PSSM amino-acid composition based rules for gene identification

Abstract

With the advancement of genome sequence data, a large number of gene predicting programs have come into existence. Big data associated with the problem of identification of genes can be projected into subspaces or clusters so that the given problem can be divided into sub-problems. Each sub-problem can then be optimized with an appropriate model. One of the approaches of dividing a given problem into subproblems involves projecting the big data to various sub-space grids [1−4], where each sub-space grid represents a sub-problem. Each sub-problem can then be represented independently with various models which can be combined by using a rule based system [5].The gene identification from large genome sequence is found to be one of the significant issues to solve in the field of bioinformatics. There is an essential requirement of developing gene finding methods and their corresponding functions. One of the foremost problems in the process of gene forecasting is to discover the protein coding genes in genomic DNA sequence.