Metadata-Version: 2.1
Name: crema-ms
Version: 0.0.4
Summary: Confidence estimation for peptide detection in mass spectrometry proteomics
Home-page: https://github.com/Noble-Lab/crema
Author: William E. Fondrie
Author-email: fondriew@gmail.com
License: Apache 2.0
Description: <img src="https://raw.githubusercontent.com/Noble-Lab/crema/master/static/crema_logo.svg" width=300>
         
        ---
        
        Confidence Estimation for Mass Spectrometry Proteomics
        
        **crema** is a Python package that implements various methods to estimate false discovery rates (FDR)
        in mass spectrometry proteomics experiments. crema focuses on
        methods that rely on the concept of "target-decoy competition." The sole purposes of crema is to do decoy-based FDR
        estimation, and to do it well. As a result, crema is lightweight and flexible. It has minimal dependencies and
        supports a wide range of input and output formats. On top of that, it is extremely simple to use.
        
        For more information, check out our
        [documentation](https://crema-ms.readthedocs.io).  
        
        ## Installation  
        
        crema requires Python 3.6+ and can be installed with pip:  
        
        ```
        $ pip3 install crema-ms
        ```
        
        ## Basic Usage  
        
        Before using crema, you need one or more files, each containing a collection of
        peptide-spectrum matches (PSMs) in tab-delimited format. Note that crema defaults
        to reading files via [crux](http://crux.ms/index.html) format, but can easily be
        manipulated to accept files in formats that use differing column headers.
        
        Simple crema calculations can be performed at the command line:
        
        ```Bash
        $ crema data/single_basic.csv
        ```
        
        Alternatively, the Python API can be used to calculate confidence estimates in the Python
        interpreter and affords greater flexibility:
        
        ```Python
            >>> import crema
            >>> input_files = ["data/multi_target.csv", "data/multi_decoy.csv"]
            >>> psms = crema.read_file(input_files)
            >>> results = crema.calculate_tdc(psms)
            >>> results.write_file(ouput_dir="example_output_dir")
        ```
        
        Check out our [documentation](hhttps://crema-ms.readthedocs.io) for more details
        on how to make full use of crema.
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
