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David K.G. Ma, Christian Stolte, Sandeep Kaur, Michael Bain, and Seán I. O’Donoghue (384 Victoria St Darlinghurst NSW 2010 Australia)
Visual analysis of time-series data on protein phosphorylation presents a particular challenge: bioinformatics tools currently available for visualising 'omics' data in time series have been developed primarily to study gene expression, and cannot easily be adopted to phosphorylation data, where a single protein typically has multiple phosphosites. In this study, we worked with an experimental research group that is applying very recent methods in high-throughput experimental proteomics to study the time course of protein phosphorylation events in human cells in vitro following stimulation by insulin, as part of a broader study of diabetes and obesity. We applied several existing visual analytics approaches with the goal of organising the data to facilitate new insight into underlying molecular processes. We developed a novel layout strategy called 'Minardo' that is loosely based on cell topology and ordered by time and causality. This layout utilises a frame of reference familiar to life scientists and helpful for organising and interpreting time-series data. This strategy proved to be useful, leading to new insights into the insulin response pathway. We are working on generalisin