Parallelization of a local similarity algorithm

Xiaoqiu Huang, Webb Miller, Scott Schwartz, Ross Cameron Hardison

Research output: Contribution to journalArticle

23 Citations (Scopus)

Abstract

The local similarity problem is to determine the similar regions within two given sequences. We recently developed a dynamic programming algorithm for the local similarity problem that requires only space proportional to the sum of the two sequence lengths, whereas earlier methods use space proportional to the product of the lengths. In this paper, we describe how to parallelize the new algorithm and present results of experimental studies on an Intel hypercube. The parallel method provides rapid, high-resolution alignments for users of our software toolkit for pairwise sequence comparison, as illustrated here by a comparison of the chloroplast genomes of tobacco and liverwort.

Original languageEnglish (US)
Pages (from-to)155-165
Number of pages11
JournalBioinformatics
Volume8
Issue number2
DOIs
StatePublished - Apr 1 1992

Fingerprint

Parallelization
Directly proportional
Hepatophyta
Chloroplast Genome
Sequence Comparison
Tobacco
Parallel Methods
Pairwise Comparisons
Chloroplast
Hypercube
Dynamic programming
Dynamic Programming
Experimental Study
Genome
Alignment
High Resolution
Software
Genes
Similarity

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this

Huang, Xiaoqiu ; Miller, Webb ; Schwartz, Scott ; Hardison, Ross Cameron. / Parallelization of a local similarity algorithm. In: Bioinformatics. 1992 ; Vol. 8, No. 2. pp. 155-165.
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Parallelization of a local similarity algorithm. / Huang, Xiaoqiu; Miller, Webb; Schwartz, Scott; Hardison, Ross Cameron.

In: Bioinformatics, Vol. 8, No. 2, 01.04.1992, p. 155-165.

Research output: Contribution to journalArticle

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