HUPO Brain Proteome Project: Summary of the pilot phase and introduction of a comprehensive data reprocessing strategy

Hamacher, Michael; Apweiler, Rolf; Arnold, Georg J.; Becker, Albert; Blüggel, Martin; Carrette, Odile; Colvis, Christine M.; Dünn, Michael J.; Fröhlich, Thomas; Fountoulakis, Michael; Hall, André van; Herberg, Friedrich W.; Ji, Jianguo; Kretzschmar, Hans A.; Lewczuk, Piotr; Lubec, Gert; Marcus, Katrin; Martens, Lennart; Bustamante, Nadine Palacios; Park, Young Mok; Pennington, Stephen R.; Robben, Johan; Stühler, Kai; Reidegeld, Kai A.; Riederer, Peter; Rossier, Jean; Sanchez, Jean‐Charles; Schrader, Michael; Stephan, Christian; Tagle, Danilo A.; Thiele, Herbert; Wang, Jing; Wiltfang, Jens; Yoo, Jong Shin; Zhang, Chenggang; Klose, Joachim; Meyer, Helmut E.

doi:10.1002/pmic.200600295

Cited by 44 publications

(16 citation statements)

References 23 publications

(27 reference statements)

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“…The detailed analysis of two projects using the IPI database confirmed the above mentioned results: the data from the Human Proteome Organization (HUPO) Plasma Proteome Project (PPP) (PRIDE accession numbers 4–98 (24)) and the data from the HUPO Brain Proteome Project (BPP) (PRIDE accession numbers 1669–1750 (25)) (see supplemental Fig. S4).…”

Section: Resultssupporting

confidence: 61%

Published and Perished? The Influence of the Searched Protein Database on the Long-Term Storage of Proteomics Data

Griss

Côté

Gerner

et al. 2011

Molecular & Cellular Proteomics

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In proteomics, protein identifications are reported and stored using an unstable reference system: protein identifiers. These proprietary identifiers are created individually by every protein database and can change or may even be deleted over time.To estimate the effect of the searched protein sequence database on the long-term storage of proteomics data we analyzed the changes of reported protein identifiers from all public experiments in the Proteomics Identifications (PRIDE) database by November 2010. To map the submitted protein identifier to a currently active entry, two distinct approaches were used. The first approach used the Protein Identifier Cross Referencing (PICR) service at the EBI, which maps protein identifiers based on 100% sequence identity. The second one (called logical mapping algorithm) accessed the source databases and retrieved the current status of the reported identifier.Our analysis showed the differences between the main protein databases (International Protein Index (IPI), UniProt Knowledgebase (UniProtKB), National Center for Biotechnological Information nr database (NCBI nr), and Ensembl) in respect to identifier stability. For example, whereas 20% of submitted IPI entries were deleted after two years, virtually all UniProtKB entries remained either active or replaced. Furthermore, the two mapping algorithms produced markedly different results. For example, the PICR service reported 10% more IPI entries deleted compared with the logical mapping algorithm. We found several cases where experiments contained more than 10% deleted identifiers already at the time of publication. We also assessed the proportion of peptide identifications in these data sets that still fitted the originally identified protein sequences. Finally, we performed the same overall analysis on all records from IPI, Ensembl, and UniProtKB: two releases per year were used, from 2005. This analysis showed for the first time the true effect of changing protein identifiers on proteomics data. Based on these findings, UniProtKB seems the best database for applications that rely on the long-term storage of proteomics data.

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Section: Resultssupporting

confidence: 61%

Published and Perished? The Influence of the Searched Protein Database on the Long-Term Storage of Proteomics Data

Griss

Côté

Gerner

et al. 2011

Molecular & Cellular Proteomics

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“…Similarly a collaborative program has been established to explore systematically the human proteome (42,43). Such initiatives afford the opportunity to refine gene models created during the genome annotation process, recognize genes that may give rise to multiple proteins by means of alternative splicing or post-translational modification, assess tissue-and/or process-specific protein regulation, and avail a resource for the selection of specific proteins such as candidate disease biomarkers.…”

Section: Discussionmentioning

confidence: 99%

Analysis of the Zebrafish Proteome during Embryonic Development

Lucitt

Price

Pizarro

et al. 2008

Molecular & Cellular Proteomics

116

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The model organism zebrafish (Danio rerio) is particularly amenable to studies deciphering regulatory genetic networks in vertebrate development, biology, and pharmacology. Unraveling the functional dynamics of such networks requires precise quantitation of protein expression during organismal growth, which is incrementally challenging with progressive complexity of the systems. In an approach toward such quantitative studies of dynamic network behavior, we applied mass spectrometric methodology and rigorous statistical analysis to create comprehensive, high quality profiles of proteins expressed at two stages of zebrafish development. Proteins of embryos 72 and 120 h postfertilization (hpf) were isolated and analyzed both by two-dimensional (2D) LC followed by ESI-MS/MS and by 2D PAGE followed by MALDI-TOF/TOF protein identification. We detected 1384 proteins from 327,906 peptide sequence identifications at 72 and 120 hpf with false identification rates of less than 1% using 2D LC-ESI-MS/MS. These included only ϳ30% of proteins that were identified by 2D PAGE-MALDI-TOF/TOF. Roughly 10% of all detected proteins were derived from hypothetical or predicted gene models or were entirely unannotated. Comparison of proteins expression by 2D DIGE revealed that proteins involved in energy production and transcription/translation were relatively more abundant at 72 hpf consistent with faster synthesis of cellular proteins during organismal growth at this time compared with 120 hpf. The data are accessible in a database that links protein identifications to existing resources including the Zebrafish Information Network database. This new resource should facilitate the selection of candidate proteins for targeted quantitation and refine systematic genetic network analysis in vertebrate development and biology. Molecular & Cellular Proteomics 7:981-994, 2008.

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“…Proteomics identifications database (PRIDE), www.ebi.ac.uk/pride/, is a public data repository designed to store, disseminate, and analyze MS based proteomics data sets [20], originally developed for the HUPO Plasma Proteome Project [21]. It also serves as data repository for HUPO brain [22] and HUPO liver [23]. The Human Protein Reference Database http://www.hprd.org, for systems biology approach [24,25], represents a centralized platform to visually depict and integrate information relating to domain architecture, post-translational modifications, interaction networks and disease association for each protein in the human proteome.…”

Section: Introductionmentioning

confidence: 99%

Introducing the CPL/MUW proteome database: Interpretation of human liver and liver cancer proteome profiles by referring to isolated primary cells

et al. 2009

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Interpretation of proteome data with a focus on biomarker discovery largely relies on comparative proteome analyses. Here, we introduce a database-assisted interpretation strategy based on proteome profiles of primary cells. Both 2-D-PAGE and shotgun proteomics are applied. We obtain high data concordance with these two different techniques. When applying mass analysis of tryptic spot digests from 2-D gels of cytoplasmic fractions, we typically identify several hundred proteins. Using the same protein fractions, we usually identify more than thousand proteins by shotgun proteomics. The data consistency obtained when comparing these independent data sets exceeds 99% of the proteins identified in the 2-D gels. Many characteristic differences in protein expression of different cells can thus be independently confirmed. Our self-designed SQL database (CPL/MUW - database of the Clinical Proteomics Laboratories at the Medical University of Vienna accessible via www.meduniwien.ac.at/proteomics/database) facilitates (i) quality management of protein identification data, which are based on MS, (ii) the detection of cell type-specific proteins and (iii) of molecular signatures of specific functional cell states. Here, we demonstrate, how the interpretation of proteome profiles obtained from human liver tissue and hepatocellular carcinoma tissue is assisted by the Clinical Proteomics Laboratories at the Medical University of Vienna-database. Therefore, we suggest that the use of reference experiments supported by a tailored database may substantially facilitate data interpretation of proteome profiling experiments.

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HUPO Brain Proteome Project: Summary of the pilot phase and introduction of a comprehensive data reprocessing strategy

Cited by 44 publications

References 23 publications

Published and Perished? The Influence of the Searched Protein Database on the Long-Term Storage of Proteomics Data

Published and Perished? The Influence of the Searched Protein Database on the Long-Term Storage of Proteomics Data

Analysis of the Zebrafish Proteome during Embryonic Development

Introducing the CPL/MUW proteome database: Interpretation of human liver and liver cancer proteome profiles by referring to isolated primary cells

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