Abstract
The search of a multiple sequence alignment (MSA) is a well-known problem in bioinformatics that consists in finding a sequence alignment of three or more biological sequences. In this paper, we propose a parallel iterative algorithm for the global alignment of multiple biological sequences. In this algorithm, a number of processes work independently at the same time searching for the best MSA of a set of sequences. It uses a Longest Common Subsequence (LCS) technique in order to generate a first MSA. An iterative process improves the MSA by applying a number of operators that have been implemented to produce more accurate alignments. Simulations were made using sequences from the UniProKB protein database. A preliminary performance analysis and comparison with several common methods for MSA shows promising results. The implementation was developed on a cluster platform through the use of the standard Message Passing Interface (MPI) library.
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