Metadata-Version: 1.1
Name: mhcflurry
Version: 1.2.4
Summary: MHC Binding Predictor
Home-page: https://github.com/hammerlab/mhcflurry
Author: Tim O'Donnell and Alex Rubinsteyn
Author-email: timodonnell@gmail.com
License: http://www.apache.org/licenses/LICENSE-2.0.html
Description: [![Build Status](https://travis-ci.org/openvax/mhcflurry.svg?branch=master)](https://travis-ci.org/openvax/mhcflurry)
        
        # mhcflurry
        [MHC I](https://en.wikipedia.org/wiki/MHC_class_I) ligand
        prediction package with competitive accuracy and a fast and 
        [documented](http://openvax.github.io/mhcflurry/) implementation.
        
        MHCflurry supports Class I peptide/MHC binding affinity prediction using
        ensembles of allele-specific models. It runs on Python 2.7 and 3.4+ using
        the [keras](https://keras.io) neural network library. It exposes [command-line](http://openvax.github.io/mhcflurry/commandline_tutorial.html)
        and [Python library](http://openvax.github.io/mhcflurry/python_tutorial.html) interfaces.
        
        If you find MHCflurry useful in your research please cite:
        
        > T. J. O’Donnell, A. Rubinsteyn, M. Bonsack, A. B. Riemer, U. Laserson, and J. Hammerbacher, "MHCflurry: Open-Source Class I MHC Binding Affinity Prediction," *Cell Systems*, 2018. Available at: https://www.cell.com/cell-systems/fulltext/S2405-4712(18)30232-1.
        
        ## Installation (pip)
        
        Install the package:
        
        ```
        $ pip install mhcflurry
        ```
        
        Then download our datasets and trained models:
        
        ```
        $ mhcflurry-downloads fetch
        ```
        
        You can now generate predictions:
        
        ```
        $ mhcflurry-predict \
               --alleles HLA-A0201 HLA-A0301 \
               --peptides SIINFEKL SIINFEKD SIINFEKQ \
               --out /tmp/predictions.csv
               
        Wrote: /tmp/predictions.csv
        ```
        
        See the [documentation](http://openvax.github.io/mhcflurry/) for more details.
        
        ## MHCflurry model variants and mass spec 
        
        The default MHCflurry models are trained
        on affinity measurements. Mass spec datasets are incorporated only in
        the model selection step. We also release experimental predictors whose training data directly
        includes mass spec. To download these predictors, run:
        
        ```
        $ mhcflurry-downloads fetch models_class1_trained_with_mass_spec
        ```
        
        and then to make them used by default:
        
        ```
        $ export MHCFLURRY_DEFAULT_CLASS1_MODELS="$(mhcflurry-downloads path models_class1_trained_with_mass_spec)/models"
        ```
        
        We also release predictors that do not use mass spec datasets at all. To use
        these predictors, run:
        
        ```
        $ mhcflurry-downloads fetch models_class1_selected_no_mass_spec
        export MHCFLURRY_DEFAULT_CLASS1_MODELS="$(mhcflurry-downloads path models_class1_selected_no_mass_spec)/models"
        ```
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
