A Reinforcement Learning Based Approach to Multiple Sequence Alignment

Multiple sequence alignment plays an important role in comparative genomic sequence analysis, being one of the most challenging problems in bioinformatics. This problem refers to the process of arranging the primary sequences of DNA, RNA or protein to identify regions of similarity that may be a consequence of functional, structural or evolutionary relationships between the sequences. In this paper we tackle multiple sequence alignment from a computational perspective and we introduce a novel approach, based on reinforcement learning, for addressing it. The experimental evaluation is performed on several DNA data sets, two of which contain human DNA sequences. The efficiency of our algorithm is shown by the obtained results, which prove that our technique outperforms other methods existing in the literature and which also indicate the potential of our proposal.

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