Deep-4mCW2V: A sequence-based predictor to identify N4-methylcytosine sites in Escherichia coli

•An deep learning-based method was developed to identify N4-methylcytosine site in Escherichia coli.•A natural language processing scheme called Word2Vec were proposed to formulate modification samples.•The distribution of nucleotides around positive and negative sequences were statistical analysis. N4-methylcytosine (4mC) is a type of DNA modification which could regulate several biological progressions such as transcription regulation, replication and gene expressions. Precisely recognizing 4mC sites in genomic sequences can provide specific knowledge about their genetic roles. This study aimed to develop a deep learning-based model to predict 4mC sites in the Escherichia coli. In the model, DNA sequences were encoded by word embedding technique ‘word2vec’. The obtained features were inputted into 1-D convolutional neural network (CNN) to discriminate 4mC sites from non-4mC sites in Escherichia coli genome. The examination on independent dataset showed that our model could yield the overall accuracy of 0.861, which was about 4.3% higher than the existing model. To provide convenience to scholars, we provided the data and source code of the model which can be freely download from https://github.com/linDing-groups/Deep-4mCW2V.