GAOPP: Operon Prediction in Prokaryotes Using Genetic Algorithm

Title:GAOPP: Operon Prediction in Prokaryotes Using Genetic Algorithm

Volume: 10 Issue: 3

Author(s): Kanhu C. Moharana, Manas R. Dikhit, Bikash R. Sahoo, Ganesh C. Sahoo and Pradeep Das

Affiliation:

Keywords: GAOPP, operon, operon prediction, prokaryote operon.

Abstract: Operons, consisting of functionally related or correlated genes, are commonly found in prokaryotes and have enormous importance in understanding microbial genomics. To identify such genes, we developed a stand-alone tool (GAOPP) for predicting operons utilizing Genetic Algorithm approach. We also aimed to use minimum and easily available data set as input to obtain modest accuracy, and provide a graphical user interface for easy accessibility. Prediction relies completely on a single input (.ptt) file, but the optional pathway file form KEGG can increase the accuracy. The tool was successfully tested on the set of experimentally defined operons in E. coli using three different fitness functions, namely Fuzzy Guided Scoring System, Rules Guided Scoring Scheme and Bayesian Scoring Scheme based Particle Swarm Optimization, with accuracies of 89.1, 86.5 and 93.4%, respectively. The availability of different fitness functions also enhances the importance of GAOPP. This tool will be helpful in predicting operons in newly sequenced prokaryotes. The tool is freely available at http://biomedinformri.com/gaopp/

Cite this article as:

Moharana C. Kanhu, Dikhit R. Manas, Sahoo R. Bikash, Sahoo C. Ganesh and Das Pradeep, GAOPP: Operon Prediction in Prokaryotes Using Genetic Algorithm, Current Bioinformatics 2015; 10 (3) . https://dx.doi.org/10.2174/157489361003150723134646