A Quantum Genetic Algorithm for Operon Prediction

Operon is a fundamental unit of transcription which is usually used to understand gene regulations and functions in entire genomes. Detecting operon experimentally is difficult and time-consuming, thus many bioinformatics algorithms have been proposed to predict operon. In this paper, we use an improved discrete genetic algorithm based on quantum theory for operon prediction. It is simpler and more powerful than the algorithms available, and thus avoids local optima while searching for a better solution. We utilize intergenic distance, participation in the same metabolic pathway and cluster of orthologous groups (COG) gene functions to design fitness function base on reward and penalty (RP). The RP operation can improve fitness value of chromosome in proportion to the accuracy. Experimental results show that the detection accuracy of our method reached 0.872, 0.925, 0.943, 0.954 and 0.926 respectively for the E. coli, B. subtilis, P. aeruginosa PA01, S. aureus and M. tuberculosis genomes. Results demonstrate that our proposed method can predict operons with high accuracy.