Improving Bloom Filter Performance on Sequence Data Using k-mer Bloom Filters
Identifieur interne : 001837 ( Ncbi/Merge ); précédent : 001836; suivant : 001838Improving Bloom Filter Performance on Sequence Data Using k-mer Bloom Filters
Auteurs : David Pellow ; Darya Filippova ; Carl KingsfordSource :
- Journal of Computational Biology [ 1066-5277 ] ; 2017.
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DOI: 10.1089/cmb.2016.0155
PubMed: 27828710
PubMed Central: 5467106
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Improving Bloom Filter Performance on Sequence Data Using <italic>k</italic>-mer Bloom Filters</title><author><name sortKey="Pellow, David" sort="Pellow, David" uniqKey="Pellow D" first="David" last="Pellow">David Pellow</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author><author><name sortKey="Filippova, Darya" sort="Filippova, Darya" uniqKey="Filippova D" first="Darya" last="Filippova">Darya Filippova</name><affiliation><nlm:aff id="aff2"></nlm:aff></affiliation></author><author><name sortKey="Kingsford, Carl" sort="Kingsford, Carl" uniqKey="Kingsford C" first="Carl" last="Kingsford">Carl Kingsford</name><affiliation><nlm:aff id="aff3"></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">27828710</idno><idno type="pmc">5467106</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5467106</idno><idno type="RBID">PMC:5467106</idno><idno type="doi">10.1089/cmb.2016.0155</idno><date when="2017">2017</date><idno type="wicri:Area/Pmc/Corpus">000D98</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000D98</idno><idno type="wicri:Area/Pmc/Curation">000D98</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000D98</idno><idno type="wicri:Area/Pmc/Checkpoint">000823</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">000823</idno><idno type="wicri:source">PubMed</idno><idno type="RBID">pubmed:27828710</idno><idno type="wicri:Area/PubMed/Corpus">000E99</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000E99</idno><idno type="wicri:Area/PubMed/Curation">000E99</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000E99</idno><idno type="wicri:Area/PubMed/Checkpoint">000C62</idno><idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">000C62</idno><idno type="wicri:Area/Ncbi/Merge">001837</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Improving Bloom Filter Performance on Sequence Data Using <italic>k</italic>-mer Bloom Filters</title><author><name sortKey="Pellow, David" sort="Pellow, David" uniqKey="Pellow D" first="David" last="Pellow">David Pellow</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author><author><name sortKey="Filippova, Darya" sort="Filippova, Darya" uniqKey="Filippova D" first="Darya" last="Filippova">Darya Filippova</name><affiliation><nlm:aff id="aff2"></nlm:aff></affiliation></author><author><name sortKey="Kingsford, Carl" sort="Kingsford, Carl" uniqKey="Kingsford C" first="Carl" last="Kingsford">Carl Kingsford</name><affiliation><nlm:aff id="aff3"></nlm:aff></affiliation></author></analytic><series><title level="j">Journal of Computational Biology</title><idno type="ISSN">1066-5277</idno><idno type="eISSN">1557-8666</idno><imprint><date when="2017">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Computational Biology (methods)</term><term>Computer Simulation</term><term>Humans</term><term>Probability</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>Biologie informatique ()</term><term>Humains</term><term>Logiciel</term><term>Probabilité</term><term>Simulation numérique</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Computational Biology</term><term>Sequence Analysis, DNA</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Computer Simulation</term><term>Humans</term><term>Probability</term><term>Software</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Analyse de séquence d'ADN</term><term>Biologie informatique</term><term>Humains</term><term>Logiciel</term><term>Probabilité</term><term>Simulation numérique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><title>Abstract</title><p><bold>Using a sequence's <italic>k</italic>-mer content rather than the full sequence directly has enabled significant performance improvements in several sequencing applications, such as metagenomic species identification, estimation of transcript abundances, and alignment-free comparison of sequencing data. As <italic>k</italic>-mer sets often reach hundreds of millions of elements, traditional data structures are often impractical for <italic>k</italic>-mer set storage, and Bloom filters (BFs) and their variants are used instead. BFs reduce the memory footprint required to store millions of <italic>k</italic>-mers while allowing for fast set containment queries, at the cost of a low false positive rate (FPR). We show that, because <italic>k</italic>-mers are derived from sequencing reads, the information about <italic>k</italic>-mer overlap in the original sequence can be used to reduce the FPR up to 30 × with little or no additional memory and with set containment queries that are only 1.3 – 1.6 times slower. Alternatively, we can leverage <italic>k</italic>-mer overlap information to store <italic>k</italic>-mer sets in about half the space while maintaining the original FPR. We consider several variants of such <italic>k</italic>-mer Bloom filters (<italic>k</italic>BFs), derive theoretical upper bounds for their FPR, and discuss their range of applications and limitations.</bold></p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Pellow, D" uniqKey="Pellow D">D. Pellow</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Benoit, G" uniqKey="Benoit G">G. Benoit</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bloom, B" uniqKey="Bloom B">B. Bloom</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Broder, A" uniqKey="Broder A">A. Broder</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Chikhi, R" uniqKey="Chikhi R">R. Chikhi</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Heo, Y" uniqKey="Heo Y">Y. Heo</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Holley, G" uniqKey="Holley G">G. Holley</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Malde, K" uniqKey="Malde K">K. Malde</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Marcais, G" uniqKey="Marcais G">G. Marçais</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Patro, R" uniqKey="Patro R">R. Patro</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Pell, J" uniqKey="Pell J">J. Pell</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Pellow, D" uniqKey="Pellow D">D. Pellow</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Rozov, R" uniqKey="Rozov R">R. Rozov</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Salikhov, K" uniqKey="Salikhov K">K. Salikhov</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Shi, H" uniqKey="Shi H">H. Shi</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Solomon, B" uniqKey="Solomon B">B. Solomon</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Song, L" uniqKey="Song L">L. Song</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Stranneheim, H" uniqKey="Stranneheim H">H. Stranneheim</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wood, D" uniqKey="Wood D">D. Wood</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Yu, Y" uniqKey="Yu Y">Y. Yu</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Zerbino, D" uniqKey="Zerbino D">D. Zerbino</name></author></analytic></biblStruct></listBibl></div1></back></TEI><double pmid="27828710"><pmc><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Improving Bloom Filter Performance on Sequence Data Using <italic>k</italic>-mer Bloom Filters</title><author><name sortKey="Pellow, David" sort="Pellow, David" uniqKey="Pellow D" first="David" last="Pellow">David Pellow</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author><author><name sortKey="Filippova, Darya" sort="Filippova, Darya" uniqKey="Filippova D" first="Darya" last="Filippova">Darya Filippova</name><affiliation><nlm:aff id="aff2"></nlm:aff></affiliation></author><author><name sortKey="Kingsford, Carl" sort="Kingsford, Carl" uniqKey="Kingsford C" first="Carl" last="Kingsford">Carl Kingsford</name><affiliation><nlm:aff id="aff3"></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">27828710</idno><idno type="pmc">5467106</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5467106</idno><idno type="RBID">PMC:5467106</idno><idno type="doi">10.1089/cmb.2016.0155</idno><date when="2017">2017</date><idno type="wicri:Area/Pmc/Corpus">000D98</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000D98</idno><idno type="wicri:Area/Pmc/Curation">000D98</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000D98</idno><idno type="wicri:Area/Pmc/Checkpoint">000823</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">000823</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Improving Bloom Filter Performance on Sequence Data Using <italic>k</italic>-mer Bloom Filters</title><author><name sortKey="Pellow, David" sort="Pellow, David" uniqKey="Pellow D" first="David" last="Pellow">David Pellow</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author><author><name sortKey="Filippova, Darya" sort="Filippova, Darya" uniqKey="Filippova D" first="Darya" last="Filippova">Darya Filippova</name><affiliation><nlm:aff id="aff2"></nlm:aff></affiliation></author><author><name sortKey="Kingsford, Carl" sort="Kingsford, Carl" uniqKey="Kingsford C" first="Carl" last="Kingsford">Carl Kingsford</name><affiliation><nlm:aff id="aff3"></nlm:aff></affiliation></author></analytic><series><title level="j">Journal of Computational Biology</title><idno type="ISSN">1066-5277</idno><idno type="eISSN">1557-8666</idno><imprint><date when="2017">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><title>Abstract</title><p><bold>Using a sequence's <italic>k</italic>-mer content rather than the full sequence directly has enabled significant performance improvements in several sequencing applications, such as metagenomic species identification, estimation of transcript abundances, and alignment-free comparison of sequencing data. As <italic>k</italic>-mer sets often reach hundreds of millions of elements, traditional data structures are often impractical for <italic>k</italic>-mer set storage, and Bloom filters (BFs) and their variants are used instead. BFs reduce the memory footprint required to store millions of <italic>k</italic>-mers while allowing for fast set containment queries, at the cost of a low false positive rate (FPR). We show that, because <italic>k</italic>-mers are derived from sequencing reads, the information about <italic>k</italic>-mer overlap in the original sequence can be used to reduce the FPR up to 30 × with little or no additional memory and with set containment queries that are only 1.3 – 1.6 times slower. Alternatively, we can leverage <italic>k</italic>-mer overlap information to store <italic>k</italic>-mer sets in about half the space while maintaining the original FPR. We consider several variants of such <italic>k</italic>-mer Bloom filters (<italic>k</italic>BFs), derive theoretical upper bounds for their FPR, and discuss their range of applications and limitations.</bold></p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Pellow, D" uniqKey="Pellow D">D. Pellow</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Benoit, G" uniqKey="Benoit G">G. Benoit</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bloom, B" uniqKey="Bloom B">B. Bloom</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Broder, A" uniqKey="Broder A">A. Broder</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Chikhi, R" uniqKey="Chikhi R">R. Chikhi</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Heo, Y" uniqKey="Heo Y">Y. Heo</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Holley, G" uniqKey="Holley G">G. Holley</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Malde, K" uniqKey="Malde K">K. Malde</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Marcais, G" uniqKey="Marcais G">G. Marçais</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Patro, R" uniqKey="Patro R">R. Patro</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Pell, J" uniqKey="Pell J">J. Pell</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Pellow, D" uniqKey="Pellow D">D. Pellow</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Rozov, R" uniqKey="Rozov R">R. Rozov</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Salikhov, K" uniqKey="Salikhov K">K. Salikhov</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Shi, H" uniqKey="Shi H">H. Shi</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Solomon, B" uniqKey="Solomon B">B. Solomon</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Song, L" uniqKey="Song L">L. Song</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Stranneheim, H" uniqKey="Stranneheim H">H. Stranneheim</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wood, D" uniqKey="Wood D">D. Wood</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Yu, Y" uniqKey="Yu Y">Y. Yu</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Zerbino, D" uniqKey="Zerbino D">D. Zerbino</name></author></analytic></biblStruct></listBibl></div1></back></TEI></pmc><pubmed><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Improving Bloom Filter Performance on Sequence Data Using k-mer Bloom Filters.</title><author><name sortKey="Pellow, David" sort="Pellow, David" uniqKey="Pellow D" first="David" last="Pellow">David Pellow</name><affiliation wicri:level="1"><nlm:affiliation>1 The Blavatnik School of Computer Science, Tel Aviv University , Tel Aviv, Israel .</nlm:affiliation><country xml:lang="fr">Israël</country><wicri:regionArea>1 The Blavatnik School of Computer Science, Tel Aviv University , Tel Aviv</wicri:regionArea><wicri:noRegion>Tel Aviv</wicri:noRegion></affiliation></author><author><name sortKey="Filippova, Darya" sort="Filippova, Darya" uniqKey="Filippova D" first="Darya" last="Filippova">Darya Filippova</name><affiliation wicri:level="2"><nlm:affiliation>2 Roche Sequencing Solutions , Pleasanton, California.</nlm:affiliation><country>États-Unis</country><placeName><region type="state">Californie</region></placeName><wicri:cityArea>2 Roche Sequencing Solutions , Pleasanton</wicri:cityArea></affiliation></author><author><name sortKey="Kingsford, Carl" sort="Kingsford, Carl" uniqKey="Kingsford C" first="Carl" last="Kingsford">Carl Kingsford</name><affiliation wicri:level="2"><nlm:affiliation>3 Computational Biology Department, School of Computer Science, Carnegie Mellon University , Pittsburgh, Pennsylvania.</nlm:affiliation><country xml:lang="fr">États-Unis</country><placeName><region type="state">Pennsylvanie</region></placeName><wicri:cityArea>3 Computational Biology Department, School of Computer Science, Carnegie Mellon University , Pittsburgh</wicri:cityArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2017">2017</date><idno type="RBID">pubmed:27828710</idno><idno type="pmid">27828710</idno><idno type="doi">10.1089/cmb.2016.0155</idno><idno type="wicri:Area/PubMed/Corpus">000E99</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000E99</idno><idno type="wicri:Area/PubMed/Curation">000E99</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000E99</idno><idno type="wicri:Area/PubMed/Checkpoint">000C62</idno><idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">000C62</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Improving Bloom Filter Performance on Sequence Data Using k-mer Bloom Filters.</title><author><name sortKey="Pellow, David" sort="Pellow, David" uniqKey="Pellow D" first="David" last="Pellow">David Pellow</name><affiliation wicri:level="1"><nlm:affiliation>1 The Blavatnik School of Computer Science, Tel Aviv University , Tel Aviv, Israel .</nlm:affiliation><country xml:lang="fr">Israël</country><wicri:regionArea>1 The Blavatnik School of Computer Science, Tel Aviv University , Tel Aviv</wicri:regionArea><wicri:noRegion>Tel Aviv</wicri:noRegion></affiliation></author><author><name sortKey="Filippova, Darya" sort="Filippova, Darya" uniqKey="Filippova D" first="Darya" last="Filippova">Darya Filippova</name><affiliation wicri:level="2"><nlm:affiliation>2 Roche Sequencing Solutions , Pleasanton, California.</nlm:affiliation><country>États-Unis</country><placeName><region type="state">Californie</region></placeName><wicri:cityArea>2 Roche Sequencing Solutions , Pleasanton</wicri:cityArea></affiliation></author><author><name sortKey="Kingsford, Carl" sort="Kingsford, Carl" uniqKey="Kingsford C" first="Carl" last="Kingsford">Carl Kingsford</name><affiliation wicri:level="2"><nlm:affiliation>3 Computational Biology Department, School of Computer Science, Carnegie Mellon University , Pittsburgh, Pennsylvania.</nlm:affiliation><country xml:lang="fr">États-Unis</country><placeName><region type="state">Pennsylvanie</region></placeName><wicri:cityArea>3 Computational Biology Department, School of Computer Science, Carnegie Mellon University , Pittsburgh</wicri:cityArea></affiliation></author></analytic><series><title level="j">Journal of computational biology : a journal of computational molecular cell biology</title><idno type="eISSN">1557-8666</idno><imprint><date when="2017" type="published">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Computational Biology (methods)</term><term>Computer Simulation</term><term>Humans</term><term>Probability</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>Biologie informatique ()</term><term>Humains</term><term>Logiciel</term><term>Probabilité</term><term>Simulation numérique</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Computational Biology</term><term>Sequence Analysis, DNA</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Computer Simulation</term><term>Humans</term><term>Probability</term><term>Software</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Analyse de séquence d'ADN</term><term>Biologie informatique</term><term>Humains</term><term>Logiciel</term><term>Probabilité</term><term>Simulation numérique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Using a sequence's k-mer content rather than the full sequence directly has enabled significant performance improvements in several sequencing applications, such as metagenomic species identification, estimation of transcript abundances, and alignment-free comparison of sequencing data. As k-mer sets often reach hundreds of millions of elements, traditional data structures are often impractical for k-mer set storage, and Bloom filters (BFs) and their variants are used instead. BFs reduce the memory footprint required to store millions of k-mers while allowing for fast set containment queries, at the cost of a low false positive rate (FPR). We show that, because k-mers are derived from sequencing reads, the information about k-mer overlap in the original sequence can be used to reduce the FPR up to 30 × with little or no additional memory and with set containment queries that are only 1.3 - 1.6 times slower. Alternatively, we can leverage k-mer overlap information to store k-mer sets in about half the space while maintaining the original FPR. We consider several variants of such k-mer Bloom filters (kBFs), derive theoretical upper bounds for their FPR, and discuss their range of applications and limitations.</div></front></TEI></pubmed></double></record>Pour manipuler ce document sous Unix (Dilib)
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