Sequence motif finder using memetic algorithm

Abstract

Background: De novo prediction of Transcription Factor Binding Sites (TFBS) using computational methods is a difficult task and it is an important problem in Bioinformatics. The correct recognition of TFBS plays an important role in understanding the mechanisms of gene regulation and helps to develop new drugs. Results: We here present Memetic Framework for Motif Discovery (MFMD), an algorithm that uses semi-greedy constructive heuristics as a local optimizer. In addition, we used a hybridization of the classic genetic algorithm as a global optimizer to refine the solutions initially found. MFMD can find and classify overrepresented patterns in DNA sequences and predict their respective initial positions. MFMD performance was assessed using ChIP-seq data retrieved from the JASPAR site, promoter sequences extracted from the ABS site, and artificially generated synthetic data. The MFMD was evaluated and compared with well-known approaches in the literature, called MEME and Gibbs Motif Sampler, achieving a higher f-score in the most datasets used in this work. Conclusions: We have developed an approach for detecting motifs in biopolymers sequences. MFMD is a freely available software that can be promising as an alternative to the development of new tools for de novo motif discovery. Its open-source software can be downloaded at https://github.com/jadermcg/mfmd.