Construction of precise support vector machine based models for predicting promoter strength

Abstract

Background: The prediction of the prokaryotic promoter strength based on its sequence is of great importance not only in the fundamental research of life sciences but also in the applied aspect of synthetic biology. Much advance has been made to build quantitative models for strength prediction, especially the introduction of machine learning methods such as artificial neural network (ANN) has significantly improve the prediction accuracy. As one of the most important machine learning methods, support vector machine (SVM) is more powerful to learn knowledge from small sample dataset and thus supposed to work in this problem.

Methods: To confirm this, we constructed SVM based models to quantitatively predict the promoter strength. A library of 100 promoter sequences and strength values was randomly divided into two datasets, including a training set (≥10 sequences) for model training and a test set (≥10 sequences) for model test.

Results: The results indicate that the prediction performance increases with an increase of the size of training set, and the best performance was achieved at the size of 90 sequences. After optimization of the model parameters, a high-performance model was finally trained, with a high squared correlation coefficient for fitting the training set (R²>0.99) and the test set (R²>0.98), both of which are better than that of ANN obtained by our previous work.

Conclusions: Our results demonstrate the SVM-based models can be employed for the quantitative prediction of promoter strength.

Graphical abstract

Keywords

support vector machine model / quantitative prediction / promoter strength / machine learning

References

[1] Blount, B. A., Weenink, T., Vasylechko, S. and Ellis, T. (2012) Rational diversification of a promoter providing fine-tuned expression and orthogonal regulation for synthetic biology. PLoS One, 7, e33279

[2] Qin, X., Qian, J., Yao, G., Zhuang, Y., Zhang, S. and Chu, J. (2011) GAP promoter library for fine-tuning of gene expression in Pichia pastoris. Appl. Environ. Microbiol., 77, 3600–3608

[3] Alper, H., Fischer, C., Nevoigt, E. and Stephanopoulos, G. (2005) Tuning genetic control through promoter engineering. Proc. Natl. Acad. Sci. USA, 102, 12678–12683.

[4] Salis, H. M., Mirsky, E. A. and Voigt, C. A. (2009) Automated design of synthetic ribosome binding sites to control protein expression. Nat. Biotechnol., 27, 946–950.

[5] Lou, C., Stanton, B., Chen, Y. J., Munsky, B. and Voigt, C. A. (2012) Ribozyme-based insulator parts buffer synthetic circuits from genetic context. Nat. Biotechnol., 30, 1137–1142.

[6] Rhodius, V. A. and Mutalik, V. K. (2010) Predicting strength and function for promoters of the Escherichia coli alternative sigma factor, σ^E. Proc. Natl. Acad. Sci. USA, 107, 2854–2859

[7] De Mey, M., Maertens, J., Lequeux, G. J., Soetaert, W. K. and Vandamme, E. J. (2007) Construction and model-based analysis of a promoter library for E. coli: an indispensable tool for metabolic engineering. BMC Biotechnol., 7, 34

[8] Meng, H., Wang, J., Xiong, Z., Xu, F., Zhao, G. and Wang, Y. (2013) Quantitative design of regulatory elements based on high-precision strength prediction using artificial neural network. PLoS One, 8, e60288

[9] Meng, H. and Wang, Y. (2015) Cis-acting regulatory elements: from random screening to quantitative design. Quant. Biol., 3, 107–114.

[10] Vapnik, V. N. (2000) The Nature of Statistical Learning Theory. New York: Springer-Verlag

[11] Vapnik, V. N. (1999) An overview of statistical learning theory. IEEE Trans. Neural Netw., 10, 988–999.

[12] Hassanien, A. E., Al-Shammari, E. T. and Ghali, N. I. (2013) Computational intelligence techniques in bioinformatics. Comput. Biol. Chem., 47, 37–47.

[13] Ho, H. K., Zhang, L., Ramamohanarao, K. and Martin, S. (2013) A survey of machine learning methods for secondary and supersecondary protein structure prediction. In Methods and Protocols: Methods in Molecular Biology, 932, 87–106. New York: Humana Press

[14] Cheng, J., Tegge, A. N. and Baldi, P. (2008) Machine learning methods for protein structure prediction. IEEE Rev. Biomed. Eng., 1, 41–49.

[15] Zhao, Y. and Wang, Z. (2008) RNA secondary structure prediction based on support vector machine classification. Chinese Journal of Biotechnology, 24, 1140–1148.

[16] Towsey, M. W., Gordon, J. J. and Hogan, J. M. (2006) The prediction of bacterial transcription start sites using SVMs. Int. J. Neural Syst., 16, 363–370.

[17] Ichikawa, D., Saito, T., Ujita, W. and Oyama, H. (2016) How can machine-learning methods assist in virtual screening for hyperuricemia? A healthcare machine-learning approach. J. Biomed. Inform., 64, 20–24.

[18] Vyas, R., Bapat, S., Jain, E., Tambe, S. S., Karthikeyan, M. and Kulkarni, B. D. (2015) A study of applications of machine learning based classification methods for virtual screening of lead molecules. Comb. Chem. High Throughput Screen., 18, 658–672.

[19] Burton, J., Ijjaali, I., Petitet, F., Michel, A. and Vercauteren, D. P. (2009) Virtual screening for cytochromes p450: successes of machine learning filters. Comb. Chem. High Throughput Screen., 12, 369–382.

[20] Melville, J. L., Burke, E. K. and Hirst, J. D. (2009) Machine learning in virtual screening. Comb. Chem. High Throughput Screen., 12, 332–343.

[21] Fox, T. and Kriegl, J. M. (2006) Machine learning techniques for in silico modeling of drug metabolism. Curr. Top. Med. Chem., 6, 1579–1591.

[22] Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V. and Fotiadis, D. I. (2015) Machine learning applications in cancer prognosis and prediction. Comput. Struct. Biotechnol. J., 13, 8–17.

[23] Polley, M. Y., Freidlin, B., Korn, E. L., Conley, B. A., Abrams, J. S. and McShane, L. M. (2013) Statistical and practical considerations for clinical evaluation of predictive biomarkers. J. Natl. Cancer Inst., 105, 1677–1683.

[24] Liang, G. and Li, Z. (2007) Scores of generalized base properties for quantitative sequence-activity modelings for E. coli promoters based on support vector machine. J. Mol. Graph. Model., 26, 269–281.

[25] Towsey, M., Timms, P., Hogan, J. and Mathews, S. A. (2008) The cross-species prediction of bacterial promoters using a support vector machine. Comput. Biol. Chem., 32, 359–366.

[26] Xu, W., Zhang, L. and Lu, Y. (2016) SD-MSAEs: promoter recognition in human genome based on deep feature extraction. J. Biomed. Inform., 61, 55–62.

[27] Sato, M. (2012) Promoter analysis with wavelets and support vector machines. Procedia Comput. Sci., 12, 432–437.

[28] Holloway, D. T., Kon, M. and Delisi, C. (2007) Machine learning for regulatory analysis and transcription factor target prediction in yeast. Syst. Synth. Biol., 1, 25–46.

[29] Anwar, F., Baker, S. M., Jabid, T., Mehedi Hasan, M., Shoyaib, M., Khan, H. and Walshe, R. (2008) Pol II promoter prediction using characteristic 4-mer motifs: a machine learning approach. BMC Bioinformatics, 9, 414

[30] Carvalho, S. G., Guerra-Sá R. and de C Merschmann, L. H. (2015) The impact of sequence length and number of sequences on promoter prediction performance. BMC Bioinformatics, 16, S5

[31] Hwang, W., Oliver, V. F., Merbs, S. L., Zhu, H. and Qian, J. (2015) Prediction of promoters and enhancers using multiple DNA methylation-associated features. BMC Genomics, 16, S11

[32] Li, Y., Lee, K. K., Walsh, S., Smith, C., Hadingham, S., Sorefan, K., Cawley, G. and Bevan, M. W. (2006) Establishing glucose- and ABA-regulated transcription networks in Arabidopsis by microarray analysis and promoter classification using a Relevance Vector Machine. Genome Res., 16, 414–427.

[33] Sandhu, R. S., Coyne, E. J., Feinstein, H. L. and Youman, C. E. (1996) Role based access control models. IEEE Computer, 29, 38–47.

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