Metadata-Version: 1.1
Name: mhctools
Version: 1.5.0
Summary: Python interface to running command-line and web-based MHC binding predictors
Home-page: https://github.com/hammerlab/mhctools
Author: Alex Rubinsteyn
Author-email: alex.rubinsteyn@mssm.edu
License: http://www.apache.org/licenses/LICENSE-2.0.html
Description: `|Build Status| <https://travis-ci.org/hammerlab/mhctools>`_ `|Coverage
        Status| <https://coveralls.io/r/hammerlab/mhctools?branch=master>`_
        `|DOI| <https://zenodo.org/badge/latestdoi/18834/hammerlab/mhctools>`_
        
        mhctools
        ========
        
        Python interface to running command-line and web-based MHC binding
        predictors.
        
        Example
        -------
        
        \`\`\`python from mhctools import NetMHCpan # Run NetMHCpan for alleles
        HLA-A*01:01 and HLA-A*02:01 predictor = NetMHCpan(alleles=["A\*02:01",
        "hla-a0101"])
        
        scan the short proteins 1L2Y and 1L3Y for epitopes
        ==================================================
        
        protein\_sequences = { "1L2Y": "NLYIQWLKDGGPSSGRPPPS", "1L3Y":
        "ECDTINCERYNGQVCGGPGRGLCFCGKCRCHPGFEGSACQA" }
        
        binding\_predictions =
        predictor.predict\_subsequences(protein\_sequences,
        peptide\_lengths=[9])
        
        flatten binding predictions into a Pandas DataFrame
        ===================================================
        
        df = binding\_predictions.to\_dataframe()
        
        epitope collection is sorted by percentile rank
        ===============================================
        
        of binding predictions
        ======================
        
        for binding\_prediction in binding\_predictions: if
        binding\_prediction.affinity < 100: print("Strong binder: %s" %
        (binding\_prediction,)) \`\`\` ## API
        
        The following MHC binding predictors are available in ``mhctools``: \*
        ``MHCflurry``: open source predictor installed by default with
        ``mhctools``, requires the user run ``mhcflurry-downloads fetch`` first
        to download MHCflurry models \* ``NetMHC3``: requires locally installed
        version of `NetMHC 3.x <http://www.cbs.dtu.dk/services/NetMHC-3.4/>`_ \*
        ``NetMHC4``: requires locally installed version of `NetMHC
        4.x <http://www.cbs.dtu.dk/services/NetMHC/>`_ \* ``NetMHC``: a wrapper
        function to automatically use ``NetMHC3`` or ``NetMHC4`` depending on
        what's installed. \* ``NetMHCpan``: requires locally installed version
        of `NetMHCpan <http://www.cbs.dtu.dk/services/NetMHCpan/>`_ \*
        ``NetMHCIIpan``: requires locally installed version of
        `NetMHCIIpan <http://www.cbs.dtu.dk/services/NetMHCIIpan/>`_ \*
        ``NetMHCcons``: requires locally installed version of
        `NetMHCcons <http://www.cbs.dtu.dk/services/NetMHCcons/>`_ \*
        ``IedbMhcClass1``: Uses IEDB's REST API for class I binding predictions.
        \* ``IedbMhcClass2``: Uses IEDB's REST API for class II binding
        predictions. \* ``RandomBindingPredictor``: Creates binding predictions
        with random IC50 and percentile rank values.
        
        Every binding predictor is constructed with an ``alleles`` argument
        specifying the HLA type for which to make predictions. Predictions are
        generated by calling the ``predict`` method with a dictionary mapping
        sequence IDs or names to amino acid sequences.
        
        Additionally there is a module for running the
        `NetChop <http://www.cbs.dtu.dk/services/NetChop>`_ proteosomal cleavage
        predictor: \* ``NetChop``: requires locally installed version of
        `NetChop-3.1 <http://www.cbs.dtu.dk/services/NetChop/>`_
        
        .. |Build
        Status| image:: https://travis-ci.org/hammerlab/mhctools.svg?branch=master
        .. |Coverage
        Status| image:: https://coveralls.io/repos/hammerlab/mhctools/badge.svg?branch=master
        .. |DOI| image:: https://zenodo.org/badge/18834/hammerlab/mhctools.svg
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
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
