Bio Rafael Rosengarten was born and raised in the salt marshes of McClellanville, South Carolina. Rafael majored in Ecology and Evolutionary Biology at Dartmouth College and earned his PhD in Molecular, Cellular and Developmental Biology from Yale University. He performed post-doctoral research at the Joint BioEnergy Institute in Emeryville, CA, and at Baylor College of Medicine in Houston, TX. Rafael left the lab last October to join the software company Genialis, focused on empowering biologists to explore big sequencing data. He founded the U.S. office in Houston,Texas, where he is the president and chief product officer. In addition to his interests in science and technology, Rafael is an avid rock climber and cook. Title: Genialis Expressions: Driving Discovery from Clinical Expression Datasets Abstract Clinical researchers rely on high throughput gene expression profiling to provide greater insight into the molecular phenotype of patients and their diseases. The scale of the resulting datasets is game changing, especially when associated with the hundreds or even thousands of parameters collected for each sample or patient. Drawing insights from transcriptome data requires close coordination between the clinician or life scientist and data experts. Time and time again we have heard from both life- and data scientists that they yearn for a better way to collaborate. Life scientists wish to be more autonomous—that is, they would prefer to have a means of exploring data on their own, following their curiosity. Meanwhile, data experts often complain they are treated as service providers, expected to execute analyses and reformat results files on demand, without the deference deserved of talented scientists. A solution should provide life scientists an absurdly intuitive, interactive workspace, and data experts the ability to automate pipelines and streamline their support. We have developed a web application—Genialis Expressions—to do just that, enabling exploration of data from the tranSMART database. Our solution grants click-and-play access to hundreds of expression profiles and more than thousand of demographic and clinical variables for each patient. The web-app is powered by Genialis Platform software, employing Real-Time Interactive Visualizations technology to drive discovery and generate insights from big data. Further, the dataflow engine and bioinformatics libraries are open-source, inviting contributions and connections from the entire community. In addition to the hyper-interactive environment, we have carefully crafted intuitive workflows so that life scientists can quickly identify patterns and generate new hypotheses. For example, life scientists should be able to explore the transcriptomes of entire patient cohorts for novel genes of interest, applying their unique knowledge and experience to identify promising leads. From this point, the data scientist collaborator may wish to devise a rigorous statistical test to confirm or refute the role of those candidate genes. As a proof of concept, one of our research partners is using the app to explore clinical microarray data from a major multi-country, multi-year population study. Our software fosters more dynamic, meaningful conversations and makes the discovery process more collegial and efficient.