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Preview of electronic edition @ stanford.edu (open access)
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Summary

Computer-aided sequence analysis is crucial in biological research, with sequence information from genome sequencing projects filling databases quickly, making human examination impossible. Computer algorithms are used to identify important sequences, but simple algorithms used on simple data representations can lead to missed hits due to lack of sensitivity. The implementation of sensitive sequence analysis algorithms, such as Smith-Waterman and Hidden Markov Models, on the Kestrel parallel processor, a single-instruction multiple-data (SIMD) processor, is described. The Kestrel processor achieves performance 6-20 times faster than standard computers on these algorithms, addressing the computational bottleneck in sequence analysis.

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Computer-aided sequence analysis is crucial in biological research, with sequence information from genome sequencing projects filling databases quickly, making...

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  • File Format: PDF
  • File Size: 472 KB
  • Pages: 12
  • Language: EN
  • Total Downloads: 99
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