mokapot 0.1a3

Semi-supervised learning for peptide detection by pretrained models

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Fast and flexible semi-supervised learning for peptide detection in Python using the Percolator algorithm.

mokapot is fundamentally a Python implementation of the semi-supervised learning algorithm 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, but using mokapot as a Python package unlocks maximum flexibility.

For more information, check out our documentation.

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 (often referred to as the Percolator input, or "PIN", file format).

Simple mokapot analyses can be performed at the command line:

$ mokapot psms.pin

Alternatively, the Python API can be used to perform analyses in the Python interpreter and affords greater flexibility:

>>> import mokapot
>>> psms = mokapot.read_pin("psms.pin")
>>> results = mokapot.brew(psms)
>>> results.to_txt()

Check out our documentation for more details and examples of mokapot in action.