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Improving transparency of computational tools for variant effect prediction

Nature Genetics volume 56, pages 1324–1326 (2024)Cite this article

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Efforts to integrate computational tools for variant effect prediction into the process of clinical decision-making are in progress. However, for such efforts to succeed and help to provide more informed clinical decisions, it is necessary to enhance transparency and address the current limitations of computational predictors.

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Author information

Authors and Affiliations

  1. The Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD, USA

    Rachel Karchin

  2. Departments of Biomedical Engineering, Oncology, and Computer Science, The Johns Hopkins University, Baltimore, MD, USA

    Rachel Karchin

  3. Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA

    Predrag Radivojac

  4. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Anne O’Donnell-Luria

  5. Division of Genetics and Genomics, Boston Children’s Hospital, Boston, MA, USA

    Anne O’Donnell-Luria

  6. Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

    Anne O’Donnell-Luria

  7. Department of Medicine and University of Vermont Cancer Center, University of Vermont, Larner College of Medicine, Burlington, VT, USA

    Marc S. Greenblatt

  8. Department of Informatics and Analytics, Dana-Farber Cancer Institute, Boston, MA, USA

    Michael Y. Tolstorukov

  9. National Cancer Institute, Division of Cancer Treatment and Diagnosis, Rockville, MD, USA

    Dmitriy Sonkin

Authors
  1. Rachel Karchin
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  2. Predrag Radivojac
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  3. Anne O’Donnell-Luria
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  4. Marc S. Greenblatt
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  5. Michael Y. Tolstorukov
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  6. Dmitriy Sonkin
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Contributions

D.S. conceptualized the manuscript. D.S. and R.K. wrote the initial draft of the manuscript. All authors participated in the manuscript editing.

Corresponding authors

Correspondence to Rachel Karchin or Dmitriy Sonkin.

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Competing interests

The authors declare no competing interests.

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Peer review information

Nature Genetics thanks Andreas Ziegler and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Karchin, R., Radivojac, P., O’Donnell-Luria, A. et al. Improving transparency of computational tools for variant effect prediction. Nat Genet 56, 1324–1326 (2024). https://doi.org/10.1038/s41588-024-01821-8

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  • DOI: https://doi.org/10.1038/s41588-024-01821-8

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