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J Proteomics. 2013 Dec 16;95:84-8. doi: 10.1016/j.jprot.2013.02.026. Epub 2013 Mar 14.

Guidelines for reporting quantitative mass spectrometry based experiments in proteomics.

Author information

1
Proteomics Facility, Centro Nacional de Biotecnología-Consejo Superior de Investigaciones Científicas (CNB-CSIC), ProteoRed ISCIII, Madrid, Spain. Electronic address: smartinez@proteored.org.

Abstract

Mass spectrometry is already a well-established protein identification tool and recent methodological and technological developments have also made possible the extraction of quantitative data of protein abundance in large-scale studies. Several strategies for absolute and relative quantitative proteomics and the statistical assessment of quantifications are possible, each having specific measurements and therefore, different data analysis workflows. The guidelines for Mass Spectrometry Quantification allow the description of a wide range of quantitative approaches, including labeled and label-free techniques and also targeted approaches such as Selected Reaction Monitoring (SRM).

BIOLOGICAL SIGNIFICANCE:

The HUPO Proteomics Standards Initiative (HUPO-PSI) has invested considerable efforts to improve the standardization of proteomics data handling, representation and sharing through the development of data standards, reporting guidelines, controlled vocabularies and tooling. In this manuscript, we describe a key output from the HUPO-PSI-namely the MIAPE Quant guidelines, which have developed in parallel with the corresponding data exchange format mzQuantML [1]. The MIAPE Quant guidelines describe the HUPO-PSI proposal concerning the minimum information to be reported when a quantitative data set, derived from mass spectrometry (MS), is submitted to a database or as supplementary information to a journal. The guidelines have been developed with input from a broad spectrum of stakeholders in the proteomics field to represent a true consensus view of the most important data types and metadata, required for a quantitative experiment to be analyzed critically or a data analysis pipeline to be reproduced. It is anticipated that they will influence or be directly adopted as part of journal guidelines for publication and by public proteomics databases and thus may have an impact on proteomics laboratories across the world. This article is part of a Special Issue entitled: Standardization and Quality Control.

PMID:
23500130
DOI:
10.1016/j.jprot.2013.02.026
[Indexed for MEDLINE]

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