MFEA: An evolutionary approach for motif finding in DNA sequences

Abstract

Identification of short repeating patterns in biological sequences, mostly known as motif, is important for understanding the genetic regulatory system of a living being. But weak conservation of motifs makes it an NP-hard problem and poses a challenge in computational biology. In this work, we have modeled the motif search problem from meta-heuristic perspective. We have proposed and evaluated an evolutionary approach, in which, we will search candidate motifs with a heuristic so that we can find the real motifs of the data set without exploring rigorously. Our method minimizes the trade between exploration and exploitation of the search space with a defined mutation technique using normal distribution and finds an efficient way to measure the fitness of a candidate motif to be real motif. We have used benchmark data set to evaluate the fitness of found motifs for each species, and our approach gives accurate motifs for each of them.