Genome sequence assembly using metaheuristics

Genome sequencing has revolutionized the field of medicine. However, it is a time-consuming process, and even with existing technology, it takes more than a day to sequence a human genome. Hence, there is a need for technology that can reduce the time required for genome sequence processing. In this chapter, we discuss the fundamental concepts behind the formulation of genome sequencing as an optimization problem and detail the steps required to solve this problem using metaheuristics. We use Particle Swarm Optimization (PSO), Cuckoo Search (CS), and Gray Wolf Optimizer (GWO), which are three well-known and well-researched algorithms, to demonstrate the application of metaheuristic algorithms for the genome sequencing problem. The comparative performance of these algorithms is demonstrated on eight benchmark datasets from the GenFrag repository. The conducted experiments show that GWO provides better solutions than the other two algorithms by consistently delivering solutions with better fitness.