Efficient assembling of genome fragments using genetic algorithm enhanced by heuristic search

Abstract-- Shotgun sequencing is the state-of-the-art to decode genome sequence. However this technique needs a lot of fragments. Combining those fragments correctly requires enormous computational cost. In our previous work we have shown how Genetic Algorithm (GA) could solve this problem efficiently. In this work, we added two heuristic ideas with GA to make it more efficient. One is Chromosome Reduction (CRed) step which shorten the length of the chromosomes, participating in genetic search, to improve the efficiency. The other is Chromosome Refinement (CRef) step which is a greedy heuristics, rearranging the bits using domain knowledge, to locally improve the fitness of chromosomes. With this hybridization and simple scaffold list, we could obtain longer contigs and scaffolds using GA. We experimented using three actual genome data to test our algorithm. We succeed in restructuring contigs covering about 90% of target genome sequences, and assembling about 500 1,000 fragments into...

• Type: Conference
• Year: 2007
• Where: CEC
• Authors: Satoko Kikuchi, Goutam Chakraborty
• Keywords: Actual Genome Data, Artificial Intelligence, CEC 2007, Enormous Computational Cost, Genome Sequences