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An analytical framework for optimizing variant discovery from personal genomes

Author

Listed:
  • Gareth Highnam

    (Gene by Gene Ltd)

  • Jason J. Wang

    (Gene by Gene Ltd)

  • Dean Kusler

    (Gene by Gene Ltd)

  • Justin Zook

    (National Institute of Standards and Technology)

  • Vinaya Vijayan

    (Virginia Bioinformatics Institute, Virginia Tech)

  • Nir Leibovich

    (Gene by Gene Ltd)

  • David Mittelman

    (Gene by Gene Ltd
    Virginia Bioinformatics Institute, Virginia Tech)

Abstract

The standardization and performance testing of analysis tools is a prerequisite to widespread adoption of genome-wide sequencing, particularly in the clinic. However, performance testing is currently complicated by the paucity of standards and comparison metrics, as well as by the heterogeneity in sequencing platforms, applications and protocols. Here we present the genome comparison and analytic testing (GCAT) platform to facilitate development of performance metrics and comparisons of analysis tools across these metrics. Performance is reported through interactive visualizations of benchmark and performance testing data, with support for data slicing and filtering. The platform is freely accessible at http://www.bioplanet.com/gcat .

Suggested Citation

  • Gareth Highnam & Jason J. Wang & Dean Kusler & Justin Zook & Vinaya Vijayan & Nir Leibovich & David Mittelman, 2015. "An analytical framework for optimizing variant discovery from personal genomes," Nature Communications, Nature, vol. 6(1), pages 1-6, May.
    • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms7275
    DOI: 10.1038/ncomms7275
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    Cited by:

    1. Jiajin Li & Brandon Jew & Lingyu Zhan & Sungoo Hwang & Giovanni Coppola & Nelson B Freimer & Jae Hoon Sul, 2019. "ForestQC: Quality control on genetic variants from next-generation sequencing data using random forest," PLOS Computational Biology, Public Library of Science, vol. 15(12), pages 1-30, December.

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