DeepAc4C, which identifies ac4C using convolutional neural networks (CNNs) using hybrid features composed of physico-chemical patterns and a distributed representation of nucleic acids.
A webserver is available at: http://lab.malab.cn/~wangchao/softs/DeepAc4C/ or at http://39.100.246.211:10503/.
The source code and datasets(both training and testing datasets) can be freely download from the github and the webserver page.
Before running, please make sure the following packages are installed in Python environment:
gensim==3.4.0
pandas==1.0.3
tensorflow==2.3.0
python==3.7.3
biopython==1.7.8
numpy==1.19.2
For convenience, we strongly recommended users to install the Anaconda Python 3.7.3 (or above) in your local computer.
Changing working dir to DeepAc4C, and then running the following command:
python DeepAc4C.py -i ./sequence/input_query.fasta -o prediction_results.csv
-i: input file in fasta format
-o output file name
The output file (in ".csv" format) can be found in results folder, which including sequence name, predicted probability and redicted result.
Sequence with predicted probability > 0.5 was regared as ac4C site.
Chao Wang et al. 2021. DeepAc4C: A convolutional neural network model with hybrid features com-posed of physicochemical patterns and distributed representation information for identification of N4-acetylcytidine in mRNA. Bioinformatics (Accepted).