Publications

Abstract

Data visualisation is usually a crucial first step in analysing and exploring large-scale complex data. The visualisation of proteomics time-course data on post-translational modifications presents a particular challenge that is largely unmet by existing tools and methods. To this end, we present Minardo, a novel visualisation strategy tailored for such proteomics data, in which data layout is driven by both cellular topology and temporal order. In this work, we utilised the Minardo strategy to visualise a dataset showing phosphorylation events in response to insulin. We evaluated the visualisation together with experts in diabetes and obesity, which led to new insights into the insulin response pathway. Based on this success, we outline how this layout strategy could be automated into a web-based tool for visualising a broad range of proteomics time-course data. We also discuss how the approach could be extended to include protein 3D structure information, as well as higher dimensional data, such as a range of experimental conditions. We also discuss our entry of Minardo in the international DREAM8 competition.