Metadata-Version: 1.1
Name: mhcflurry
Version: 1.6.0
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|
        
        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 implements class I peptide/MHC binding affinity prediction.
        The current version provides pan-MHC I predictors supporting any MHC
        allele of known sequence. MHCflurry runs on Python 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.
        
        Starting in version 1.6.0, MHCflurry also includes two expermental
        predictors, an “antigen processing” predictor that attempts to model MHC
        allele-independent effects such as proteosomal cleavage and a
        “presentation” predictor that integrates processing predictions with
        binding affinity predictions to give a composite “presentation score.”
        Both models are trained on mass spec-identified MHC ligands.
        
        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.
            https://www.cell.com/cell-systems/fulltext/S2405-4712(18)30232-1.
        
        Please file an issue if you have questions or encounter problems.
        
        Have a bugfix or other contribution? We would love your help. See our
        `contributing guidelines <CONTRIBUTING.md>`__.
        
        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
        
        Or scan protein sequences for potential epitopes:
        
        ::
        
            $ mhcflurry-predict-scan \
                    --sequences MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHS \
                    --alleles HLA-A*02:01 \
                    --out /tmp/predictions.csv
                    
            Wrote: /tmp/predictions.csv  
        
        See the `documentation <http://openvax.github.io/mhcflurry/>`__ for more
        details.
        
        Older allele-specific models
        ----------------------------
        
        Previous versions of MHCflurry used models trained on affinity
        measurements, one allele per model (i.e. allele-specific). Mass spec
        datasets were incorporated in the model selection step.
        
        These models are still available to use with the latest version of
        MHCflurry. To download these predictors, run:
        
        ::
        
            $ mhcflurry-downloads fetch models_class1
        
        and specify ``--models`` when you call ``mhcflurry-predict``:
        
        ::
        
            $ mhcflurry-predict \
                   --alleles HLA-A0201 HLA-A0301 \
                   --peptides SIINFEKL SIINFEKD SIINFEKQ \
                   --models "$(mhcflurry-downloads path models_class1)/models"
                   --out /tmp/predictions.csv
                   
            Wrote: /tmp/predictions.csv
        
        Common issues and fixes
        -----------------------
        
        Problems downloading data and models
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        Some users have reported HTTP connection issues when using
        ``mhcflurry-downloads fetch``. As a workaround, you can download the
        data manually (e.g. using ``wget``) and then use ``mhcflurry-downloads``
        just to copy the data to the right place.
        
        To do this, first get the URL(s) of the downloads you need using
        ``mhcflurry-downloads url``:
        
        ::
        
            $ mhcflurry-downloads url models_class1_presentation
            https://github.com/openvax/mhcflurry/releases/download/1.6.0/models_class1_presentation.20200205.tar.bz2```
        
        Then make a directory and download the needed files to this directory:
        
        ::
        
            $ mkdir downloads
            $ wget  --directory-prefix downloads https://github.com/openvax/mhcflurry/releases/download/1.6.0/models_class1_presentation.20200205.tar.bz2```
        
            HTTP request sent, awaiting response... 200 OK
            Length: 72616448 (69M) [application/octet-stream]
            Saving to: 'downloads/models_class1_presentation.20200205.tar.bz2'
        
        Now call ``mhcflurry-downloads fetch`` with the
        ``--already-downloaded-dir`` option to indicate that the downloads
        should be retrived from the specified directory:
        
        ::
        
            $ mhcflurry-downloads fetch models_class1_presentation --already-downloaded-dir downloads
        
        Problems deserializing models
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        If you encounter errors loading the MHCflurry models, such as:
        
        ::
        
            ...
              File "/usr/local/lib/python3.6/site-packages/keras/engine/topology.py", line 293, in __init__
                raise TypeError('Keyword argument not understood:', kwarg)
            TypeError: ('Keyword argument not understood:', 'data_format')
        
        You may need to upgrade Keras:
        
        ::
        
            pip install --upgrade Keras
        
        .. |Build Status| image:: https://travis-ci.org/openvax/mhcflurry.svg?branch=master
           :target: https://travis-ci.org/openvax/mhcflurry
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
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
