An optimized approach for annotation of large eukaryotic genomic sequences using genetic algorithm.

摘要: Detection of important functional and/or structural elements and identification their positions in a large eukaryotic genomic sequence are an active research area. Gene is unit DNA. The computation gene prediction is, therefore, very essential for detailed genome annotation. In this paper, we propose new technique based on Genetic Algorithm (GA) to determine the optimal exons chromosome or genome. correct coding non-coding regions difficult computationally demanding. proposed genetic-based method, named Prediction with (GPGA), reduces problem by searching only one exon at time instead all along its introns. This representation carries significant advantage that it breaks entire gene-finding into number smaller sub-problems, thereby reducing computational complexity. We tested performance GPGA existing benchmark datasets compared results well-known relevant techniques. comparison shows better comparable method. also used annotating human 21 (HS21) using cross-species comparisons mouse orthologs. It was noted predicted true genes accuracy than other approaches.