ArticleBMC bioinformatics2010
Efficient motif finding algorithms for large-alphabet inputs.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A Review on Planted (Sensors (Basel, Switzerland) · 2022Review
- Article
- PMS6MC: A Multicore Algorithm for Motif Discovery.Algorithms · 2013Article
- A fast weak motif-finding algorithm based on community detection in graphs.BMC bioinformatics · 2013Article
- Efficient algorithms for biological stems search.BMC bioinformatics · 2013Article
- PMS6: A Fast Algorithm for Motif Discovery.IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences · 2012Article
- Virtual interactomics of proteins from biochemical standpoint.Molecular biology international · 2012Article
- Article
- Article
- Review of Different Sequence Motif Finding Algorithms.Avicenna journal of medical biotechnologyReview
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2 authors.
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Abstract
backgroundWe consider the problem of identifying motifs, recurring or conserved patterns, in the biological sequence data sets. To solve this task, we present a new deterministic algorithm for finding patterns that are embedded as exact or inexact instances in all or most of the input strings.
resultsThe proposed algorithm (1) improves search efficiency compared to existing algorithms, and (2) scales well with the size of alphabet. On a synthetic planted DNA motif finding problem our algorithm is over 10× more efficient than MITRA, PMSPrune, and RISOTTO for long motifs. Improvements are orders of magnitude higher in the same setting with large alphabets. On benchmark TF-binding site problems (FNP, CRP, LexA) we observed reduction in running time of over 12×, with high detection accuracy. The algorithm was also successful in rapidly identifying protein motifs in Lipocalin, Zinc metallopeptidase, and supersecondary structure motifs for Cadherin and Immunoglobin families.
conclusionsOur algorithm reduces computational complexity of the current motif finding algorithms and demonstrate strong running time improvements over existing exact algorithms, especially in important and difficult cases of large-alphabet sequences.
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