A systematic approach to modeling, capturing, and disseminating proteomics experimental data

Taylor, Chris; Paton, Norman W.; Garwood, Kevin; Kirby, Paul D.; Stead, David; Yin, Zhikang; Deutsch, Eric W.; Selway, Laura; Walker, Janet L.; Riba-Garcia, Isabel; Mohammed, Shabaz; Deery, Michael J.; Howard, Julie A.; Dunkley, Tom; Aebersold, Ruedi; Kell, Douglas B.; Lilley, Kathryn S.; Roepstorff, Peter; Yates, John R.; Brass, Andy; Brown, Alistair J. P.; Cash, Phillip; Gaskell, Simon J.; Hubbard, Simon J.; Oliver, Stephen G.

doi:10.1038/nbt0303-247

Cited by 232 publications

(138 citation statements)

References 14 publications

Supporting

Mentioning

138

Contrasting

Order By: Relevance

“…A proposal for a standard representation of both, the methods used and the data generated in proteomics experiments was presented recently (Taylor et al, 2003;Garwood et al, 2004a). It is analogous to that of the minimum information about a microarray experiment (MIAME) guidelines for ''transcriptomics'' and the associated microarray gene expression (MAGE) object model including a extensible mark-up language (XML) implementation.…”

Section: The Need For a Common Standard…mentioning

confidence: 99%

A Bioinformatics Perspective on Proteomics: Data Storage, Analysis, and Integration

2005

View full text Add to dashboard Cite

The field of proteomics is advancing rapidly as a result of powerful new technologies and proteomics experiments yield a vast and increasing amount of information. Data regarding protein occurrence, abundance, identity, sequence, structure, properties, and interactions need to be stored. Currently, a common standard has not yet been established and open access to results is needed for further development of robust analysis algorithms. Databases for proteomics will evolve from pure storage into knowledge resources, providing a repository for information (meta-data) which is mainly not stored in simple flat files. This review will shed light on recent steps towards the generation of a common standard in proteomics data storage and integration, but is not meant to be a comprehensive overview of all available databases and tools in the proteomics community.

show abstract

Section: The Need For a Common Standard…mentioning

confidence: 99%

A Bioinformatics Perspective on Proteomics: Data Storage, Analysis, and Integration

2005

View full text Add to dashboard Cite

show abstract

“…proposed two general criteria for proteomics data storage systems: sufficiency and natural organization [23]. Taylor et al developed an advanced infrastructure for capture, storage, and dissemination strategies for proteomics data.…”

Section: Discussionmentioning

confidence: 99%

“…Many tools [20,21,23,24] such as PEDRo [25], PRIDE (www.ebi.ac.uk/pride), ProteinScape [26], HSC-2DPAGE [27] provide web interfaces to enable data retrieval and dissemination [28]. ProteinScape [26] is able to handle MS-based protein identification data [22].…”

Section: Introductionmentioning

confidence: 99%

PROTEOMER: A workflow‐optimized laboratory information management system for 2‐D electrophoresis‐centered proteomics

Nebrich¹,

Herrmann²,

Hartl³

et al. 2009

Proteomics

View full text Add to dashboard Cite

In recent years proteomics became increasingly important to functional genomics. Although a large amount of data is generated by high throughput large-scale techniques, a connection of these mostly heterogeneous data from different analytical platforms and of different experiments is limited. Data mining procedures and algorithms are often insufficient to extract meaningful results from large datasets and therefore limit the exploitation of the generated biological information. In our proteomic core facility, which almost exclusively focuses on 2-DE/MS-based proteomics, we developed a proteomic database custom tailored to our needs aiming at connecting MS protein identification information to 2-DE derived protein expression profiles. The tools developed should not only enable an automatic evaluation of single experiments, but also link multiple 2-DE experiments with MS-data on different levels and thereby helping to create a comprehensive network of our proteomics data. Therefore the key feature of our "PROTEOMER" database is its high cross-referencing capacity, enabling integration of a wide range of experimental data. To illustrate the workflow and utility of the system, two practical examples are provided to demonstrate that proper data cross-referencing can transform information into biological knowledge.

show abstract

“…Based on the experience gained from the large-scale genomics projects, there is widespread recognition that the proteomics field needs to invest early on in significant multi-laboratory efforts devoted to improving data quality [1], to making cross-laboratory and cross-platform data comparisons [2], and to developing data standards [3]. One such effort is the Plasma Proteomics Initiative, one of the first endeavors of the Human Proteome Organization Plasma Proteome Project (HUPO PPP) [4], which also includes liver and brain initiatives [5].…”

Section: Introductionmentioning

confidence: 99%

“…One of these issues is to encourage open and direct comparisons of methods and technologies, using sample(s) made commonly available to many laboratories [3][4][5] and facilitating the development of a central repository for results with unified data standards. With such a centralized and unified data system in place, a much greater impact of proteomic efforts will be realized, such as more effective mining of results, development of better data analysis tools, more confident protein identifications, and a deeper understanding of the relative strengths and weaknesses of various technologies.…”

Section: Introductionmentioning

confidence: 99%

A proteomic study of the HUPO Plasma Proteome Project's pilot samples using an accurate mass and time tag strategy

et al. 2005

View full text Add to dashboard Cite

Characterization of the human blood plasma proteome is critical to the discovery of routinely useful clinical biomarkers. We used an Accurate Mass and Time (AMT) tag strategy with high-resolution mass accuracy capillary liquid chromatography Fourier-Transform Ion Cyclotron Resonance Mass Spectrometry (cLC-FTICR MS) to perform a global proteomic analysis of pilot study samples as part of the HUPO Plasma Proteome Project. HUPO reference serum and citrated plasma samples from African Americans, Asian Americans, and Caucasian Americans were analyzed, in addition to a Pacific Northwest National Laboratory reference serum and plasma. The AMT tag strategy allowed us to leverage two previously published "shotgun" proteomics experiments to perform global analyses on these samples in triplicate in less than 4 days total analysis time. A total of 722 (22% with multiple peptide identifications) International Protein Index (IPI) redundant proteins, or 377 protein families by ProteinProphet, were identified over the 6 individual HUPO serum and plasma samples. The samples yielded a similar number of identified redundant proteins in the plasma samples (average 446 +/−23) as found in the serum samples (average 440+/−20). These proteins were identified by an average of 956+/−35 unique peptides in plasma and 930+/−11 unique peptides in serum. In addition to this high-throughput analysis, the AMT tag approach was used with a Z-score normalization to compare relative protein abundances. This analysis highlighted both known differences in serum and citrated plasma such as fibrinogens, and reproducible differences in peptide abundances from proteins such as soluble activin receptor-like kinase 7b and glycoprotein m6b. The AMT tag strategy not only improved our sample throughput, and provided a basis for estimated quantitation.

show abstract

A systematic approach to modeling, capturing, and disseminating proteomics experimental data

Cited by 232 publications

References 14 publications

A Bioinformatics Perspective on Proteomics: Data Storage, Analysis, and Integration

A Bioinformatics Perspective on Proteomics: Data Storage, Analysis, and Integration

PROTEOMER: A workflow‐optimized laboratory information management system for 2‐D electrophoresis‐centered proteomics

A proteomic study of the HUPO Plasma Proteome Project's pilot samples using an accurate mass and time tag strategy

Contact Info

Product

Resources

About