The proposed project is aimed at significant improvement of high throughput (HTP) proteomics analysis for biomedical research and providing a common point of reference for scientific community to share results and compare methods. The goals of this proposal are: (i) introduce novel experimental standards that are realistic known complex protein samples, uniquely based on the diverse proteins of a single organism (S. oneidensis) and (ii) implement a flexible, transparent, and statistically sound computational platform. Typically, protein standards have been very simple mixtures primarily used for calibration of instruments rather than as tools for scientific research. Fig. 1 shows a general flow diagram for HTP proteomics analysis of complex biological samples. The development of analytical tools at each step in this pipeline is being hindered by inability of researchers to verify and validate the performance of these tools on complex biological mixtures. These new experimental standards are aimed at breaking through this bottleneck in HTP proteomics. The computational platform will include new statistical models and software tools for HTP proteomics to serve both as the method and the means of validation of HTP proteomics studies. Portions of the pipeline that are of particular importance are peptide and protein identification and the establishment of reliable quantitation of relative protein expression (i.e. comparing """"""""disease"""""""" versus """"""""control"""""""" samples). The complex experimental standards are the central part of the process of training, validating, performing comparative analysis on models for HTP proteomics (illustrated in Fig. 2). The analytical methods we will implement uniquely combine with the experimental standards to provide capabilities for peptide and protein identification and measures for relative protein expression that can be customized to fit specific research purposes, experimental conditions and instrumentation. We will then disseminate all of the data, software and developed capabilities to the scientific community. These capabilities will enable researchers to quantitatively assess their new and/or currently used methods (analytical, experimental, and technical) and analyses (software packages, algorithms, and statistical models) for HTP proteomics. While the development of methods for all steps in the general HTP proteomics pipeline (Fig. 1) is well beyond the scope of any single project, the experimental standards and computational platform can provide the basis for development, validation, and comparative analysis of models and methods at each step in the pipeline. ? ? ? ?
| Kolker, Eugene; Higdon, Roger; Haynes, Winston et al. (2012) MOPED: Model Organism Protein Expression Database. Nucleic Acids Res 40:D1093-9 |
| Higdon, Roger; Reiter, Lukas; Hather, Gregory et al. (2011) IPM: An integrated protein model for false discovery rate estimation and identification in high-throughput proteomics. J Proteomics 75:116-21 |
| Kolker, Eugene; Higdon, Roger; Morgan, Phil et al. (2011) SPIRE: Systematic protein investigative research environment. J Proteomics 75:122-6 |
| Bauman, Andrew; Higdon, Roger; Rapson, Sean et al. (2011) Design and initial characterization of the SC-200 proteomics standard mixture. OMICS 15:73-82 |
| Galperin, Michael Y; Higdon, Roger; Kolker, Eugene (2010) Interplay of heritage and habitat in the distribution of bacterial signal transduction systems. Mol Biosyst 6:721-8 |
| Higdon, Roger; Louie, Brenton; Kolker, Eugene (2010) Modeling sequence and function similarity between proteins for protein functional annotation. Proc Int Symp High Perform Distrib Comput 2010:499-502 |
| Louie, Brenton; Higdon, Roger; Kolker, Eugene (2010) The necessity of adjusting tests of protein category enrichment in discovery proteomics. Bioinformatics 26:3007-11 |
| Hather, Gregory J; Haynes, Winston; Higdon, Roger et al. (2010) The United States of America and scientific research. PLoS One 5:e12203 |
| Hather, Gregory; Higdon, Roger; Bauman, Andrew et al. (2010) Estimating false discovery rates for peptide and protein identification using randomized databases. Proteomics 10:2369-76 |
| Higdon, Roger; Haynes, Winston; Kolker, Eugene (2010) Meta-analysis for protein identification: a case study on yeast data. OMICS 14:309-14 |
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