Metadata-Version: 1.1
Name: mhcflurry
Version: 1.2.1
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-Content-Type: UNKNOWN
Description: |Build Status|
        
        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:
        
            O’Donnell, T. et al., 2017. MHCflurry: open-source class I MHC
            binding affinity prediction. bioRxiv. Available at:
            http://www.biorxiv.org/content/early/2017/08/09/174243.
        
        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"
        
        .. |Build Status| image:: https://travis-ci.org/openvax/mhcflurry.svg?branch=master
           :target: https://travis-ci.org/openvax/mhcflurry
        
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
