Abstract
The prediction of operons is critical to reconstruction of regulatory networks at the whole genome level. In this paper, a multi-approach guided genetic algorithm is developed to prediction of operon. The fitness function is created by using intergenic distance of local entropy-minimization, participation of the same metabolic pathway, log-likelihood of COG gene functions and correlation coefficient of microarray expression data, which have been used individually for predicting operons. The gene pairs within operons have high fitness value by using these four scoring criteria, whereas those across transcription unit borders have low fitness value. On the other hand, it is easy to predict operons and makes the prediction ability stronger by using these four scoring criteria. The proposed method is examined on 683 known operons of Escherichia coli K12 and an accuracy of 85.9987% is obtained.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Yeh, P., Tschumi, A.I., Kishony, R.: Functional classification of drugs by properties of their pairwise interactions. Nature Genetics 38, 489–494 (2006)
Chen, X., Su, Z., Dam, P., Palenik, B., Xu, Y., Jiang, T.: Operon prediction by comparative genomics: An application to the Synechococcus sp. WH8102 genome. Nucleic Acids Res. 32, 2147–2157 (2004)
Yada, T., Nakao, M., Totoki, Y., Nakai, K.: Modeling and predicting transcriptional units of Escherichia coli genes using hidden Markov models. Bioinformatics 15, 987–993 (1999)
Salgado, H., Moreno-Hagelsieb, G., Smith, T., Collado-Vides, J.: Operons in Escherichia coli: genomic analyses and predictions. Proc. Natl. Acad. Sci. 97, 6652–6657 (2000)
Overbeek, R., Fonstein, M., D’Souza, M., Pusch, G.D., Maltsev, N.: The use of gene clusters to infer functional coupling. Proc. Natl. Acad. Sci. 96, 2896–2901 (1999)
Zheng, Y., Szustakowski, J.D., Fortnow, L., Roberts, R.J., Kasif, S.: Computational identification of operons in microbial genopmes. Genome Res. 12, 1221–1230 (2002)
Sabatti, C., Rohlin, L., Oh, M.K., Liao, J.C.: Co-expression pattern from DNA microarray experiments as a tool for operon prediction. Nucleic Acids Res. 30, 2886–2893 (2002)
Craven, M., Page, D., Shavlik, J., Bockhorst, J., Glasner, J.: A probabilistic learning approach to whole-genome operon prediction. In: Proc. 8th International Conference on Intelligent Systems for Mol. Biol., pp. 116–127 (2000)
Chen, X., Su, Z.C., Xu, Y., Jiang, T.: Computational Prediction of Operons in Synechococcus sp.WH8102. Genome Informatics. Genome Informatics 15, 211–222 (2004)
Dam, P., Olman, V., Xu, Y.: Improving Operon Prediction in E. coli. In: 2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW), pp. 69–70 (2005)
Jacob, E., Sasikumar, R., Nair, K.N.R.: A fuzzy guided genetic algorithm for operon prediction. Bioinformatics 21, 1403–1407 (2005)
Ogata, H., Fujibuchi, W., Goto, S., Kanehisa, M.: Aheuristic graph comparison algorithm and its application to detect functionally related enzyme clusters. Nucleic Acids Res. 28, 4021–4028 (2000)
Moreno-Hagelsieb, G., Collado-Vides, J.: A powerful non-homology method for the prediction of operons in prokaryotes. Bioinformatics 18, 329–336 (2002)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Wang, S. et al. (2007). A Multi-gene-Feature-Based Genetic Algorithm for Prediction of Operon. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71618-1_33
Download citation
DOI: https://doi.org/10.1007/978-3-540-71618-1_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71589-4
Online ISBN: 978-3-540-71618-1
eBook Packages: Computer ScienceComputer Science (R0)