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BMC Bioinformatics. 2018 Nov 30;19(Suppl 15):433. doi: 10.1186/s12859-018-2416-7.

Challenging popular tools for the annotation of genetic variations with a real case, pathogenic mutations of lysosomal alpha-galactosidase.

Author information

1
Dipartimento di Biologia, Complesso di Monte Sant'Angelo, Università Federico II, VIA Cinthia, 80126, Napoli, Italy.
2
Istituto di Chimica Biomolecolare, Consiglio Nazionale delle Ricerche, Pozzuoli, Italy.
3
Dipartimento di Scienze e Tecnologie Ambientali, Biologiche e Farmaceutiche, Università della Campania "Luigi Vanvitelli", Caserta, Italy.
4
Dipartimento di Biologia, Complesso di Monte Sant'Angelo, Università Federico II, VIA Cinthia, 80126, Napoli, Italy. cubellis@unina.it.
5
Dipartimento di Agraria, Università Federico II, Napoli, Italy.

Abstract

BACKGROUND:

Severity gradation of missense mutations is a big challenge for exome annotation. Predictors of deleteriousness that are most frequently used to filter variants found by next generation sequencing, produce qualitative predictions, but also numerical scores. It has never been tested if these scores correlate with disease severity.

RESULTS:

wANNOVAR, a popular tool that can generate several different types of deleteriousness-prediction scores, was tested on Fabry disease. This pathology, which is caused by a deficit of lysosomal alpha-galactosidase, has a very large genotypic and phenotypic spectrum and offers the possibility of associating a quantitative measure of the damage caused by mutations to the functioning of the enzyme in the cells. Some predictors, and in particular VEST3 and PolyPhen2 provide scores that correlate with the severity of lysosomal alpha-galactosidase mutations in a statistically significant way.

CONCLUSIONS:

Sorting disease mutations by severity is possible and offers advantages over binary classification. Dataset for testing and training in silico predictors can be obtained by transient transfection and evaluation of residual activity of mutants in cell extracts. This approach consents to quantitative data for severe, mild and non pathological variants.

KEYWORDS:

Bioinformatics; Clinical informatics; Fabry disease; Rare disease; Variant analysis

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