Welcome to Fabry

Prediction of the functional significance of missense variants of Fabry syndrome

Analyse your variant

Know our pathogenicity predictors

To assess the functional significance of missense variants, we have developed a specific predictor for GLA protein. Predict your variant or check our examples to see the result


Prediction of the functional significance of missense variants affecting GLA protein involved in Fabry disease, a rare genetic lysosomal storage disease

Disclaimer This resource is uniquely intended for research purposes. The authors are not responsible for neither its use nor misuse. The data provided are not intended as advice of any kind. The authors have worked with care in the development of this server, but assume no liability or responsibility for any error, weakness, incompleteness or temporariness of the resource and of the data provided.

How to interpret a prediction?

Label Plot

Prediction Label

The variants are classified as pathogenic or neutral by a supervised machine learning algorithm

The label depends in the numeric score. Variants above 0.5 are pathogenic while variants low 0.5 are neutral

Score Plot

Prediction Score

Beyond the label, a variant is characterized with a numerical score of its pathogenicity. We provide you with this score, so you can have a more precise sense of the consequence of the variant

This score has a continuous range between 0 to 1, being 0 a neutral and 1 a pathogenic variant. The threshold between pathogenic and neutral variant is at 0.5

Reliability Plot

Prediction Reliability

Such as any other measurement, a predictions has an associated error. This metric measures the accuracy of the prediction

The reliability has a continuous range between 0 to 1, being 1 a trueful prediction

Performance Plot

Predictor Performance

We have evaluated our predictors with rigorous quality controls and compared to the state of the art predictors such as PolyPhen2, PON-P2, SIFT or CADD

To evaluate our predictor, we perform a leave-one-out cross-validation. From it, we derived several performance metrics such as sensitivity, specificity, accuracy, AUC and MCC

About us

Supporting Institutions

Ministerio de Economía y Empresa