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.
Chapter PDF
Similar content being viewed by others
Keywords
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
Bateman, A., et al.: The PFAM Protein Families Database. Nucleic Acid Research 32, 138–141 (2004)
Chukkapalli, G., Guda, C., Subramaniam, S.: SledgeHMMER: a web server for batch searching the pfam database. Nucleic Acid Research 32, W542–W544 (2004)
Di Blas, A., et al.: The UCSC Kestrel Parallel Processor. IEEE Transactions on Parallel and Distributed Systems 16(1), 80–92 (2005)
Durbin, R., Eddy, S., Krogh, A., Mitchison, G.: Biologcial Sequence Analysis. In: Probabilistic models of proteins and nucleic acids. Cambridge University Press, Cambridge (1998)
Eddy, S.R.: HMMER: Profile HMMs for protein sequence analysis (2003), http://hmmer.wustl.edu
Eddy, S.R.: Profile Hidden Markov Models. Bioinformatics 14, 755–763 (1998)
Horn, D.R., Houston, M., Hanrahan, P.: ClawHMMER: A Streaming HMMer-Search Implementation. In: ACM/IEEE Conference on Supercomputing (2005)
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)
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)
Schmidt, B., Schröder, H.: Massively Parallel Sequence Analysis with Hidden Markov Models. In: International Conference on Scientific & Engineering Computation. World Scientific, Singapore (2002)
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)
Viterbi, A.J.: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory 13(2), 260–269 (1967)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Oliver, T.F., Schmidt, B., Jakop, Y., Maskell, D.L. (2006). Accelerating the Viterbi Algorithm for Profile Hidden Markov Models Using Reconfigurable Hardware. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science – ICCS 2006. ICCS 2006. Lecture Notes in Computer Science, vol 3991. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11758501_71
Download citation
DOI: https://doi.org/10.1007/11758501_71
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34379-0
Online ISBN: 978-3-540-34380-6
eBook Packages: Computer ScienceComputer Science (R0)