im6A-TS-CNN: identifying N6-methyladenine site in multiple tissues by using convolutional neural network

Abstract N6-methyladenosine (m6A) is the most abundant post-transcriptional modification and involves in a series of important biological processes. Therefore, accurate detection of 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. Considering the spatial specificity of m6A modification, it’s necessary to develop methods able to detect 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 onehot encoding scheme. The results from both 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 .