********************************************************************************************** ** ** ** SNPs&GO ** ** Predicting disease associated variation using GO terms ** ** ** ********************************************************************************************** Sequence File: fileseq.seq Mutation Prediction RI Probability Method A31V Neutral 7 0.145 PhD-SNP: F[A]=44% F[V]=0% Nali=71 A31V Unclassified NA NA PANTHER: F[A]=NA F[V]=NA A31V Neutral 9 0.039 SNPs&GO K55N Neutral 3 0.359 PhD-SNP: F[K]=56% F[N]=1% Nali=222 K55N Unclassified NA NA PANTHER: F[K]=NA F[N]=NA K55N Neutral 9 0.057 SNPs&GO V69E Disease 5 0.763 PhD-SNP: F[V]=35% F[E]=7% Nali=285 V69E Neutral 1 0.455 PANTHER: F[V]=39% F[E]=1% V69E Disease 1 0.558 SNPs&GO T169A Neutral 8 0.107 PhD-SNP: F[T]=17% F[A]=6% Nali=173 T169A Neutral 5 0.242 PANTHER: F[T]=51% F[A]=7% T169A Neutral 8 0.083 SNPs&GO R453C Disease 0 0.500 PhD-SNP: F[R]=34% F[C]=2% Nali=340 R453C Disease 6 0.784 PANTHER: F[R]=58% F[C]=0% R453C Neutral 3 0.367 SNPs&GO S703L Neutral 8 0.075 PhD-SNP: F[S]=59% F[L]=0% Nali=16 S703L Neutral 6 0.182 PANTHER: F[S]=35% F[L]=3% S703L Neutral 10 0.013 SNPs&GO S784T Neutral 7 0.132 PhD-SNP: F[S]=57% F[T]=0% Nali=6 S784T Unclassified NA NA PANTHER: F[S]=NA F[T]=NA S784T Neutral 10 0.014 SNPs&GO M785K Neutral 5 0.242 PhD-SNP: F[M]=38% F[K]=0% Nali=7 M785K Unclassified NA NA PANTHER: F[M]=NA F[K]=NA M785K Neutral 9 0.031 SNPs&GO Q787H Neutral 8 0.090 PhD-SNP: F[Q]=50% F[H]=0% Nali=7 Q787H Unclassified NA NA PANTHER: F[Q]=NA F[H]=NA Q787H Neutral 10 0.010 SNPs&GO Q1015P Neutral 7 0.171 PhD-SNP: F[Q]=57% F[P]=14% Nali=6 Q1015P Unclassified NA NA PANTHER: F[Q]=NA F[P]=NA Q1015P Neutral 10 0.016 SNPs&GO H1021P Neutral 5 0.252 PhD-SNP: F[H]=80% F[P]=0% Nali=4 H1021P Unclassified NA NA PANTHER: F[H]=NA F[P]=NA H1021P Neutral 9 0.026 SNPs&GO H1021Q Neutral 9 0.052 PhD-SNP: F[H]=80% F[Q]=20% Nali=4 H1021Q Unclassified NA NA PANTHER: F[H]=NA F[Q]=NA H1021Q Neutral 10 0.005 SNPs&GO G1025R Neutral 0 0.491 PhD-SNP: F[G]=80% F[R]=0% Nali=4 G1025R Unclassified NA NA PANTHER: F[G]=NA F[R]=NA G1025R Neutral 9 0.060 SNPs&GO Q1026H Neutral 7 0.166 PhD-SNP: F[Q]=88% F[H]=0% Nali=7 Q1026H Unclassified NA NA PANTHER: F[Q]=NA F[H]=NA Q1026H Neutral 10 0.012 SNPs&GO Q1027H Neutral 8 0.077 PhD-SNP: F[Q]=78% F[H]=11% Nali=8 Q1027H Unclassified NA NA PANTHER: F[Q]=NA F[H]=NA Q1027H Neutral 10 0.006 SNPs&GO K1035N Neutral 2 0.382 PhD-SNP: F[K]=67% F[N]=0% Nali=8 K1035N Unclassified NA NA PANTHER: F[K]=NA F[N]=NA K1035N Neutral 9 0.048 SNPs&GO E1113D Disease 3 0.670 PhD-SNP: F[E]=100% F[D]=0% Nali=9 E1113D Unclassified NA NA PANTHER: F[E]=NA F[D]=NA E1113D Neutral 5 0.263 SNPs&GO R1254W Disease 5 0.740 PhD-SNP: F[R]=63% F[W]=0% Nali=18 R1254W Unclassified NA NA PANTHER: F[R]=NA F[W]=NA R1254W Neutral 3 0.329 SNPs&GO R1256W Disease 3 0.654 PhD-SNP: F[R]=40% F[W]=0% Nali=19 R1256W Unclassified NA NA PANTHER: F[R]=NA F[W]=NA R1256W Neutral 5 0.248 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. ** ** ** **********************************************************************************************