********************************************************************************************** ** ** ** SNPs&GO ** ** Predicting disease associated variation using GO terms ** ** ** ********************************************************************************************** Sequence File: MMP8_HUMAN.seq Mutation Prediction RI Probability Method D169Y Disease 9 0.959 PhD-SNP: F[D]=88% F[Y]=1% Nali=701 D169Y Disease 9 0.947 PANTHER: F[D]=83% F[Y]=0% D169Y Disease 9 0.946 SNPs&GO D174Y Disease 9 0.950 PhD-SNP: F[D]=95% F[Y]=1% Nali=715 D174Y Disease 10 0.997 PANTHER: F[D]=97% F[Y]=0% D174Y Disease 9 0.939 SNPs&GO G175R Disease 9 0.930 PhD-SNP: F[G]=95% F[R]=0% Nali=719 G175R Disease 9 0.974 PANTHER: F[G]=94% F[R]=0% G175R Disease 8 0.907 SNPs&GO A215D Disease 9 0.958 PhD-SNP: F[A]=89% F[D]=0% Nali=728 A215D Disease 9 0.947 PANTHER: F[A]=86% F[D]=0% A215D Disease 8 0.899 SNPs&GO D253N Disease 8 0.898 PhD-SNP: F[D]=97% F[N]=0% Nali=714 D253N Disease 10 0.991 PANTHER: F[D]=97% F[N]=0% D253N Disease 7 0.871 SNPs&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 PhD-SNP: SVM input is the sequence and profile at the mutated position SNPs&GO: SVM input is all the input in PhD-SNP, PANTHER and GO term features F[X]: Frequency of residue X in the sequence profile Nali: Number of aligned sequences in the mutated site ********************************************************************************************** ** ** ** 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. ** ** ** **********************************************************************************************