A novel computational analysis platform is proposed, which enables collaboration in the discovery and characterization of proteomics biomarkers. The proposed system is a broad academic-commercial collaboration, integrates several prominent proteomics efforts, and marks first major use in proteomics of virtual cluster technology. Consequently, the proposed platform offers new solutions to the security, scalability, and ease of use problems that have hampered proteomics collaborations to date. The proposed software brings together several popular open-source proteomics tools projects, together with experts in the development of scientific computing solutions. The resulting system is validated by applying it to the study the human plasma proteome, in conjunction with the international collaborative HUPO Human Plasma Proteome Project. This research builds on a successful collaboration of experts in proteomics and software development. The impressive research team includes pioneers in proteomics, public health, and bioinformatics. The proposed software platform will be widely applicable to the study of biological mechanisms and measurement of complex protein systems. to public health:
The aim of this research is to enable a new proteomics technology that will both lower the cost of proteomics experiments and make the resulting data more biologically relevant. A successful outcome would increase the potential health benefit of a broad array of current and future research. The HUPO Human Plasma Proteome Project is the largest and broadest proteomics collaboration ever attempted. The goals of this project are comprehensive analysis of plasma and serum protein constituents in people, and identification of biological sources of variation within individuals over time, with validation of biomarkers. By working closely with this project, the proposed research has the potential for a substantial positive influence on human health. ? ? ?
| Farrah, Terry; Deutsch, Eric W; Omenn, Gilbert S et al. (2011) A high-confidence human plasma proteome reference set with estimated concentrations in PeptideAtlas. Mol Cell Proteomics 10:M110.006353 |
| Heinecke, N L; Pratt, B S; Vaisar, T et al. (2010) PepC: proteomics software for identifying differentially expressed proteins based on spectral counting. Bioinformatics 26:1574-5 |
| Deutsch, Eric W; Mendoza, Luis; Shteynberg, David et al. (2010) A guided tour of the Trans-Proteomic Pipeline. Proteomics 10:1150-9 |
