High speed biological sequence analysis with hiddenMarkov models on reconfigurable platforms

Authors:
Timothy F. Oliver;Bertil Schmidt;Yanto Jakop;Douglas L. Maskell
Affiliations:
School of Computer Engineering, Nanyang Technological University, Singapore, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore, Singapore
Venue:
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Year:
2009

Citing 13
Cited 4

P-NAC: A Systolic Array for Comparing Nucleic Acid Sequences

Computer
A parallel computing approach to genetic sequence comparison: the master-worker paradigm with interworker communication

Computers and Biomedical Research
Processing in Memory: The Terasys Massively Parallel PIM Array

Computer
Parallel Computation in Biological Sequence Analysis

IEEE Transactions on Parallel and Distributed Systems
On Parallel Search of DNA Sequence Databases

Proceedings of the Fifth SIAM Conference on Parallel Processing for Scientific Computing
Implementing Parallel Hmm-pfam on the EARTH Multithreaded Architecture

CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
The UCSC Kestrel Parallel Processor

IEEE Transactions on Parallel and Distributed Systems
Hardware Acceleration of Hidden Markov Model Decoding for Person Detection

Proceedings of the conference on Design, Automation and Test in Europe - Volume 3
RC-BLAST: Towards a Portable, Cost-Effective Open Source Hardware Implementation

IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 7 - Volume 08
A reconfigurable, power-efficient adaptive Viterbi decoder

IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Using reconfigurable hardware to accelerate multiple sequence alignment with ClustalW

Bioinformatics
A Systolic FPGA Architecture of Two-Level Dynamic Programming for Connected Speech Recognition

IEICE - Transactions on Information and Systems
Speech silicon: an FPGA architecture for real-time hidden Markov-model-based speech recognition

EURASIP Journal on Embedded Systems

A novel approach to multiple sequence alignment using hadoop data grids

Proceedings of the 2010 Workshop on Massive Data Analytics on the Cloud
A HMMER hardware accelerator using divergences

Proceedings of the Conference on Design, Automation and Test in Europe
A novel approach to Multiple Sequence Alignment using hadoop data grids

International Journal of Bioinformatics Research and Applications
A protein sequence analysis hardware accelerator based on divergences

International Journal of Reconfigurable Computing - Special issue on High-Performance Reconfigurable Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Molecular biologists use hidden Markov models (HMMs) as a popular tool to statistically describe biological sequence 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. Efficient dynamic-programming algorithms exist for solving this problem; however, current solutions still require significant scan times. These scan time requirements are likely to become even more severe due to the rapid growth in the size of these databases. This paper shows how reconfigurable architectures can be used to derive an efficient fine-grained parallelization of the dynamic programming calculation. We describe how this technique leads to significant runtime savings for HMM database scanning on a standard off-the-shelf field-programmable gate array (FPGA).