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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| Veröffentlicht in: | Proteomics (Weinheim) Jg. 24; H. 8; S. e2300112 - n/a |
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01.04.2024
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| 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. |
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| AbstractList | 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. 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.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. |
| Author | Hamood, Firas Jensen, Cecilia Bang Shouman, Omar Gabriel, Wassim Lautenbacher, Ludwig Picciani, Mario Soleymaniniya, Armin Kalhor, Mostafa Müller, Julian Wilhelm, Mathias The, Matthew Giurcoiu, Victor‐George Kuster, Bernhard |
| Author_xml | – sequence: 1 givenname: Mario orcidid: 0000-0003-0428-1703 surname: Picciani fullname: Picciani, Mario organization: Technical University of Munich – sequence: 2 givenname: Wassim orcidid: 0000-0001-6440-9794 surname: Gabriel fullname: Gabriel, Wassim organization: Technical University of Munich – sequence: 3 givenname: Victor‐George orcidid: 0000-0002-1190-6954 surname: Giurcoiu fullname: Giurcoiu, Victor‐George organization: Technical University of Munich – sequence: 4 givenname: Omar orcidid: 0000-0002-9077-3036 surname: Shouman fullname: Shouman, Omar organization: Technical University of Munich – sequence: 5 givenname: Firas orcidid: 0000-0002-4141-7051 surname: Hamood fullname: Hamood, Firas organization: Technical University of Munich – sequence: 6 givenname: Ludwig orcidid: 0000-0002-1540-5911 surname: Lautenbacher fullname: Lautenbacher, Ludwig organization: Technical University of Munich – sequence: 7 givenname: Cecilia Bang orcidid: 0009-0007-7227-3840 surname: Jensen fullname: Jensen, Cecilia Bang organization: Technical University of Munich – sequence: 8 givenname: Julian orcidid: 0000-0003-4108-7926 surname: Müller fullname: Müller, Julian organization: Technical University of Munich – sequence: 9 givenname: Mostafa orcidid: 0009-0006-2548-4154 surname: Kalhor fullname: Kalhor, Mostafa organization: Technical University of Munich – sequence: 10 givenname: Armin orcidid: 0000-0002-7799-6091 surname: Soleymaniniya fullname: Soleymaniniya, Armin organization: Technical University of Munich – sequence: 11 givenname: Bernhard orcidid: 0000-0002-9094-1677 surname: Kuster fullname: Kuster, Bernhard organization: Technical University of Munich – sequence: 12 givenname: Matthew orcidid: 0000-0002-5401-5553 surname: The fullname: The, Matthew organization: Technical University of Munich – sequence: 13 givenname: Mathias orcidid: 0000-0002-9224-3258 surname: Wilhelm fullname: Wilhelm, Mathias email: mathias.wilhelm@tum.de organization: Technical University of Munich |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37672792$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | 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 |
| Title | Oktoberfest: Open‐source spectral library generation and rescoring pipeline based on Prosit |
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