title: A case study of high-throughput biological data processing on parallel platforms.
abstract: BACKGROUND
  Analysis of large biological data sets 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 algorithm in a massively parallel (PAR) mode for finding pairwise protein structure similarities and aligning protein structures from the Protein Data Bank. CEPAR design and implementation are described and results provided for the efficiency of the algorithm when run on a large number of processors.
  
  
  BACKGROUND
  Source code is available by contacting one of the authors.
authors.affiliation: San Diego Supercomputer Center, University of California San Diego, La Jolla 92093, USA.
authors.forename: D
authors.initials: D
authors.lastname: Pekurovsky
authors.forename: I N
authors.initials: IN
authors.lastname: Shindyalov
authors.forename: P E
authors.initials: PE
authors.lastname: Bourne
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003205:Computing Methodologies; D016247:Information Storage and Retrieval / Q000379:methods*; D008958:Models, Molecular; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000145:classification*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid
chemical_list: D011506:Proteins