im6A-TS-CNN: Identifying the N⁶-Methyladenine Site in Multiple Tissues by Using the Convolutional Neural Network

N⁶-methyladenosine (m⁶A) is the most abundant post-transcriptional modification and involves a series of important biological processes. Therefore, accurate detection of the m⁶A site is very important for revealing its biological functions and impacts on diseases. Although both experimental and computational methods have been proposed for identifying m⁶A sites, few of them are able to detect m⁶A sites in different tissues. With the consideration of the spatial specificity of m⁶A modification, it is necessary to develop methods able to detect the m⁶A 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 m⁶A 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.