ArticleBMC bioinformatics2014
Efficient sequential and parallel algorithms for planted motif search.
Article in BMC bioinformatics, 2014. 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
- Freezing firefly algorithm for efficient planted (ℓ, d) motif search.Medical & biological engineering & computing · 2022Article
- A Clustering Approach for Motif Discovery in ChIP-Seq Dataset.Entropy (Basel, Switzerland) · 2019Article
- Novel algorithms for LDD motif search.BMC genomics · 2019Article
- SamSelect: a sample sequence selection algorithm for quorum planted motif search on large DNA datasets.BMC bioinformatics · 2018Article
- Efficient sequential and parallel algorithms for finding edit distance based motifs.BMC genomics · 2016Article
- RefSelect: a reference sequence selection algorithm for planted (l, d) motif search.BMC bioinformatics · 2016Article
- PairMotifChIP: A Fast Algorithm for Discovery of Patterns Conserved in Large ChIP-seq Data Sets.BioMed research international · 2016Article
- qPMS9: an efficient algorithm for quorum Planted Motif Search.Scientific reports · 2015Article
- An Affinity Propagation-Based DNA Motif Discovery Algorithm.BioMed research international · 2015Article
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2 authors.
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Abstract
backgroundMotif searching is an important step in the detection of rare events occurring in a set of DNA or protein sequences. One formulation of the problem is known as (l,d)-motif search or Planted Motif Search (PMS). In PMS we are given two integers l and d and n biological sequences. We want to find all sequences of length l that appear in each of the input sequences with at most d mismatches. The PMS problem is NP-complete. PMS algorithms are typically evaluated on certain instances considered challenging. Despite ample research in the area, a considerable performance gap exists because many state of the art algorithms have large runtimes even for moderately challenging instances.
resultsThis paper presents a fast exact parallel PMS algorithm called PMS8. PMS8 is the first algorithm to solve the challenging (l,d) instances (25,10) and (26,11). PMS8 is also efficient on instances with larger l and d such as (50,21). We include a comparison of PMS8 with several state of the art algorithms on multiple problem instances. This paper also presents necessary and sufficient conditions for 3 l-mers to have a common d-neighbor. The program is freely available at http://engr.uconn.edu/~man09004/PMS8/.
conclusionsWe present PMS8, an efficient exact algorithm for Planted Motif Search. PMS8 introduces novel ideas for generating common neighborhoods. We have also implemented a parallel version for this algorithm. PMS8 can solve instances not solved by any previous algorithms.
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