CFP last date
20 May 2024
Reseach Article

Article:A Survey of Bioinformatics Applications on Parallel Architectures

by Binay Kumar Pandey, Sanjay Kumar Pandey, Digvijay Pandey
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 23 - Number 4
Year of Publication: 2011
Authors: Binay Kumar Pandey, Sanjay Kumar Pandey, Digvijay Pandey
10.5120/2877-3744

Binay Kumar Pandey, Sanjay Kumar Pandey, Digvijay Pandey . Article:A Survey of Bioinformatics Applications on Parallel Architectures. International Journal of Computer Applications. 23, 4 ( June 2011), 21-25. DOI=10.5120/2877-3744

@article{ 10.5120/2877-3744,
author = { Binay Kumar Pandey, Sanjay Kumar Pandey, Digvijay Pandey },
title = { Article:A Survey of Bioinformatics Applications on Parallel Architectures },
journal = { International Journal of Computer Applications },
issue_date = { June 2011 },
volume = { 23 },
number = { 4 },
month = { June },
year = { 2011 },
issn = { 0975-8887 },
pages = { 21-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume23/number4/2877-3744/ },
doi = { 10.5120/2877-3744 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:09:18.677925+05:30
%A Binay Kumar Pandey
%A Sanjay Kumar Pandey
%A Digvijay Pandey
%T Article:A Survey of Bioinformatics Applications on Parallel Architectures
%J International Journal of Computer Applications
%@ 0975-8887
%V 23
%N 4
%P 21-25
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Based on bioinformatics algorithm, there are a wide range of implementations. With the urge for program speed, many applications take the heuristic approach to compensate running time. One of the most critical shortcomings of this technique is the loss of optimality, i.e. the desired results may not always be found. To overcome this problem, many different hardware architectures have been experimented for bioinformatics algorithm such as cell broadband engine, cluster and compute unified device architecture where, the main technique for obtaining high performance is to parallelize the task to be run simultaneously by multiple vector execution units with single instruction multiple data and by multiple processors with multiple instruction multiple data. In this paper we presents a survey of data intensive bioinformatics applications on variety of parallel Architecture that are available for accelerating the processing of large biological data set.

References
  1. Y. L. Kuo, C. T. Yang, C. L. Lai and T. M. Tseng, “Construct a Grid Computing Environment for Bioinformatics”, Proceedings of the Seventh International Symposium on Parallel Architectures, Algorithms and Networks, Hong Kong, 10-12 May 2004
  2. B. Turgeon, D. McCourt, J. Cowper, F. Palmer, S. McClean and W. Dubitzky, “Can the Grid help to solve the data integration problems in molecular biology”, Third IEEE/ACM International Symposium in Cluster Computing and the Grid, Tokyo (Japan), Page(s):594 – 600, 12-14 May 2003
  3. B. Wang, Y. Liu, J. Yun and S. Liu, “Application Research of Protein Structure Prediction Based Support Vector Machine”, In International Symposium on knowledge Acquisition and Modelling, Wuhan (China), Page(s): 581-584, 21-22 Dec 2008
  4. S. Parthasarathy and M. Coatney, “Efficient Discovery of Common Substructures in Macromolecules” , In Proceedings of IEEE International Conference of Data Mining, Maebashi City (Japan), Page(s):362-369, 9-12 Dec 2002
  5. E. R. Mardis, “Engineering in genomics: Technical improvements in high throughput genome sequencing”, Engineering in Medicine and Biology Magazine, vol.14 (6), Page(s):794 – 797, Nov-Dec 1995
  6. S. Zhaofeng, Q. Hongze, Z. Daming and H. Feng, “New Evolutionary Subset: Application to Symbiotic Evolutionary Algorithm for Job-Shop Scheduling Problem”, Fourth International Conference Natural Computation, vol.1, Jinan (China), Page(s):470 – 475, 18-20 Oct. 2008
  7. C. Kwangmin, A. Saple and S. Kim, “Genome Data Type: a Vehicle to Deliver a Genome Comparison System on the Web”, Sixth IEEE International Conference on Data Mining, Hong Kong (China), Page(s):142 – 146, 18-22 Dec- 2006
  8. Y. Liu, T. Yan and R. Zhang, “Protein Sequence Analysis Based on Smooth PST”, Third International Conference on Bioinformatics and Biomedical Engineering, Beijing (China), Page(s): 1-4, 11-13 June 2009
  9. K. H. Emden, “Molecular Phylogenetic Analysis and real Life Data”, Computing in Science and Engineering, vol.7 (3), Page(s): 86-91, May-June 2005
  10. Y. B. Choi and I. K. Cheang, “Providing white page service to the Human Genome Project”, In Proceedings IEEE International Conference on Communication Systems, vol.3, Singapore, Pages(s): 1185-1189, 14-18 Nov 1994
  11. T. Almas, Z. Tesfave and I. D. Donn, “Simulation of Electrical Conduction in Cardiac Tissues on High Performance Computing”, 19th International Symposium on High Performance Computing and Application, Guelph Ontario (Canda), Pages(s):244-250, 14-18 May 2005
  12. Z. Aung, W. Fu and K. L. Tan, “An efficient index-based protein structure database searching method” International Conference on Database System for Advance Application, Kyoto(Japan), Page(s):311-318, 26-28 March 2003
  13. C. Wilks and S. Khuri, “A Fast Shotgun Assembly Heuristic”, IEEE Computational Systems and Bioinformatics Conference, Stanford (CA), Page(s):122-123, 8-11 August 2005
  14. I. Gorton, P. Greenfield, A. Szalay and R. Williams, “Data-Intensive Computing in the 21st Century”, In Computer Magazine of IEEE Computer Society, vol41(4), Page(s) 30-32, April 2008
  15. V. Sachdeva, M. Kistler, E. Speight and T. H. K. Tzeng, “Exploring the Viability of the Cell Broadband Engine for Bioinformatics Applications”, IEEE international Parallel and Distributed Processing Symposium, Long Beach California (USA), Page(s):1-8, 26-30 March 2007
  16. T. F. Smith and M. S. Waterman, “Identification of Common Molecular Subsequences”, Journal of Molecular Biology, vol. 147(1), Page(s): 195-197, 1981
  17. Y. Chen, S. Yu and M. Leng, “Parallel Sequence Alignment Algoritms for Clustering System”, International Federation for Information Processing (IFIP) (Boston: Springer), vol.207, Page(s): 311-321, 2006
  18. A. Wirawan, K. C. Keong and B. Schmidt, “Parallel DNA Sequence Alignment on the Cell Broadband Engine”, Springer-Verlag Berlin Heidelberg, Page(s): 1249-1256, 2008
  19. G. Minervini, G. L. Rocca, P. L. Luisi and F. Polticelli, “ High Throughput Protein Structure Prediction in a Grid Environment”, Journal of Bio-Algorithms and Med-System, vol.3(5), Page(s): 39-43, 2007
  20. H. Zhang, B. Schmidt and W. M. Witting, “ Accelerating BLASTP on the Cell broadband Engine”, In Proceedings of the Third International Conference on Pattern Recognition in Bioinformatics, vol.5265, Lecture Notes in Bioinformatics, Springer Berlin Heidelberg, Page(s) 460-470, 8 Oct 2008
  21. J. Ebedes and A. Datta, “Multiple Sequence Alignment in Parallel on a Workstation cluster”, Oxford University Press, vol.20 (77), Page(s):1193-1195, 2004
Index Terms

Computer Science
Information Sciences

Keywords

Cell broadband engine Clusters CUDA Suffix tree Weighted suffix tree (key words)