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. © Springer-Verlag Berlin Heidelberg 2006.
Cite
CITATION STYLE
Oliver, T. F., Schmidt, B., Jakop, Y., & Maskell, D. L. (2006). Accelerating the viterbi algorithm for profile Hidden Markov Models using reconfigurable hardware. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3991 LNCS-I, pp. 522–529). Springer Verlag. https://doi.org/10.1007/11758501_71
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
