An Analysis Of Bayesian & Non – Bayesian Functions For Motif Detection Using Genetic Algorithm

A motif, in the context of biological sequence analysis, is a consensus pattern of DNA bases or amino acids which accurately captures a conserved feature common to a group of DNA or protein sequences. Finding motif- patterns of conserved residues-within nucleotide and protein sequence is a key part of understanding function and regulation within biological system. Computational motif discovery has been used with some success in simple organisms like yeast. However, when moves to higher organisms with more complex genomes, more sensitive methods are needed. Genetic Algorithm is an efficient method for detecting motifs, since it has greater freedom of movement between different configurations than simpler algorithms. This paper analyses the genetic algorithm method for the detection of motifs by using Bayesian and NonBayesian functions as fitness function and compares it with the other existing tools.