Omics technologies are revolutionizing scientific research, health care and the world. It has become easy to screen the whole genome, transcriptome or metabolome for individual human being and living organisms. These omics profiles enable personalized/precision medicine, and make it possible to solve complex biological problems (aging, brain function etc) or diseases (cancers, obesity etc). However, it remains a major challenge to effectively analyze and interpret these big and complex datasets. The PI's group recently developed the Pathview tool set to map omics data onto molecular pathways (or fundamental biochemical or cellular processes). This project will fully develop Pathview as a systematic solution for omics data visualization, integration and analytics based on pathways. In addition, the whole research community will have open access to this tool and future development. Pathview has generated a global impact within a short time. As an open source project, Pathview not only creates a big user base (tens of thousands) directly, but also fosters a community of dependent projects. Pathview has been adopted by numerous courses in major research institutions and online tutorials. The Pathview project also recruits and trains students and developers and prepare them for careers in the STEM fields. Beyond academia, Pathview is widely used in hospitals, biotech, pharmaceutical and agriculture industry. With this NSF award, the next iteration of the Pathview software will have much deeper and broader impacts.
Pathway analysis has been widely used in omics studies, biomedical research and industry. Pathview maps, integrates and renders a large variety of biological data on molecular and genetic pathways, and produces interpretable graphs with publication quality. Pathview was quickly adopted and widely used by thousands of scientists worldwide. In this Development project, Pathview will be fully developed as a systematic solution for pathway based analytics and visualization. First, a Pathview Web server will be developed, which not only provides a graphic and user-friendly online portal to Pathview, but also extends the static package into an interactive bioinformatics server. Second, the Pathview R package will be expanded in its core functions. It will offer stronger graphic controls for better visualization, and support for more commonly used molecular data and pathway data types. Third, multiple automated pathway analysis workflows will be developed based on Pathview for transcriptomic, genomic and other omics data analysis. More information about the Pathview project will be available at: http://pathview.uncc.edu.
