Biological sequence analysis with hidden markov models on an FPGA

Authors:
Jacop Yanto;Timothy F. Oliver;Bertil Schmidt;Douglas L. Maskell
Affiliations:
School of Computer Engineering, Nanyang Technological University, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore
Venue:
ACSAC'05 Proceedings of the 10th Asia-Pacific conference on Advances in Computer Systems Architecture
Year:
2005

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Abstract

Molecular biologists use Hidden Markov Models (HMMs) as a popular tool to statistically describe protein families. This statistical description can then be used for sensitive and selective database scanning, e.g. new protein sequences are compared with a set of HMMs to detect functional similarities. Even though efficient dynamic programming algorithms exist for the problem, the required scanning time is still very high, and because of the rapid database growth finding fast solutions is of high importance to research in this area. In this paper we present how reconfigurable architectures can be used to derive an efficient fine-grained parallelization of the dynamic programming calculation. It is described how this technique leads to significant runtime savings for HMM database scanning on a standard off-the-shelf FPGA.