Visualizing Co-occurrence of Events in Populations of Viral Genome Sequences

Abstract

Virologists are not only interested in point mutations in a genome, but also in relationships between mutations. In this work, we present a design study to support the discovery of correlated mutation events (called co-occurrences) in populations of viral genomes. The key challenge is to identify potentially interesting pairs of events within the vast space of event combinations. In our work, we identify analyst requirements and develop a prototype through a participatory process. The key ideas of our approach are to use interest metrics to create dynamic filtering that guides the viewer to interesting and relevant correlations of genome mutations, and to provide visual encodings designed to fit scientists' mental map of the data, along with dynamic filtering techniques. We demonstrate the strength of our approach in virology-situated case studies, and offer suggestions for extending our strategy to other sequence-based domains.