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Getting Started

mokapot uses a semi-supervised learning approach to enhance peptide detection from bottom-up proteomics experiments. It takes features describing putative peptide-spectrum matches (PSMs) from database search engines as input, re-scores them, and yields statistical measures—confidence estimates, such as q-values and posterior error probabilities—indicating their quality.


If you use mokapot in your work, please cite:

Fondrie W. E. & Noble W. S. mokapot: Fast and Flexible Semisupervised Learning for Peptide Detection. J Proteome Res (2021) doi: 10.1021/acs.jproteome.0c01010. PMID: 33596079. [Link]


Nearly every analysis of a bottom-up proteomics begins by using a search engine to assign putative peptides to the acquired mass spectra, yielding a collection of peptide-spectrum matches (PSMs). However, post-processing tools such as Percolator and PeptideProphet have proven invaluable for improving the sensitivity of peptide detection and providing consistent statistical frameworks for interpreting these detections.

mokapot is fundamentally a Python implementation of the semi-supervised learning algorithm introduced by Percolator. We developed mokapot to add additional flexibility for 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.

Ready to try mokapot for your analyses? See below for details on how to install and use mokapot. Additionally, check out the Vignettes for other examples of mokapot in action.


Before you can install and use mokapot, you’ll need to have Python 3.6+ installed. If you think it may be installed, you can check with:

$ python3 --version

If you need to install Python, we recommend using the Anaconda Python distribution. This distribution comes with most of the mokapot dependencies installed and provides the conda package manager.

mokapot also depends on several Python packages:

We recommend using conda to install mokapot. Missing dependencies will also be installed automatically:

$ conda install -c bioconda mokapot

You can also install mokapot with pip:

$ pip3 install mokapot

Basic Usage

Before you can use mokapot, you need PSMs assigned by a search engine available in either the Percolator tab-delimited file format (often referred to as the Percolator input, or “PIN”, file format) or as a PepXML file. These files can be generated from various search engines, such as Comet or Tide (which is part of the Crux mass spectrometry toolkit).

If you need an example file to get started with, a selection of PSMs from Hogrebe et al. 1 is available to download from the mokapot repository, This is the file we’ll use in the examples below.

Run mokapot from the command line

Simple mokapot analyses can be performed from the command line:

$ mokapot

That’s it. Your results will be saved in your working directory as two tab-delimited files, mokapot.psms.txt and mokapot.peptides.txt. For a full list of parameters, see the Command Line Interface.

Use mokapot as a Python package

It is easy to run the above analysis from the Python interpreter as well. First start up the Python interpreter:

$ python3

Then conduct your mokapot analysis:

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

This is great for when your upstream and/or downstream analyses are also conducted in Python. Additionally, a good deal more flexibility is available when using mokapot from the Python interpreter. For more details, see the Python API as well as our Cookbook and Vignettes for common use cases.

Need help?

Do you still have questions after reading the documentation on this site? The best way to get help is to poster your question to the mokapot discussion board. Chances are, if you have a question, someone else has a similar one.


Hogrebe, Alexander, et al. “Benchmarking common quantification strategies for large-scale phosphoproteomics.” Nature communications 9.1 (2018): 1-13.