Metadata-Version: 2.1
Name: psap
Version: 1.0.6
Summary: CLI interface for the PSAP classifier. PSAP implements a RandomForest approach to predict the probability of proteins to mediate protein phase separation (PPS).
Home-page: https://github.com/vanheeringen-lab/psap
Author: ['Juriaan Jansen', 'Tilman Schaefers <tilman.schaefers@ru.nl>']
License: MIT license
Description: ========
        psap
        ========
        
        
        .. image:: https://github.com/vanheeringen-lab/psap/actions/workflows/python-app.yml/badge.svg
           :target:  https://github.com/vanheeringen-lab/psap
        
        .. image:: https://github.com/vanheeringen-lab/psap/actions/workflows/continuous-deployment.yml/badge.svg
           :target:  https://github.com/vanheeringen-lab/psap
        
        .. image:: https://badge.fury.io/py/psap.svg
           :target:  https://pypi.org/project/psap/
        
        CLI interface for the PSAP classifier. PSAP implements a RandomForest approach to predict the probability of proteins to mediate protein phase separation (PPS). Initially, a set of protein sequences is annotated with biochemical features wich are subsequently used to train a RandomForest (scikit-learn) classifier. The trained classifier is exported to json format and can be used to predict the llps class probability (PSAP_score) for new samples. 
        
        The default model was trained on the `human reference proteome <ftp://ftp.ebi.ac.uk/pub/databases/reference_proteomes/QfO/Eukaryota/UP000005640_9606.fasta.gz>`_ with a list of literature curated PPS proteins for positive class labeling. Both can be found in /data.   
        
        **Publication**
        | Mierlo, G., Jansen, J. R. G., Wang, J., Poser, I., van Heeringen, S. J., & Vermeulen, M. (2021). Predicting protein condensate formation using machine learning. Cell Reports, 34(5), 108705. https://doi.org/10.1016/j.celrep.2021.108705.
        
        
        * Free software: MIT license
        
        ================
        Getting Started
        ================
        
        1. *Install psap*
        ----------------------
        .. code-block:: bash
           
           pip install psap
           
        2. *Train classifier*
        -----------------------
        .. code-block:: python
        
           psap train -f /path/to/peptide-trainingset.fasta -l /path/top/known/pps-proteins.txt (optional)  -o /output/directory (optional)
              
        The trained RandomForest classifier is exported to json format and stored in the output directory.
        
        3. *Predict llps score for peptide instances*
        -----------------------------------------------
        .. code-block:: python
        
           psap predict -f /path/to/peptid-testset.fasta -m /path/to/model.json (optional) -o /output/directory (optional)
           
        When no model (-m) is provided psap loads the default classifier stored in /data/model.
        
        4. *Annotate petides (optional)*
        ---------------------------------
        .. code-block:: python
        
           psap annotate -f /path/to/peptide.fasta  -l /path/top/known/pps-proteins.txt (optional) -o /output/directory (optional)    
        
        Annotates a peptide fasta with biochemical features. This step is included in train and predict.
        
        
        
        Credits
        -------
        
        This package was adapted from the cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
        
        .. _Cookiecutter: https://github.com/audreyr/cookiecutter
        .. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
        
Keywords: psap
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.7
Description-Content-Type: text/x-rst
