Deep-2′-O-Me: Predicting 2′-O-methylation sites by Convolutional Neural Networks.

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Abstract

2′-O-methylation (2′-O-me) of ribose moiety is one of the significant and ubiquitous post-transcriptional RNA modifications which is vital for metabolism and functions of RNA. Although recent development of new technology (Nmseq) enabled biologists to find precise location of 2′-O-me in RNA sequences, there is still a lack of computational tools that can also provide high resolution prediction of this RNA modification. In this paper, we propose a deep learning based method that takes advantage of an embedding method to learn complex feature representation of pre-mRNA sequences and employs a Convolutional Neural Network to fine-tune the features required for accurate prediction of such alteration. Specifically, we adopted dna2vec, a biological sequence embedding method originally inspired by the word2vec model of text analysis, to yield embedded representation of sequences that may or may not contain 2-O-me sites before feeding those features into CNN for classification. Our model was trained using the data collected from Nm-seq experiment. The proposed method achieved AUC and auPRC scores of 90% outperforming existing state-of-the-art algorithms by a significant margin in both balanced and unbalanced class testing scenarios.