A speedup technique for (l, d)-motif finding algorithms

Abstract

BackgroundThe discovery of patterns in DNA, RNA, and protein sequences has led to the solution of many vital biological problems. For instance, the identification of patterns in nucleic acid sequences has resulted in the determination of open reading frames, identification of promoter elements of genes, identification of intron/exon splicing sites, identification of SH RNAs, location of RNA degradation signals, identification of alternative splicing sites, etc. In protein sequences, patterns have proven to be extremely helpful in domain identification, location of protease cleavage sites, identification of signal peptides, protein interactions, determination of protein degradation elements, identification of protein trafficking elements, etc. Motifs are important patterns that are helpful in finding transcriptional regulatory elements, transcription factor binding sites, functional genomics, drug design, etc. As a result, numerous papers have been written to solve the motif search problem.ResultsThree versions of the motif search problem have been proposed in the literature: Simple Motif Search (SMS), (l, d)-motif search (or Planted Motif Search (PMS)), and Edit-distance-based Motif Search (EMS). In this paper we focus on PMS. Two kinds of algorithms can be found in the literature for solving the PMS problem: exact and approximate. An exact algorithm identifies the motifs always and an approximate algorithm may fail to identify some or all of the motifs. The exact version of PMS problem has been shown to be NP-hard. Exact algorithms proposed in the literature for PMS take time that is exponential in some of the underlying parameters. In this paper we propose a generic technique that can be used to speedup PMS algorithms.ConclusionsWe present a speedup technique that can be used on any PMS algorithm. We have tested our speedup technique on a number of algorithms. These experimental results show that our speedup technique is indeed very effective. The implementation of algorithms is freely available on the web at http://www.engr.uconn.edu/rajasek/PMS4.zip

A New Idea for Improving the Running Time of PMS Algorithm

“…All existing exact algorithms solve PMS problem in exponential time in some of its parameters. Some of the most important exact algorithms are PMS1 [5], PMS2 [5], PMS3 [5], PMSi [6], PMSP [6], PMSP4 [7], Stemming [8], PMS5 [9], PMS6 [10], PMS8 [11], qPMS9 [12], PMS Prune [13], Algorithm Voting [14] and RISSOTO [15]. On the other side, approximate algorithms take less time than exact algorithms.…”

Motif Discovery and Data Mining in Bioinformatics

“…Finding motifs can be equally crucial for analyzing interactions between viruses and cells or identification of diseaselinked patterns. In protein sequences, patterns have proven to be extremely helpful in domain identification, location of protease cleavage sites, identification of signal peptides, protein interactions, determination of protein degradation elements, identification of protein trafficking elements, discovery of short functional motifs, …etc [49]. Motif discovery for protein sequences is important for identifying structurally or functionally important regions and understanding proteins' functional components, or active sites [15].…”

“…These algorithms attempt to find sequential patterns based on specifying motif related information, such as length of each sequence and length of motifs. They currently suffer from limitations of sequence length and input file size, and they need a long time of execution exceeding months or years [9,49,59]. For example, M a r c h 3 1 , 2 0 1 4 DNA monad motif discovery tools need information about data such as DNA sequences in FASTA format, and the number of sequences must be between 5 and 500, the length of each sequence must be between 15 and 1000 DNA letters, motif length up to 23 characters may require days to process.…”

PMS6MC: A multicore algorithm for motif discovery

“…PMS1, PMS2 and PMS3 [24] first sorts the d-neighborhood of input l-mers using radix sort and then intersects them to find the motifs. PMS4 [25] proposes a very general technique to reduce the run time of any exact algorithm by examining only k input sequences out of total n input sequences. PMSP [9] extends this idea further by only examining the dneighborhood of the l-mers from the first input sequence.…”