Improving Bloom Filter Performance on Sequence Data Using k-mer Bloom Filters
Identifieur interne : 001837 ( Ncbi/Curation ); 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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Abstract
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DOI: 10.1089/cmb.2016.0155
PubMed: 27828710
PubMed Central: 5467106
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PMC:5467106Le document en format XML
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-mer Bloom Filters</title>
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<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>
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<author><name sortKey="Filippova, Darya" sort="Filippova, Darya" uniqKey="Filippova D" first="Darya" last="Filippova">Darya Filippova</name>
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<author><name sortKey="Kingsford, Carl" sort="Kingsford, Carl" uniqKey="Kingsford C" first="Carl" last="Kingsford">Carl Kingsford</name>
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<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>
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<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>
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<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>
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<biblStruct><analytic><author><name sortKey="Benoit, G" uniqKey="Benoit G">G. Benoit</name>
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<biblStruct><analytic><author><name sortKey="Bloom, B" uniqKey="Bloom B">B. Bloom</name>
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<biblStruct><analytic><author><name sortKey="Broder, A" uniqKey="Broder A">A. Broder</name>
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<biblStruct><analytic><author><name sortKey="Chikhi, R" uniqKey="Chikhi R">R. Chikhi</name>
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<biblStruct><analytic><author><name sortKey="Heo, Y" uniqKey="Heo Y">Y. Heo</name>
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<biblStruct><analytic><author><name sortKey="Marcais, G" uniqKey="Marcais G">G. Marçais</name>
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<biblStruct><analytic><author><name sortKey="Pell, J" uniqKey="Pell J">J. Pell</name>
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<biblStruct><analytic><author><name sortKey="Pellow, D" uniqKey="Pellow D">D. Pellow</name>
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<biblStruct><analytic><author><name sortKey="Rozov, R" uniqKey="Rozov R">R. Rozov</name>
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<biblStruct><analytic><author><name sortKey="Salikhov, K" uniqKey="Salikhov K">K. Salikhov</name>
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<biblStruct><analytic><author><name sortKey="Shi, H" uniqKey="Shi H">H. Shi</name>
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<biblStruct><analytic><author><name sortKey="Wood, D" uniqKey="Wood D">D. Wood</name>
</author>
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<biblStruct><analytic><author><name sortKey="Yu, Y" uniqKey="Yu Y">Y. Yu</name>
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<biblStruct><analytic><author><name sortKey="Zerbino, D" uniqKey="Zerbino D">D. Zerbino</name>
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