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dc.contributor.authorSarikaya, Alperen_US
dc.contributor.authorCorrell, Michaelen_US
dc.contributor.authorDinis, Jorge M.en_US
dc.contributor.authorO'Connor, David H.en_US
dc.contributor.authorGleicher, Michaelen_US
dc.contributor.editorKwan-Liu Ma and Giuseppe Santucci and Jarke van Wijken_US
dc.date.accessioned2016-06-09T09:32:38Z
dc.date.available2016-06-09T09:32:38Z
dc.date.issued2016en_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttp://dx.doi.org/10.1111/cgf.12891en_US
dc.identifier.urihttps://diglib.eg.org:443/handle/10
dc.description.abstractVirologists 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.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectJ.3 [Life and Medical Sciences]en_US
dc.subjectBiology and Geneticsen_US
dc.titleVisualizing Co-occurrence of Events in Populations of Viral Genome Sequencesen_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.sectionheadersBiological Data Visualizationen_US
dc.description.volume35en_US
dc.description.number3en_US
dc.identifier.doi10.1111/cgf.12891en_US
dc.identifier.pages151-160en_US


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