Advances in technology have generated vast amounts of “omics” data: genomic, epigenomic, transcriptomic, proteomic and metabolomic changes for all types of specimens. Bridging the gap between data generation and investigators’ ability to retrieve and interpret data is essential to realize the biological and clinical value of this wealth of information.
Bing Zhang, Ph.D., and colleagues previously developed NetGestalt, which generates a framework for the study of “omics” data in the context of biological networks. The investigators have now applied NetGestalt to data from The Cancer Genome Atlas (TCGA) colorectal cancer cohort, the first tumor dataset with complete molecular measurements at DNA, RNA and protein levels. Using three case studies — retrieving information for a gene, retrieving information for a gene set and prioritizing epigenetically silenced genes — they demonstrate that the NetGestalt portal provides user-friendly data query and visualization, and enables integration of information over biological networks.
This research was supported by a contract from Leidos Biomedical Research, Inc. and was conducted in part using resources of the Advanced Computing Center for Research and Education at Vanderbilt University