ReviewFrontiers in genetics2022
Computational approaches for predicting variant impact: An overview from resources, principles to applications.
Review in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Empirically determined baseline masking strategies and other considerations for gene-level burden tests.Nature genetics · 2026Pooled it
- A CLN8 biallelic missense variant causes epilepsy with severe treatment-resistant psychosis.Molecular genetics and genomics : MGG · 2026Article
- A machine learning framework for predictive interpretation of variants of uncertain significance in hereditary cancer.Frontiers in systems biology · 2026Article
- Deep DNA and protein level feature integration for robust clinical variant interpretation using probabilistic gradient boosting.Frontiers in digital health · 2026Article
- Disease-Specific Prediction of Missense Variant Pathogenicity with DNA Language Models and Graph Neural Networks.Bioengineering (Basel, Switzerland) · 2025Article
- MOLGENIS VIP: an end-to-end DNA variant interpretation pipeline for research and diagnostics configurable to support rapid implementation of new methods.NAR genomics and bioinformatics · 2025Article
- Review
- Evaluating variant pathogenicity prediction tools to establish African inclusive guidelines for germline genetic testing.Communications medicine · 2025Article
- Guidelines for releasing a variant effect predictor.Genome biology · 2025Article
- Navigating Uncertainty: Assessing Variants of Uncertain Significance in the CDKL5 Gene for Developmental and Epileptic Encephalopathy Using In Silico Prediction Tools and Computational Analysis.Journal of molecular neuroscience : MN · 2025Article
- NAVIP: Unraveling the influence of neighboring small sequence variants on functional impact prediction.PLoS computational biology · 2025Article
- Translating pharmacogenomic sequencing data into drug response predictions-How to interpret variants of unknown significance.British journal of clinical pharmacology · 2025Review
- Human disease-causing missense genetic variants are enriched in the evolutionarily ancient domains of the cytosolic aminoacyl-tRNA synthetase proteins.IUBMB life · 2025Article
- Language Modelling Techniques for Analysing the Impact of Human Genetic Variation.Bioinformatics and biology insights · 2025Review
- Understanding the heterogeneous performance of variant effect predictors across human protein-coding genes.Scientific reports · 2024Article
- Functional analysis of ESRP1/2 gene variants and CTNND1 isoforms in orofacial cleft pathogenesis.Communications biology · 2024Article
- Functional validation of EIF2AK4 (GCN2) missense variants associated with pulmonary arterial hypertension.Human molecular genetics · 2024Article
- Using computational approaches to enhance the interpretation of missense variants in the PAX6 gene.European journal of human genetics : EJHG · 2024Article
- Pharmacogenomics: A Genetic Approach to Drug Development and Therapy.Pharmaceuticals (Basel, Switzerland) · 2024Review
- Functional analysis ofbioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One objective of human genetics is to unveil the variants that contribute to human diseases. With the rapid development and wide use of next-generation sequencing (NGS), massive genomic sequence data have been created, making personal genetic information available. Conventional experimental evidence is critical in establishing the relationship between sequence variants and phenotype but with low efficiency. Due to the lack of comprehensive databases and resources which present clinical and experimental evidence on genotype-phenotype relationship, as well as accumulating variants found from NGS, different computational tools that can predict the impact of the variants on phenotype have been greatly developed to bridge the gap. In this review, we present a brief introduction and discussion about the computational approaches for variant impact prediction. Following an innovative manner, we mainly focus on approaches for non-synonymous variants (nsSNVs) impact prediction and categorize them into six classes. Their underlying rationale and constraints, together with the concerns and remedies raised from comparative studies are discussed. We also present how the predictive approaches employed in different research. Although diverse constraints exist, the computational predictive approaches are indispensable in exploring genotype-phenotype relationship.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.