staudingerweg 9, Mainz, 55435 Germany</aff>
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<pub-date pub-type="epub"><day>3</day>
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<month>9</month>
<year>2016</year>
</date>
<date date-type="accepted"><day>16</day>
<month>12</month>
<year>2016</year>
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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>
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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>
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Accelerating metagenomic read classification on CUDA-enabled GPUs

Accelerating metagenomic read classification on CUDA-enabled GPUs

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Abstract

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