What is BRCA2 predictor?

BRCA2 predictor is a tool to predict the functional significance of missense variants of BRCA2 from the Clinical and Translational Bioinformatics research group at Vall d'Hebron Institute of Research.

How can I predict my variant?

You can submit your variant in our query page indicating the native amino acid, the residue and the mutated amino acid. Afterwards, you will be redirect to the prediction page.

Why is my variant not accepted?

We use as a reference the database UniProt, a comprehensive, high-quality and freely accessible resource of protein sequence and functional information. In particular, we use the most prevalent isoform, the canonical isoform. So, if you are using another isoform or another database for protein sequence reference such as NCBI or Ensembl, you can find some small differences.

Which metrics has a pathogenicity prediction?

We provide you with three metrics:

  • Label: the variants are classified as pathogenic or neutral according to its functional consequence.

  • Score: the numerical score of the functional consequence of the variant. It has a continuous scale from 0 to 1, being 0 a neutral and 1 a pathogenic variant. The threshold between pathogenic and neutral variant is at 0.5.

  • Reliability: measures the accuracy of the prediction. It has a continuous scale from 0 to 1, being 1 a truthful prediction.

Score Plot

Which metrics has a functional prediction?

We provide you with two metrics:

How are the pathogenicity predictions calculated?

These predictions are calculated by a machine learning algorithm previously trained with a set of already known variants from the literature. To develop the predictor, we followed these steps:

  1. Collect the pathogenic and neutral variants of the protein

  2. Decipher the features able to discriminate between pathogenic and neutral variants

  3. Build the model by training the machine learning algorithm with the set of features of the known variants

  4. Estimate the model performance by cross-validation to ensure the reliability of the predictor

Predictor schema

Riera et alt., Human Mutation, 2016

How are the functional predictions calculated?

These predictions are calculated by a multiple linear regression model previously trained with a set of already known variants from the literature. To develop the predictor, we followed these steps:

  1. Collect the pathogenic and neutral variants of BRCA2 and their HDR experimental value from the paper of Farrugia et al.

  2. Decipher the features able to discriminate between pathogenic and neutral variants

  3. Build the model of multiple linear regression with the set of features of the known variants

  4. Estimate the model performance by cross-validation to ensure the reliability of the predictor

Which is the performance of BRCA2 predictor?

The BRCA2 predictor has been evaluated and compared to the state of the art predictors. Compare the performance metrics per predictor:

Sensitivity Specificity Accuracy MCC Coverage
BRCA2 pathogenicity 0.833 0.885 0.857 0.716 100%
BRCA2 functional 0.75 0.848 0.823 0.566 100%
Align-GVGD 0.87 0.856 0.877 0.722 99%
PON-P2 0.983 0.222 0.732 0.242 19%
PolyPhen-2 0.928 0.282 0.566 0.232 100%
CADD 0.972 0.334 0.61 0.358 31%
SIFT 0.0 0.0 0.0 0.0 0%

Can I download all the predictions for my protein?

You can download all the pre-calculated predictions to make your own queries. The file is in csv format containing the following columns:

# Field Description
1 Gene HGNC official gene symbol
2 Protein Uniprot accession number
3 Variant Missense variant from the canonical isoform
4 Prediction Predicted functional consequence of the variant
5 Score Numerical score of the pathogenic prediction

Which other relevant information do you provide?

The results report a great amount of information related to the variant divided in different sections: