期刊
METHODS
卷 203, 期 -, 页码 558-563出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ymeth.2021.07.011
关键词
Convolutional neural network; Modification; Feature extraction; Word embedding; N4-methylcytosine
资金
- National Natural Science Foundation of China [61772119]
- Sichuan Provincial Science Fund for Distinguished Young Scholars [2020JDJQ0012]
This study developed a deep learning-based model to predict 4mC sites in Escherichia coli. By encoding DNA sequences and utilizing convolutional neural networks for classification, the model can accurately identify 4mC sites, providing a convenient approach for studying 4mC modification.
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.
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