oktoberfest

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.

How to cite

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.

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

oktoberfest-0.11.1.tar.gz (59.3 kB view details)

Uploaded Jul 16, 2026 Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

oktoberfest-0.11.1-py3-none-any.whl (67.0 kB view details)

Uploaded Jul 16, 2026 Python 3

File details

Details for the file oktoberfest-0.11.1.tar.gz.

File metadata

Download URL: oktoberfest-0.11.1.tar.gz
Upload date: Jul 16, 2026
Size: 59.3 kB
Tags: Source
Uploaded using Trusted Publishing? Yes
Uploaded via: twine/6.1.0 CPython/3.13.13

File hashes

Hashes for oktoberfest-0.11.1.tar.gz
Algorithm	Hash digest
SHA256	`0f9d54d62a24a0e39e695d638791c294a3f97cbd9fa3676e174d3da9209beca0`
MD5	`4cc1d04cefff510d64bba0be23b9d626`
BLAKE2b-256	`ccd6bfd3af12ba95daeafbc4b846ec2b8886b7c128fbcc2408849e57626b3747`

See more details on using hashes here.

Provenance

The following attestation bundles were made for oktoberfest-0.11.1.tar.gz:

Publisher: publish_package.yml on wilhelm-lab/oktoberfest

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Statement:
- Statement type: https://in-toto.io/Statement/v1
- Predicate type: https://docs.pypi.org/attestations/publish/v1
- Subject name: oktoberfest-0.11.1.tar.gz
- Subject digest: 0f9d54d62a24a0e39e695d638791c294a3f97cbd9fa3676e174d3da9209beca0
- Sigstore transparency entry: 2184807358
- Sigstore integration time: Jul 16, 2026
Source repository:
- Permalink: wilhelm-lab/oktoberfest@8ba32202a8b1231c124380a24a1070566b4114f0
- Branch / Tag: refs/tags/v0.11.1
- Owner: https://github.com/wilhelm-lab
- Access: public
Publication detail:
- Token Issuer: https://token.actions.githubusercontent.com
- Runner Environment: github-hosted
- Publication workflow: publish_package.yml@8ba32202a8b1231c124380a24a1070566b4114f0
- Trigger Event: release