GASCO: Genetic Algorithm Simulation for Codon Optimization

Article type: Research Article

Authors: Sandhu, Kuljeet Singh | Pandey, Sunil | Maiti, Souvik | Pillai, Beena

Affiliations: GN Ramachandran Knowledge Center for Genome Informatics, Institute of Genomics and Integrative Biology (IGIB), Mall Road, Delhi-110007, India

Note: [] Corresponding author. Kuljeet Singh Sandhu, Department of Animal Development and Genetics, Evolutionary Biology Center, Uppsala University, Norbyvagen 18a, Uppsala 75236, Sweden. E-mail: kuljeet.singh@ebc.uu.se

Abstract: Codon optimization is a generic technique to achieve optimum expression of a foreign gene in the host's cell system. Selection of optimum codons depends on codon usage of the host genome and the presence of several desirable and undesirable sequence motifs. Searching these motifs in all possible combinations of the codons increases the search space exponentially with respect to sequence length. GASCO is an algorithm developed for the optimum codon selection using genetic algorithms. The algorithm reduces the search space and provides an approximate solution to the problem. The algorithm has applications in DNA vaccine design for successfully eliciting potent immune responses and synthetic gene design for metabolic pathway engineering. The software for the proposed algorithm is available on http://miracle.igib.res.in/gasco/.

Keywords: Codon optimization, genetic algorithm, DNA vaccine, CpG motif

Journal: In Silico Biology, vol. 8, no. 2, pp. 187-192, 2008