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Reseach Article

Performance Optimization of the Database Sequencing Applications

by Talal Bonny
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 112 - Number 5
Year of Publication: 2015
Authors: Talal Bonny
10.5120/19659-1315

Talal Bonny . Performance Optimization of the Database Sequencing Applications. International Journal of Computer Applications. 112, 5 ( February 2015), 1-8. DOI=10.5120/19659-1315

@article{ 10.5120/19659-1315,
author = { Talal Bonny },
title = { Performance Optimization of the Database Sequencing Applications },
journal = { International Journal of Computer Applications },
issue_date = { February 2015 },
volume = { 112 },
number = { 5 },
month = { February },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume112/number5/19659-1315/ },
doi = { 10.5120/19659-1315 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:48:36.746756+05:30
%A Talal Bonny
%T Performance Optimization of the Database Sequencing Applications
%J International Journal of Computer Applications
%@ 0975-8887
%V 112
%N 5
%P 1-8
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Database sequencing applications such as sequence comparison process large size of sequences and considered to be high consumers of computation time. Heuristic algorithms have the problem of sensitivity since they trim the search and miss unexpected but important homologies. Traditional optimal methods apply these applications on the whole database to find the most matched sequences but this consumes very high computation time. We introduce novel and efficient technique which optimizes the performance of the database sequencing applications by reducing the computation time of finding the optimal matched sequence in a large database. Our technique uses our new similarity functions which are based on the mathematical parameters: frequency and mean of the codes of each sequence in the database. Using our technique, we explicitly accelerate the database sequencing applications by 60% in comparison to the traditional known methods.

References
  1. S. Needleman and C. A. Wunsch. General method applicable to the search for similarities in the amino acid sequence of two sequences. Journal of Molecular Biology. Pages 443453, 1970
  2. T. F. Smith and M. S. Watermann. Identification of common molecular subsequence. Journal of Molecular Biology. Pages 196197, 1981
  3. B. Halpin and T. W. Chan. Class Careers as Sequences: An Optimal Matching Analysis of Work-Life Histories. European Sociological Review 14(2). Pages 111-30, 1998
  4. European Bioinformatics Institute Home Page, FASTA searching program, 2003. http://www. ebi. ac. uk/fasta33/.
  5. National Center for Biotechnology Information. NCBI BLAST home page, 2003. http://www. ncbi. nlm. nih. gov/blast.
  6. L. Lesnard. Optimal Matching And Social Sciences. Working Paper, Centre de Recherche en Economie et Statistique. Institut Nationale de la Statistique et des Etudes Economiques, Paris, France. 2006
  7. M. C. Schatz, C. Trapnell and A. Varshney. High-throughput sequence alignment using graphics processing units. BMC Bioinformatics. Page 10, 2007
  8. G. Pollock. Holistic Trajectories: A Study of Combined Employment, Housing and Family Careers by Using Multiple- Sequence Analysis. Journal of the Royal Statistical Society: Series A 170(1). Pages:167-83, 2007
  9. Y. Munekawa and K. Hagihara. Design and implementation of the smith-waterman algorithm on the cuda-compatible gpu. In 8th IEEE International Conference on BioInformatics and Bio- Engineering. pages 16, 2008
  10. P. F. Marteau. TimeWarp Edit Distance with Stiffness Adjustment for Time Series Matching. IEEE Transactions on Pattern Analysis and Machine Intelligence. Pages 306-318. 2008
  11. S. A. Manavski and G. Valle. Cuda compatible gpu cards as efficient hardware accelerators for smith-waterman sequence alignment. Journal of Molecular Biology. Page 9, 2008
  12. Talal Bonny, M. A. Z. and Salama, K. N. An adaptive hybrid multiprocessor technique for bioinformatics sequence alignment. In the 5th Cairo International Conference on Biomedical Engineering. pages 112115, 2010
  13. M. Affan Zidan, T. B. and Salama, K. N. High performance technique for database applications using a hybrid gpu/cpu platform. IEEE/ACM 21st Great Lake Symposium on VLSI. pages 8590, 2011
  14. H. Yousefi, M. Ahmadi and H. R. Roshani. Fast sequence alignment algorithm using bloom filters. In 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP). Pages 484-489. 2012
  15. A. Chakraborty and S. Bandyopadhyay. Clustering of web sessions by FOGSAA. In IEEE Recent Advances in Intelligent Computational Systems (RAICS). Pages 282-287. 2013
  16. S. Kim, Y. J. Yoo, J. So, J. G. Lee and J. Kim. , Design and Implementation of Binary File Similarity Evaluation System. International Journal of Multimedia and Ubiquitous Engineering, Vol. 9, No. 1. Pages 1-10, 2014
  17. Manal Al Ghamdi and Yoshihiko Gotoh. Alignment of nearlyrepetitive contents in a video stream with manifold embedding. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Pages 1255-1259, 2014
  18. Talal Bonny and Bassel Soudan. Filtering Technique for High Speed Database Sequence Comparison. To be published in the Ninth IEEE International Conference on Semantic Computing. Anaheim, USA. February,2015
  19. DNA Data Bank of Japan (ddbj): http://www. ddbj. nig. ac. jp/
  20. Needleman-Wunsch Algorithm": http://blast. ncbi. nlm. nih. gov/
Index Terms

Computer Science
Information Sciences

Keywords

Database sequence comparison Sequence Analysis