Abstract
Transcription factor binding site (TFBS), one of the DNA-protein binding sites, plays important roles in understanding regulation of gene expression and drug design. Recently, deep-learning based methods have been widely used in the prediction of TFBS. In this work, we propose a novel deep-learning model, called Combination of Multi-Scale Convolutional Network and Long Short-Term Memory Network (MCNN-LSTM), which utilizes multi-scale convolution for feature processing, and the long short-term memory network to recognize TFBS in DNA sequences. Moreover, we design a new encoding method, called multi-nucleotide one-hot (MNOH), which considers the correlation between nucleotides in adjacent positions, to further improve the prediction performance of TFBS. Stringent cross-validation and independent tests on benchmark datasets demonstrated the efficacy of MNOH and MCNN-LSTM. Based on the proposed methods, we further implement a new TFBS predictor, called DeepTF. The computational experimental results show that our predictor outperformed several existing TFBS predictors.
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References
Lee, D., et al.: A method to predict the impact of regulatory variants from DNA sequence. Nat. Genet. 47(8), 955 (2015)
Kharchenko, P.V., Tolstorukov, M.Y., Park, P.J.: Design and analysis of ChIP-seq experiments for DNA-binding proteins. Nat. Biotechnol. 26(12), 1351 (2008)
Ji, H., Jiang, H., Ma, W., Johnson, D.S., Myers, R.M., Wong, W.H.: An integrated software system for analyzing ChIP-chip and ChIP-seq data. Nat. Biotechnol. 26(11), 1293 (2008)
Siggers, T., Gordân, R.: Protein-DNA binding: complexities and multi-protein codes. Nucleic Acids Res. 42(4), 2099–2111 (2013)
Fletez-Brant, C., Lee, D., McCallion, A.S., Beer, M.A.: kmer-SVM: a web server for identifying predictive regulatory sequence features in genomic data sets. Nucleic Acids Res. 41(W1), W544–W556 (2013)
Wong, K.C., Chan, T.M., Peng, C., Li, Y., Zhang, Z.: DNA motif elucidation using belief propagation. Nucleic Acids Res. 41(16), e153–e153 (2013)
Ghandi, M., Lee, D., Mohammad-Noori, M., Beer, M.A.: Enhanced regulatory sequence prediction using gapped k-mer features. PLoS Comput. Biol. 10(7), e1003711 (2014)
Nutiu, R., et al.: Direct measurement of DNA affinity landscapes on a high-throughput sequencing instrument. Nat. Biotechnol. 29(7), 659 (2011)
Alipanahi, B., Delong, A., Weirauch, M.T., Frey, B.J.: Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning. Nat. Biotechnol. 33(8), 831 (2015)
Zeng, H., Edwards, M.D., Liu, G., Gifford, D.K.: Convolutional neural network architectures for predicting DNA–protein binding. Bioinformatics 32(12), i121–i127 (2016)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)
Hassanzadeh, H.R., Wang, M.D.: DeeperBind: enhancing prediction of sequence specificities of DNA binding proteins. In: 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 178–183. IEEE (2016)
Zhou, J., Troyanskaya, O.G.: Predicting effects of noncoding variants with deep learning–based sequence model. Nat. Methods 12(10), 931 (2015)
Siebert, M., Söding, J.: Bayesian Markov models consistently outperform PWMs at predicting motifs in nucleotide sequences. Nucleic Acids Res. 44(13), 6055–6069 (2016)
Salekin, S., Zhang, J.M., Huang, Y.: Base-pair resolution detection of transcription factor binding site by deep deconvolutional network. Bioinformatics 34(20), 3446–3453 (2018)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence & Statistics, AISTATS, vol. 130, p. 297 (2011)
Graves, A., Jaitly, N., Mohamed, A.R.: Hybrid speech recognition with deep bidirectional LSTM. In: 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 273–278. IEEE (2013)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Hu, J., Zhou, X., Zhu, Y.H., Yu, D.J., Zhang, G.: TargetDBP: accurate DNA-binding protein prediction via sequence-based multi-view feature learning. IEEE/ACM Trans. Comput. Biol. Bioinform. (2019)
Zhu, Y.H., Hu, J., Song, X.N., Yu, D.J.: DNAPred: accurate identification of DNA-binding sites from protein sequence by ensembled hyperplane-distance-based support vector machines. J. Chem. Inf. Model. (2019)
Ren, H., Shen, Y.: RNA-binding residues prediction using structural features. BMC Bioinform. 16(1), 249 (2015)
Chen, K., Mizianty, M.J., Kurgan, L.: ATPsite: sequence-based prediction of ATP-binding residues. In: Proteome Science, vol. 9, p. S4. BioMed Central (2011)
Acknowledgments
This work was supported by the National Natural Science Foundation of China (No. 61772273, 61373062) and the Fundamental Research Funds for the Central Universities (No. 30918011104).
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Bao, XR., Zhu, YH., Yu, DJ. (2019). DeepTF: Accurate Prediction of Transcription Factor Binding Sites by Combining Multi-scale Convolution and Long Short-Term Memory Neural Network. In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (eds) Intelligence Science and Big Data Engineering. Big Data and Machine Learning. IScIDE 2019. Lecture Notes in Computer Science(), vol 11936. Springer, Cham. https://doi.org/10.1007/978-3-030-36204-1_10
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