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Reseach Article

Motif Finding with Application to the Transcription Factor Binding Sites Problem

by Vilas Machhi, Maulika S Patel, Jyoti Degama
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 120 - Number 15
Year of Publication: 2015
Authors: Vilas Machhi, Maulika S Patel, Jyoti Degama
10.5120/21301-3918

Vilas Machhi, Maulika S Patel, Jyoti Degama . Motif Finding with Application to the Transcription Factor Binding Sites Problem. International Journal of Computer Applications. 120, 15 ( June 2015), 7-10. DOI=10.5120/21301-3918

@article{ 10.5120/21301-3918,
author = { Vilas Machhi, Maulika S Patel, Jyoti Degama },
title = { Motif Finding with Application to the Transcription Factor Binding Sites Problem },
journal = { International Journal of Computer Applications },
issue_date = { June 2015 },
volume = { 120 },
number = { 15 },
month = { June },
year = { 2015 },
issn = { 0975-8887 },
pages = { 7-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume120/number15/21301-3918/ },
doi = { 10.5120/21301-3918 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:06:16.786172+05:30
%A Vilas Machhi
%A Maulika S Patel
%A Jyoti Degama
%T Motif Finding with Application to the Transcription Factor Binding Sites Problem
%J International Journal of Computer Applications
%@ 0975-8887
%V 120
%N 15
%P 7-10
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

DNA sequencing of different species has resulted in the generation of huge amount of biological data. There is an increasing need to develop computational techniques to search for relevant information in the DNA data. Discovering motifs involves determining short sequence segments which have a high probability of repeated occurrences over many sequences in different species. Motifs are useful in finding transcription factor binding sites, transcriptional regulatory elements and so on. Transcription factor binding sites (TFBSs) is important for understanding the genetic regulatory system. Our method is based on the Ant Colony Optimization (ACO) and Gibbs sampling algorithm to discover DNA motifs (collections of TFBSs) in a set of DNA-sequences. We first applied an ACO algorithm to find a set of better candidate positions for the motif. The resultant positions are given as input to the Gibbs sampler method for calculating score for each sequence. Based on the score, motif for TF binding sites is identified.

References
  1. Rahul Chauhan, Dr. Pankaj Agarwal,A Review: Applying Genetic Algorithms for Motif Discovery, (2012).
  2. HieuDinh, Sanguthevar Rajasekaranand Vamsi K Kundeti, PMS5: an efficient exact algorithm for the (?, d) - motif finding problem, (2011).
  3. Ying-Jer Liao, Chang-Biau Yang and Shyue-Horng Shia, Motif Finding in Biological Sequences, Masters' Thesis, National Sun Yat-sen University, 2003.
  4. G. D. Stormo, DNA binding sites: representation and discovery, Bioinformatics, vol. 16, 2000, pp. 16-23.
  5. S. E. Halford and J. F. Marko, How do site-specific DNA-binding proteins find their targets? Nucleic Acids Research, vol. 32, 2004, pp. 3040-52.
  6. D. J. Galas and A. Schmitz, DNAse footprinting: a simple method for the detection of protein-DNA binding specificity, Nucleic Acids Research, vol. 5, 1978, pp. 3157-70.
  7. M. M. Garner and A. Revzin, A gel electrophoresis method for quantifying the binding of proteins to specific DNA regions: application to components of the Escherichia coli lactose operon regulatory system, Nucleic Acids Research, vol. 9, 1981, pp. 3047-60.
  8. M. Dorigo, V. Maniezzo, and A. Colorni, The ant system: Optimization by a colony of cooperating agents, IEEE Transactions on Systems, Man, and Cybernetics - Part B, Vol. 26, No. 1, pp. 29–42, 1996.
  9. C. E. Lawrence, S. F. Altschul, M. S. Boguski, J. S. Liu, A. F. Neuwald, and J. C. Wootton, Detecting subtle sequence signals: A Gibbs sampling strategy for multiple alignment, Science, Vol. 262, pp. 208–214, Oct. 1993.
  10. Xin Chen and Tao Jiang, An improved Gibbs sampling method for motif discovery via sequence weighting, Comput Syst Bioinformatics Conf. 2006:239-47.
  11. Rafael S. Parpinelli, Heitor S. Lopes, and Alex A. Freitas, Data Mining with an Ant Colony Optimization Algorithm.
  12. S. Goss, S. Aron, J. L. Deneubourg, and J. M. Pasteels, self-organized Shortcuts in the Argentine Ant, Unit of Behavioural Ecology, C. P. 231, Universit6 Libre de Bruxelles, B- 1050 Bruxelles.
  13. G. D. Caro and M. Dorigo, Antnet: Distributed stigmergetic control for communications networks, Journal of Artificial Intelligence Research (JAIR), Vol. 9, pp. 317–365, Dec. 1998.
  14. E. Rocke and M. Tompa, An algorithm for finding novel gapped motifs in DNA sequences, Proceedings of the Second Annual International Conference on ComputationalMolecular Biology, pp. 228–233,Mar. 1998.
  15. D. S. Latchman, Transcription factors: an overview, The International Journal of Biochemistry & Cell Biology, vol. 29, 1997, pp. 1305-12.
  16. Modan K Das, and Ho-Kwok Dai, A survey of DNA motif finding algorithms, BMC Bioinformatics 2007, 8(Suppl 7):S21
  17. www. ncbi. nlm. nih. gov
Index Terms

Computer Science
Information Sciences

Keywords

Motif transcription factor binding sites (TFBSs) Gibbs Sampling Ant colony Optimization.