Metadata-Version: 2.1
Name: EmulsiPred
Version: 0.0.1.1
Summary: A package to predict emulsifying potential of peptides
Home-page: https://github.com/MarcatiliLab/EmulsiPred
Author: Paolo Marcatili, Tobias Olsen, Egon Hansen
Author-email: pamar@dtu.dk
License: UNKNOWN
Description: # EmulsiPred
        Prediction of Emulsifying Peptides, based on protein sequences (in fasta format) and
        their corresponding results from NetSurfP-2 (http://www.cbs.dtu.dk/services/NetSurfP/).
        The NetSurfP-2 file should be in the NetSurfP-1 Format (retrieved when clicking 'Export All'
        in the upper right side of NetSurfP's 'Server Output' window).
        
        
        #### Prerequisites and installation
        
        The package can either be cloned from github and installed 
        locally or installed with pip. In both cases, python-3.6 or 
        higher needs to be installed on your PC. Additionally, it is 
        recommended to install the package in a new environment.
        
        The following commands are run in the command line.
        
        1: Set up a new environment.
        ~~~.sh  
            python3 -m venv EmulsiPred_env
        ~~~
        2: Enter (activate) the environment.
        ~~~.sh
            source EmulsiPred_env/bin/activate
        ~~~
        3a: Install EmulsiPred within the activated environment with pip.
        ~~~.sh
            pip install EmulsiPred
        ~~~
            
        3b: Install EmulsiPred by installing from github with pip.
        
        ~~~.sh
            pip install "git+https://github.com/MarcatiliLab/EmulsiPred.git"
        ~~~ 
        
        After either running 3a or 3b EmulsiPred is installed within the
        activated environment (in our case EmulsiPred_env).
        
        ---
        #### Running EmulsiPred
        
        After installation, EmulsiPred can be run from the terminal or
        within a python script.
        
        As mentioned above, EmulsiPred requires 2 inputs.
        1) A fasta file containing the protein sequences to check for emulsifiers (termed sequence.fsa).
        2) A NetSurfP file containing secondary structure information of the sequences in sequence.fsa (termed netsurfp.txt)  
        
        Additionally, there are also 3 variable parameters. 
        1) o (out_dir): Output directory (default is the current directory).
        2) nr_seq: Results will only include peptides present in this number of sequences or higher (default 1).
        3) ls (lower score): Results will only include peptides with a score higher than this score (default 2).  
        
        EmulsiPred can be run directly in the terminal with the following
        command.
        ~~~.sh
            python -m EmulsiPred -s path/to/sequence.fsa -n path/to/netsurfp.txt -o path/to/out_dir --nr_seq 1 --ls 2
        ~~~ 
        Furthermore, it can be imported and run in a python script.
        
        ~~~~~~~~~~~~~~~~~~~~~python
        import EmulsiPred as ep
        
        ep.EmulsiPred(sequences='path/to/sequence.fsa', netsurfp_results='path/to/netsurfp.txt', out_dir='path/to/out_dir', nr_seq=1, lower_score=2)
        ~~~~~~~~~~~~~~~~~~~~~
        
        #### Interpretation of predictions
        
        The predicted values are a relative ordering 
        of the peptides by chance of being an emulsifier. 
        In other words, a higher score implies a higher chance 
        of being an emulsifier. 
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python :: 3.6
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
