Helping scientists see: supporting healthcare and bioinformatics through visual analytics.

• Online Access: http://hdl.handle.net/2047/D20289813

Scientific research and discovery in the field of bioinformatics have seen a tremendous increase in recent years through the advent of low-cost genetic sequencing and better healthcare programs. At the same time, this situation poses new important challenges for the analyses of genetic data, as many of the current visualization software were not originally designed to manage the large datasets that continue to become available on a regular basis. While active in many other domains such as finance and journalism, most data visualization designers have remained as bystanders in the fields of healthcare and life sciences, and new visualization tools are steadily being developed without the expertise and knowledge of these design professionals. However, the visualization tools for the exploration of these data can no longer be only developed by bioinformaticians if we wish to realize the full potential of modern technologies and to extract actionable insights from new large datasets. In this thesis, I propose that designers can play an important role as mediators in transdisciplinary groups that come together to create user-centered digital products for non-linear visual analytics. This thesis explores the creation of visualization tools for the analysis of large genomic datasets, especially by proposing a redesign of Multiple Sequence Alignment visualizations, while at the same time presenting a replicable model for collaboration for the design of these tools in healthcare and bioinformatics.