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Mokapot: Fast and Flexible Semisupervised Learning for Peptide Detection.

ABSTRACT: 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.

SUBMITTER: Fondrie WE

PROVIDER: S-EPMC8022319 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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mokapot: Fast and Flexible Semisupervised Learning for Peptide Detection.

Fondrie William E WE Noble William S WS

Journal of proteome research 20210217 4

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 sin ...[more]

PMID: 33596079

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