ArticleMolecular genetics & genomic medicine2016
Computational assessment of feature combinations for pathogenic variant prediction.
Article in Molecular genetics & genomic medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Correspondence between functional scores from deep mutational scans and predicted effects on protein stability.Protein science : a publication of the Protein Society · 2023Article
- ASCARIS: Positional feature annotation and protein structure-based representation of single amino acid variations.Computational and structural biotechnology journal · 2023Article
- Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery.BioMed research international · 2019Review
- Performance evaluation of pathogenicity-computation methods for missense variants.Nucleic acids research · 2018Article
- Exploring digenic inheritance in arrhythmogenic cardiomyopathy.BMC medical genetics · 2017Article
- Rapid functional analysis of computationally complex rare human IRF6 gene variants using a novel zebrafish model.PLoS genetics · 2017Article
- PON-P and PON-P2 predictor performance in CAGI challenges: Lessons learned.Human mutation · 2017Article
- Acute Intermittent Porphyria: Predicted Pathogenicity of HMBS Variants Indicates Extremely Low Penetrance of the Autosomal Dominant Disease.Human mutation · 2016Article
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Authors and funding
3 authors.
Funding
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Abstract
backgroundAlthough several methods have been proposed for predicting the effects of genetic variants and their role in disease, it is still a challenge to identify and prioritize pathogenic variants within sequencing studies.
methodsHere, we compare different variant and gene-specific features as well as existing methods and investigate their best combination to explore potential performance gains.
resultsWe found that combining the number of "biological process" Gene Ontology annotations of a gene with the methods PON-P2, and PROVEAN significantly improves prediction of pathogenic variants, outperforming all individual methods. A comprehensive analysis of the Gene Ontology feature suggests that it is not a variant-dependent annotation bias but reflects the multifunctional nature of disease genes. Furthermore, we identified a set of difficult variants where different prediction methods fail.
conclusionExisting pathogenicity prediction methods can be further improved.
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