PON-P2

PON-P2 predicts the pathogenicity (harmfulness) of amino acid substitutions. It is a machine learning-based approach and utilizes amino acid features, Gene Ontology (GO) annotations, evolutionary conservation, and if available, annotations of functional sites. Note that, PON-P2 is NOT a meta-predictor. PON-P2 estimates the reliability of predictions and groups the variants into pathogenic, neutral and unknown classes. Read more

Performance of PON-P2 has been extensively tested. For details, see here. Performance of PON-P2 on additional datasets such as predictSNPSelected and SwissVarSelected datasets are also available here. PON-P2 has been shown to work also on cancer variants. PON-P2 predictions for amino acid substitutions in COSMIC (v68) and data published in Harmful somatic amino acid substitutions affect key pathways in cancers is publicly available here.

PON-P2 was the best performing method in a recent comparison and outperformed protein-specific predictors in 85% of the proteins (Riera et. al. 2016).

NEWS: PON-P2 API and a VEP Plugin for PON-P2 is now available.

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Predictions for somatic variations leading to amino acid substitutions using PON-P2.
The predictions of PON-P2 are used to analyze the amino acid substitutions from 7,042 samples in 30 types. This page contains the predictions used in the analysis. If you use this data, please cite the following publication.

Niroula A, Vihinen M (2015) Harmful somatic amino acid substitutions affect key pathways in cancers. BMC Med Genomics 8:53

Predictions

COSMIC
ALL
AML
Bladder
Breast
Cervix
CLL
Colorectum
Esophageal
Glioblastoma
Glioma Low Grade
Head and Neck
Kidney Chromophobe
Kideny Clear Cell
Kidney Papillary
Liver
Lung Adeno
Lung Small Cell
Lung Squamous
Lymphoma B-cell
Medulloblastoma
Melanoma
Myeloma
Neuroblastoma
Ovary
Pancreas
Pilocytic Astrocytoma
Prostate
Stomach
Thyroid
Uterus