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Bioinformatics Advance Access published online on March 25, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth184
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received August 31, 2003
Revised February 4, 2004
Accepted February 7, 2004

Article

A case study of high-throughput biological data processing on parallel platforms

D. Pekurovsky 1, I. N. Shindyalov 1, P. E. Bourne 2*

1 San Diego Supercomputer Center, MC 0505, 9500 Gilman Drive, La Jolla, California 92093, USA
2 Department of Pharmacology, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA

* To whom correspondence should be addressed. E-mail: bourne{at}sdsc.edu.


  Abstract

Motivation: Analysis of large biological datasets using a variety of parallel processor computer architectures is a common task in bioinformatics. The efficiency of the analysis can be significantly improved by properly handling redundancy present in these data combined with taking advantage of the unique features of these compute architectures.

Results: We describe a generalized approach to this analysis, but present specific results using the program CEPAR, an efficient implementation of the Combinatorial Extension (CE) algorithm in a massively parallel (PAR) mode for finding pair-wise protein structure similarities and aligning protein structures from the Protein Data Bank (PDB). CEPAR design and implementation are described and results provided for the efficiency of the algorithm when run on a large number of processors.

Availability: Source code is available by contacting one of the authors.


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