Metadata-Version: 2.1
Name: mokapot
Version: 0.2.1
Summary: Semi-supervised learning for peptide detection by pretrained models
Home-page: https://github.com/wfondrie/mokapot
Author: William E. Fondrie
Author-email: fondriew@gmail.com
License: Apache 2.0
Description: <img src="https://raw.githubusercontent.com/wfondrie/mokapot/master/static/mokapot_logo_dark.svg" width=300>  
        
        ---
        
        [![PyPI version](https://badge.fury.io/py/mokapot.svg)](https://badge.fury.io/py/mokapot)
        [![tests](https://github.com/wfondrie/mokapot/workflows/tests/badge.svg)](https://github.com/wfondrie/mokapot/actions?query=workflow%3Atests)
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        Fast and flexible semi-supervised learning for peptide detection.  
        
        mokapot is fundamentally a Python implementation of the semi-supervised learning
        algorithm first introduced by Percolator. We developed mokapot to add additional
        flexibility to our analyses, whether to try something experimental---such as
        swapping Percolator's linear support vector machine classifier for a non-linear,
        gradient boosting classifier---or to train a joint model across experiments
        while retaining valid, per-experiment confidence estimates. We designed mokapot
        to be extensible and support the analysis of additional types of proteomics
        data, such as cross-linked peptides from cross-linking mass spectrometry
        experiments. mokapot offers basic functionality from the command line, but using
        mokapot as a Python package unlocks maximum flexibility.
        
        For more information, check out our
        [documentation](https://mokapot.readthedocs.io).  
        
        ## Installation  
        
        mokapot requires Python 3.6+ and can be installed with pip:  
        
        ```
        $ pip3 install mokapot
        ```
        
        Additionally, you can install the development version directly from GitHub:  
        
        ```
        $ pip3 install git+git://github.com/wfondrie/mokapot
        ```
        
        ## Basic Usage  
        
        Before you can use mokapot, you need PSMs assigned by a search engine available
        in the [Percolator tab-delimited file
        format](https://github.com/percolator/percolator/wiki/Interface#tab-delimited-file-format)
        (often referred to as the Percolator input, or "PIN", file format). 
        
        Simple mokapot analyses can be performed at the command line:
        
        ```Bash
        $ mokapot psms.pin
        ```
        
        Alternatively, the Python API can be used to perform analyses in the Python
        interpreter and affords greater flexibility:
        
        ```Python
        >>> import mokapot
        >>> psms = mokapot.read_pin("psms.pin")
        >>> results, models = mokapot.brew(psms)
        >>> results.to_txt()
        ```
        
        Check out our [documentation](https://mokapot.readthedocs.io) for more details
        and examples of mokapot in action.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Description-Content-Type: text/markdown
Provides-Extra: docs
Provides-Extra: dev
