Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA

N-6-Methyladenosine (m(6)A) refers to methylation modification of the adenosine nucleotide acid at the nitrogen-6 position. Many conventional computational methods for identifying N-6-methyladenosine sites are limited by the small amount of data available. Taking advantage of the thousands of m(6)A sites detected by high-throughput sequencing, it is now possible to discover the characteristics of m(6)A 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 m(6)A prediction from mRNA sequences. Using four deep neural networks, we developed a model inferred from a larger sequence shifting window that can predict m(6)A 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.