Modern genomics research generates massive amounts of data. But these data sets are too big and complex to be useful on their own. Researchers must first analyze and interpret biological data to better understand them and turn them into meaningful information. This information can then be used to help solve real-world problems, such as developing new tools or strategies to better diagnose and treat patients, increasing crop yields or monitoring the environment. Increasingly, the ability of the human end-user to interpret the data is the key factor limiting researchers from delivering these much-needed solutions more quickly.
Dr. Paul C. Boutros of the Ontario Institute for Cancer Research is leading a team developing ways of making “big data” results more easily understood by improving the way it is visualized and interpreted. The team will create interactive visualization tools that will integrate tightly with databases scientists already use routinely. The team will use crowdsourcing to capture the best visualization ideas from a broad community of scientists, graphic designers and citizen-scientists. The project will build on the human brain’s ability to interpret images, to make the conclusions of biological data more readily accessible and accelerate the rate of biological discovery and innovation.