The University of Minnesota -- Twin Cities is awarded a grant to develop a framework for the integration of tools for mass spectrometry (MS) proteomics research that builds on the community-driven analytical framework for genomic data analysis named Galaxy. Developed at Penn State University, Galaxy is designed specifically to enable existing applications to become usable by life scientists while enhancing reproducibility functions through data capture, workflow development and sharing. This project will extend this framework by deploying and integrating a series of key software programs for MS-based proteomics data analysis, thus creating Galaxy Tool Modules for Proteomics which will be Galaxy-P (http://getgalaxyp.org/). Reliance on computation and informatics is inherent to system-wide molecular studies at the level of the genome, proteome and metabolome using high throughput analytical platforms. Analysis and management of such data requires informatics solutions, including software for capture, processing, analysis, annotation and dissemination of the data and experimental procedures used. Unfortunately, in many cases these informatics solutions do not meet the needs of the researchers, detracting from their use and adoption and thus creating an analytical bottle-neck in biological research using high throughput technologies. This is especially the case in the still maturing field of MS-based proteomics where a significant shortcoming is the difficulty in accessing and assembling useful software into integrated workflows or pipelines, especially by users who lack computational expertise. Too often data analysis is left to the core proteomics lab or expert in computation with limited involvement of the project researcher. Also lacking is an effective means to document and share experimental procedures and workflows and the more systematic adoption of scientific community standards. Consequently, process transparency, repeatability and dissemination of effective, complex data analyses suffer. Due to these collective shortcomings the full depth and richness of information of MS-based proteomics data is not realized, limiting new discoveries. Development of Galaxy-P will not only provide informatics solutions to Minnesota researchers, but also a freely-available platform enabling integration of genomic and proteomic data and analytics for a broad community of systems biology researchers. In addition, the project will leverage unique training resources in place at the Minnesota Supercomputing Institute (MSI) and area undergraduate colleges, developing a local area network and mechanism for training opportunities for young scientists in systems biology.
