This talk describes our work to implement a portable, cross-platform proteomics data analysis infrastructure that leverage existing open source tools where they exist, and create them where they do not, to improve the quality and scientific content of high-throughput mass-spectrometry-based proteomics efforts, allowing these datasets to be integrated with DNA microarray, gene sequence and ultimately gene annotation databases.

Mass spectrometry is the method of choice for identifying proteins present in biological mixtures. The primary advantages of mass spectrometry are its high-sensitivity, accuracy and capacity. However, practical problems continue to plague mass-spectrometry-based high throughput proteomics efforts. First, the algorithms used to identify proteins using indirect correlation of mass spectra with DNA and protein sequence databases are computationally demanding and frequently ineffective. Second, the field suffers from a lack of standardized data visualization and analysis tools. As the scope of protein analysis has shifted from a molecule-by-molecule approach to a genomic scale, the ability of both academia and industry to generate new mass spectrometry data has dramatically outstripped the corresponding ability to validate, manage, and interrogate the data.