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Improved parallel and sequential walking tree methods for biological string alignments

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Title: Improved parallel and sequential walking tree methods for biological string alignments
Authors: Oregon State University. Department of Computer Science, Cull, Paul, 1943-, Hsu, Tai C.
Publisher Information: Oregon State University, Dept. of Computer Science
Collection: ScholarsArchive@OSU (Oregon State University)
Subject Terms: Sequence alignment (Bioinformatics) -- Computer programs, Matching theory, Nucleotide sequence -- Computer programs, Heuristic programming, Parallel algorithms
Description: Approximate string matching is commonly used to align genetic sequences (DNA or RNA) to determine their shared characteristics. Most genetic string matching methods are based on the edit-distance model, which does not provide alignments for inversions and translocations. Recently, a heuristic called the Walking Tree Method [2, 3] has been developed to solve this problem. Unlike other heuristics, it can handle more than one level of inversion, i.e., inversions within inversions. Furthermore, it tends to capture the matched strings’ genes while other heuristics fail. There are two versions of the original walking tree heuristics: the score version gives only the alignment score, the alignment version gives both the score and the alignment mapping between the strings. The score version runs in quadratic time and uses linear space while the alignment version uses an extra log factor for time and space. In this paper, we will briefly describe the walking tree method and the original sequential and parallel algorithms. We will explain why different parallel algorithms are needed for a network of workstations rather than the original algorithm which worked well on a symmetric multi-processor. Our improved parallel method also led to a quadratic time sequential algorithm that uses less space. We give an example of our parallel method. We describe several experiments that show speedup linear in the number of processors, but eventual drop off in speedup as the communication network saturates. For big enough strings, we found linear speedup for all processors we had available. These results suggest that our improved parallel method will scale up as both the size of the problem and the number of processors increase. We include two figures that use real biological data and show that the walking tree methods can find translocations and inversions in DNA sequences and also discover unknown genes. ; Keywords: heuristic, inversions, sequence alignment, parallel algorithm, walking tree, dynamic programming, genome alignment, ...
Document Type: report
Language: English
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Relation: https://ir.library.oregonstate.edu/concern/technical_reports/xd07gv26f
Availability: https://ir.library.oregonstate.edu/concern/technical_reports/xd07gv26f
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  – Url: https://ir.library.oregonstate.edu/concern/technical_reports/xd07gv26f#
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  Group: Ti
  Data: Improved parallel and sequential walking tree methods for biological string alignments
– Name: Author
  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Oregon+State+University%2E+Department+of+Computer+Science%22">Oregon State University. Department of Computer Science</searchLink><br /><searchLink fieldCode="AR" term="%22Cull%2C+Paul%2C+1943-%22">Cull, Paul, 1943-</searchLink><br /><searchLink fieldCode="AR" term="%22Hsu%2C+Tai+C%2E%22">Hsu, Tai C.</searchLink>
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  Data: Oregon State University, Dept. of Computer Science
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  Data: ScholarsArchive@OSU (Oregon State University)
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  Data: <searchLink fieldCode="DE" term="%22Sequence+alignment+%28Bioinformatics%29+--+Computer+programs%22">Sequence alignment (Bioinformatics) -- Computer programs</searchLink><br /><searchLink fieldCode="DE" term="%22Matching+theory%22">Matching theory</searchLink><br /><searchLink fieldCode="DE" term="%22Nucleotide+sequence+--+Computer+programs%22">Nucleotide sequence -- Computer programs</searchLink><br /><searchLink fieldCode="DE" term="%22Heuristic+programming%22">Heuristic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+algorithms%22">Parallel algorithms</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: Approximate string matching is commonly used to align genetic sequences (DNA or RNA) to determine their shared characteristics. Most genetic string matching methods are based on the edit-distance model, which does not provide alignments for inversions and translocations. Recently, a heuristic called the Walking Tree Method [2, 3] has been developed to solve this problem. Unlike other heuristics, it can handle more than one level of inversion, i.e., inversions within inversions. Furthermore, it tends to capture the matched strings’ genes while other heuristics fail. There are two versions of the original walking tree heuristics: the score version gives only the alignment score, the alignment version gives both the score and the alignment mapping between the strings. The score version runs in quadratic time and uses linear space while the alignment version uses an extra log factor for time and space. In this paper, we will briefly describe the walking tree method and the original sequential and parallel algorithms. We will explain why different parallel algorithms are needed for a network of workstations rather than the original algorithm which worked well on a symmetric multi-processor. Our improved parallel method also led to a quadratic time sequential algorithm that uses less space. We give an example of our parallel method. We describe several experiments that show speedup linear in the number of processors, but eventual drop off in speedup as the communication network saturates. For big enough strings, we found linear speedup for all processors we had available. These results suggest that our improved parallel method will scale up as both the size of the problem and the number of processors increase. We include two figures that use real biological data and show that the walking tree methods can find translocations and inversions in DNA sequences and also discover unknown genes. ; Keywords: heuristic, inversions, sequence alignment, parallel algorithm, walking tree, dynamic programming, genome alignment, ...
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  Data: https://ir.library.oregonstate.edu/concern/technical_reports/xd07gv26f
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      – Text: English
      – Text: unknown
    Subjects:
      – SubjectFull: Sequence alignment (Bioinformatics) -- Computer programs
        Type: general
      – SubjectFull: Matching theory
        Type: general
      – SubjectFull: Nucleotide sequence -- Computer programs
        Type: general
      – SubjectFull: Heuristic programming
        Type: general
      – SubjectFull: Parallel algorithms
        Type: general
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      – TitleFull: Improved parallel and sequential walking tree methods for biological string alignments
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            NameFull: Oregon State University. Department of Computer Science
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            NameFull: Cull, Paul, 1943-
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            NameFull: Hsu, Tai C.
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