Oktoberfest: Open‐source spectral library generation and rescoring pipeline based on Prosit
-
Published:2023-09-06
Issue:
Volume:
Page:
-
ISSN:1615-9853
-
Container-title:PROTEOMICS
-
language:en
-
Short-container-title:Proteomics
Author:
Picciani Mario1ORCID,
Gabriel Wassim1ORCID,
Giurcoiu Victor‐George1ORCID,
Shouman Omar1ORCID,
Hamood Firas2ORCID,
Lautenbacher Ludwig1ORCID,
Jensen Cecilia Bang2ORCID,
Müller Julian2ORCID,
Kalhor Mostafa1ORCID,
Soleymaniniya Armin1ORCID,
Kuster Bernhard2ORCID,
The Matthew2ORCID,
Wilhelm Mathias1ORCID
Affiliation:
1. Computational Mass Spectrometry TUM School of Life Sciences Technical University of Munich Freising Germany
2. Chair of Proteomics and Bioanalytics TUM School of Life Sciences Technical University of Munich Freising Germany
Abstract
AbstractMachine 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.
Funder
Elitenetzwerk Bayern
European Proteomics Infrastructure Consortium providing access
European Research Council
H2020 Marie Skłodowska-Curie Actions
Bundesministerium für Bildung und Forschung
Subject
Molecular Biology,Biochemistry
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献