Staem5: a novel stacked ensemble method for prediction of m5C site
Before running m5C_predict, users shuold make sure all the following packages are installed in their Python enviroment:
numpy == 1.19.5
pandas == 1.2.4
sklearn == 0.21.3
xgboost == 0.90
lightgbm == 2.3.0
mlxtend == 0.17.3
Bio == 0.4.1
keras==2.30
tensorflow == 1.14
python == 3.7
For advanced users who want to perform prediction by using their own data:
To get the information the user needs to enter for help, run:
python m5C_predict.py --help
or
python m5C_predict.py -h
as follows:
python prom_pred.py -h
Using TensorFlow backend.
usage: m5C_predict.py [-h] --input inputpath [--output OUTPUTFILE] --species SPECIESFILE
optional arguments:
-h, --help show this help message and exit
--input inputpath query RNA sequences to be predicted in fasta format.
--output OUTPUTFILE save the prediction results.
--species SPECIESFILE
--species indicates the specific species,
currently we accept 'Arabidopsis' or 'Mouse'.