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Sequence analysis with the Kestrel SIMD parallel processor.

L Grate¹, M Diekhans, D Dahle

¹Department of Computer Engineering, University of California, Santa Cruz, CA 95064, USA. leslie@cse.ucsc.edu

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

|March 27, 2001

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Sensitive computer algorithms accelerate biological research by analyzing vast genomic data. Implementing algorithms like Smith-Waterman on the Kestrel parallel processor significantly speeds up sequence analysis, improving discovery rates.

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Biological research generates massive genomic datasets, overwhelming manual analysis.
Computer algorithms are essential for identifying significant biological information within large databases.
Current methods often use simplified algorithms due to computational demands, leading to missed discoveries.

Purpose of the Study:

To implement sensitive sequence analysis algorithms on a parallel processing architecture.
To evaluate the performance of these algorithms for biological data analysis.
To enhance the efficiency and sensitivity of genomic data examination.

Main Methods:

Implementation of Smith-Waterman and Hidden Markov Model algorithms.
Utilized Viterbi and Expectation Maximization methods for analysis.

Related Experiment Videos

Deployed on the Kestrel single-instruction multiple-data (SIMD) parallel processor.

Main Results:

Achieved performance improvements ranging from 6 to 20 times faster than standard computers.
Demonstrated the effectiveness of sensitive algorithms on parallel hardware.
Successfully processed complex biological sequence data with increased speed.

Conclusions:

Parallel processing significantly accelerates sensitive sequence analysis.
The Kestrel processor offers a viable platform for high-performance bioinformatics.
Enhanced computational speed improves the capacity for biological discovery from genomic data.