In silico analysis of missense substitutions using sequence-alignment based methods
Open accessAbstract
Genetic testing for mutations in high-risk cancer susceptibility genes often reveals missense substitutions that are not easily classified as pathogenic or neutral. Among the methods that can help in their classification are computational analyses. Predictions of pathogenic vs. neutral, or the probability that a variant is pathogenic, can be made based on: 1) inferences from evolutionary conservation using protein multiple sequence alignments (PMSAs) of the gene of interest for almost any missense sequence variant; and 2) for many variants, structural features of wild-type and variant proteins. These in silico methods have improved considerably in recent years. In this work, we review and/or make suggestions with respect to: 1) the rationale for using in silico methods to help predict the consequences of missense variants; 2) important aspects of creating PMSAs that are informative for classification; 3) specific features of algorithms that have been used for classification of clinically-observed variants; 4) validation studies demonstrating that computational analyses can have predictive values (PVs) of approximately 75 to 95%; 5) current limitations of data sets and algorithms that need to be addressed to improve the computational classifiers; and 6) how in silico algorithms can be a part of the "integrated analysis" of multiple lines of evidence to help classify variants. We conclude that carefully validated computational algorithms, in the context of other evidence, can be an important tool for classification of missense variants.
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Several efforts have evaluated the performance of available prediction software, to compare them to each other and to assess their ability to predict known disease-causing variants.
“Several efforts have evaluated the performance of available prediction software, to compare them to each other and to assess their ability to predict 'known' disease-causing variants. 14-17”
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- Authors
- Tavtigian, S. V., Greenblatt, M. S., Lesueur, F., Byrnes, G. B., & for the IARC Unclassified Genetic Variants Working Group
- Affiliations
- Centre international de recherche sur le cancer (FR)
- University of Vermont (US)
- Corresponding author
- Tavtigian, Sean V.
- Venue
- Human Mutation
- Publisher
- Wiley
- Published
- 2008-11
- Volume
- 29
- Issue
- 11
- Pages
- 1327-1336
- ISSN
- 1059-7794 (ISSN-L)
- 1098-1004
- PMC
- PMC3431198
- OpenAlex
- W1984576497
- Cited by
- 199
- References
- 53
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 3 · 2023: 5 · 2022: 4 · 2021: 10 · 2020: 13 · 2019: 14 · 2018: 14 · 2017: 6 · 2016: 7 · 2015: 13 · 2014: 20 · 2013: 11 · 2012: 20
- FWCI
- 9.71
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Human Mutation (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Cancer Institute (R55CA096536, CA 96536, R01CA096536)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › BRCA gene mutations in cancer
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Keywords
- missense mutations, variant pathogenicity prediction, protein multiple sequence alignment, evolutionary conservation, cancer susceptibility genes, predictive value, protein structure
- MeSH terms
- Algorithms, Amino Acid Sequence, Genetic Testing, Humans, Molecular Sequence Data, Neoplastic Syndromes, Hereditary, Predictive Value of Tests, Genetic Variation, Sequence Alignment, Evolution, Molecular, Computational Biology, Genetic Predisposition to Disease, Mutation, Missense, Genes, Neoplasm
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