Skip Navigation



Bioinformatics Advance Access published online on November 25, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti156
Bioinformatics © Oxford University Press 2004; all rights reserved
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow supplementary data
Right arrow All Versions of this Article:
21/8/1403    most recent
bti156v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Jacob, E.
Right arrow Articles by Nair, K. N. R.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Jacob, E.
Right arrow Articles by Nair, K. N. R.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Received May 3, 2004
Revised October 28, 2004
Accepted November 13, 2004

Article

A fuzzy guided genetic algorithm for operon prediction

E. Jacob 1, R. Sasikumar 1*, and K. N. R. Nair 2

1 Department of Computational Modeling and Simulation, Regional Research Laboratory (CSIR), Trivandrum, 695019 India
2 School of Computer Science, Mahatma Gandhi University, Kottayam, 686560 India

* To whom correspondence should be addressed.
R. Sasikumar, E-mail: roschen_csir{at}rediffmail.com


  Abstract

Motivation: The operon structure of the prokaryotic genome is a critical input for the reconstruction of regulatory networks at the whole genome level. As experimental methods for detection of operons are difficult and time consuming, efforts are being put into developing computational methods that can use available biological information to predict operons.

Method: A genetic algorithm is developed to evolve a starting population of putative operon maps of the genome into progressively better predictions. Fuzzy scoring functions based on multiple criteria are used for assessing the "fitness" of the newly evolved operon maps and guiding their evolution.

Results: The algorithm organizes the whole genome into operons. The fuzzy guided genetic algorithm-based approach makes it possible to use diverse biological information like genome sequence data, functional annotations and conservation across multiple genomes, to guide the organization process. This approach does not require any prior training with experimental operons. The predictions from this algorithm for E. coli K12 and B. subtilis are evaluated against experimentally discovered operons for these organisms. The accuracy of the method is evaluated using an ROC analysis. The area under the ROC curve is around 0.9, which indicates excellent accuracy.

Supplementary Information: List of predicted operons for E. coli K12 and B. subtilis.

The fuzzy rule base for generating fitness scores for putative operons.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 Brief BioinformHome page
R. W. W. Brouwer, O. P. Kuipers, and S. A. F. T. van Hijum
The relative value of operon predictions
Brief Bioinform, September 1, 2008; 9(5): 367 - 375.
[Abstract] [Full Text] [PDF]

Home page
 Brief BioinformHome page
M. Brilli, R. Fani, and P. Lio
Current trends in the bioinformatic sequence analysis of metabolic pathways in prokaryotes
Brief Bioinform, January 1, 2008; 9(1): 34 - 45.
[Abstract] [Full Text] [PDF]

Home page
 Nucleic Acids ResHome page
T. T. Tran, P. Dam, Z. Su, F. L. Poole II, M. W. W. Adams, G. T. Zhou, and Y. Xu
Operon prediction in Pyrococcus furiosus
Nucleic Acids Res., January 12, 2007; 35(1): 11 - 20.
[Abstract] [Full Text] [PDF]

Home page
 Nucleic Acids ResHome page
P. Dam, V. Olman, K. Harris, Z. Su, and Y. Xu
Operon prediction using both genome-specific and general genomic information
Nucleic Acids Res., January 12, 2007; 35(1): 288 - 298.
[Abstract] [Full Text] [PDF]

Home page
 Nucleic Acids ResHome page
S. C. Janga, W. F. Lamboy, A. M. Huerta, and G. Moreno-Hagelsieb
The distinctive signatures of promoter regions and operon junctions across prokaryotes
Nucleic Acids Res., September 1, 2006; 34(14): 3980 - 3987.
[Abstract] [Full Text] [PDF]

Home page
 Brief BioinformHome page
P. Larranaga, B. Calvo, R. Santana, C. Bielza, J. Galdiano, I. Inza, J. A. Lozano, R. Armananzas, G. Santafe, A. Perez, et al.
Machine learning in bioinformatics
Brief Bioinform, March 1, 2006; 7(1): 86 - 112.
[Abstract] [Full Text] [PDF]


Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.