Data Mining in Genomics and ProteomicsView this Special Issue
Research article | Open Access
Protein Coding Sequence Identification by Simultaneously Characterizing the Periodic and Random Features of DNA Sequences
Most codon indices used today are based on highly biased nonrandom usage of codons in coding regions. The background of a coding or noncoding DNA sequence, however, is fairly random, and can be characterized as a random fractal. When a gene-finding algorithm incorporates multiple sources of information about coding regions, it becomes more successful. It is thus highly desirable to develop new and efficient codon indices by simultaneously characterizing the fractal and periodic features of a DNA sequence. In this paper, we describe a novel way of achieving this goal. The efficiency of the new codon index is evaluated by studying all of the 16 yeast chromosomes. In particular, we show that the method automatically and correctly identifies which of the three reading frames is the one that contains a gene.
Copyright © 2005 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.