Exploiting parallelism on progressive alignment methods

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 com...

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Bibliographic Details
Published inJournal of supercomputing Vol. 58; no. 2; pp. 186 - 194
Main Authors Orobitg, Miquel, Guirado, Fernando, Notredame, Cedric, Cores, Fernando
Format Journal Article Conference Proceeding
LanguageEnglish
Published Boston Springer US 01.11.2011
Springer
Subjects
Applied sciences
Biological and medical sciences
Compilers
Computer Science
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Data processing. List processing. Character string processing
Exact sciences and technology
Fundamental and applied biological sciences. Psychology
General aspects
Interpreters
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Memory organisation. Data processing
Processor Architectures
Programming Languages
Software
Parallelism
T-Coffee
Multiple Sequence Alignment
Supercomputer
Bottleneck
High performance
Parallel algorithm
Tree(graph)
Sequence alignment
Phylogeny
Distributed computing
Bioinformatics
Execution time
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Summary: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.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-009-0359-5