KMC 2: fast and resource-frugal k-mer counting.
Identifieur interne : 001719 ( PubMed/Corpus ); précédent : 001718; suivant : 001720KMC 2: fast and resource-frugal k-mer counting.
Auteurs : Sebastian Deorowicz ; Marek Kokot ; Szymon Grabowski ; Agnieszka Debudaj-GrabyszSource :
- Bioinformatics (Oxford, England) [ 1367-4811 ] ; 2015.
English descriptors
- KwdEn :
- MESH :
- methods : Computational Biology, Sequence Alignment, Sequence Analysis, DNA.
- Algorithms, Animals, Humans, Software.
Abstract
Building the histogram of occurrences of every k-symbol long substring of nucleotide data is a standard step in many bioinformatics applications, known under the name of k-mer counting. Its applications include developing de Bruijn graph genome assemblers, fast multiple sequence alignment and repeat detection. The tremendous amounts of NGS data require fast algorithms for k-mer counting, preferably using moderate amounts of memory.
DOI: 10.1093/bioinformatics/btv022
PubMed: 25609798
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pubmed:25609798Le document en format XML
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<front><div type="abstract" xml:lang="en">Building the histogram of occurrences of every k-symbol long substring of nucleotide data is a standard step in many bioinformatics applications, known under the name of k-mer counting. Its applications include developing de Bruijn graph genome assemblers, fast multiple sequence alignment and repeat detection. The tremendous amounts of NGS data require fast algorithms for k-mer counting, preferably using moderate amounts of memory.</div>
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<ArticleTitle>KMC 2: fast and resource-frugal k-mer counting.</ArticleTitle>
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<Abstract><AbstractText Label="MOTIVATION" NlmCategory="BACKGROUND">Building the histogram of occurrences of every k-symbol long substring of nucleotide data is a standard step in many bioinformatics applications, known under the name of k-mer counting. Its applications include developing de Bruijn graph genome assemblers, fast multiple sequence alignment and repeat detection. The tremendous amounts of NGS data require fast algorithms for k-mer counting, preferably using moderate amounts of memory.</AbstractText>
<AbstractText Label="RESULTS" NlmCategory="RESULTS">We present a novel method for k-mer counting, on large datasets about twice faster than the strongest competitors (Jellyfish 2, KMC 1), using about 12 GB (or less) of RAM. Our disk-based method bears some resemblance to MSPKmerCounter, yet replacing the original minimizers with signatures (a carefully selected subset of all minimizers) and using (k, x)-mers allows to significantly reduce the I/O and a highly parallel overall architecture allows to achieve unprecedented processing speeds. For example, KMC 2 counts the 28-mers of a human reads collection with 44-fold coverage (106 GB of compressed size) in about 20 min, on a 6-core Intel i7 PC with an solid-state disk.</AbstractText>
<CopyrightInformation>© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.</CopyrightInformation>
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<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Deorowicz</LastName>
<ForeName>Sebastian</ForeName>
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<Author ValidYN="Y"><LastName>Debudaj-Grabysz</LastName>
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