A COMPARISON OF METHODS FOR CLASSIFYING PROMOTER REGIONS IN E. COLI BASED ON STRUCTURAL PROPERTIES OF DNA
Abstract
One of the major challenges in biology is the correct identification of promoter regions. Computational methods based on motif searching have been the traditional approach taken. Studies have shown that DNA structural properties, such as free energy, curvature, and stress-induced duplex destabilization (SIDD) are useful in promoter classification, as well. In this paper, these properties were compared for their effectiveness in correctly classifying promoters. When using a decision tree for promoter classification based on DNA structural properties, SIDD showed a slight improvement over free energy and curvature, with f-score values 70.9%, 67.1%, and 61.5%, respectively.
About the Authors
Carmen Wright
Department of Mathematics, Jackson State University
United States
United States
Ph.D, Assistant Professor
Jackson, MS
J. Kaur
Mississippi Valley State University
United States
United States
Graduate Student, Bioinformatics Program
Itta Bena, MS
A. S. Newsome
Mississippi Valley State University
United States
United States
Ph.D, Director of Bioinformatics and Associate Professor
Itta Bena, MS
Charles Bland
Mississippi Valley State University
United States
United States
Ph.D, Assistant Professor
Itta Bena, MS
References
1. Gerald Z. Hertz, Gary D. Stormo. “Escherichia coli promoter sequences: analysis and prediction”; Methods in Enzymology, Volume 273, Pages 30-42, 1996.
2. Araceli M. Huerta, Julio Collado-Vides. “Sigma 70 promoters in Escherichia coli: specific transcription in dense regions of overlapping promoter-like signals”. Journal of Molecular Biology, Volume 333, Issue 2, Pages 261-278, October 2003.
3. Czuee Morey, Sushmita Mookherjee, Ganesan Rajasekaran, Manju Bansal. “DNA Free Energy-Based Promoter Prediction and Comparative Analysis of Arabidopsis and Rice Genomes”. Plant Physiology, Volume 156, Issue 3, Pages 1300-1315, April 2011.
4. Charles Bland, Abigail S. Newsome, Aleksandra Markovets. “Promoter prediction in E. coli based on SIDD profiles and Artificial Neural Networks”. BMC Bioinformatics, Volume 11, Supplement 6, October 2010.
5. Huiqan Wang, Craig J. Benham. “Promoter prediction and annotation of microbial genomes based on DNA sequence and structural responses to superhelical stress". BMC Bioinformatics, 7: 248, May 2006.
6. Aditi Kanhere, Manju Bansal. “A novel method for prokaryotic promoter prediction based on DNA stability”. BMC Bioinformatics, 6:1, January 2005.
7. Aditi Kanhere, Manju Bansal. “Structural properties of promoters: similarities and differences between prokaryotes and eukaryotes”. Nucleic Acids Research, Volume 33, Issue 10, Pages 3165-3175, June 2005.
8. Limor Kozobay-Avraham, Sergey Hosid, Alexander Bolshoy. “Involvement of DNA curvature in intergenic regions of prokaryotes”. Nucleic Acids Research, Volume 34, Issue 8, Pages 2316–2327, May 2006.
9. Huiqan Wang, Michiel Noordewier, Craig J. Benham. “Stress-induced DNA duplex destabilization (SIDD) in the E. coli genome: SIDD sites are closely associated with promoters”. Genome Research, Volume 14, Issue 8, Pages 1575-1584, August 2004.
10. RequlonDB [http://regulondb.ccg.unam.mx]
11. John SantaLucia Jr. “A unified view of polymer, dumbbell, and oligonucleotide DNA nearest-neighbor thermodynamics”. Proceedings of the National Academy of Sciences USA, Volume 95, Number 4, Pages 1460-1465, February 1998.
12. E.S. Shpigelman, E.N. Trifonov, A. Bolshoy. “Curvature: software for the analysis of curved DNA”. Computer Applications in the Biosciences, Volume 9, Issue 4, Pages 435-440, August 1993.
13. Curvature [http://www.lfd.uci.edu/~gohlke/dnacurve/]
14. Weka [http://www.cs.waikato.ac.nz]
Review
For citations:
Wright C., Kaur J., Newsome A.S., Bland Ch. A COMPARISON OF METHODS FOR CLASSIFYING PROMOTER REGIONS IN E. COLI BASED ON STRUCTURAL PROPERTIES OF DNA. Bulletin of Kazakh National Women's Teacher Training University. 2018;(2):6-10.
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