ArticleBMC medical genomics2023
Evaluation of in silico pathogenicity prediction tools for the classification of small in-frame indels.
Article in BMC medical genomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 17 papers.
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17 citing papers in PubMed.
- From conservation scores to language models: a feature engineering perspective on machine learning for genetic variant interpretation.Briefings in bioinformatics · 2026Review
- Unsupervised Deep Representation Learning and Probabilistic Clustering for the Systems-Level Discovery of Germline Mutation Signatures in Pediatric Cancers.Biomedicines · 2026Article
- Semantic embedding of variant effect annotations enables rapid and accurate pathogenicity prediction with VUS.Life.Scientific reports · 2026Article
- Causal variants in animal genomes: approaches to identification, phenotypic impact, and implications for selective breeding.BMC genomics · 2026Review
- Calibration of in-frame indel variant effect predictors for clinical variant classification.bioRxiv : the preprint server for biology · 2026Article
- Leveraging protein language models and a scoring function for indel characterization and transfer learning.Patterns (New York, N.Y.) · 2026Article
- Comprehensive evaluation of ACMG/AMP-based variant classification tools.Bioinformatics (Oxford, England) · 2026Article
- PON-Del predictor for sequence retaining protein deletions.PLoS computational biology · 2026Article
- In Silico Functional and Structural Analysis ofGenes · 2026Article
- Assessment of In-Frame Indel Variants in an Unsolved Cohort of Inherited Retinal Diseases Using Machine Learning.Human mutation · 2026Article
- Deep DNA and protein level feature integration for robust clinical variant interpretation using probabilistic gradient boosting.Frontiers in digital health · 2026Article
- Dynamicasome-a molecular dynamics-guided and AI-driven pathogenicity prediction catalogue for all genetic mutations.Communications biology · 2025Article
- Evaluating variant pathogenicity prediction tools to establish African inclusive guidelines for germline genetic testing.Communications medicine · 2025Article
- Case Report: Simultaneous presentation of end-stage heart failure with cardiogenic shock requiring emergency transplantation in monozygotic twins with myofibrillar myopathy: a previously unknown genetic disease?Frontiers in cardiovascular medicine · 2025Article
- AI-derived comparative assessment of the performance of pathogenicity prediction tools on missense variants of breast cancer genes.Human genomics · 2024Article
- CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions.Nucleic acids research · 2024Article
- Correction: Evaluation of in silico pathogenicity prediction tools for the classification of small in-frame indels.BMC medical genomics · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
Abstract
backgroundThe use of in silico pathogenicity predictions as evidence when interpreting genetic variants is widely accepted as part of standard variant classification guidelines. Although numerous algorithms have been developed and evaluated for classifying missense variants, in-frame insertions/deletions (indels) have been much less well studied.
methodsWe created a dataset of 3964 small (< 100 bp) indels predicted to result in in-frame amino acid insertions or deletions using data from gnomAD v3.1 (minor allele frequency of 1-5%), ClinVar and the Deciphering Developmental Disorders (DDD) study. We used this dataset to evaluate the performance of nine pathogenicity predictor tools: CADD, CAPICE, FATHMM-indel, MutPred-Indel, MutationTaster2021, PROVEAN, SIFT-indel, VEST-indel and VVP.
resultsOur dataset consisted of 2224 benign/likely benign and 1740 pathogenic/likely pathogenic variants from gnomAD (n = 809), ClinVar (n = 2882) and, DDD (n = 273). We were able to generate scores across all tools for 91% of the variants, with areas under the ROC curve (AUC) of 0.81-0.96 based on the published recommended thresholds. To avoid biases caused by inclusion of our dataset in the tools' training data, we also evaluated just DDD variants not present in either gnomAD or ClinVar (70 pathogenic and 81 benign). Using this subset, the AUC of all tools decreased substantially to 0.64-0.87. Several of the tools performed similarly however, VEST-indel had the highest AUCs of 0.93 (full dataset) and 0.87 (DDD subset).
conclusionsAlgorithms designed for predicting the pathogenicity of in-frame indels perform well enough to aid clinical variant classification in a similar manner to missense prediction tools.
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