DeepMRMP: A new predictor for multiple types of RNA modification sites using deep learning

Pingping Sun; Yongbing Chen; Bo Liu; Yanxin Gao; Ye Han; Fei He; Jinchao Ji; Pingping Sun; Yongbing Chen; Bo Liu; Yanxin Gao; Ye Han; Fei He; Jinchao Ji

doi:10.3934/mbe.2019310

Mathematical Biosciences and Engineering

2019, Volume 16, Issue 6: 6231-6241. doi: 10.3934/mbe.2019310

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Research article Special Issues

DeepMRMP: A new predictor for multiple types of RNA modification sites using deep learning

1.
School of Information Science and Technology, Northeast Normal University, Changchun, China
2.
School of Physical Education, Northeast Normal University, Changchun, China
3.
School of Information Technology, Jilin Agriculture University, Changchun, China

Received: 01 March 2019 Accepted: 20 June 2019 Published: 04 July 2019

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 N¹-methyladenosine (m¹A), pseudouridine (Ψ), 5-methylcytosine (m⁵C) modification sites.
- bidirectional gated recurrent unit,
- transfer learning,
- N¹-methyladenosine,
- pseudouridine,
- 5-methylcytosine,
- modification
Citation: Pingping Sun, Yongbing Chen, Bo Liu, Yanxin Gao, Ye Han, Fei He, Jinchao Ji. DeepMRMP: A new predictor for multiple types of RNA modification sites using deep learning[J]. Mathematical Biosciences and Engineering, 2019, 16(6): 6231-6241. doi: 10.3934/mbe.2019310

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Abstract

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 N¹-methyladenosine (m¹A), pseudouridine (Ψ), 5-methylcytosine (m⁵C) modification sites.

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