KCMBT: a k-mer Counter based on Multiple Burst Trees.
Bioinformatics
; 32(18): 2783-90, 2016 09 15.
Article
em En
| MEDLINE
| ID: mdl-27283950
ABSTRACT
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. 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. AVAILABILITY AND IMPLEMENTATION KCMBT is freely available on GitHub (https//github.com/abdullah009/kcmbt_mt). CONTACT rajasek@engr.uconn.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Base de dados:
MEDLINE
Assunto principal:
Algoritmos
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Alinhamento de Sequência
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Análise de Sequência de DNA
Limite:
Humans
Idioma:
En
Ano de publicação:
2016
Tipo de documento:
Article