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MultiPro: DDA-PASEF and diaPASEF acquired cell line proteomic datasets with deliberate batch effects
Mass spectrometry-based proteomics plays a critical role in current biological and clinical research. Technical issues like data integration, missing value imputation, batch effect correction and the exploration of inter-connections amongst these technical issues, can produce errors but are not well...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10693559/ https://www.ncbi.nlm.nih.gov/pubmed/38042886 http://dx.doi.org/10.1038/s41597-023-02779-8 |
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author | Wang, He Lim, Kai Peng Kong, Weijia Gao, Huanhuan Wong, Bertrand Jern Han Phua, Ser Xian Guo, Tiannan Goh, Wilson Wen Bin |
author_facet | Wang, He Lim, Kai Peng Kong, Weijia Gao, Huanhuan Wong, Bertrand Jern Han Phua, Ser Xian Guo, Tiannan Goh, Wilson Wen Bin |
author_sort | Wang, He |
collection | PubMed |
description | Mass spectrometry-based proteomics plays a critical role in current biological and clinical research. Technical issues like data integration, missing value imputation, batch effect correction and the exploration of inter-connections amongst these technical issues, can produce errors but are not well studied. Although proteomic technologies have improved significantly in recent years, this alone cannot resolve these issues. What is needed are better algorithms and data processing knowledge. But to obtain these, we need appropriate proteomics datasets for exploration, investigation, and benchmarking. To meet this need, we developed MultiPro (Multi-purpose Proteome Resource), a resource comprising four comprehensive large-scale proteomics datasets with deliberate batch effects using the latest parallel accumulation-serial fragmentation in both Data-Dependent Acquisition (DDA) and Data Independent Acquisition (DIA) modes. Each dataset contains a balanced two-class design based on well-characterized and widely studied cell lines (A549 vs K562 or HCC1806 vs HS578T) with 48 or 36 biological and technical replicates altogether, allowing for investigation of a multitude of technical issues. These datasets allow for investigation of inter-connections between class and batch factors, or to develop approaches to compare and integrate data from DDA and DIA platforms. |
format | Online Article Text |
id | pubmed-10693559 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106935592023-12-04 MultiPro: DDA-PASEF and diaPASEF acquired cell line proteomic datasets with deliberate batch effects Wang, He Lim, Kai Peng Kong, Weijia Gao, Huanhuan Wong, Bertrand Jern Han Phua, Ser Xian Guo, Tiannan Goh, Wilson Wen Bin Sci Data Data Descriptor Mass spectrometry-based proteomics plays a critical role in current biological and clinical research. Technical issues like data integration, missing value imputation, batch effect correction and the exploration of inter-connections amongst these technical issues, can produce errors but are not well studied. Although proteomic technologies have improved significantly in recent years, this alone cannot resolve these issues. What is needed are better algorithms and data processing knowledge. But to obtain these, we need appropriate proteomics datasets for exploration, investigation, and benchmarking. To meet this need, we developed MultiPro (Multi-purpose Proteome Resource), a resource comprising four comprehensive large-scale proteomics datasets with deliberate batch effects using the latest parallel accumulation-serial fragmentation in both Data-Dependent Acquisition (DDA) and Data Independent Acquisition (DIA) modes. Each dataset contains a balanced two-class design based on well-characterized and widely studied cell lines (A549 vs K562 or HCC1806 vs HS578T) with 48 or 36 biological and technical replicates altogether, allowing for investigation of a multitude of technical issues. These datasets allow for investigation of inter-connections between class and batch factors, or to develop approaches to compare and integrate data from DDA and DIA platforms. Nature Publishing Group UK 2023-12-02 /pmc/articles/PMC10693559/ /pubmed/38042886 http://dx.doi.org/10.1038/s41597-023-02779-8 Text en © The Author(s) 2023 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Wang, He Lim, Kai Peng Kong, Weijia Gao, Huanhuan Wong, Bertrand Jern Han Phua, Ser Xian Guo, Tiannan Goh, Wilson Wen Bin MultiPro: DDA-PASEF and diaPASEF acquired cell line proteomic datasets with deliberate batch effects |
title | MultiPro: DDA-PASEF and diaPASEF acquired cell line proteomic datasets with deliberate batch effects |
title_full | MultiPro: DDA-PASEF and diaPASEF acquired cell line proteomic datasets with deliberate batch effects |
title_fullStr | MultiPro: DDA-PASEF and diaPASEF acquired cell line proteomic datasets with deliberate batch effects |
title_full_unstemmed | MultiPro: DDA-PASEF and diaPASEF acquired cell line proteomic datasets with deliberate batch effects |
title_short | MultiPro: DDA-PASEF and diaPASEF acquired cell line proteomic datasets with deliberate batch effects |
title_sort | multipro: dda-pasef and diapasef acquired cell line proteomic datasets with deliberate batch effects |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10693559/ https://www.ncbi.nlm.nih.gov/pubmed/38042886 http://dx.doi.org/10.1038/s41597-023-02779-8 |
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