DVA: predicting the functional impact of single nucleotide missense variants
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-03-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-024-05709-6 |
| _version_ | 1797266544477077504 |
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| author | Dong Wang Jie Li Edwin Wang Yadong Wang |
| author_facet | Dong Wang Jie Li Edwin Wang Yadong Wang |
| author_sort | Dong Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-25T01:02:23Z |
| format | Article |
| id | doaj.art-12baea084ff24b9d8a260140fe81e38e |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-25T01:02:23Z |
| publishDate | 2024-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-12baea084ff24b9d8a260140fe81e38e2024-03-10T12:23:23ZengBMCBMC Bioinformatics1471-21052024-03-0125S111610.1186/s12859-024-05709-6DVA: predicting the functional impact of single nucleotide missense variantsDong Wang0Jie Li1Edwin Wang2Yadong Wang3School of Computer Science and Technology, Harbin Institute of Technology HarbinSchool of Computer Science and Technology, Harbin Institute of Technology HarbinCumming School of Medicine, University of CalgarySchool of Computer Science and Technology, Harbin Institute of Technology HarbinAbstract 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.https://doi.org/10.1186/s12859-024-05709-6Missense variantsFunctional impactVariant annotationDisease-related |
| spellingShingle | Dong Wang Jie Li Edwin Wang Yadong Wang DVA: predicting the functional impact of single nucleotide missense variants BMC Bioinformatics Missense variants Functional impact Variant annotation Disease-related |
| title | DVA: predicting the functional impact of single nucleotide missense variants |
| title_full | DVA: predicting the functional impact of single nucleotide missense variants |
| title_fullStr | DVA: predicting the functional impact of single nucleotide missense variants |
| title_full_unstemmed | DVA: predicting the functional impact of single nucleotide missense variants |
| title_short | DVA: predicting the functional impact of single nucleotide missense variants |
| title_sort | dva predicting the functional impact of single nucleotide missense variants |
| topic | Missense variants Functional impact Variant annotation Disease-related |
| url | https://doi.org/10.1186/s12859-024-05709-6 |
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