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

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 r...

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Vydané v:Proteomics (Weinheim) Ročník 24; číslo 8; s. e2300112 - n/a
Hlavní autori: Picciani, Mario, Gabriel, Wassim, Giurcoiu, Victor‐George, Shouman, Omar, Hamood, Firas, Lautenbacher, Ludwig, Jensen, Cecilia Bang, Müller, Julian, Kalhor, Mostafa, Soleymaniniya, Armin, Kuster, Bernhard, The, Matthew, Wilhelm, Mathias
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Germany Wiley Subscription Services, Inc 01.04.2024
Predmet:
bioinformatics
bottom‐up proteomics
Data analysis
data processing and analysis
Deep learning
Libraries
Machine learning
mass spectrometry LC‐MS/MS
Proteomics
Python
Search engines
technology
bottom‐up proteomics
technology
data processing and analysis
bioinformatics
mass spectrometry LC‐MS/MS
ISSN:1615-9853, 1615-9861, 1615-9861
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Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
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ISSN:1615-9853
1615-9861
1615-9861
DOI:10.1002/pmic.202300112