Accelerating the Viterbi Algorithm for Profile Hidden Markov Models Using Reconfigurable Hardware

  • Timothy F. Oliver
  • Bertil Schmidt
  • Yanto Jakop
  • Douglas L. Maskell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3991)


Profile Hidden Markov Models (PHMMs) are used as a popular tool in bioinformatics for probabilistic sequence database searching. The search operation consists of computing the Viterbi score for each sequence in the database with respect to a given query PHMM. Because of the rapid growth of biological sequence databases, finding fast solutions is of highest importance to research in this area. Unfortunately, the required scan times of currently available sequential software implementations are very high. In this paper we show how reconfigurable hardware can be used as a computational platform to accelerate this application by two orders of magnitude.


Viterbi Algorithm Processor Array Query Model Profile Hide Markov Model Reconfigurable Hardware 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Timothy F. Oliver
    • 1
  • Bertil Schmidt
    • 1
  • Yanto Jakop
    • 1
  • Douglas L. Maskell
    • 1
  1. 1.School of Computer EngineeringNanyang Technological UniversitySingapore

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