The long term success of high throughput proteomics depends in part on the ability of the researchers to compile, manage, and analyze data sets generated from state of the art protein separation and protein identification technologies. Systems are needed that can reduce the human effort required for a thorough analysis of these large data sets.
Researchers at the University of Michigan developed a system to gather manage, and process data generated by laboratories: sample tracking of gels, relative and differential expression of gels, data from spot pickers, and digestion robots, mass spectrometers, results from mass spectrometry database search engines, and iTRAQ quantification information. Curation tools are continuously being developed.
Applications and Advantages
- High throughput proteomics
- Highly scalable, secure, and extendable