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Genes Immun. 2017 Dec 4. doi: 10.1038/s41435-017-0002-z. [Epub ahead of print]

Frequently used bioinformatics tools overestimate the damaging effect of allelic variants.

Author information

1
Department of Molecular Biology and Genetics, Aarhus University, 8000, Aarhus, Denmark.
2
Department of Infectious Diseases, Aarhus University Hospital, Skejby, 8200, Aarhus, Denmark.
3
Department of Molecular Medicine, University of Bonn, 53113, Bonn, Germany.
4
Gene Center and Department of Biochemistry, Ludwig-Maximilians-Universität München, 81377, Munich, Germany.
5
Department of Clinical Immunology, Aarhus University Hospital, Skejby, 8200, Aarhus, Denmark.
6
Department of Biomedicine and Department of Clinical Medicine, Aarhus University, 8000, Aarhus, Denmark.
7
Department of Molecular Biology and Genetics, Aarhus University, 8000, Aarhus, Denmark. rh@mbg.au.dk.

Abstract

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.

PMID:
29217828
DOI:
10.1038/s41435-017-0002-z

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