A Bayesian Scoring Scheme based Particle Swarm Optimization algorithm to identify transcription factor binding sites

Identification of transcription factor binding sites is a vital task in contemporary biology, since it helps researchers to comprehend the regulatory mechanism of gene expression. Computational tools to perform this task have gained great attention since they are good alternatives to expensive and laborious biological experiments. In this paper, we propose a Particle Swarm Optimization based motif-finding method that utilizes a proven Bayesian Scoring Scheme as the fitness function. Since PSO is designed to work in multidimensional continuous domains, this paper presents required developments to adapt PSO for the motif finding application. Furthermore, this paper presents a benchmark of PSO variants with four separate population topologies, GBest, Bidirectional Ring, Random and Von Neumann. Simulations performed over synthetic and real data sets have shown that the proposed method is efficient and also superior to some well-known existing tools. Additionally, the Bidirectional Ring topology appears to be remarkable for the motif-finding application.

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► A PSO algorithm with a proven Bayesian fitness model to find DNA motifs is developed.
► Algorithm adaptations to fit regular PSO to DNA motif finding task is presented.
► Four PSO variants based on neighborhood topologies are evaluated for this task.
► Experiments are conducted based on synthetic and real datasets.
► The proposed model is efficient at finding TFBS instances residing on DNA sequences.