Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA
- 1Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, 610051 Chengdu, China
- 2School of Computer Science and Technology, Tianjin University, 300350 Tianjin, China
- 3School of Computer Science and Technology, Harbin Institute of Technology, 150001 Shenzhen, China
- Corresponding authors: zouquan{at}nclab.net, bliu{at}hit.edu.cn
Abstract
N6-Methyladenosine (m6A) refers to methylation modification of the adenosine nucleotide acid at the nitrogen-6 position. Many conventional computational methods for identifying N6-methyladenosine sites are limited by the small amount of data available. Taking advantage of the thousands of m6A sites detected by high-throughput sequencing, it is now possible to discover the characteristics of m6A sequences using deep learning techniques. To the best of our knowledge, our work is the first attempt to use word embedding and deep neural networks for m6A prediction from mRNA sequences. Using four deep neural networks, we developed a model inferred from a larger sequence shifting window that can predict m6A accurately and robustly. Four prediction schemes were built with various RNA sequence representations and optimized convolutional neural networks. The soft voting results from the four deep networks were shown to outperform all of the state-of-the-art methods. We evaluated these predictors mentioned above on a rigorous independent test data set and proved that our proposed method outperforms the state-of-the-art predictors. The training, independent, and cross-species testing data sets are much larger than in previous studies, which could help to avoid the problem of overfitting. Furthermore, an online prediction web server implementing the four proposed predictors has been built and is available at http://server.malab.cn/Gene2vec/.
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Article is online at http://www.rnajournal.org/cgi/doi/10.1261/rna.069112.118.
- Received October 3, 2018.
- Accepted November 1, 2018.
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