KCMBT: a k-mer Counter based on Multiple Burst Trees.
Identifieur interne : 001656 ( Ncbi/Merge ); précédent : 001655; suivant : 001657KCMBT: a k-mer Counter based on Multiple Burst Trees.
Auteurs : Abdullah-Al Mamun [États-Unis] ; Soumitra Pal [États-Unis] ; Sanguthevar Rajasekaran [États-Unis]Source :
- Bioinformatics (Oxford, England) [ 1367-4811 ] ; 2016.
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Abstract
A massive number of bioinformatics applications require counting of k-length substrings in genetically important long strings. A k-mer counter generates the frequencies of each k-length substring in genome sequences. Genome assembly, repeat detection, multiple sequence alignment, error detection and many other related applications use a k-mer counter as a building block. Very fast and efficient algorithms are necessary to count k-mers in large data sets to be useful in such applications.
DOI: 10.1093/bioinformatics/btw345
PubMed: 27283950
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pubmed:27283950Le document en format XML
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<term>Humans</term>
<term>Sequence Alignment</term>
<term>Sequence Analysis, DNA</term>
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<term>Génome</term>
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<term>Logiciel</term>
<term>Séquence nucléotidique</term>
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<front><div type="abstract" xml:lang="en">A massive number of bioinformatics applications require counting of k-length substrings in genetically important long strings. A k-mer counter generates the frequencies of each k-length substring in genome sequences. Genome assembly, repeat detection, multiple sequence alignment, error detection and many other related applications use a k-mer counter as a building block. Very fast and efficient algorithms are necessary to count k-mers in large data sets to be useful in such applications.</div>
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<Abstract><AbstractText Label="MOTIVATION">A massive number of bioinformatics applications require counting of k-length substrings in genetically important long strings. A k-mer counter generates the frequencies of each k-length substring in genome sequences. Genome assembly, repeat detection, multiple sequence alignment, error detection and many other related applications use a k-mer counter as a building block. Very fast and efficient algorithms are necessary to count k-mers in large data sets to be useful in such applications.</AbstractText>
<AbstractText Label="RESULTS">We propose a novel trie-based algorithm for this k-mer counting problem. We compare our devised algorithm k-mer Counter based on Multiple Burst Trees (KCMBT) with available all well-known algorithms. Our experimental results show that KCMBT is around 30% faster than the previous best-performing algorithm KMC2 for human genome dataset. As another example, our algorithm is around six times faster than Jellyfish2. Overall, KCMBT is 20-30% faster than KMC2 on five benchmark data sets when both the algorithms were run using multiple threads.</AbstractText>
<AbstractText Label="AVAILABILITY AND IMPLEMENTATION">KCMBT is freely available on GitHub: (https://github.com/abdullah009/kcmbt_mt).</AbstractText>
<AbstractText Label="CONTACT">rajasek@engr.uconn.edu</AbstractText>
<AbstractText Label="SUPPLEMENTARY INFORMATION">Supplementary data are available at Bioinformatics online.</AbstractText>
<CopyrightInformation>© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.</CopyrightInformation>
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