Dr Nils Gehlenborg gives his keynote at 1st BiVi in 2014. The rapid proliferation of high quality, low cost genome-wide measurement technologies such as whole-genome and transcriptome sequencing, as well as advances in epigenomics and proteomics, are enabling researchers to perform studies that generate heterogeneous datasets for cohorts of thousands of individuals. A common feature of these studies is that a collection of genome-wide, molecular data types and phenotypic or clinical characterizations are available for each individual. These data can be used to identify the molecular basis of diseases and to characterize and describe the variations that are relevant for improved diagnosis, prognosis and targeted treatment of patients. An example for a study in which this approach has been successfully applied is The Cancer Genome Atlas project (http://cancergenome.nih.gov).In this talk Dr Gehlenborg discusses how visualization approaches can be applied to enable exploration and support analysis of data generated by such studies. Specifically, he reviews techniques and tools for visual exploration of individual omics data types, their ability to scale to large numbers of individuals or samples, and emerging techniques that integrate multiple omics data types for interactive visual analysis. He also examines technical and legal challenges that developers of such visualization tools are facing. To conclude the talk, he outlines research opportunities for the biological data visualization community that address major challenges in this domain.
Created at: 1st BiVi Annual Meeting.