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Published on: July 6, 2012
Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA
Quan Zou1,2, Pengwei Xing2, Leyi Wei2
1Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, 610051 Chengdu, China.
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/.
Insights
This study introduces a novel deep learning approach for predicting N6-Methyladenosine (m6A) sites in mRNA sequences. The method accurately identifies m6A sites, outperforming existing computational tools.
Area of Science:
- Epigenetics
- Computational Biology
- Bioinformatics
Background:
- N6-Methyladenosine (m6A) is a crucial RNA modification, but its identification is challenging.
- Conventional computational methods for m6A site prediction are limited by small datasets and accuracy.
- High-throughput sequencing has enabled the discovery of numerous m6A sites, paving the way for advanced computational analysis.
Purpose of the Study:
- To develop a robust and accurate computational model for predicting m6A sites in mRNA sequences.
- To leverage deep learning techniques, specifically word embedding and deep neural networks, for m6A prediction.
- To overcome the limitations of existing methods by utilizing larger datasets and advanced algorithms.
Main Methods:
- Utilized word embedding techniques to represent RNA sequences.
- Developed and optimized four deep neural networks, including convolutional neural networks, for m6A prediction.
- Implemented a soft voting strategy combining predictions from multiple deep networks.
- Employed large-scale training, independent, and cross-species testing datasets to ensure robustness and avoid overfitting.
Main Results:
- The proposed deep learning model demonstrated high accuracy and robustness in predicting m6A sites.
- The soft voting ensemble of four deep neural networks outperformed all state-of-the-art prediction methods.
- Rigorous evaluation on an independent test dataset confirmed the superior performance of the proposed method.
- The use of larger datasets mitigated the issue of overfitting, enhancing model generalizability.
Conclusions:
- The novel deep learning framework, combining word embedding and deep neural networks, provides a powerful tool for m6A site prediction.
- This approach significantly advances the accuracy and reliability of computational m6A identification.
- An accessible online web server has been developed to facilitate the use of these predictors in biological research.
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