Visualization is an interdisciplinary field related to data science and cognition, concerned with visual representations of data. Visualization research is often conducted in collaboration with researchers from other disciplines, for example, climate and environmental sciences, urban planning, and medicine. One such application area is genomics data, which is increasingly being used within healthcare for diagnosis and treatment selection. However, genomics data is often large and error-prone and requires interpretation to be of use to the treating physician. These challenges can be addressed with visualization. 

This thesis maps out the challenges and requirements of genomics data in clinical contexts from a visualization perspective. It contains an analysis of the tasks users performed and what data features were important to them. Several visualization environments for genomics data were designed and evaluated, based on these analyses. In general, the emphasis of the designs was on position and navigation, along with data aggregation and comparison of different data types. This was especially important for the work on copy number variant review, where several data sources were combined in a glyph. Therefore, genomics visualizations often benefit from combining multiple views, zooming, and different visual representations. 

Several of the challenges that were encountered in developing the genomics visualization environments generalize to other sequence data visualizations, such as timelines. Therefore, we studied how the sequence axis should be designed for these visualizations in general. In one study, graphs with straight layouts were compared to circular layouts. Another study investigated whether the x-axis could be stretched and compressed to reduce whitespace. The findings indicated that compressing axes can increase item visibility while the viewer maintains an understanding of the data distribution.  

A prominent recurring theme in the thesis is the utilization of position and the possibility of modifying the graph axes. Which axis type is most appropriate depends on the data, the task, and the user. In particular, it depends on how important the position along the axis is in comparison with all other data features. For genomics data, the position is typically not the most important feature. Therefore, axis manipulations can be both justified and appropriate. 

Visual representations based on circular shapes are frequently used in visualization applications. One example are circos plots within bioinformatics, which bend graphs into a wheel of information with connective lines running through the center like spokes. The results are aesthetically appealing and impressive visualizations that fit long data sequences into a small quadratic space. However, the authors' experiences are that when asked, a visualization researcher would generally advise against making visualizations with radial layouts. Upon reviewing the literature we found that there is evidence that circular layouts are preferable in some cases, but we found no clear evidence for what layout is preferable for matrices and connective data in particular, which both are common data types in circos plots. In this work, we thus performed a user study to compare circular and linear layouts. The tasks are inspired by genomics data, but our results generalize to many other application areas, involving comparison and connective data. To build the prototype we utilized Gosling, a grammar for visualizing genomics data. We contribute empirical evidence on the suitedness of linear versus circular layouts, adding to the specific and general knowledge concerning perception of circular graphs. In addition, we contribute a case study evaluation of the grammar Gosling as a rapid prototyping language, confirming its utility and providing guidance on suitable areas for future development.

In this perspective article we discuss a certain type of research on visualization for bioinformatics data, namely, methods targeting clinical use. We argue that in this subarea additional complex challenges come into play, particularly so in genomics. We here describe four such challenge areas, elicited from a domain characterization effort in clinical genomics. We also list opportunities for visualization research to address clinical challenges in genomics that were uncovered in the case study. The findings are shown to have parallels with experiences from the diagnostic imaging domain.