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J Proteome Res. 2019 Dec 6;18(12):4108-4116. doi: 10.1021/acs.jproteome.9b00542. Epub 2019 Oct 21.

Human Proteome Project Mass Spectrometry Data Interpretation Guidelines 3.0.

Author information

1
Institute for Systems Biology , Seattle , Washington 98109 , United States.
2
SIB Swiss Institute of Bioinformatics and Department of Microbiology and Molecular Medicine, Faculty of Medicine , University of Geneva , CMU, Michel Servet 1 , 1211 Geneva 4 , Switzerland.
3
Centre for Blood Research, Departments of Oral Biological & Medical Sciences, and Biochemistry & Molecular Biology, Faculty of Dentistry , The University of British Columbia , Vancouver , BC V6T 1Z4 , Canada.
4
Center for Computational Mass Spectrometry and Department of Computer Science and Engineering, Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California San Diego , La Jolla , California 92093 , United States.
5
Department of Biomedical Sciences, Faculty of Medicine and Health Science , Macquarie University , Macquarie Park , NSW 2109 , Australia.
6
Univ. Rennes , Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail) - UMR_S 1085 , F-35042 Rennes cedex , France.
7
Functional Proteomics Laboratory, Centro Nacional de Biotecnología , Spanish Research Council , ProteoRed-.ISCIII , Madrid 117 , Spain.
8
European Molecular Biology Laboratory , European Bioinformatics Institute (EMBL-EBI) , Wellcome Trust Genome Campus , Hinxton , Cambridge CB10 1SD , U.K.
9
Advanced Clinical Biosystems Research Institute, The Smidt Heart Institute, Department of Medicine , Cedars Sinai Medical Center , Los Angeles , California 90048 , United States.
10
Yonsei Proteome Research Center , Yonsei University , 50 Yonsei-ro , Sudaemoon-ku , Seoul 03720 , Korea.
11
The University of Texas Health Science Center at San Antonio , San Antonio , Texas 78229 , United States.
12
Univ. Grenoble Alpes , CEA, INSERM, IRIG-BGE, U1038 , F-38000 Grenoble , France.
13
Departments of Computational Medicine & Bioinformatics, Internal Medicine, and Human Genetics and School of Public Health , University of Michigan , Ann Arbor , Michigan 48109-2218 , United States.

Abstract

The Human Proteome Organization's (HUPO) Human Proteome Project (HPP) developed Mass Spectrometry (MS) Data Interpretation Guidelines that have been applied since 2016. These guidelines have helped ensure that the emerging draft of the complete human proteome is highly accurate and with low numbers of false-positive protein identifications. Here, we describe an update to these guidelines based on consensus-reaching discussions with the wider HPP community over the past year. The revised 3.0 guidelines address several major and minor identified gaps. We have added guidelines for emerging data independent acquisition (DIA) MS workflows and for use of the new Universal Spectrum Identifier (USI) system being developed by the HUPO Proteomics Standards Initiative (PSI). In addition, we discuss updates to the standard HPP pipeline for collecting MS evidence for all proteins in the HPP, including refinements to minimum evidence. We present a new plan for incorporating MassIVE-KB into the HPP pipeline for the next (HPP 2020) cycle in order to obtain more comprehensive coverage of public MS data sets. The main checklist has been reorganized under headings and subitems, and related guidelines have been grouped. In sum, Version 2.1 of the HPP MS Data Interpretation Guidelines has served well, and this timely update to version 3.0 will aid the HPP as it approaches its goal of collecting and curating MS evidence of translation and expression for all predicted ∼20 000 human proteins encoded by the human genome.

KEYWORDS:

B/D-HPP; C-HPP; HPP; Human Proteome Project; Universal Spectrum Identifier (USI); false-discovery rates; guidelines; mass spectrometry; standards; unicity checker

PMID:
31599596
PMCID:
PMC6986310
[Available on 2020-03-06]
DOI:
10.1021/acs.jproteome.9b00542

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