Genome Assembly Using Reinforcement Learning

Abstract

Reinforcement learning (RL) aims to build intelligent agents able to optimally act after the training process to solve a given goal task in an autonomous and non-deterministic fashion. It has been successfully employed in several areas; however, few RL-based approaches related to genome assembly have been found, especially when considering real input datasets. De novo genome assembly is a crucial step in a number of genome projects, but due to its high complexity, the outcome of state-of-art assemblers is still insufficient to assist researchers in answering all their scientific questions properly. Hence, the development of better assembler is desirable and perhaps necessary, and preliminary studies suggest that RL has the potential to solve this computational task. In this sense, this paper presents an empirical analysis to evaluate this hypothesis, particularly in higher scale, through performance assessment along with time and space complexity analysis of a theoretical approach to the problem of assembly proposed by [2] using the RL algorithm Q-learning. Our analysis shows that, although space and time complexities are limiting scale issues, RL is shown as a viable alternative for solving the DNA fragment assembly problem.