Metadata-Version: 1.1
Name: mhctools
Version: 0.0.11
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 {dot} rubinsteyn {at} mssm {dot} edu
License: http://www.apache.org/licenses/LICENSE-2.0.html
Description: # 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"
        }
        
        epitope_collection = predictor.predict(protein_sequences)
        
        # flatten binding predictions into a Pandas DataFrame
        df = epitope_collection.dataframe()
        
        # epitope collection is sorted by percentile rank
        # of binding predictions
        strongest_predicted_binder = epitope_collection[0]
        ```
        ## API
        
        The following models are available in `mhctools`: 
        * `NetMHCpan`: requires locally installed version of [NetMHCpan](http://www.cbs.dtu.dk/services/NetMHCpan/)
        * `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 model 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.
        
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
