Frequently used bioinformatics tools overestimate the damaging effect of allelic variants
| Autor: | Trine H. Mogensen, Line Lykke Andersen, Veit Hornung, Nanna Mørk, Rune Hartmann, Klara Höning, Carsten Scavenius, Jan J. Enghild, Ewa Terczyńska-Dyla, Mette Christiansen |
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| Rok vydání: | 2017 |
| Předmět: |
Male
0301 basic medicine Encephalitis Herpes Simplex/genetics Genome-Wide Association Study/standards Immunology Mutation Missense Single-nucleotide polymorphism Genome-wide association study Biology Bioinformatics Polymorphism Single Nucleotide 03 medical and health sciences 0302 clinical medicine Genetics medicine False positive paradox Humans Missense mutation False Positive Reactions Genetic Testing Receptors Cytokine Allele Child Genetic Testing/standards Gene Genetics (clinical) Receptors Interferon Genetic testing medicine.diagnostic_test Receptors Cytokine/genetics Computational Biology HEK293 Cells 030104 developmental biology Mutation (genetic algorithm) Software/standards Female Encephalitis Herpes Simplex Computational Biology/standards Software Genome-Wide Association Study 030215 immunology |
| Zdroj: | Andersen, L L, Terczyńska-Dyla, E, Mørk, N, Scavenius, C, Enghild, J J, Höning, K, Hornung, V, Christiansen, M, Mogensen, T H & Hartmann, R 2019, ' Frequently used bioinformatics tools overestimate the damaging effect of allelic variants ', Genes and Immunity, vol. 20, no. 1, pp. 10-22 . https://doi.org/10.1038/s41435-017-0002-z |
| ISSN: | 1476-5470 1466-4879 |
| DOI: | 10.1038/s41435-017-0002-z |
| Popis: | We selected two sets of naturally occurring human missense allelic variants within innate immune genes. The first set represented eleven non-synonymous variants in six different genes involved in interferon (IFN) induction, present in a cohort of patients suffering from herpes simplex encephalitis (HSE) and the second set represented sixteen allelic variants of the IFNLR1 gene. We recreated the variants in vitro and tested their effect on protein function in a HEK293T cell based assay. We then used an array of 14 available bioinformatics tools to predict the effect of these variants upon protein function. To our surprise two of the most commonly used tools, CADD and SIFT, produced a high rate of false positives, whereas SNPs&GO exhibited the lowest rate of false positives in our test. As the problem in our test in general was false positive variants, inclusion of mutation significance cutoff (MSC) did not improve accuracy. |
| Databáze: | OpenAIRE |
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