Disk-based k-mer counting on a PC.
Identifieur interne : 001B60 ( PubMed/Checkpoint ); précédent : 001B59; suivant : 001B61Disk-based k-mer counting on a PC.
Auteurs : Sebastian Deorowicz [Pologne] ; Agnieszka Debudaj-Grabysz ; Szymon GrabowskiSource :
- BMC bioinformatics [ 1471-2105 ] ; 2013.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- genetics : Caenorhabditis elegans.
- methods : Genomics.
- Algorithms, Animals, Genome, Human, Humans, Microcomputers, Sequence Alignment, Sequence Analysis, DNA, Software.
Abstract
The k-mer counting problem, which is to build the histogram of occurrences of every k-symbol long substring in a given text, is important for many bioinformatics applications. They include developing de Bruijn graph genome assemblers, fast multiple sequence alignment and repeat detection.
DOI: 10.1186/1471-2105-14-160
PubMed: 23679007
Affiliations:
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pubmed:23679007Le document en format XML
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<term>Genomics (methods)</term>
<term>Humans</term>
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<term>Caenorhabditis elegans (génétique)</term>
<term>Génome humain</term>
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<front><div type="abstract" xml:lang="en">The k-mer counting problem, which is to build the histogram of occurrences of every k-symbol long substring in a given text, is important for many bioinformatics applications. They include developing de Bruijn graph genome assemblers, fast multiple sequence alignment and repeat detection.</div>
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<Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">The k-mer counting problem, which is to build the histogram of occurrences of every k-symbol long substring in a given text, is important for many bioinformatics applications. They include developing de Bruijn graph genome assemblers, fast multiple sequence alignment and repeat detection.</AbstractText>
<AbstractText Label="RESULTS" NlmCategory="RESULTS">We propose a simple, yet efficient, parallel disk-based algorithm for counting k-mers. Experiments show that it usually offers the fastest solution to the considered problem, while demanding a relatively small amount of memory. In particular, it is capable of counting the statistics for short-read human genome data, in input gzipped FASTQ file, in less than 40 minutes on a PC with 16 GB of RAM and 6 CPU cores, and for long-read human genome data in less than 70 minutes. On a more powerful machine, using 32 GB of RAM and 32 CPU cores, the tasks are accomplished in less than half the time. No other algorithm for most tested settings of this problem and mammalian-size data can accomplish this task in comparable time. Our solution also belongs to memory-frugal ones; most competitive algorithms cannot efficiently work on a PC with 16 GB of memory for such massive data.</AbstractText>
<AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">By making use of cheap disk space and exploiting CPU and I/O parallelism we propose a very competitive k-mer counting procedure, called KMC. Our results suggest that judicious resource management may allow to solve at least some bioinformatics problems with massive data on a commodity personal computer.</AbstractText>
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