Update README.md

README.md

+# <Effector-GAN>
+   
+# Effector-GAN
+
+Effector-GAN: prediction of fungal effector proteins based on pre-trained deep representation learning methods and generative adversarial networks 
+
+Effector-GAN is an open-source Python-based toolkit, which operates depending on the Python environment (Python Version 3.7). 
+
+Before running Effector-GAN, users should make sure all the following packages are installed in their Python environment: 
+
+joblib==1.0.1
+tape_proteins==0.4
+torch==1.2.0+cpu
+numpy==1.19.2
+pandas==1.2.0
+Bio==0.4.1
+tape==1.0
+sklearn
+
+
+## **Prerequisites**
+
+### 1. **iFeature**
+    
+    To obtain iFeature, please download from https://github.com/Superzchen/iFeature/.
+    
+   **Add iFeature into environment variables (~/.bashrc)** 
+   
+    export PATH=$PATH:/xxxx/xxxx/xxxxx/iFeature
+   
+
+## **Installation**
+
+###**prPred**
+
+    git clone git@github.com:Wangys-prog/prPred.git  
+
+**Add prPred into into environment variables**
+
+**(./prPred/dist/prPred)**
+
+    export PATH=$PATH:/xxxx/xxxx/xxxx/prPred/dist/prPred  
+
+
+## Input parameters
+    prPred -h  
+    $ -i  inputfile in FASTA format  
+    $ -o  output folder  
+### usage
+    prPred -i /xxxx/xxxx/test/test.fasta -o result  
+**or**
+**Using absolute path to invoke prPred.py (/xxxx/xxxx/prPred/prPred.py)**
+
+    python xxxx/xxxx/prPred/prPred.py -i /xxxx/xxxx/test/test.fasta -o /xxxx/xxxxx/result  
+      
+###**Output file**
+
+    domain_result   
+    R_protein_possibility.fasta
+
+## For split_fasta.py
+**Analyze your sequences one by one**  
+**Before using the script,please add prPred into into environment variables**
+    
+      python split_fasta.py -i your fasta file
+ 
+## For windows 10 or later
+
+**Download Ubuntu xx.x LTS from Microsoft Store**
+
+    cd ../../
+    cd mnt/x/xxxx/xxxx/
+    git clone git@github.com:Wangys-prog/prPred.git
+    cd mnt/x/xxxx/xxxx/prPred/  
+
+## **If you use prPred, please cite:** 
+    (1) Wang Y, Wang P, Guo Y, et al. prPred: A Predictor to Identify Plant Resistance Proteins by Incorporating k-Spaced Amino Acid (Group) Pairs[J]. Frontiers in bioengineering and biotechnology, 2021, 8: 1593.
+    (2) Yansu Wang, Murong Zhou, Quan Zou, Lei Xu. Machine learning for phytopathology: from the molecular scale towards the network scale. Briefings in Bioinformatics. 2021, Doi: 10.1093/bib/bbab037
+    (3) Yansu Wang, Lei Xu, Quan Zou, Chen Lin. prPred-DRLF: plant R protein predictor using deep representation learning features. Proteomics. 2021. DOI: 10.1002/pmic.202100161