  Various methods have been developed to analyze the association between
organisms and their genomic sequences. Among them, sequence alignment is the
most frequently used for comparative analysis of biological genomes. However,
the traditional sequence alignment method is considerably complicated in
proportion to the sequences' length, and it is significantly challenging to
align long sequences such as a human genome. Currently, several multiple
sequence alignment algorithms are available that can reduce the complexity and
improve the alignment performance of various genomes. However, there have been
relatively fewer attempts to improve the alignment performance of the pairwise
alignment algorithm. After grasping these problems, we intend to propose a new
sequence alignment method using deep reinforcement learning. This research
shows the application method of the deep reinforcement learning to the sequence
alignment system and the way how the deep reinforcement learning can improve
the conventional sequence alignment method.
