mokapot: Fast and Flexible Semisupervised Learning for Peptide Detection

J Proteome Res. 2021 Apr 2;20(4):1966-1971. doi: 10.1021/acs.jproteome.0c01010. Epub 2021 Feb 17.


Proteomics studies rely on the accurate assignment of peptides to the acquired tandem mass spectra-a task where machine learning algorithms have proven invaluable. We describe mokapot, which provides a flexible semisupervised learning algorithm that allows for highly customized analyses. We demonstrate some of the unique features of mokapot by improving the detection of RNA-cross-linked peptides from an analysis of RNA-binding proteins and increasing the consistency of peptide detection in a single-cell proteomics study.

Keywords: SVM; bioinformatics; confidence estimation; machine learning; peptide identification; percolator; proteomics; single-cell mass spectrometry; support vector machine; tandem mass spectrometry.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms
  • Databases, Protein
  • Peptides*
  • Proteomics*
  • Tandem Mass Spectrometry


  • Peptides