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Exploiting parallelism on progressive alignment methods.

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Τίτλος: Exploiting parallelism on progressive alignment methods.
Συγγραφείς: Orobitg, Miquel, Guirado, Fernando, Notredame, Cedric, Cores, Fernando
Πηγή: Journal of Supercomputing; Nov2011, Vol. 58 Issue 2, p186-194, 9p
Θεματικοί όροι: Sequence alignment, Parallel programs (Computer programs), Heuristic algorithms, Computer programming, Amino acid sequence
Περίληψη: Multiple Sequence Alignment (MSA) constitutes an extremely powerful tool for important biological applications such as phylogenetic analysis, identification of conserved motifs and domains and structure prediction. In spite of the improvement in speed and accuracy introduced by MSA programs, the computational requirements for large-scale alignments requires high-performance computing and parallel applications. In this paper we present an improvement to a parallel implementation of T-Coffee, a widely used MSA package. Our approximation resolves the bottleneck of the progressive alignment stage on MSA. This is achieved by increasing the degree of parallelism by balancing the guide tree that drives the progressive alignment process. The experimental results show improvements in execution time of over 68% while maintaining the biological accuracy. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Supercomputing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  – Url: https://dx.doi.org/doi:10.1007/s11227-009-0359-5
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  Data: Exploiting parallelism on progressive alignment methods.
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  Data: <searchLink fieldCode="AR" term="%22Orobitg%2C+Miquel%22">Orobitg, Miquel</searchLink><br /><searchLink fieldCode="AR" term="%22Guirado%2C+Fernando%22">Guirado, Fernando</searchLink><br /><searchLink fieldCode="AR" term="%22Notredame%2C+Cedric%22">Notredame, Cedric</searchLink><br /><searchLink fieldCode="AR" term="%22Cores%2C+Fernando%22">Cores, Fernando</searchLink>
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  Data: Journal of Supercomputing; Nov2011, Vol. 58 Issue 2, p186-194, 9p
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  Data: <searchLink fieldCode="DE" term="%22Sequence+alignment%22">Sequence alignment</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+programs+%28Computer+programs%29%22">Parallel programs (Computer programs)</searchLink><br /><searchLink fieldCode="DE" term="%22Heuristic+algorithms%22">Heuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+programming%22">Computer programming</searchLink><br /><searchLink fieldCode="DE" term="%22Amino+acid+sequence%22">Amino acid sequence</searchLink>
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  Data: Multiple Sequence Alignment (MSA) constitutes an extremely powerful tool for important biological applications such as phylogenetic analysis, identification of conserved motifs and domains and structure prediction. In spite of the improvement in speed and accuracy introduced by MSA programs, the computational requirements for large-scale alignments requires high-performance computing and parallel applications. In this paper we present an improvement to a parallel implementation of T-Coffee, a widely used MSA package. Our approximation resolves the bottleneck of the progressive alignment stage on MSA. This is achieved by increasing the degree of parallelism by balancing the guide tree that drives the progressive alignment process. The experimental results show improvements in execution time of over 68% while maintaining the biological accuracy. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Supercomputing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Heuristic algorithms
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              Text: Nov2011
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