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

Jesper V Olsen; Matthias Mann

doi:10.1126/scisignal.2001839

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

Jesper V Olsen, Matthias Mann

The NNF Center for Protein Research

13 Citationer (Scopus)

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.

Originalsprog	Engelsk
Tidsskrift	Science Signaling
Vol/bind	4
Udgave nummer	160
Sider (fra-til)	pe7
ISSN	1945-0877
DOI	https://doi.org/10.1126/scisignal.2001839
Status	Udgivet - 15 feb. 2011

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10.1126/scisignal.2001839

Citationsformater

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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.",

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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.

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KW - Databases, Protein

KW - Humans

KW - Mass Spectrometry

KW - Proteomics

KW - Signal Transduction

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JO - Science Signaling

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

Abstract

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