im6A-TS-CNN: Identifying the N(6)-Methyladenine Site in Multiple Tissues by Using the Convolutional Neural Network

N(6)-methyladenosine (m(6)A) is the most abundant post-transcriptional modification and involves a series of important biological processes. Therefore, accurate detection of the m(6)A site is very important for revealing its biological functions and impacts on diseases. Although both experimental and computational methods have been proposed for identifying m(6)A sites, few of them are able to detect m(6)A sites in different tissues. With the consideration of the spatial specificity of m(6)A modification, it is necessary to develop methods able to detect the m(6)A site in different tissues. In this work, by using the convolutional neural network (CNN), we proposed a new method, called im6A-TS-CNN, that can identify m(6)A sites in brain, liver, kidney, heart, and testis of Homo sapiens, Mus musculus, and Rattus norvegicus. In im6A-TS-CNN, the samples were encoded by using the one-hot encoding scheme. The results from both a 5-fold cross-validation test and independent dataset test demonstrate that im6A-TS-CNN is better than the existing method for the same purpose. The command-line version of im6A-TS-CNN is available at https://github.com/liukeweiaway/DeepM6A_cnn.