Enhancers are crucial for precisely regulation of gene expression, while enhancers identification and strength prediction is challenging because of their freely distribution and tremendous of similar fractions in genome. Enhancer-FRL, a two-layer predictor proposed for identifying enhancers (enhancers or non-enhancers) and their activities (strong and weak) using the feature representation learning scheme.
2.1. Webserver is available at: http://39.100.246.211:10505/DeepSoluE/
http://lab.malab.cn/~wangchao/softwares/Enhancer-FRL/ http://39.100.246.211:10504/
https://github.com/wangchao-malab/Enhancer-FRL and http://lab.malab.cn/~wangchao/softwares/Enhancer-FRL/
Before running, please make sure the following packages are installed in Python environment:
gensim==3.4.0
pandas==1.1.3
python==3.7.3
biopython==1.7.8
numpy==1.19.2
scikit-learn==0.22.1
For convenience, we strongly recommended users to install the Anaconda Python 3.7.3 (or above) in your local computer.
Changing working dir to Enhancer-FRL_master, and then running the following command:
python Enhancer-FRL.py -i input.fasta -o results.csv
-i: name of input_file in fasta format # folder “sequence” is the default file path of the input_file
-o name of output_file # folder “results” is the default file path for result save.
Notes: We have set the default working dir to 'C:\Users\Administer\Desktop\Enhancer-FRL_master', Of course, you can change the working dir by fix the Enhancer-FRL.py scripts according to your environment.
The output file (in ".csv" format) can be found in results folder, which including sequence_id, Enhancer_prediction, Enhancer_prediction_probability, Enhancer_classification, Enhancer_classification_probability.
Chao Wang et al. 2022. Enhancer-FRL: improved and robust identification of enhancers and their activities using feature representation learning (Submited).