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Visualizing and interpreting cancer genomics data via the Xena platform

Nature Biotechnology volume 38, pages 675–678 (2020)Cite this article

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To the Editor — There is a great need for easy-to-use cancer genomics visualization tools for both large public data resources such as TCGA (The Cancer Genome Atlas)1 and the GDC (Genomic Data Commons)2, as well as smaller-scale datasets generated by individual labs. Commonly used interactive visualization tools are either web-based portals or desktop applications. Data portals have a dedicated back end and are a powerful means of viewing centrally hosted resource datasets (for example, Xena’s predecessor, the University of California, Santa Cruz (UCSC) Cancer Browser (currently retired3), cBioPortal4, ICGC (International Cancer Genomics Consortium) Data Portal5, GDC Data Portal2). However, researchers wishing to use a data portal to explore their own data have to either redeploy the entire platform, a difficult task even for bioinformaticians, or upload private data to a server outside the user’s control, a non-starter for protected patient data, such as germline variants (for example, MAGI (Mutation Annotation and Genome Interpretation6), WebMeV7 or Ordino8). Desktop tools can view a user’s own data securely (for example, Integrated Genomics Viewer (IGV)9, Gitools10), but lack well-maintained, prebuilt files for the ever-evolving and expanding public data resources. This dichotomy between data portals and desktop tools highlights the challenge of using a single platform for both large public data and smaller-scale datasets generated by individual labs.

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Fig. 1: Xena’s architecture to securely join public and private data.
Fig. 2: An example Xena Browser Visual Spreadsheet examining published ERG–TMPRSS2 (ETS transcription factor–transmembrane serine protease 2) fusion calls in TCGA PRAD (prostate cancer) by combining data from local and public Xena Hubs.

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Acknowledgements

Research reported in this publication was supported by the National Cancer Institute of the US National Institutes of Health under award numbers 5U24CA180951-04 and 5U24CA210974-02. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This project has also been made possible in part by grant number 2018-182812 from the Chan Zuckerberg Initiative DAF, an advised fund of the Silicon Valley Community Foundation. We would also like to thank AWS Cloud Credits for Research and Google Summer of Code.

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Author notes

  1. These authors contributed equally: Mary Goldman, Brian Craft.

Authors and Affiliations

  1. UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA

    Mary J. Goldman, Brian Craft, Angela N. Brooks, Jingchun Zhu & David Haussler

  2. Clever Canary, New York, NY, USA

    Mim Hastie, Fran McDade & Dave Rogers

  3. Vilnius University, Vilnius, Lithuania

    Kristupas Repečka

  4. Birla Institute of Technology and Science, Goa, India

    Akhil Kamath

  5. National Institute of Technology, Durgapur, India

    Ayan Banerjee

  6. Department of Genetics, Stanford University, Stanford, CA, USA

    Yunhai Luo

  7. Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA

    Angela N. Brooks

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Correspondence to Mary J. Goldman.

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Editorial note: This article has been peer reviewed.

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Supplementary Information

Supplementary Figs. 1–10, Supplementary Table 1, Supplementary Note and Supplementary Methods

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Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol 38, 675–678 (2020). https://doi.org/10.1038/s41587-020-0546-8

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