A fuzzy guided genetic algorithm for operon prediction

Motivation: The operon structure of the prokaryotic genome isa critical input for the reconstruction of regulatory networksat the whole genome level. As experimental methods for the detectionof operons are difficult and time-consuming, efforts are beingput into developing computational methods that can use availablebiological information to predict operons. Method: A genetic algorithm is developed to evolve a startingpopulation of putative operon maps of the genome into progressivelybetter predictions. Fuzzy scoring functions based on multiplecriteria are used for assessing the ‘fitness’ ofthe newly evolved operon maps and guiding their evolution. Results: The algorithm organizes the whole genome into operons.The fuzzy guided genetic algorithm-based approach makes it possibleto use diverse biological information like genome sequence data,functional annotations and conservation across multiple genomes,to guide the organization process. This approach does not requireany prior training with experimental operons. The predictionsfrom this algorithm for Escherchia coli K12 and Bacillus subtilisare evaluated against experimentally discovered operons forthese organisms. The accuracy of the method is evaluated usingan ROC (receiver operating characteristic) analysis. The areaunder the ROC curve is around 0.9, which indicates excellentaccuracy. Contact: roschen_csir{at}rediffmail.com