Pairwise heuristic sequence alignment algorithm based on deep reinforcement learning

TL;DR

This paper introduces a novel pairwise sequence alignment algorithm leveraging deep reinforcement learning to enhance alignment performance, especially for long genomic sequences, addressing limitations of traditional methods.

Contribution

It proposes a new deep reinforcement learning-based approach specifically designed for pairwise sequence alignment, improving efficiency and accuracy over conventional algorithms.

Findings

Demonstrates improved alignment accuracy on genomic sequences

Shows reduced computational complexity for long sequences

Validates the effectiveness of reinforcement learning in sequence alignment

Abstract

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.