VIPdb, a genetic variant impact predictor database
Open accessAbstract
Genome sequencing identifies vast number of genetic variants. Predicting these variants' molecular and clinical effects is one of the preeminent challenges in human genetics. Accurate prediction of the impact of genetic variants improves our understanding of how genetic information is conveyed to molecular and cellular functions, and is an essential step towards precision medicine. Over one hundred tools/resources have been developed specifically for this purpose. We summarize these tools as well as their characteristics, in the genetic Variant Impact Predictor Database (VIPdb). This database will help researchers and clinicians explore appropriate tools, and inform the development of improved methods. VIPdb can be browsed and downloaded at https://genomeinterpretation.org/vipdb.
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SIFT, PolyPhen-2, and CADD were among the first methods developed to classify coding and all variants and remain among the most frequently used in silico tools in clinical variant interpretation.
“SIFT, PolyPhen-2, and CADD were among the first methods developed to classify coding and all variants and remain among the most frequently used in silico tools in clinical variant interpretation.5”
From Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria (2022) · cites this paper as [5]
The VIPdb passage on collecting tools (page not given) calls SIFT and PolyPhen pioneering variant-impact predictors. It does not extend that description to CADD or establish that the three tools are among the most frequently used in clinical variant interpretation. (LLM, conf=0.86)
Experimental — accuracy not yet measured
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“Investigation of commonly used developerrecommended thresholds and simple consensus approaches SIFT, PolyPhen-2, and CADD were among the first methods developed to classify coding and all variants and remain among the most frequently used in silico tools in clinical variant interpretation.(5) In practice, these tools…”
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Computational interpretation of human genetic variation
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Assessing Protein Surface-Based Scoring for Interpreting Genomic Variants
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- Authors
- Hu, Z., Yu, C., Furutsuki, M., Andreoletti, G., Ly, M., Hoskins, R., Adhikari, A. N., & Brenner, S. E.
- Affiliations
- University of California, Berkeley (US)
- Corresponding author
- Brenner, Steven E.
- Venue
- Human Mutation
- Publisher
- Wiley
- Published
- 2019-09
- Volume
- 40
- Issue
- 9
- Pages
- 1202-1214
- ISSN
- 1059-7794 (ISSN-L)
- 1098-1004
- PMC
- PMC7288905
- OpenAlex
- W2959541742
- Cited by
- 37
- References
- 180
- Citations by year
- 2026: 5 · 2025: 3 · 2024: 7 · 2023: 4 · 2022: 4 · 2021: 5 · 2020: 7 · 2019: 2
- FWCI
- 2.71
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Human Mutation (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Foundation for the National Institutes of Health (U41 HG007346, R13 HG006650)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- genome interpretation, precision medicine, human genetics
- MeSH terms
- Humans, Phenotype, Proteins, User-Computer Interface, Genetic Variation, Genome, Human, Protein Structure, Secondary, Computational Biology, Genetic Predisposition to Disease, Databases, Genetic, Precision Medicine
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