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Oktoberfest: Open‐source spectral library generation and rescoring pipeline based on Prosit

2023; Wiley; Volume: 24; Issue: 8 Linguagem: Inglês

10.1002/pmic.202300112

1615-9861

Mario Picciani, Wassim Gabriel, Victor Giurcoiu, Omar Shouman, Firas Hamood, Ludwig Lautenbacher, Cecilia Bang Jensen, Julian Müller, Mostafa Kalhor, Armin Soleymaniniya, Bernhard Küster, Matthew The, Mathias Wilhelm,

Machine Learning in Bioinformatics

Abstract Machine learning (ML) and deep learning (DL) models for peptide property prediction such as Prosit have enabled the creation of high quality in silico reference libraries. These libraries are used in various applications, ranging from data‐independent acquisition (DIA) data analysis to data‐driven rescoring of search engine results. Here, we present Oktoberfest, an open source Python package of our spectral library generation and rescoring pipeline originally only available online via ProteomicsDB. Oktoberfest is largely search engine agnostic and provides access to online peptide property predictions, promoting the adoption of state‐of‐the‐art ML/DL models in proteomics analysis pipelines. We demonstrate its ability to reproduce and even improve our results from previously published rescoring analyses on two distinct use cases. Oktoberfest is freely available on GitHub ( https://github.com/wilhelm‐lab/oktoberfest ) and can easily be installed locally through the cross‐platform PyPI Python package.