Planted (l, d) - Motif Finding using Particle Swarm Optimization

Abstract

In Bioinformatics, Motif Finding is one of the most popular problems, which has many applications. Generally, it is to locate recurring patterns in the sequence of nucleotides or amino acids. As we can't expect the pattern to be exact matching copies owing to biological mutations, the motif finding turns to be an NPcomplete problem. By approximating the same in different aspects, scientists have provided many solutions in the literature. The most of the algorithms suffer with local optima. Particle swarm optimization (PSO) is a new global optimization technique which has wide applications. It finds the global best solution by simply adjusting the trajectory of each individual towards its own best location and towards the best particle of the swarm at each generation. We have adopted the features of the PSO to solve the Planted Motif Finding Problem and have designed a sequential algorithm. We have performed experiments with simulated data it outperforms MbGA and PbGA. The PMbPSO also applied for real biological data sets and observe that the algorithm is also able to detect known TFBS accurately when there are no mutations.

Adaptation of cuckoo search algorithm for the Motif Finding problem

“…After the objective function is determined, the goal is to find the patterns of high objective function value. The score function described by Reddy and Arock [18] has been used as the objective function in the motif fmding problem.…”

“…These patterns are not exact copies due to biological reasons. Accordingly, the motif prediction problem turns to be an NP-Complete Problem and is one of the key areas of interest for a number of computer science researchers [2] Pevzner and Sze [3] designed a set of challenging test cases, so-called (I,d) motif, where motifs of length I are planted in each input string with at most d mismatches. The goal of the planted motif problem is to find all the occurrences of the (I, d)-motifs that appear in each of the input strings.…”

A particle swarm optimization solution for challenging planted(l, d)-Motif problem

“…In our previous work [25], we have adopted the features of the PSO to solve challenging instances of Planted (l, d) -Motif Finding Problem. The results also show that the proposed algorithm works better for longer size motifs.…”

Mdabc

“…Zhou et al (2005) proposed a novel algorithm IPSO-GA by integrating an improved particle swarm optimization with genetic algorithm to search sequence motifs from co expressed genes regulated by the NFkb transcription factor. Reddy et al (2010) have adopted the features of the PSO to solve the Planted Motif Finding Problem and have designed a sequential algorithm. Lei, C. and Ruan, J.…”