Operon Prediction Using Decision Tree Approach

Overview

This page contains related information for our operon prediction with the decision tree approach (operonDT). operonDT mainly uses three kinds of features: intergenic distance, COG pattern and gene order conservation.

The feature datasets generated for machine learning approachs were generated by our program - operonFT. The decision tree and other machine learning approaches used for prediction can been freely downloaded from the website of WEKA. The predicted WEKA outputs are further processed into operon structures by using our program - operonST.

Download

• operonDT Package: operonDTv0.1.tar.gz(Linux version)

• Decision Tree Models
  • Escherichia coli: [eco_c0005][eco_c010][eco_c025]
  • Bacillus subtilis: [bsu_c0005][bsu_c005][bsu_c010][bsu_c025]
  • Bacillus subtilis + Escherichia coli: [bsu_eco_c0005][bsu_eco_c005][bsu_eco_c010][bsu_eco_c025]

• Predicted Operons:
  • Escherichia coli: [pred_eco_op_0423.txt]
  • Bacillus subtilis: [pred_bsu_op_0423.txt]
  • Agrobacterium tumefaciens: [pred_agro_op_0423.txt]
  • Pseudomonas aeruginosa: [pred_pae_op_0423.txt]

Documentation

• Please see README file here

Reference

• Che D., Zhao, J., Cai, L. and Xu, Y., Operon Prediction in Microbial Genomes Using Decision Tree Approach. Proceedings of IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2007), pp 135-142.
• Che D., Zhao, J., Cai, L. and Xu, Y., Decision Tree Modeling Predicts Operon Structures of Prokaryotic Genomes. International Journal of Information Technology and Intelligent Computing (in press).

Contact

Email: che@cs.uga.edu, xyn@bmb.uga.edu

Dongsheng Che and Ying Xu

Last updated 06/05/2007