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. 2024 Mar 6;25(Suppl 1):100.
doi: 10.1186/s12859-024-05709-6.

DVA: predicting the functional impact of single nucleotide missense variants

Dong Wang  1 , Jie Li  2 , Edwin Wang  3 , Yadong Wang  1
Affiliations

DVA: predicting the functional impact of single nucleotide missense variants

Dong Wang et al. BMC Bioinformatics. .

Abstract

Background: In the past decade, single nucleotide variants (SNVs) have been identified as having a significant relationship with the development and treatment of diseases. Among them, prioritizing missense variants for further functional impact investigation is an essential challenge in the study of common disease and cancer. Although several computational methods have been developed to predict the functional impacts of variants, the predictive ability of these methods is still insufficient in the Mendelian and cancer missense variants.

Results: We present a novel prediction method called the disease-related variant annotation (DVA) method that predicts the effect of missense variants based on a comprehensive feature set of variants, notably, the allele frequency and protein-protein interaction network feature based on graph embedding. Benchmarked against datasets of single nucleotide missense variants, the DVA method outperforms the state-of-the-art methods by up to 0.473 in the area under receiver operating characteristic curve. The results demonstrate that the proposed method can accurately predict the functional impact of single nucleotide missense variants and substantially outperforms existing methods.

Conclusions: DVA is an effective framework for identifying the functional impact of disease missense variants based on a comprehensive feature set. Based on different datasets, DVA shows its generalization ability and robustness, and it also provides innovative ideas for the study of the functional mechanism and impact of SNVs.

Keywords: Disease-related; Functional impact; Missense variants; Variant annotation.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
The AUROCs of 15 different prediction methods using somatic cancer missense variants
Fig. 2
Fig. 2
The AUROCs of 15 different prediction methods using missense variants in the ClinVar database
Fig. 3
Fig. 3
The AUROCs of 15 different prediction methods using missense variants in the VariBench database
Fig. 4
Fig. 4
The AUROCs of 15 different prediction methods on COSMIC dataset
Fig. 5
Fig. 5
The degree of contribution from top 20 features used by DVA
Fig. 6
Fig. 6
The overview of the DVA method
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