Gene transcript clustering: a comparison of parallel approaches

  • Authors:

  • Todd E. Scheetz;Nishank Trivedi;Kevin T. Pedretti;Terry A. Braun;Thomas L. Casavant

  • Affiliations:

  • Departments of Electrical and Computer Engineering, Biomedical Engineering, and Ophthalmology and Visual Sciences, Center for Bioinformatics and Computational Biology, The University of Iowa, Iowa ...;Departments of Electrical and Computer Engineering, Biomedical Engineering, and Ophthalmology and Visual Sciences, Center for Bioinformatics and Computational Biology, The University of Iowa, Iowa ...;Sandia National Laboratories, Albuquerque, New Mexico;Departments of Electrical and Computer Engineering, Biomedical Engineering, and Ophthalmology and Visual Sciences, Center for Bioinformatics and Computational Biology, The University of Iowa, Iowa ...;Departments of Electrical and Computer Engineering, Biomedical Engineering, and Ophthalmology and Visual Sciences, Center for Bioinformatics and Computational Biology, The University of Iowa, Iowa ...

  • Venue:
  • Future Generation Computer Systems - Special issue: Parallel computing technologies
  • Year:
  • 2005

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Abstract

One of the fundamental components of large-scale gene discovery projects is that of clustering of expressed sequence tags (ESTs) from complementary DNA (cDNA) clone libraries. Clustering is used to create non-redundant catalogs and indices of these sequences. In particular, clustering of ESTs is frequently used to estimate the number of genes derived from cDNA-based gene discovery efforts. This paper presents a novel parallel extension to an EST clustering program, UIeluster4, that incorporates alternative splicing information and a new parallelization strategy. The results are compared to other parallelized EST clustering systems in terms of overall processing time and in accuracy of the resulting clustering.