Know our pathogenicity predictors
To assess the functional significance of nonsynonymous variants, we have developed a set of specific predictors. Predict your variant or check our examples to see the result
BRCA Specific Software (BRASS) for the prediction of the functional significance of nonsynonymous variants in BRCA1 and BRCA2 involved in hereditary breast and ovarian cancer (HBOC)
Prediction of the functional significance of nonsynonymous variants affecting NOTCH3 protein involved in CADASIL syndrome, the most common form of hereditary stroke disorder
Prediction of the functional significance of nonsynonymous variants affecting GLA protein involved in Fabry disease, a rare genetic lysosomal storage disease
Prediction of the functional significance of nonsynonymous variants in more than 80 proteins involved in diseases with an inheritance component, both common and rare diseases
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?
The variants are classified as pathogenic or neutral by a supervised machine learning algorithm according to its functional consequence
The label is a categorical metric derived from the numerical score. Variants above 0.5 are pathogenic while variants below 0.5 are neutral
Beyond the label, a variant is characterized with a numerical score which indicates its pathogenicity. We provide you with this score, so you can have a more precise metric of the functional 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
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
We have evaluated our predictors with rigorous validation techniques and compared to the state of the art predictors such as PolyPhen2, PON-P2, SIFT or CADD
We performed a leave-one-out cross-validation and calculated several performance metrics such as sensitivity, specificity, accuracy, AUC and MCC that we make available to users