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.
Which metrics has a functional prediction?
We provide you with two metrics:
Label: the variants are classified as HDR activity or No HDR activity according to its functional consequence.
Score: the numerical score of the functional consequence of the variant its calculated based on the paper of Farrugia et al.
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:
Collect the pathogenic and neutral variants of the protein
Decipher the features able to discriminate between pathogenic and neutral variants
Build the model by training the machine learning algorithm with the set of features of the known variants
Estimate the model performance by cross-validation to ensure the reliability of the predictor
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:
Collect the pathogenic and neutral variants of BRCA2 and their HDR experimental value from the paper of Farrugia et al.
Decipher the features able to discriminate between pathogenic and neutral variants
Build the model of multiple linear regression with the set of features of the known variants
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:
Prediction: prediction of the functional consequence of the variant along with its score and reliability.
Other Predictors: functional consequence of the variant predicted by other standard tools such as Align-GVGD, PON-P2, PolyPhen-2, SIFT and CADD predictors.
Variant Annotation: known the clinical evidence, biological relevance, population allele frequency and other information about your variant from several databases such as ClinVar, UniProt, dbSNP and ExAC.
Biomedical Information: links to several resources about the disease (DECIPHER, HPO, GeneReview, Malacards, MedGen, OMIM and Orphanet databases), the protein (UniProt database), the tridimensional structure (PDB database), the protein-protein interactions (STRING database), the metabolic pathways (REACTOME database), and the gene (Ensembl, GeneCards, HGNC and NCBI databases).
Protein Plot: distribution of several features along the protein such as known pathogenic and neutral variants, biological relevant residues, functional domains and gene exons.
Predicted functional consequence: localization of the score of the variant in the distribution of scores of known pathogenic and neutral variants.
Explanatory variables of the prediction: localization of the features of the variant in the distribution of features of known pathogenic and neutral variants.