Oktoberfest: Rescoring, Collision Energy Calibration and Spectral Library Generation for Proteomics

Oktoberfest is a python tool for collision energy calibration, rescoring search results and generating spectral libraries for proteomics research within the Prosit ecosystem. It offers an end to end pipeline that takes search results, predicts peptide properties using koina, plots summaries and quality control figures and performs FDR estimation with either mokapot or percolator.

Documentation

The official Oktoberfest documentation can be found at https://oktoberfest.readthedocs.io.

How to cite

Please always cite the main publication:

[Oktoberfest] Picciani M, Gabriel W, Giurcoiu VG et al. (2023), Oktoberfest: Open-source spectral library generation and rescoring pipeline based on Prosit, Proteomics

Should you make use of peptide property predictions through Oktoberfest using one of the supported Prosit models, please also cite the following:

When using Prosit

[Prosit] Gessulat S, Schmidt T, Zolg DP et al. (2019), PROSIT: Proteome-wide prediction of peptide tandem mass spectra by deep learning, Nature Methods

[Prosit-HLA] Wilhelm M, Zolg DP, Graber M et al. (2021), Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics, Nature Communications

When using Prosit-TMT

[Prosit-TMT] Gabriel W, The M, Zolg D et al. (2022), TriMap: Prosit-TMT: Deep Learning Boosts Identification of TMT-Labeled Peptides, Analytical Chemistry

When using Prosit-timsTOF

[Prosit-timsTOF] Adams C, Gabriel W, Laukens K et al. (2023), Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF, BioRxiv