Source:http://linkedlifedata.com/resource/pubmed/id/19642267
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:dateCreated |
2009-7-31
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| pubmed:abstractText |
Finding motifs in many sequences is an important problem in computational biology, especially in identification of regulatory motifs in DNA sequences. Let c be a motif sequence. Given a set of sequences, each is planted with a mutated version of c at an unknown position, the motif finding problem is to find these planted motifs and the original c. In this paper, we study the VM model of the planted motif problem, which is proposed by Pevzner and Sze. We give a simple Selecting One Voting algorithm and a more powerful Selecting k Voting algorithm. When the length of motif and the number of input sequences are large enough, we prove that the two algorithms can find the unknown motif consensus with high probability. In the proof, we show why a large number of input sequences is so important for finding motifs, which is believed by most researchers. Experimental results on simulated data also support the claim. Selecting k Voting algorithm is powerful, but computational intensive. To speed up the algorithm, we propose a progressive filtering algorithm, which improves the running time significantly and has good accuracy in finding motifs. Our experimental results show that Selecting k Voting algorithm with progressive filtering performs very well in practice and it outperforms some best known algorithms. AVAILABILITY: The software is available upon request.
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| pubmed:language |
eng
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| pubmed:journal | |
| pubmed:citationSubset |
IM
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| pubmed:status |
MEDLINE
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| pubmed:issn |
1752-7791
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| pubmed:author | |
| pubmed:issnType |
Print
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| pubmed:volume |
7
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| pubmed:owner |
NLM
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| pubmed:authorsComplete |
Y
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| pubmed:pagination |
37-47
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| pubmed:meshHeading | |
| pubmed:year |
2008
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| pubmed:articleTitle |
Voting algorithms for the motif finding problem.
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| pubmed:affiliation |
Department of Computer Science, University of Western Ontario, London, ON, Canada. liuxiaowencs@gmail.com
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| pubmed:publicationType |
Journal Article
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