A configware approach for high-speed parallel analysis of genomic data

Journal of Circuits, Systems and Computers

Volumn 16, Issue 4, 2007, Pages 527-540

  Lopes, Heitor S ^a   Erig Lima, Carlos R ^a   Murata, Norton J ^a  

^a UNIVERSIDADE TECNOLÓGICA FEDERAL DO PARANÁ   (Brazil)

Author keywords

Decision tree; DNA; FPGA; Pattern recognition

Indexed keywords

BIOINFORMATICS; COMPUTER SOFTWARE; DECISION TREES; DNA; FIELD PROGRAMMABLE GATE ARRAYS (FPGA); MOLECULAR BIOLOGY; PATTERN RECOGNITION;

BOOLEAN MINIMIZATION; CUSTOM-COMPUTING MACHINES; GENERAL-PURPOSE PROCESSORS; RECONFIGURABLE SYSTEMS; SPLICE JUNCTION DETECTION;

DATA REDUCTION;

EID: 37149031306     PISSN: 02181266     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218126607003885     Document Type: Article

References (24)

1. M. Kanehisa and P. Bork, Bioinformatics in the post-sequence era, Nat. Genet. 22(suppl.) (2003) 305-310.
2. R. Luethy and C. Hoover, Hardware and software systems for accelerating common bioinformatics sequence analysis algorithms, DDT: Biosilico 2 (2004) 12-17.
3. J. Becker and R. Hartenstein, Configware and morphware going mainstream, J. Syst. Architec. 49 (2003) 127-142.
4. S. Hauck, The roles of FPGA's in reprogrammable systems, P. IEEE 86 (1998) 615-636.
5. H. S. Lopes and G. L. Moritz, A distributed approach for multiple sequence alignment using a parallel virtual machine, Proc. 27th Ann. Int. Conf. IEEE EMBS (2005), pp. 2843-2846.
6. A. Marongiu, P. Palazzari and V. Rosato, Designing hardware for protein sequence analysis, Bioinformatics 19 (2003) 1739-1740.
7. A. L. Lehninger, D. L. Nelson and M. M. Cox, Principles of Biochemistry, 2nd edn. (Worth Publishers, New York, 1998), pp. 134-137.
8. J. D. Watson, H. H. Hopkins, J. W. Roberts, J. A. Steitz and A. M. Weiner, The Molecular Biology of the Gene (Benjamin-Cummings, Menlo-Park, 1987).
9. The Institute of Electrical and Electronics Engineers, 1076 IEEE Standard VHDL Language Reference Manual (IEEE Press, New York, 2002).
10. S. A. Ito and L. Carro, A comparison of microcontrollers targeted to FPGA-based embedded applications, Proc. IEEE 13th Symp. Integrated Circuits and Systems Design (2000), pp. 397-402.
11. D. Michie, D. J. Spiegehalter and C. C. Taylor, Machine Learning, Neural and Statistical Classification (Ellis Horwood, Chichester, 1994).
12. I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (Morgan Kaufmann, San Francisco, 2000).
13. M. O. Noordewier, G. G. Towell and J. W. Shavlik, Training knowledge-based neural networks to recognize genes in DNA sequences, Advances in Neural Information Processing Systems, Vol. 3, eds. R. Lippmann et al. (Morgan Kaufmann, San Francisco, 1991).
14. M. Canella, F. Miglioli, A. Bogliolo, E. Petraglio and E. Sanchez, Performing DNA comparison on a bio-inspired tissue of FPGAs, Proc. IEEE Int. Parallel and Distributed Processing Symp. (2003), pp. 193-199.
15. T. Oliver, B. Schmidt, D. Natghan, R., Clemens and D. Maskell, Using reconfigurable hardware to accelerate multiple sequence alignment with ClustaIW, Bioinformatics 21 (2005) 3431-3432.
16. Y. Yamaguchi, T. Maruyama and A. Konagaya, High speed homology search with FPGAs, Proc. Pacific Symp. Biocomputing (2002), pp. 271-282.
17. N. B. Armstrong, Jr., H. S. Lopes and C. R. E. Lima, Preliminary steps towards protein folding prediction using reconfigurable computing, Proc. IEEE Int. Conf. Reconfigurable Computing and FPGA's, San Luis de Potosi, Mexico (IEEE Computer Press, Piscataway, 2006), pp. 92-98.
18. S. Hettich, C. L. Blake and C. J. Merz, UCI repository of machine learning databases, University of California at Irvine (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html.
19. G. G. Towell, Symbolic knowledge and neural networks: Insertion, refinement and extraction, PhD. thesis, Department of Computer Science, University of Wisconsin-Madison (1991).
20. D. A. Benson, I. Karsch-Mizrachi, D. J. Lipman, J. Ostell and D. L. Wheeler, GenBank, Nucleic Acids Res. 34 (2006) D16-D20.
21. J. R. Quinlan, C4-5: Programs for Machine Learning (Morgan Kaufmann, San Francisco, 1993).
22. Boolean minimizer, http://www.gmdsoft.de/mitsch/software/boolmin/.
23. R. Rudell and A. Sangiovanni-Vincentelli, Exact minimization of multiple-valued functions for PLA, Proc. Int. Conf. Computer-Aided Design (1986), pp. 352-355.
24. Altera Corporation, http://www.altera.com/.