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.
The official Oktoberfest documentation can be found at https://oktoberfest.readthedocs.io.
[Oktoberfest] Picciani M, Gabriel W, Giurcoiu VG et al. (2023), Oktoberfest: Open-source spectral library generation and rescoring pipeline based on Prosit, Proteomics
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), 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. (2024), Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF, Nature Communications
[PickedGroupFDR] The M, Samaras P, Kuster B, Wilhelm, M. (2022), Reanalysis of ProteomicsDB using an accurate, sensitive, and scalable false discovery rate estimation approach for protein groups, Molecular & Cellular Proteomics