Discovering of gapped motifs using particle swarm optimisation

In bioinformatics, motif discovery is one of the fundamental and important computational problems. Identifying these recurring patterns in biological sequences helps us to better understand the mechanisms that regulate gene expression. Recently several evolutionary algorithms have been developed to solve motif discovery problem, because of their efficiency in searching multidimensional solution space. HPSO, IPSO-GA, PMbPSO and PSO+ are based on particle swarm optimisation (PSO) algorithms. Among these, PSO+ is the first one to be proposed for finding gapped motifs. PSO+ is less efficient in finding gapped motifs that are located at the centre of a motif. Here, our contribution is, to find gapped motifs that are present at the centre of two conserved regions efficiently by adopting features of PSO to solve the problem. We performed experiments on simulated and real biological datasets. It is observed that our approach is able to detect known gapped TFBS more accurately and efficiently.