crema-ms 0.0.9

Confidence estimation for proteomics experiments

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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 purpose 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.

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 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:

$ crema data/tide-search.target.psms.txt data/tide-search.decoy.psms.txt

Alternatively, the Python API can be used to calculate confidence estimates in the Python interpreter and affords greater flexibility:

    >>> import crema
    >>> input_files = ["data/tide-search.target.psms.txt", "data/tide-search.decoy.psms.txt"]
    >>> psms = crema.read_tide(input_files)
    >>> results =  psms.assign_confidence()
    >>> results.to_txt(ouput_dir="example_output_dir")

Check out our documentation for more details on how to make full use of crema.

Disclaimer

This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor Battelle, nor any of their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

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