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Efficient clustering of large EST data sets on parallel computers.

Anantharaman Kalyanaraman1, Srinivas Aluru, Suresh Kothari

  • 1Department of Computer Science, Iowa State University, Ames, IA 50011, USA.

Nucleic Acids Research
|May 29, 2003
PubMed
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PaCE software efficiently clusters expressed sequence tags (ESTs) on parallel computers, enabling faster gene identification and analysis of large datasets. This tool significantly reduces computational time and memory requirements for EST clustering.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Clustering expressed sequence tags (ESTs) is crucial for gene identification, expression studies, and genetic variation discovery.
  • Existing software often struggles with the computational demands of large-scale EST datasets.

Purpose of the Study:

  • To develop and evaluate PaCE (Parallel Clustering of ESTs), a software program designed for rapid and efficient EST clustering on parallel systems.
  • To address limitations in memory usage and computational time associated with large-scale EST data analysis.

Main Methods:

  • Developed memory-efficient algorithms to achieve linear memory complexity relative to input size.
  • Integrated algorithmic techniques to optimize computational work without compromising clustering quality.

Related Experiment Videos

  • Utilized parallel processing to accelerate runtime and enable the analysis of larger EST datasets.

    • Main Results:

    • Successfully clustered 168,200 Arabidopsis ESTs in 15 minutes on a 30-node IBM xSeries cluster.
    • Clustered 327,632 rat ESTs in 47 minutes and 420,694 Triticum aestivum ESTs in 3.25 hours.
    • Demonstrated high-quality clustering comparable to CAP3 and enabled analysis of significantly larger datasets.

    Conclusions:

    • PaCE offers a significant advancement in the speed and scalability of EST clustering.
    • The software facilitates multiple analysis runs, empowering biologists with enhanced EST data interpretation tools.
    • PaCE has been successfully applied to EST data from 23 plant species, with results available on the PlantGDB website.