Effective representation and storage of mass spectrometry-based proteomic data sets for the scientific community

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Effective representation and storage of mass spectrometry-based proteomic data sets for the scientific community. / Olsen, Jesper V; Mann, Matthias.

In: Science Signaling, Vol. 4, No. 160, 01.01.2011, p. pe7.

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

Harvard

Olsen, JV & Mann, M 2011, 'Effective representation and storage of mass spectrometry-based proteomic data sets for the scientific community', Science Signaling, vol. 4, no. 160, pp. pe7. https://doi.org/10.1126/scisignal.2001839

APA

Olsen, J. V., & Mann, M. (2011). Effective representation and storage of mass spectrometry-based proteomic data sets for the scientific community. Science Signaling, 4(160), pe7. https://doi.org/10.1126/scisignal.2001839

Vancouver

Olsen JV, Mann M. Effective representation and storage of mass spectrometry-based proteomic data sets for the scientific community. Science Signaling. 2011 Jan 1;4(160):pe7. https://doi.org/10.1126/scisignal.2001839

Author

Olsen, Jesper V ; Mann, Matthias. / Effective representation and storage of mass spectrometry-based proteomic data sets for the scientific community. In: Science Signaling. 2011 ; Vol. 4, No. 160. pp. pe7.

Bibtex

@article{a5f7f7a083704d898458df8c5f6c7de7,
title = "Effective representation and storage of mass spectrometry-based proteomic data sets for the scientific community",
abstract = "Mass spectrometry-based proteomics has emerged as a technology of choice for global analysis of cell signaling networks. However, reporting and sharing of MS data are often haphazard, limiting the usefulness of proteomics to the signaling community. We argue that raw data should always be provided with proteomics studies together with detailed peptide and protein identification and quantification information. Statistical criteria for peptide identification and their posttranslational modifications have largely been established for individual projects. However, the current practice of indiscriminately incorporating these individual results into databases such as UniProt is problematic. Because of the vast differences in underlying data quality, we advocate a differentiated annotation of data by level of reliability. Requirements for the reporting of quantitative data are being developed, but there are few mechanisms for community-wide sharing of these data.",
keywords = "Database Management Systems, Databases, Protein, Humans, Mass Spectrometry, Proteomics, Signal Transduction",
author = "Olsen, {Jesper V} and Matthias Mann",
year = "2011",
month = jan,
day = "1",
doi = "10.1126/scisignal.2001839",
language = "English",
volume = "4",
pages = "pe7",
journal = "Science Signaling",
issn = "1945-0877",
publisher = "American Association for the Advancement of Science",
number = "160",

}

RIS

TY - JOUR

T1 - Effective representation and storage of mass spectrometry-based proteomic data sets for the scientific community

AU - Olsen, Jesper V

AU - Mann, Matthias

PY - 2011/1/1

Y1 - 2011/1/1

N2 - Mass spectrometry-based proteomics has emerged as a technology of choice for global analysis of cell signaling networks. However, reporting and sharing of MS data are often haphazard, limiting the usefulness of proteomics to the signaling community. We argue that raw data should always be provided with proteomics studies together with detailed peptide and protein identification and quantification information. Statistical criteria for peptide identification and their posttranslational modifications have largely been established for individual projects. However, the current practice of indiscriminately incorporating these individual results into databases such as UniProt is problematic. Because of the vast differences in underlying data quality, we advocate a differentiated annotation of data by level of reliability. Requirements for the reporting of quantitative data are being developed, but there are few mechanisms for community-wide sharing of these data.

AB - Mass spectrometry-based proteomics has emerged as a technology of choice for global analysis of cell signaling networks. However, reporting and sharing of MS data are often haphazard, limiting the usefulness of proteomics to the signaling community. We argue that raw data should always be provided with proteomics studies together with detailed peptide and protein identification and quantification information. Statistical criteria for peptide identification and their posttranslational modifications have largely been established for individual projects. However, the current practice of indiscriminately incorporating these individual results into databases such as UniProt is problematic. Because of the vast differences in underlying data quality, we advocate a differentiated annotation of data by level of reliability. Requirements for the reporting of quantitative data are being developed, but there are few mechanisms for community-wide sharing of these data.

KW - Database Management Systems

KW - Databases, Protein

KW - Humans

KW - Mass Spectrometry

KW - Proteomics

KW - Signal Transduction

U2 - 10.1126/scisignal.2001839

DO - 10.1126/scisignal.2001839

M3 - Journal article

C2 - 21325203

VL - 4

SP - pe7

JO - Science Signaling

JF - Science Signaling

SN - 1945-0877

IS - 160

ER -

ID: 34339699