Classification of Hot and Cold Recombination Regions in Saccharomyces cerevisiae: Comparative Analysis of Two Machine Learning Techniques

Abstract

Recombination plays a crucial role generating natural genetic variations. Nevertheless, the recombination does not occur randomly in the genome, but with higher allure in some genomic regions while low in other regions, former are called hot recombination regions and later are called cold regions for recombination. With the advancement of genome sequencing techniques, computational methods are required which can efficiently classify the recombination regions. For this we have developed artificial neural network based model which uses amino acid composition features of DNA sequences. Compositional features were used to incorporate its local or short range sequence order information. High accuracy and sensitivity indicates that this model may become a useful tool for identifying the recombination hotspots. Moreover we compared the performance artificial neural network model with support vector machine model with the target class as hot and cold. We found that compositional features gives good classification result which probably reflect the structural and functional characteristics of hot and cold spots.