PA-Star: A disk-assisted parallel A-Star strategy with locality-sensitive hash for multiple sequence alignment
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 mini...
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| Published in | Journal of parallel and distributed computing Vol. 112; pp. 154 - 165 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier Inc
01.02.2018
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| Online Access | Get full text |
| ISSN | 0743-7315 1096-0848 |
| DOI | 10.1016/j.jpdc.2017.04.014 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Boukerche, Azzedine Sundfeld, Daniel Razzolini, Caina Teodoro, George de Melo, Alba Cristina Magalhaes Alves |
| Author_xml | – sequence: 1 givenname: Daniel surname: Sundfeld fullname: Sundfeld, Daniel email: sund@unb.br organization: Department of Computer Science, University of Brasilia (UnB), Brazil – sequence: 2 givenname: Caina orcidid: 0000-0002-7179-2020 surname: Razzolini fullname: Razzolini, Caina email: cfbrazzolini@gmail.com organization: Department of Computer Science, University of Brasilia (UnB), Brazil – sequence: 3 givenname: George surname: Teodoro fullname: Teodoro, George email: teodoro@cic.unb.br organization: Department of Computer Science, University of Brasilia (UnB), Brazil – sequence: 4 givenname: Azzedine surname: Boukerche fullname: Boukerche, Azzedine email: boukerch@site.uottawa.ca organization: School of Information Technology and Engineering (SITE), University of Ottawa, Canada – sequence: 5 givenname: Alba Cristina Magalhaes Alves orcidid: 0000-0001-5191-5209 surname: de Melo fullname: de Melo, Alba Cristina Magalhaes Alves email: alves@unb.br organization: Department of Computer Science, University of Brasilia (UnB), Brazil |
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| Cites_doi | 10.1089/cmb.1994.1.337 10.1609/aaai.v29i1.9367 10.1016/j.artint.2012.10.007 10.1089/106652701446134 10.1093/bioinformatics/15.1.87 10.1137/0148063 10.1145/1455248.1455250 10.1016/0022-2836(70)90057-4 10.1609/aaai.v27i1.8626 10.1109/TSSC.1968.300136 10.1609/aaai.v30i1.10065 10.1137/0149094 10.1007/978-3-319-02624-4_5 |
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