Disk-based k-mer counting on a PC
Identifieur interne : 000A48 ( Ncbi/Merge ); précédent : 000A47; suivant : 000A49Disk-based k-mer counting on a PC
Auteurs : Sebastian Deorowicz [Pologne] ; Agnieszka Debudaj-Grabysz [Pologne] ; Szymon Grabowski [Pologne]Source :
- BMC Bioinformatics [ 1471-2105 ] ; 2013.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- genetics : Caenorhabditis elegans.
- methods : Genomics.
- Algorithms, Animals, Genome, Human, Humans, Microcomputers, Sequence Alignment, Sequence Analysis, DNA, Software.
Abstract
The
We propose a simple, yet efficient, parallel disk-based algorithm for counting
By making use of cheap disk space and exploiting CPU and I/O parallelism we propose a very competitive
Url:
DOI: 10.1186/1471-2105-14-160
PubMed: 23679007
PubMed Central: 3680041
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PMC:3680041Le document en format XML
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-mer counting on a PC</title>
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<term>Animals</term>
<term>Caenorhabditis elegans (genetics)</term>
<term>Genome, Human</term>
<term>Genomics (methods)</term>
<term>Humans</term>
<term>Microcomputers</term>
<term>Sequence Alignment</term>
<term>Sequence Analysis, DNA</term>
<term>Software</term>
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<term>Alignement de séquences</term>
<term>Analyse de séquence d'ADN</term>
<term>Animaux</term>
<term>Caenorhabditis elegans (génétique)</term>
<term>Génome humain</term>
<term>Génomique ()</term>
<term>Humains</term>
<term>Logiciel</term>
<term>Micro-ordinateurs</term>
</keywords>
<keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Caenorhabditis elegans</term>
</keywords>
<keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Caenorhabditis elegans</term>
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<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Genomics</term>
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<keywords scheme="MESH" xml:lang="en"><term>Algorithms</term>
<term>Animals</term>
<term>Genome, Human</term>
<term>Humans</term>
<term>Microcomputers</term>
<term>Sequence Alignment</term>
<term>Sequence Analysis, DNA</term>
<term>Software</term>
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<keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term>
<term>Alignement de séquences</term>
<term>Analyse de séquence d'ADN</term>
<term>Animaux</term>
<term>Génome humain</term>
<term>Génomique</term>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>The <italic>k</italic>
-mer counting problem, which is to build the histogram of occurrences of every <italic>k</italic>
-symbol long substring in a given text, is important for many bioinformatics applications. They include developing de Bruijn graph genome assemblers, fast multiple sequence alignment and repeat detection.</p>
</sec>
<sec><title>Results</title>
<p>We propose a simple, yet efficient, parallel disk-based algorithm for counting <italic>k</italic>
-mers. Experiments show that it usually offers the fastest solution to the considered problem, while demanding a relatively small amount of memory. In particular, it is capable of counting the statistics for short-read human genome data, in input gzipped FASTQ file, in less than 40 minutes on a PC with 16 GB of RAM and 6 CPU cores, and for long-read human genome data in less than 70 minutes. On a more powerful machine, using 32 GB of RAM and 32 CPU cores, the tasks are accomplished in less than half the time. No other algorithm for most tested settings of this problem and mammalian-size data can accomplish this task in comparable time. Our solution also belongs to memory-frugal ones; most competitive algorithms cannot efficiently work on a PC with 16 GB of memory for such massive data.</p>
</sec>
<sec><title>Conclusions</title>
<p>By making use of cheap disk space and exploiting CPU and I/O parallelism we propose a very competitive <italic>k</italic>
-mer counting procedure, called KMC. Our results suggest that judicious resource management may allow to solve at least some bioinformatics problems with massive data on a commodity personal computer.</p>
</sec>
</div>
</front>
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<double pmid="23679007"><pmc><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Disk-based <italic>k</italic>
-mer counting on a PC</title>
<author><name sortKey="Deorowicz, Sebastian" sort="Deorowicz, Sebastian" uniqKey="Deorowicz S" first="Sebastian" last="Deorowicz">Sebastian Deorowicz</name>
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Disk-based <italic>k</italic>
-mer counting on a PC</title>
<author><name sortKey="Deorowicz, Sebastian" sort="Deorowicz, Sebastian" uniqKey="Deorowicz S" first="Sebastian" last="Deorowicz">Sebastian Deorowicz</name>
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<author><name sortKey="Debudaj Grabysz, Agnieszka" sort="Debudaj Grabysz, Agnieszka" uniqKey="Debudaj Grabysz A" first="Agnieszka" last="Debudaj-Grabysz">Agnieszka Debudaj-Grabysz</name>
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<wicri:regionArea>, Institute of Informatics, Silesian University of Technology, Akademicka 16, Gliwice, 44-100</wicri:regionArea>
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<author><name sortKey="Grabowski, Szymon" sort="Grabowski, Szymon" uniqKey="Grabowski S" first="Szymon" last="Grabowski">Szymon Grabowski</name>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>The <italic>k</italic>
-mer counting problem, which is to build the histogram of occurrences of every <italic>k</italic>
-symbol long substring in a given text, is important for many bioinformatics applications. They include developing de Bruijn graph genome assemblers, fast multiple sequence alignment and repeat detection.</p>
</sec>
<sec><title>Results</title>
<p>We propose a simple, yet efficient, parallel disk-based algorithm for counting <italic>k</italic>
-mers. Experiments show that it usually offers the fastest solution to the considered problem, while demanding a relatively small amount of memory. In particular, it is capable of counting the statistics for short-read human genome data, in input gzipped FASTQ file, in less than 40 minutes on a PC with 16 GB of RAM and 6 CPU cores, and for long-read human genome data in less than 70 minutes. On a more powerful machine, using 32 GB of RAM and 32 CPU cores, the tasks are accomplished in less than half the time. No other algorithm for most tested settings of this problem and mammalian-size data can accomplish this task in comparable time. Our solution also belongs to memory-frugal ones; most competitive algorithms cannot efficiently work on a PC with 16 GB of memory for such massive data.</p>
</sec>
<sec><title>Conclusions</title>
<p>By making use of cheap disk space and exploiting CPU and I/O parallelism we propose a very competitive <italic>k</italic>
-mer counting procedure, called KMC. Our results suggest that judicious resource management may allow to solve at least some bioinformatics problems with massive data on a commodity personal computer.</p>
</sec>
</div>
</front>
<back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Compeau, Pe" uniqKey="Compeau P">PE Compeau</name>
</author>
<author><name sortKey="Pevzner, Pa" uniqKey="Pevzner P">PA Pevzner</name>
</author>
<author><name sortKey="Tesler, G" uniqKey="Tesler G">G Tesler</name>
</author>
</analytic>
</biblStruct>
<biblStruct><analytic><author><name sortKey="Edgar, Rc" uniqKey="Edgar R">RC Edgar</name>
</author>
</analytic>
</biblStruct>
<biblStruct><analytic><author><name sortKey="Kurtz, S" uniqKey="Kurtz S">S Kurtz</name>
</author>
<author><name sortKey="Narechania, A" uniqKey="Narechania A">A Narechania</name>
</author>
<author><name sortKey="Stein, J" uniqKey="Stein J">J Stein</name>
</author>
<author><name sortKey="Ware, D" uniqKey="Ware D">D Ware</name>
</author>
</analytic>
</biblStruct>
<biblStruct><analytic><author><name sortKey="Kelley, Dr" uniqKey="Kelley D">DR Kelley</name>
</author>
<author><name sortKey="Schatz, Mc" uniqKey="Schatz M">MC Schatz</name>
</author>
<author><name sortKey="Salzberg, Sl" uniqKey="Salzberg S">SL Salzberg</name>
</author>
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</biblStruct>
<biblStruct><analytic><author><name sortKey="Miller, Jr" uniqKey="Miller J">JR Miller</name>
</author>
<author><name sortKey="Delcher, Al" uniqKey="Delcher A">AL Delcher</name>
</author>
<author><name sortKey="Koren, S" uniqKey="Koren S">S Koren</name>
</author>
<author><name sortKey="Venter, E" uniqKey="Venter E">E Venter</name>
</author>
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<pubmed><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Disk-based k-mer counting on a PC.</title>
<author><name sortKey="Deorowicz, Sebastian" sort="Deorowicz, Sebastian" uniqKey="Deorowicz S" first="Sebastian" last="Deorowicz">Sebastian Deorowicz</name>
<affiliation wicri:level="1"><nlm:affiliation>Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland. sebastian.deorowicz@polsl.pl</nlm:affiliation>
<country xml:lang="fr">Pologne</country>
<wicri:regionArea>Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice</wicri:regionArea>
<wicri:noRegion>44-100 Gliwice</wicri:noRegion>
</affiliation>
</author>
<author><name sortKey="Debudaj Grabysz, Agnieszka" sort="Debudaj Grabysz, Agnieszka" uniqKey="Debudaj Grabysz A" first="Agnieszka" last="Debudaj-Grabysz">Agnieszka Debudaj-Grabysz</name>
</author>
<author><name sortKey="Grabowski, Szymon" sort="Grabowski, Szymon" uniqKey="Grabowski S" first="Szymon" last="Grabowski">Szymon Grabowski</name>
</author>
</titleStmt>
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<date when="2013">2013</date>
<idno type="RBID">pubmed:23679007</idno>
<idno type="pmid">23679007</idno>
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<sourceDesc><biblStruct><analytic><title xml:lang="en">Disk-based k-mer counting on a PC.</title>
<author><name sortKey="Deorowicz, Sebastian" sort="Deorowicz, Sebastian" uniqKey="Deorowicz S" first="Sebastian" last="Deorowicz">Sebastian Deorowicz</name>
<affiliation wicri:level="1"><nlm:affiliation>Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland. sebastian.deorowicz@polsl.pl</nlm:affiliation>
<country xml:lang="fr">Pologne</country>
<wicri:regionArea>Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice</wicri:regionArea>
<wicri:noRegion>44-100 Gliwice</wicri:noRegion>
</affiliation>
</author>
<author><name sortKey="Debudaj Grabysz, Agnieszka" sort="Debudaj Grabysz, Agnieszka" uniqKey="Debudaj Grabysz A" first="Agnieszka" last="Debudaj-Grabysz">Agnieszka Debudaj-Grabysz</name>
</author>
<author><name sortKey="Grabowski, Szymon" sort="Grabowski, Szymon" uniqKey="Grabowski S" first="Szymon" last="Grabowski">Szymon Grabowski</name>
</author>
</analytic>
<series><title level="j">BMC bioinformatics</title>
<idno type="eISSN">1471-2105</idno>
<imprint><date when="2013" type="published">2013</date>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term>
<term>Animals</term>
<term>Caenorhabditis elegans (genetics)</term>
<term>Genome, Human</term>
<term>Genomics (methods)</term>
<term>Humans</term>
<term>Microcomputers</term>
<term>Sequence Alignment</term>
<term>Sequence Analysis, DNA</term>
<term>Software</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term>
<term>Alignement de séquences</term>
<term>Analyse de séquence d'ADN</term>
<term>Animaux</term>
<term>Caenorhabditis elegans (génétique)</term>
<term>Génome humain</term>
<term>Génomique ()</term>
<term>Humains</term>
<term>Logiciel</term>
<term>Micro-ordinateurs</term>
</keywords>
<keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Caenorhabditis elegans</term>
</keywords>
<keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Caenorhabditis elegans</term>
</keywords>
<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Genomics</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Algorithms</term>
<term>Animals</term>
<term>Genome, Human</term>
<term>Humans</term>
<term>Microcomputers</term>
<term>Sequence Alignment</term>
<term>Sequence Analysis, DNA</term>
<term>Software</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term>
<term>Alignement de séquences</term>
<term>Analyse de séquence d'ADN</term>
<term>Animaux</term>
<term>Génome humain</term>
<term>Génomique</term>
<term>Humains</term>
<term>Logiciel</term>
<term>Micro-ordinateurs</term>
</keywords>
</textClass>
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</teiHeader>
<front><div type="abstract" xml:lang="en">The k-mer counting problem, which is to build the histogram of occurrences of every k-symbol long substring in a given text, is important for many bioinformatics applications. They include developing de Bruijn graph genome assemblers, fast multiple sequence alignment and repeat detection.</div>
</front>
</TEI>
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