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Table 2 Methods for predicting the consequences of point mutations

From: Getting personalized cancer genome analysis into the clinic: the challenges in bioinformatics

Name URL How it works
SIFT http://sift.jcvi.org Uses sequence homology scores that are calculated using position-specific scoring matrices with Dirichlet priors
Polyphen 2 http://genetics.bwh.harvard.edu/pph2/ Uses sequence conservation, structure and Swiss-Prot annotations
PMUT http://mmb2.pcb.ub.es:8080/PMut/ Formulates predictions with neural networks, using internal databases, secondary structure prediction and sequence conservation
SNPs3D http://www.snps3d.org/ Based on a support vector machine that uses structural or sequence conservation parameters
PantherPSEC19 http://www.pantherdb.org/tools/csnpScoreForm.jsp Uses sequence homology scores calculated using PANTHER hidden Markov model families
Mutationassessor http://mutationassessor.org Provides predictions using additional information based on the specific patterns of conservation of protein families
VEP (Variant Effect Predictor) http://www.ensembl.org/info/docs/variation/vep This system categorizes Ensembl genomic variants in known transcripts by their potential effect
KinMut http://kinmut.bioinfo.cnio.es Prediction of the consequences of mutations in protein kinases; the system was trained with specific information about the kinase subfamilies, and together with the predictions provides general information about the corresponding proteins, a comparison with other predictors and links to the related literature