Guidelines for releasing a variant effect predictor

2024/04/16 by Benjamin Livesey, Livesey, Benjamin J., Mihaly Badonyi +33 · 1 voice · 3 citations
Biochemistry, Genetics and Molecular Biology · #Cancer Genomics and Diagnostics #Gene expression and cancer classification #Genomics and Rare Diseases

paper · pdf · doi:10.1186/s13059-025-03572-z

openalex created_date 2024/04/19 · openalex publication_date 2025/04/15 · openalex updated_date 2026/08/03

Abstract

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 to date, and there is tremendous variability in their underlying algorithms and outputs, and in the ways in which the methodologies and predictions are shared. This leads to considerable challenges for end users in knowing which VEPs to use and how to use them. Here, to address these issues, we provide guidelines and recommendations for the release of novel VEPs. Emphasising open-source availability, transparent methodologies, clear variant effect score interpretations, standardised scales, accessible predictions, and rigorous training data disclosure, we aim to improve the usability and interpretability of VEPs, and promote their integration into analysis and evaluation pipelines. We also provide a large, categorised list of currently available VEPs, aiming to facilitate the discovery and encourage the usage of novel methods within the scientific community.

Citations

• REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants
• Assessing performance of pathogenicity predictors using clinically relevant variant datasets
• Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria
• GEMME: A Simple and Fast Global Epistatic Model Predicting Mutational Effects
• The mutational constraint spectrum quantified from variation in 141,456 humans
• Accurate proteome-wide missense variant effect prediction with AlphaMissense

Cited by

• Variant effect predictor correlation with functional assays is reflective of clinical classification performance
• Reinforcement learning-based dynamic ensemble for missense variant effect prediction and tiered prioritization of VUS
• Predicting cell-specific gene expression profile and knockout impact through deep learning

Discussions

• New paper out in Genome Biology! 🎉 We lay out best-practice guidelines for releasing variant effect predictors, developed through the Atlas of Variant Effects Alliance @varianteffect.bsky.social Ope [bsky, 33 points, 2 comments]

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