CFP last date
20 January 2025
Reseach Article

Application of the Codon-based Scoring Method in Motif Detection

by Barilee Baridam
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 92 - Number 9
Year of Publication: 2014
Authors: Barilee Baridam
10.5120/16034-4811

Barilee Baridam . Application of the Codon-based Scoring Method in Motif Detection. International Journal of Computer Applications. 92, 9 ( April 2014), 1-4. DOI=10.5120/16034-4811

@article{ 10.5120/16034-4811,
author = { Barilee Baridam },
title = { Application of the Codon-based Scoring Method in Motif Detection },
journal = { International Journal of Computer Applications },
issue_date = { April 2014 },
volume = { 92 },
number = { 9 },
month = { April },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume92/number9/16034-4811/ },
doi = { 10.5120/16034-4811 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:13:48.847450+05:30
%A Barilee Baridam
%T Application of the Codon-based Scoring Method in Motif Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 92
%N 9
%P 1-4
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A RNA or DNA sequence motif is a short sequence found within a particular nucleic acid sequence families. Most amino acid and nucleic acid sequences have some level of functional or structural similarities. These similarities are mostly represented by short, contiguous sequences called motif. Motif discovery is an important aspect of molecular biology. This is because the knowledge of these sequences helps determine their structural properties, signal sites and/or ligand-binding sites. In most cases, depending on the function of the motif, these contiguous regions can be highly conserved with an homology of nearly 100%. Several algorithms have been proposed for the discovery of motifs. In this paper, the codon-based scoring method is employed to detect motifs and with their invariants. The result obtained shows the reliability and robustness of the method as motifs are discovered irrespective of their length and position in a sequence.

References
  1. T. L. Bailey and C. Elkan. The value of prior knowledge in discovering motifs with MEME. In Proceedings of the 3rd International Conference on Intelligent Systems for Molecular Biology, pages 21–29, 1995.
  2. B. B. Baridam. A scoring method for the clustering of nucleic acid sequences. International Journal of Computer Applications, 44(2):14–22, April 2012.
  3. B. B. Baridam. Codon-based Similarity Measure and Optimization Techniques for the Clustering of Nucleic Acids Se-quences. Phd thesis, University of Pretoria, 2013.
  4. B. B. Baridam and O. Owolabi. Conceptual clustering of RNA sequences with the codon usage model. Global Journal of Computer Science and Technology, 10(8):41–45, Sept 2010.
  5. J. Buhler and M. Tompa. Finding motifs using random projections. In Proceedings of the 5th Annual International Conference on Computational Molecular Biology, April 2001.
  6. B. C. H. Chang, A. Ratnaweera, and S. K. Halgamuge. Particle swarm optimization for protein motif discovery. Genetic Programming and Evolvable Machines, 5:203–214, 2004.
  7. L. R. Cordon and G. D. Stormo. Expectation maximization algorithm for identifying protein-binding sites with variable lengths from unaligned DNA fragments. Journal of Molecular Biology, 223:159–170, 1992.
  8. G. Cormode and S. Muthukrishnan. The string matching problem with moves. ACM Transactions on Algorithms, 3(1), February 2007.
  9. C. T. Hardin and E. C. Rouchka. DNA motif detection using particle swarm optimization and expectation-maximization. In Proceedings of the IEEE Swarm Intelligent Symposium, 2005.
  10. C. E. Lawrence and A. A. Reilly. An expectation maximization (EM) algorithm for the identification and characterization of common sites in unaligned biopolymer sequences. Protein Structure and Function Genetics, 7:41–51, 1990.
  11. V. I. Levenshtein. Binary codes capable of correcting deletions, insertions, and reversals. Doklady Akademii Nauk SSSR, 163(4):845–848, January 1965.
  12. D. W. Mount. Bioinformatics: Sequence and genome analysis. Cold Spring Harbor, New York, 2001.
  13. C. G. Nevill-Manning, T. D. Wu, and D. L. Brutlag. Highly specific protein sequence motifs for genome analysis. In Proceedings of National Academy of Science, volume 95, pages 5865–5871, 1998.
  14. P. A. Pevzner and S. Sze. Combinatorial approaches to finding subtle signals in dna sequences. American Association for Artificial Intelligence, 2000.
  15. H. O. Smith, T. M. Annau, and S. Chandrasegaran. Finding sequence motifs in groups of functionally related proteins. In Proceedings of National Academy of Science, volume 87, pages 826–830, 1990.
  16. G. Thijs, K. Marchal, M. Lescot, S. Rombauts, B. De Moor, P. Rouze, and Y. Moreau. A Gibbs sampling method to detect over-represented motifs in the upstream regions of co-expressed genes. Journal of Computational Biology, 9(2):447–464, 2002. 4
Index Terms

Computer Science
Information Sciences

Keywords

Codon motif homology similarity measure sequences