Current Bioinformatics

Yi-Ping Phoebe Chen
Department of Computer Science and Information Technology
La Trobe University
Melbourne
Australia

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GAOPP: Operon Prediction in Prokaryotes Using Genetic Algorithm

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

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/

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

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Article Details

VOLUME: 10
ISSUE: 3
Year: 2015
Page: [299 - 305]
Pages: 7
DOI: 10.2174/157489361003150723134646
Price: $58