Mass spectrometry-based proteomics data from thousands of HeLa control samples

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Mass spectrometry-based proteomics data from thousands of HeLa control samples. / Webel, Henry; Perez-Riverol, Yasset; Nielsen, Annelaura Bach; Rasmussen, Simon.

In: Scientific Data, Vol. 11, 112, 2024.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Webel, H, Perez-Riverol, Y, Nielsen, AB & Rasmussen, S 2024, 'Mass spectrometry-based proteomics data from thousands of HeLa control samples', Scientific Data, vol. 11, 112. https://doi.org/10.1038/s41597-024-02922-z

APA

Webel, H., Perez-Riverol, Y., Nielsen, A. B., & Rasmussen, S. (2024). Mass spectrometry-based proteomics data from thousands of HeLa control samples. Scientific Data, 11, [112]. https://doi.org/10.1038/s41597-024-02922-z

Vancouver

Webel H, Perez-Riverol Y, Nielsen AB, Rasmussen S. Mass spectrometry-based proteomics data from thousands of HeLa control samples. Scientific Data. 2024;11. 112. https://doi.org/10.1038/s41597-024-02922-z

Author

Webel, Henry ; Perez-Riverol, Yasset ; Nielsen, Annelaura Bach ; Rasmussen, Simon. / Mass spectrometry-based proteomics data from thousands of HeLa control samples. In: Scientific Data. 2024 ; Vol. 11.

Bibtex

@article{889f932bdae24dd08c5ff59e7bccd7b1,

title = "Mass spectrometry-based proteomics data from thousands of HeLa control samples",

abstract = "Here we provide a curated, large scale, label free mass spectrometry-based proteomics data set derived from HeLa cell lines for general purpose machine learning and analysis. Data access and filtering is a tedious task, which takes up considerable amounts of time for researchers. Therefore we provide machine based metadata for easy selection and overview along the 7,444 raw files and MaxQuant search output. For convenience, we provide three filtered and aggregated development datasets on the protein groups, peptides and precursors level. Next to providing easy to access training data, we provide a SDRF file annotating each raw file with instrument settings allowing automated reprocessing. We encourage others to enlarge this data set by instrument runs of further HeLa samples from different machine types by providing our workflows and analysis scripts.",

author = "Henry Webel and Yasset Perez-Riverol and Nielsen, {Annelaura Bach} and Simon Rasmussen",

note = "Publisher Copyright: {\textcopyright} 2024, The Author(s).",

year = "2024",

doi = "10.1038/s41597-024-02922-z",

language = "English",

volume = "11",

journal = "Scientific data",

issn = "2052-4463",

publisher = "nature publishing group",

}

RIS

TY - JOUR

T1 - Mass spectrometry-based proteomics data from thousands of HeLa control samples

AU - Webel, Henry

AU - Perez-Riverol, Yasset

AU - Nielsen, Annelaura Bach

AU - Rasmussen, Simon

PY - 2024

Y1 - 2024

N2 - Here we provide a curated, large scale, label free mass spectrometry-based proteomics data set derived from HeLa cell lines for general purpose machine learning and analysis. Data access and filtering is a tedious task, which takes up considerable amounts of time for researchers. Therefore we provide machine based metadata for easy selection and overview along the 7,444 raw files and MaxQuant search output. For convenience, we provide three filtered and aggregated development datasets on the protein groups, peptides and precursors level. Next to providing easy to access training data, we provide a SDRF file annotating each raw file with instrument settings allowing automated reprocessing. We encourage others to enlarge this data set by instrument runs of further HeLa samples from different machine types by providing our workflows and analysis scripts.

AB - Here we provide a curated, large scale, label free mass spectrometry-based proteomics data set derived from HeLa cell lines for general purpose machine learning and analysis. Data access and filtering is a tedious task, which takes up considerable amounts of time for researchers. Therefore we provide machine based metadata for easy selection and overview along the 7,444 raw files and MaxQuant search output. For convenience, we provide three filtered and aggregated development datasets on the protein groups, peptides and precursors level. Next to providing easy to access training data, we provide a SDRF file annotating each raw file with instrument settings allowing automated reprocessing. We encourage others to enlarge this data set by instrument runs of further HeLa samples from different machine types by providing our workflows and analysis scripts.

U2 - 10.1038/s41597-024-02922-z

DO - 10.1038/s41597-024-02922-z

M3 - Journal article

C2 - 38263211

AN - SCOPUS:85182819573

VL - 11

JO - Scientific data

JF - Scientific data

SN - 2052-4463

M1 - 112

ER -

ID: 381888756

Globe Institute