Advertisement

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)

Abstract

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.

Keywords

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.

References

  1. 1.
    Bateman, A., et al.: The PFAM Protein Families Database. Nucleic Acid Research 32, 138–141 (2004)CrossRefGoogle Scholar
  2. 2.
    Chukkapalli, G., Guda, C., Subramaniam, S.: SledgeHMMER: a web server for batch searching the pfam database. Nucleic Acid Research 32, W542–W544 (2004)CrossRefGoogle Scholar
  3. 3.
    Di Blas, A., et al.: The UCSC Kestrel Parallel Processor. IEEE Transactions on Parallel and Distributed Systems 16(1), 80–92 (2005)CrossRefGoogle Scholar
  4. 4.
    Durbin, R., Eddy, S., Krogh, A., Mitchison, G.: Biologcial Sequence Analysis. In: Probabilistic models of proteins and nucleic acids. Cambridge University Press, Cambridge (1998)CrossRefGoogle Scholar
  5. 5.
    Eddy, S.R.: HMMER: Profile HMMs for protein sequence analysis (2003), http://hmmer.wustl.edu
  6. 6.
    Eddy, S.R.: Profile Hidden Markov Models. Bioinformatics 14, 755–763 (1998)CrossRefGoogle Scholar
  7. 7.
    Horn, D.R., Houston, M., Hanrahan, P.: ClawHMMER: A Streaming HMMer-Search Implementation. In: ACM/IEEE Conference on Supercomputing (2005)Google Scholar
  8. 8.
    Krogh, A., Brown, M., Mian, S., Sjolander, K., Hausler, D.: Hidden Markov Models in computational biology: Applications to protein modeling. Journal of Molecular Biology 235, 1501–1531 (1994)CrossRefGoogle Scholar
  9. 9.
    Narukawa, K., Kadowaki, T.: Transmembrane regions prediction for G-protein-coupled receptors for hidden markov models. In: Proc. 15th Int. Conf. on Genome Informatics (2004)Google Scholar
  10. 10.
    Schmidt, B., Schröder, H.: Massively Parallel Sequence Analysis with Hidden Markov Models. In: International Conference on Scientific & Engineering Computation. World Scientific, Singapore (2002)Google Scholar
  11. 11.
    Staub, E., Mennerich, D., Rosenthal, A.: The Spin/Ssty repeat: a new motif identified in proteins involved in vertebrate development from gamete to embryo. Genome Biology 3(1) (2001)Google Scholar
  12. 12.
    Viterbi, A.J.: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory 13(2), 260–269 (1967)MATHCrossRefGoogle Scholar
  13. 13.
    Zhu, W., Niu, Y., Lu, J., Gao, G.R.: Implementing Parallel Hmm-Pfam on the EARTH Multithreaded Architecture. In: 2nd IEEE Computer Society Bioinformatics Conference, pp. 549–550 (2003)Google Scholar

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

Personalised recommendations