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Julia Schessner, Ian Wood, Paul Dupree, Georg Borner (Max-Planck Institute of Biochemistry, Martinsried, Germany; University of Cambridge, Cambridge, UK)
Immense technical advances in mass spectrometry-based proteomics enable evermore complex experimental designs, in our case comparative spatial proteomics. However, data accessibility declines with increasing complexity and the path from the raw data to any derived hypotheses often remains enigmatic to anybody but the responsible statistician. A key barrier, besides the informatic analysis, is the predominantly static nature of data visualization. Static visualization is generally limited to two dimensions, precalculated results and a small number of annotations. These limitations slow down projects, especially collaborative ones and hinder the synergy of biologically-, mathematically- and technically-oriented team members who may look at data from different perspectives and with different tools. This impacts on the whole lifecycle of a complex proteomic dataset but particularly affects targeted exploration, raw data traceback and reusability. Here, I illustrate how we use interactive online visualization using python to make comparative spatial proteomics data accessible independent of programming skills and software tools and thereby drive biological hypothesis generation.