Turtle: identifying frequent k-mers with cache-efficient algorithms.
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Auteurs : Rajat Shuvro Roy [États-Unis] ; Debashish Bhattacharya [États-Unis] ; Alexander Schliep [États-Unis]Source :
- Bioinformatics (Oxford, England) [ 1367-4811 ] ; 2014.
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Abstract
Counting the frequencies of k-mers in read libraries is often a first step in the analysis of high-throughput sequencing data. Infrequent k-mers are assumed to be a result of sequencing errors. The frequent k-mers constitute a reduced but error-free representation of the experiment, which can inform read error correction or serve as the input to de novo assembly methods. Ideally, the memory requirement for counting should be linear in the number of frequent k-mers and not in the, typically much larger, total number of k-mers in the read library.
DOI: 10.1093/bioinformatics/btu132
PubMed: 24618471
Affiliations:
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Shuvro" uniqKey="Roy R" first="Rajat Shuvro" last="Roy">Rajat Shuvro Roy</name><affiliation wicri:level="4"><nlm:affiliation>Department of Computer Science, Department of Ecology, Evolution and Natural Resources, Institute of Marine and Coastal Sciences and BioMaPS Institute for Quantitative Biology, Rutgers University, New Brunswick, NJ 08901, USADepartment of Computer Science, Department of Ecology, Evolution and Natural Resources, Institute of Marine and Coastal Sciences and BioMaPS Institute for Quantitative Biology, Rutgers University, New Brunswick, NJ 08901, USADepartment of Computer Science, Department of Ecology, Evolution and Natural Resources, Institute of Marine and Coastal Sciences and BioMaPS Institute for Quantitative Biology, Rutgers University, New Brunswick, NJ 08901, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Computer Science, Department of Ecology, Evolution and Natural Resources, Institute of Marine and Coastal Sciences and BioMaPS Institute for Quantitative Biology, Rutgers University, New Brunswick, NJ 08901, USADepartment of Computer Science, Department of Ecology, Evolution and Natural Resources, Institute of Marine and Coastal Sciences and BioMaPS Institute for Quantitative Biology, Rutgers University, New Brunswick, NJ 08901, USADepartment of Computer Science, Department of Ecology, Evolution and Natural Resources, Institute of Marine and Coastal Sciences and BioMaPS Institute for Quantitative Biology, Rutgers University, New Brunswick, NJ 08901</wicri:regionArea><placeName><region type="state">New Jersey</region><settlement type="city">New Brunswick (New Jersey)</settlement></placeName><orgName type="university">Université Rutgers</orgName></affiliation></author><author><name sortKey="Bhattacharya, Debashish" sort="Bhattacharya, Debashish" uniqKey="Bhattacharya D" first="Debashish" last="Bhattacharya">Debashish Bhattacharya</name><affiliation wicri:level="4"><nlm:affiliation>Department of Computer Science, Department of Ecology, Evolution and Natural Resources, Institute of Marine and Coastal Sciences and BioMaPS Institute for Quantitative Biology, Rutgers University, New Brunswick, NJ 08901, USADepartment of Computer Science, Department of Ecology, Evolution and Natural Resources, Institute of Marine and Coastal Sciences and BioMaPS Institute for Quantitative Biology, Rutgers University, New Brunswick, NJ 08901, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Computer Science, Department of Ecology, Evolution and Natural Resources, Institute of Marine and Coastal Sciences and BioMaPS Institute for Quantitative Biology, Rutgers University, New Brunswick, NJ 08901, USADepartment of Computer Science, Department of Ecology, Evolution and Natural Resources, Institute of Marine and Coastal Sciences and BioMaPS Institute for Quantitative Biology, Rutgers University, New Brunswick, NJ 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Ecology, Evolution and Natural Resources, Institute of Marine and Coastal Sciences and BioMaPS Institute for Quantitative Biology, Rutgers University, New Brunswick, NJ 08901, USADepartment of Computer Science, Department of Ecology, Evolution and Natural Resources, Institute of Marine and Coastal Sciences and BioMaPS Institute for Quantitative Biology, Rutgers University, New Brunswick, NJ 08901</wicri:regionArea><placeName><region type="state">New Jersey</region><settlement type="city">New Brunswick (New Jersey)</settlement></placeName><orgName type="university">Université Rutgers</orgName></affiliation></author></analytic><series><title level="j">Bioinformatics (Oxford, England)</title><idno type="eISSN">1367-4811</idno><imprint><date when="2014" type="published">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Genome, Human</term><term>High-Throughput Nucleotide Sequencing (methods)</term><term>Humans</term><term>Sequence Analysis, DNA (methods)</term><term>Software</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term><term>Analyse de séquence d'ADN ()</term><term>Génome humain</term><term>Humains</term><term>Logiciel</term><term>Séquençage nucléotidique à haut débit ()</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>High-Throughput Nucleotide Sequencing</term><term>Sequence Analysis, DNA</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Genome, Human</term><term>Humans</term><term>Software</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Analyse de séquence d'ADN</term><term>Génome humain</term><term>Humains</term><term>Logiciel</term><term>Séquençage nucléotidique à haut débit</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Counting the frequencies of k-mers in read libraries is often a first step in the analysis of high-throughput sequencing data. Infrequent k-mers are assumed to be a result of sequencing errors. The frequent k-mers constitute a reduced but error-free representation of the experiment, which can inform read error correction or serve as the input to de novo assembly methods. Ideally, the memory requirement for counting should be linear in the number of frequent k-mers and not in the, typically much larger, total number of k-mers in the read library.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>New Jersey</li></region><settlement><li>New Brunswick (New Jersey)</li></settlement><orgName><li>Université Rutgers</li></orgName></list><tree><country name="États-Unis"><region name="New Jersey"><name sortKey="Roy, Rajat Shuvro" sort="Roy, Rajat Shuvro" uniqKey="Roy R" first="Rajat Shuvro" last="Roy">Rajat Shuvro Roy</name></region><name sortKey="Bhattacharya, Debashish" sort="Bhattacharya, Debashish" uniqKey="Bhattacharya D" first="Debashish" last="Bhattacharya">Debashish Bhattacharya</name><name sortKey="Schliep, Alexander" sort="Schliep, Alexander" uniqKey="Schliep A" first="Alexander" last="Schliep">Alexander Schliep</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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