PA-Star: A disk-assisted parallel A-Star strategy with locality-sensitive hash for multiple sequence alignment

• Published in: Journal of parallel and distributed computing Vol. 112; pp. 154 - 165
• Main Authors: Sundfeld, Daniel, Razzolini, Caina, Teodoro, George, Boukerche, Azzedine, de Melo, Alba Cristina Magalhaes Alves
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.02.2018
• Subjects: A-Star; Locality-sensitive hash; Multiple sequence alignment; Parallel algorithms; A-Star; Multiple sequence alignment; Locality-sensitive hash; Parallel algorithms
• ISSN: 0743-7315; 1096-0848
• DOI: 10.1016/j.jpdc.2017.04.014

Multiple Sequence Alignment (MSA) is a basic operation in Bioinformatics, and is used to highlight the similarities among a set of sequences. The MSA problem was proven NP-Hard, thus requiring a high amount of memory and computing power. This problem can be modeled as a search for the path with minimum cost in a graph, and the A-Star algorithm has been adapted to solve it sequentially and in parallel. The design of a parallel version for MSA with A-Star is subject to challenges such as irregular dependency pattern and substantial memory requirements. In this paper, we propose PA-Star, a locality-sensitive multithreaded strategy based on A-Star, which computes optimal MSAs using both RAM and disk to store nodes. The experimental results obtained in 3 different machines show that the optimizations used in PA-Star can achieve an acceleration of 1.88× in the serial execution, and the parallel execution can attain an acceleration of 5.52× with 8 cores. We also show that PA-Star outperforms a state-of-the-art MSA tool based on A-Star, executing up to 4.77× faster. Finally, we show that our disk-assisted strategy is able to retrieve the optimal alignment when other tools fail. •An A-Star based algorithm that retrieves optimal multiple sequence alignments.•Locality-sensitive hash functions to assign work to cores.•Disk-assisted strategy which augments the amount of memory.•Better performance than state-of-the-art.