mokapot: Fast and flexible semi-supervised learning for peptide detection

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 semi-supervised 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.