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<p>Computational methods for assessing the likely impacts of mutations, known as variant effect predictors (VEPs), are widely used in the assessment and interpretation of human genetic variation, as well as in other applications like protein engineering. Many different VEPs have been released, and there is tremendous variability in their underlying algorithms, outputs, and the ways in which the methodologies and predictions are shared. This leads to considerable difficulties for users trying to navigate the selection and application of VEPs. Here, to address these issues, we provide guidelines and recommendations for the release of novel VEPs.</p>

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Guidelines for releasing a variant effect predictor

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月15日

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  • Benjamin J. Livesey,
  • Mihaly Badonyi,
  • Mafalda Dias,
  • Jonathan Frazer,
  • Sushant Kumar,
  • Kresten Lindorff-Larsen,
  • David M. McCandlish,
  • Rose Orenbuch,
  • Courtney A. Shearer,
  • Lara Muffley,
  • Julia Foreman,
  • Andrew M. Glazer,
  • Ben Lehner,
  • Debora S. Marks,
  • Frederick P. Roth,
  • Alan F. Rubin,
  • Lea M. Starita,
  • Joseph A. Marsh

摘要

Computational methods for assessing the likely impacts of mutations, known as variant effect predictors (VEPs), are widely used in the assessment and interpretation of human genetic variation, as well as in other applications like protein engineering. Many different VEPs have been released, and there is tremendous variability in their underlying algorithms, outputs, and the ways in which the methodologies and predictions are shared. This leads to considerable difficulties for users trying to navigate the selection and application of VEPs. Here, to address these issues, we provide guidelines and recommendations for the release of novel VEPs.