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Overview of the HUPO Plasma Proteome Project: Results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly‐available database
Author(s) -
Omenn Gilbert S.,
States David J.,
Adamski Marcin,
Blackwell Thomas W.,
Me Rajasree,
Hermjakob Henning,
Apweiler Rolf,
Haab Brian B.,
Simpson Richard J.,
Eddes James S.,
Kapp Eugene A.,
Moritz Robert L.,
Chan Daniel W.,
Rai Alex J.,
Admon Arie,
Aebersold Ruedi,
Eng Jimmy,
Hancock William S.,
Hefta Stanley A.,
Meyer Helmut,
Paik YoungKi,
Yoo JongShin,
Ping Peipei,
Pounds Joel,
Adkins Joshua,
Qian Xiaohong,
Wang Rong,
Wasinger Valerie,
Wu Chi Yue,
Zhao Xiaohang,
Zeng Rong,
Archakov Alexander,
Tsugita Akira,
Beer Ilan,
Pandey Akhilesh,
Pisano Michael,
Andrews Philip,
Tammen Harald,
Speicher David W.,
Hanash Samir M.
Publication year - 2005
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.200500358
Subject(s) - proteome , computational biology , computer science , bioinformatics , chemistry , biology
HUPO initiated the Plasma Proteome Project (PPP) in 2002. Its pilot phase has (1) evaluated advantages and limitations of many depletion, fractionation, and MS technology platforms; (2) compared PPP reference specimens of human serum and EDTA, heparin, and citrate‐anti‐coagulated plasma; and (3) created a publicly‐available knowledge base (www.bioinformatics.med.umich.edu/hupo/ppp; www.ebi.ac.uk/pride). Thirty‐five participating laboratories in 13 countries submitted datasets. Working groups addressed (a) specimen stability and protein concentrations; (b) protein identifications from 18 MS/MS datasets; (c) independent analyses from raw MS‐MS spectra; (d) search engine performance, subproteome analyses, and biological insights; (e) antibody arrays; and (f) direct MS/SELDI analyses. MS‐MS datasets had 15 710 different International Protein Index (IPI) protein IDs; our integration algorithm applied to multiple matches of peptide sequences yielded 9504 IPI proteins identified with one or more peptides and 3020 proteins identified with two or more peptides (the Core Dataset). These proteins have been characterized with Gene Ontology, InterPro, Novartis Atlas, OMIM, and immunoassay‐based concentration determinations. The database permits examination of many other subsets, such as 1274 proteins identified with three or more peptides. Reverse protein to DNA matching identified proteins for 118 previously unidentified ORFs. We recommend use of plasma instead of serum, with EDTA (or citrate) for anticoagulation. To improve resolution, sensitivity and reproducibility of peptide identifications and protein matches, we recommend combinations of depletion, fractionation, and MS/MS technologies, with explicit criteria for evaluation of spectra, use of search algorithms, and integration of homologous protein matches. This Special Issue of PROTEOMICS presents papers integral to the collaborative analysis plus many reports of supplementary work on various aspects of the PPP workplan. These PPP results on complexity, dynamic range, incomplete sampling, false‐positive matches, and integration of diverse datasets for plasma and serum proteins lay a foundation for development and validation of circulating protein biomarkers in health and disease.