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A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics
In the last decade, a revolution in liquid chromatography-mass spectrometry (LC-MS) based proteomics was unfolded with the introduction of dozens of novel instruments that incorporate additional data dimensions through innovative acquisition methodologies, in turn inspiring specialized data analysis...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8967878/ https://www.ncbi.nlm.nih.gov/pubmed/35354825 http://dx.doi.org/10.1038/s41597-022-01216-6 |
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author | Van Puyvelde, Bart Daled, Simon Willems, Sander Gabriels, Ralf Gonzalez de Peredo, Anne Chaoui, Karima Mouton-Barbosa, Emmanuelle Bouyssié, David Boonen, Kurt Hughes, Christopher J. Gethings, Lee A. Perez-Riverol, Yasset Bloomfield, Nic Tate, Stephen Schiltz, Odile Martens, Lennart Deforce, Dieter Dhaenens, Maarten |
author_facet | Van Puyvelde, Bart Daled, Simon Willems, Sander Gabriels, Ralf Gonzalez de Peredo, Anne Chaoui, Karima Mouton-Barbosa, Emmanuelle Bouyssié, David Boonen, Kurt Hughes, Christopher J. Gethings, Lee A. Perez-Riverol, Yasset Bloomfield, Nic Tate, Stephen Schiltz, Odile Martens, Lennart Deforce, Dieter Dhaenens, Maarten |
author_sort | Van Puyvelde, Bart |
collection | PubMed |
description | In the last decade, a revolution in liquid chromatography-mass spectrometry (LC-MS) based proteomics was unfolded with the introduction of dozens of novel instruments that incorporate additional data dimensions through innovative acquisition methodologies, in turn inspiring specialized data analysis pipelines. Simultaneously, a growing number of proteomics datasets have been made publicly available through data repositories such as ProteomeXchange, Zenodo and Skyline Panorama. However, developing algorithms to mine this data and assessing the performance on different platforms is currently hampered by the lack of a single benchmark experimental design. Therefore, we acquired a hybrid proteome mixture on different instrument platforms and in all currently available families of data acquisition. Here, we present a comprehensive Data-Dependent and Data-Independent Acquisition (DDA/DIA) dataset acquired using several of the most commonly used current day instrumental platforms. The dataset consists of over 700 LC-MS runs, including adequate replicates allowing robust statistics and covering over nearly 10 different data formats, including scanning quadrupole and ion mobility enabled acquisitions. Datasets are available via ProteomeXchange (PXD028735). |
format | Online Article Text |
id | pubmed-8967878 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-89678782022-04-20 A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics Van Puyvelde, Bart Daled, Simon Willems, Sander Gabriels, Ralf Gonzalez de Peredo, Anne Chaoui, Karima Mouton-Barbosa, Emmanuelle Bouyssié, David Boonen, Kurt Hughes, Christopher J. Gethings, Lee A. Perez-Riverol, Yasset Bloomfield, Nic Tate, Stephen Schiltz, Odile Martens, Lennart Deforce, Dieter Dhaenens, Maarten Sci Data Data Descriptor In the last decade, a revolution in liquid chromatography-mass spectrometry (LC-MS) based proteomics was unfolded with the introduction of dozens of novel instruments that incorporate additional data dimensions through innovative acquisition methodologies, in turn inspiring specialized data analysis pipelines. Simultaneously, a growing number of proteomics datasets have been made publicly available through data repositories such as ProteomeXchange, Zenodo and Skyline Panorama. However, developing algorithms to mine this data and assessing the performance on different platforms is currently hampered by the lack of a single benchmark experimental design. Therefore, we acquired a hybrid proteome mixture on different instrument platforms and in all currently available families of data acquisition. Here, we present a comprehensive Data-Dependent and Data-Independent Acquisition (DDA/DIA) dataset acquired using several of the most commonly used current day instrumental platforms. The dataset consists of over 700 LC-MS runs, including adequate replicates allowing robust statistics and covering over nearly 10 different data formats, including scanning quadrupole and ion mobility enabled acquisitions. Datasets are available via ProteomeXchange (PXD028735). Nature Publishing Group UK 2022-03-30 /pmc/articles/PMC8967878/ /pubmed/35354825 http://dx.doi.org/10.1038/s41597-022-01216-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Van Puyvelde, Bart Daled, Simon Willems, Sander Gabriels, Ralf Gonzalez de Peredo, Anne Chaoui, Karima Mouton-Barbosa, Emmanuelle Bouyssié, David Boonen, Kurt Hughes, Christopher J. Gethings, Lee A. Perez-Riverol, Yasset Bloomfield, Nic Tate, Stephen Schiltz, Odile Martens, Lennart Deforce, Dieter Dhaenens, Maarten A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics |
title | A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics |
title_full | A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics |
title_fullStr | A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics |
title_full_unstemmed | A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics |
title_short | A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics |
title_sort | comprehensive lfq benchmark dataset on modern day acquisition strategies in proteomics |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8967878/ https://www.ncbi.nlm.nih.gov/pubmed/35354825 http://dx.doi.org/10.1038/s41597-022-01216-6 |
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