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Related Concept Videos

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The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Interpreting protein variant effects with computational predictors and deep mutational scanning.

Benjamin J Livesey1, Joseph A Marsh1

  • 1MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK.

Disease Models & Mechanisms
|June 23, 2022
PubMed
Summary

Computational tools predict genetic variant effects but can be unreliable due to biased benchmarking. Deep mutational scans offer independent data to improve accuracy and assess clinical impact.

Keywords:
BenchmarkingCircularityDeep mutational scanMachine learningMultiplexed assay of variant effectVariant effect predictor

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Computational variant effect predictors are crucial for genetic research and clinical diagnostics.
  • Assessing the reliability of these predictors is challenging due to benchmarking limitations.

Purpose of the Study:

  • To review advances in computational predictor methodology.
  • To discuss current benchmarking strategies and their limitations, particularly data circularity.
  • To explore the utility of deep mutational scans for independent benchmarking and predicting clinical variant impact.

Main Methods:

  • Review of current literature on computational variant effect predictors.
  • Analysis of benchmarking strategies, including data reuse and computational bias.
  • Examination of deep mutational scanning as a method for generating independent variant effect data.

Main Results:

  • Existing benchmarking methods can lead to inflated performance estimates for predictors due to data circularity.
  • Deep mutational scans provide independent, large-scale functional data for more reliable predictor evaluation.
  • Functional assays show promise in directly predicting clinical impacts of genetic variants.

Conclusions:

  • Deep mutational scans offer a solution to data circularity in benchmarking genetic variant effect predictors.
  • These functional assays can improve the accuracy of variant effect predictions and directly inform clinical decisions.
  • The increasing use of functional assays may reduce the future reliance on purely computational prediction methods.