********************************************************************************************** ** ** ** SNPs&GO ** ** Predicting disease associated variation using GO terms ** ** ** ********************************************************************************************** PDB: pdb7zxn.spdb Chain: A Mutation Prediction RI Probability Method S198F Disease 6 0.812 PANTHER: F[S]=65% F[F]=0% S198F Disease 9 0.971 S3Ds&GO Mutation: WT+POS+NEW WT: Residue in wild-type protein POS: Residue position NEW: New residue after mutation Prediction: Neutral: Neutral variation Disease: Disease associated variation RI: Reliability Index Probability: Disease probability (if >0.5 mutation is predicted Disease) Method: SVM type and data PANTHER: Output of the PANTHER algorithm S3D-PROF: SVM input is the protein structure and profile at the mutated position SNPs&GO: SVM input is sequence and profile information and GO term features S3Ds&GO: SVM input is all the input in S3D-PROF, PANTHER and GO term features F[X]: Frequency of residue X in the sequence profile Nali: Number of aligned sequences in the mutated site RSA: Relative Solvent Accesible Area ********************************************************************************************** ** ** ** Calabrese R, Capriotti E, Fariselli P, Martelli PL, Casadio R. (2009). Functional ** ** annotations improve the predictive score of human disease-related mutations in ** ** proteins. Human Mutation. 30:1237-1244. ** ** ** ** Capriotti E, Altman RB. (2011). Improving the prediction of disease-related vari- ** ** ants using protein three-dimensional structure. BMC Bioinformatics. 12 (Sup.4) S3. ** ** ** **********************************************************************************************