×

DeepMRMP: a new predictor for multiple types of RNA modification sites using deep learning. (English) Zbl 1470.92227

Summary: RNA modification plays an indispensable role in the regulation of organisms. RNA modification site prediction offers an insight into diverse cellular processing. Regarding different types of RNA modification site prediction, it is difficult to tell the most relevant feature combinations from a variant of RNA properties. Thereby, the performance of traditional machine learning based predictors relied on the skill of feature engineering. As a data-driven approach, deep learning can detect optimal feature patterns to represent input data. In this study, we developed a predictor for multiple types of RNA modifications method called DeepMRMP (multiple types RNA modification sites predictor), which is based on the bidirectional gated recurrent unit (BGRU) and transfer learning. DeepMRMP makes full use of multiple RNA site modification data and correlation among them to build predictor for different types of RNA modification sites. Through 10-fold cross-validation of the RNA sequences of H. sapiens, M. musculus and S. cerevisiae, DeepMRMP acted as a reliable computational tool for identifying \(\text{N}^1\)-methyladenosine (\(\text{m}^1\)A), pseudouridine (\( \Psi \)), 5-methylcytosine (\(\text{m}^5\)C) modification sites.

MSC:

92D20 Protein sequences, DNA sequences
68T07 Artificial neural networks and deep learning
PDF BibTeX XML Cite
Full Text: DOI

References:

[1] S. Dunin-Horkawicz, A. Czerwoniec, M. J. Gajda, et al.
[2] J. H. Ge and Y. T. Yu, RNA pseudouridylation: New insights into an old modification, Trends Biochem. Sci., 38(2013), 210-218. 2. J. H. Ge and Y. T. Yu, RNA
[3] M. Charette and M. W. Gray, Pseudouridine in RNA: what, where, how, and why, IUBMB Life, 49(2010), 341-351. 3. M. Charette and M. W. Gray, Pseudouridine in
[4] D. R. Davis, C. A. Veltri, L. J. J. o. B. S. Nielsen, et al., An RNA model system for investigation of pseudouridine stabilization of the codon-anticodon interaction in tRNALys, tRNAHis and tRNATyr, J. Biomol. Struct. Dyn., 15(1998), 1121-1132.
[5] A. Basak and C. Query, A pseudouridine residue in the spliceosome core is part of the filamentous growth program in yeast, Cell Reports, 8(2014), 966-973.
[6] X. Yang, Y. Yang, B. F. Sun, et al., 5-methylcytosine promotes mRNA export-NSUN2 as the methyltransferase and ALYREF as an m^5C reader, Cell Res., 27(2017), 606-625.
[7] M. Frye and F. M. Watt, The RNA methyltransferase Misu (NSun2) mediates Myc-induced proliferation and is upregulated in tumors, Curr. Biol., 16(2006), 971-981.
[8] X. Wang, Z. Lu, A. Gomez, et al., N^6-methyladenosine-dependent regulation of messenger RNA stability, Nature, 505(2014), 117-120.
[9] C. Roost, S. R. Lynch, P. J. Batista, et al., Structure and thermodynamics of N^6-methyladenosine in
[10] T. Chen, Y. J. Hao, Y. Zhang, et al., m^6A RNA methylation is regulated by micrornas and promotes reprogramming to pluripotency, Cell Stem Cell, 16(2015), 289-301.
[11] S. Geula, S. Moshitch-Moshkovitz, D. Dominissini, et al., m^6A mRNA methylation facilitates resolution of naive pluripotency toward differentiation, Science, 347(2015), 1002-1006.
[12] X. Li, X. Xiong, K. Wang, et al., Transcriptome-wide mapping reveals reversible and dynamic N1-methyladenosine methylome, Nat. Chem. Biol., 12(2016), 311.
[13] S. Nachtergaele and C. J. R. B. He, The emerging biology of RNA post-transcriptional modifications, RNA Biol., 14(2016), 156-163.
[14] W. Chen, P. M. Feng, H. Tang, et al.
[15] W. Chen, H. Tang, J. Ye, et al.
[16] J. J. He, T. Fang, Z. Z. Zhang, et al.
[17] W. R. Qiu, S. Y. Jiang, Z. C. Xu, et al.
[18] J. W. Li, Y. Huang, X. Y. Yang, et al.
[19] P. M. Feng, H. Ding, H. Yang, et al.
[20] W. Chen, P. M. Feng, H. Yang, et al.
[21] Y. Huang, N. N. He, Y. Chen, et al.
[22] J. J. Xuan, W. J. Sun, P. H. Lin, et al., RMBase
[23] D. Dominissini, S. Moshitch-Moshkovitz, S. Schwartz, et al., Topology of the human and mouse m^6A RNA methylomes revealed by m^6A-seq, Nature, 485(2012), U201-U284.
[24] L. Fu, B. Niu, Z. Zhu, et al.
[25] W. Z. Li and A. Godzik
[26] L. Zhu, H. B. Zhang and D. S. J. B. Huang, Direct AUC optimization of regulatory motifs, Bioinformatics, 33(2017), i243.
[27] H. Zhang, L. Zhu and D. S. J. S. R. Huang
[28] G. H. Chuai, H. H. Ma, J. F. Yan, et al.
[29] Q. Zhang, L. Zhu and D. S. Huang, High-order convolutional neural network architecture for predicting DNA-protein binding sites, IEEE/ACM Transact. Comput. Biol. Bioinform., (2018), 1.
[30] Q. Zhang, L. Zhu, W. Bao, et al., Weakly-supervised convolutional neural network architecture for predicting protein-DNA binding, IEEE/ACM Transact. Comput. Biol. Bioinform., (2018), 1.
[31] A. Krizhevsky, I. Sutskever and G. E. Hinton, ImageNet classification with deep convolutional neural networks, NIPS. Curran Assoc. Inc., (2012).
[32] D. P. Kingma and J. J. C. S. Ba
[33] C. Tan, F. Sun, K. Tao, et al., A survey on deep transfer learning, (2018).
[34] G. Litjens, T. Kooi, B. E. Bejnordi, et al., A survey on deep learning in medical image analysis, Med. Image Anal., 42(2017), 60-88.
[35] S. Liang, R. G. Zhang, D. Y. Liang, et al., Multimodal 3D denseNet for IDH genotype prediction in gliomas, Genes, 9(2018).
[36] L. Zhu, W. L. Guo, C. Lu, et al., Collaborative completion of transcription factor binding profiles via local sensitive unified embedding, IEEE Transact. NanoBiosci., (2016), 1.
[37] J. X. Wang, L. Chen, Y. Wang, et al., A computational systems biology study for understanding salt tolerance mechanism in rice, Plos One, 8(2013), 177-194.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.