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Evolutionary diagnosis method for variants in personal exomes

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Figure 1: Performance and application of the EvoD method.

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Acknowledgements

We thank N. Gerek, C. Hepp and M. Champion for insightful comments and J. Akey for providing the 5,400 exomes data. C. Williams provided editorial support. This research was supported by a research grant from the National Library of Medicine (R01 LM010834) and a Postbaccalaureate Research Education Program grant (R25 GM071798) from the US National Institutes of Health.

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Authors and Affiliations

  1. Center for Evolutionary Medicine and Informatics, Biodesign Institute, Arizona State University, Tempe, Arizona, USA

    Sudhir Kumar, Maxwell Sanderford, Vanessa E Gray, Jieping Ye & Li Liu

  2. School of Life Sciences, Arizona State University, Tempe, Arizona, USA

    Sudhir Kumar

  3. School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, Arizona, USA

    Jieping Ye

Authors
  1. Sudhir Kumar
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  2. Maxwell Sanderford
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  3. Vanessa E Gray
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  4. Jieping Ye
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  5. Li Liu
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Correspondence to Sudhir Kumar.

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The authors declare no competing financial interests.

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Supplementary Text and Figures

Supplementary Figures 1–5, Supplementary Table 1 and Supplementary Methods (PDF 672 kb)

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Kumar, S., Sanderford, M., Gray, V. et al. Evolutionary diagnosis method for variants in personal exomes. Nat Methods 9, 855–856 (2012). https://doi.org/10.1038/nmeth.2147

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