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Parallel pattern identification in biological sequences on clusters.

Chun-Hsi Huang·Sanguthevar Rajasekaran
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Abstract

Tandem repeats are ubiquitous sequence features in both prokaryotic and eukaryotic genomes. They are known to cause several inherited neurological diseases in humans. Identifying these patterns is a highly computation-intensive process. Previous parallel implementations use straightforward domain decomposition based on existing sequential algorithms and rely on parallel machines with low-latency interconnection network and fast hardware support for processor synchronization. Our research exploits the superior cost effectiveness and flexibility achieved through low-cost clusters to speed up biological computations by designing communication-efficient parallel algorithms for pattern identification. This paper presents a low communication-overhead parallel algorithm for pattern identification in biological sequences. Given a biological sequence of length n and a pattern of length m, we conclude an algorithm with five computation/communication phases, each requiring O(n) computation time and only O(p) message units. The low communication overhead of the algorithm is essential in achieving reasonable speedups on clusters, where the inter-processor communication latency is usually higher.

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AlgorithmsCluster AnalysisComputer Communication NetworksComputing MethodologiesGene Expression ProfilingPattern Recognition, AutomatedSequence AlignmentSequence Analysis, DNATandem Repeat SequencesComparative StudyEvaluation StudyJournal ArticleResearch Support, Non-U.S. Gov'tValidation Study

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Chun-Hsi Huang, Sanguthevar Rajasekaran. 2003. Parallel pattern identification in biological sequences on clusters.. https://doi.org/10.1109/tnb.2003.810165

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