Please always cite the main publication

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

Please cite the Prosit model(s) you are using

When using Prosit

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

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

When using Prosit-TMT

[4] 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

[5] 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.

When using Prosit-XL

[6] Kalhor M, Saylan C, Picciani M et al. (2025), Prosit-XL: enhanced cross-linked peptide identification by fragment intensity prediction to study protein interactions and structures, Nature Communications.

When using Prosit-MultiFrag

[7] Levin N, Saylan CC, Lapin J et al. (2026), Integration of alternative fragmentation techniques into standard LC-MS workflows using a single deep learning model enhances proteome coverage, Nature Methods.

Please cite in case you use quantification

[8] 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.