crema-ms 0.0.2

Confidence estimation for peptide detection in mass spectrometry proteomics

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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) of peptide detection in mass spectrometry proteomics experiments. Although there are many ways to estimate FDR, crema focuses on methods that rely on the concept of target decoy competition. The sole purposes of crema is to do this, and to do this well. As a result, we developed crema to be lightweight and flexible. It has very 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/single_basic.csv

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

    >>> import crema-ms
    >>> psms = crema.read_file(["data/multi_target.csv", "data/multi_decoy.csv"])
    >>> results = crema.calculate_tdc(psms)
    >>> results.write_csv("save_to_here.txt")

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