Helping biologists see the bigger picture in diverse cancer genomics data

UCSC Xena helps biologists, both with and without computation skills, to visualize and analyze cancer genomics datasets. Our web-based data browser enables cancer researchers around the world to discover trends and test their hypotheses. Unique in the medical genomics research field, we allow users to easily view both large public resources generated by consortium around the world AND their own data or data from a publication. We accomplish this using a decoupled server-client architecture, where data is distributed across both public and private servers while being securely combined and visualized in one web-based browser.

While we are funded nationally by NCI, our users are international, with only ~40% of our traffic coming from the United States. We are committed to open-source software and aim to make our code as reusable as possible (e.g. our static interval tree[] and KM statistics[]).

Students we mentor will have the benefit of being part of both a small, focused development team and also having access to the community resources of the larger UCSC Genomics Institute[], whose mission is to create advanced technologies and open-source genomics platforms to unravel evolutionary patterns, molecular processes and the underpinnings of disease.

We aim to ensure that our students gain new knowledge and skills as well as contribute ideas and code to the project. We are committed to putting the code from students out to production and into the world where it is used by real cancer researchers. In addition, we are always looking for long-term collaborators and growing our development team. If you're interested in doing your Google Summer of Code project on cancer genomics, statistics, data visualization, or functional programing, let us know! No prior knowledge of cancer or biology required.

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UCSC Xena 2019 Projects

  • Ayan Banerjee
    Update GDC Data on Xena
    UCSC Xena is a functional genomics visualization and analysis platform. For this (genomics visualization and analysis), there are many datasets which...