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Juha Karjalainen (University Medical Center Groningen, Groningen, The Netherlands)
Visualizations help in understanding scientific data and discussing research questions, and interaction can make visualizations more useful and engaging. It’s important to create informative visualization tools that are capable of handling large datasets while remaining fast and easy to use. To make visualizations accessible, usable and interactive, the most suitable platform is the web browser. The development of web graphics libraries such as Three.js has made it convenient to visualize large datasets with JavaScript and WebGL. Here we show web tools for visualizing genomic data. We show examples with a dataset of tens of thousands of publicly available RNA-seq samples. We have used the data to build gene co-expression and co-regulation networks, to predict unknown functions of genes, and to predict which gene or genes may be causal for a patient’s disease. The tools shown here are made for a visual understanding of the data, the research process and the end results. We show tools to visualize individual gene expression profiles, to create informative point clouds, and to visualize and analyze gene networks and biological pathways.