Abstract
The identification of transcription factor binding sites (TFBSs ) is a non-trivial problem as the existing computational predictors produce a lot of false predictions. Though it is proven that combining these predictions with a meta-classifier, like Support Vector Machines (SVMs), can improve the overall results, this improvement is not as significant as expected. The reason for this is that the predictors are not reliable for the negative examples from non-binding sites in the promoter region. Therefore, using negative examples from different sources during training an SVM can be one of the solutions to this problem. In this study, we used different types of negative examples during training the classifier. These negative examples can be far away from the promoter regions or produced by randomisation or from the intronic region of genes. By using these negative examples during training, we observed their effect in improving predictions of TFBSs in the yeast. We also used a modified cross-validation method for this type of problem. Thus we observed substantial improvement in the classifier performance that could constitute a model for predicting TFBSs. Therefore, the major contribution of the analysis is that for the yeast genome, the position of binding sites could be predicted with high confidence using our technique and the predictions are of much higher quality than the predictions of the original prediction algorithms.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Tompa, M., Li, N., Bailey, T.L., Church, G.M., De Moor, B., Eskin, E., Favorov, A.V., Frith, M.C., Fu, Y., Kent, W.J., Makeev, V.J., Mironov, A.A., Noble, W.S., Pavesi, G., Pesole, G., Régnier, M., Simonis, N., Sinha, S., Thijs, G., van Helden, J., Vandenbogaert, M., Weng, Z., Workman, C., Ye, C., Zhu, Z.: Assessing computational tools for the discovery of transcription factor binding sites. Nat. Biotechnol. 23(1), 137–144 (2005)
Elnitski, L., Jin, V.X., Farnham, P.J., Jones, S.J.: Locating mammalian transcription factor binding sites: a survey of computational and experimental techniques. Genome Res. 16, 1455–1464 (2006)
Pavesi, G., Mauri, G., Pesole, G.: In silico representation and discovery of transcription factor binding sites. Brief. Bioinformatics 5, 217–236 (2004)
Hu, J., Li, B., Kihara, D.: Limitations and potentials of current motif discovery algorithms. Nucleic Acids Res. 33, 4899–4913 (2005)
Brown, C.T.: Computational approaches to finding and analyzing cis-regulatory elements. Methods Cell Biol. 87, 337–365 (2008)
Sun, Y., Robinson, M., Adams, R., Rust, A.G., Davey, N.: Using Pre and Posting-processing Methods to Improve Binding Site Predictions. Pattern Recognition 42(9), 1949–1958 (2009)
Robinson, M., Castellano, C.G., Rezwan, F., Adams, R., Davey, N., Rust, A.G., Sun, Y.: Combining experts in order to identify binding sites in yeast and mouse genomic data. Neural Networks 21(6), 856–861 (2008)
Cherry, J.M., Hong, E.L., Amundsen, C., Balakrishnan, R., Binkley, G., Chan, E.T., Christie, K.R., Costanzo, M.C., Dwight, S.S., Engel, S.R., Fisk, D.G., Hirschman, J.E., Hitz, B.C., Karra, K., Krieger, C.J., Miyasato, S.R., Nash, R.S., Park, J., Skrzypek, M.S., Simison, M., Weng, S., Wong, E.D.: Saccharomyces Genome Database: the genomics resource of budding yeast. Nucleic Acids Res. 40(Database issue), D700–D705 (2012)
Montgomery, S.B., Griffith, O.L., Sleumer, M.C., Bergman, C.M., Bilenky, M., Pleasance, E.D., Prychyna, Y., Zhang, X., Jones, S.J.M.: ORegAnno: An open access database and curation system for literature-derived promoters, transcription factor binding sites and regulatory variation. Bioinformatics (March 2006)
MacIsaac, K.D., Wang, T., Gordon, D.B., Gifford, D.K., Stormo, G., Fraenkel, E.: An improved map of conserved regulatory sites for Saccharomyces cerevisiae. BMC Bioinformatics 7, 113 (2006)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeye, W.P.: SMOTE: Synthetic minority over-sampling Technique. Journal of Artificial Intelligence Research 16, 321–357 (2002)
Rezwan, F., Sun, Y., Davey, N., Adams, R., Rust, A.G., Robinson, M.: Effect of Using Varying Negative Examples in Transcription Factor Binding Site Predictions. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds.) EvoBIO 2011. LNCS, vol. 6623, pp. 1–12. Springer, Heidelberg (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rezwan, F., Sun, Y., Davey, N., Adams, R., Rust, A.G., Robinson, M. (2012). Using Varying Negative Examples to Improve Computational Predictions of Transcription Factor Binding Sites. In: Jayne, C., Yue, S., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2012. Communications in Computer and Information Science, vol 311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32909-8_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-32909-8_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32908-1
Online ISBN: 978-3-642-32909-8
eBook Packages: Computer ScienceComputer Science (R0)