********************************************************************************************** ** ** ** SNPs&GO ** ** Predicting disease associated variation using GO terms ** ** ** ********************************************************************************************** Sequence File: fileseq.seq Mutation Prediction RI Probability Method K34R Neutral 9 0.057 PhD-SNP: F[K]=94% F[R]=0% Nali=17 K34R Unclassified NA NA PANTHER: F[K]=NA F[R]=NA K34R Neutral 10 0.016 SNPs&GO E303D Neutral 2 0.385 PhD-SNP: F[E]=81% F[D]=0% Nali=15 E303D Unclassified NA NA PANTHER: F[E]=NA F[D]=NA E303D Neutral 5 0.237 SNPs&GO T345N Neutral 3 0.333 PhD-SNP: F[T]=73% F[N]=0% Nali=14 T345N Unclassified NA NA PANTHER: F[T]=NA F[N]=NA T345N Neutral 8 0.108 SNPs&GO K599T Disease 1 0.548 PhD-SNP: F[K]=100% F[T]=0% Nali=14 K599T Unclassified NA NA PANTHER: F[K]=NA F[T]=NA K599T Neutral 6 0.204 SNPs&GO Q725H Neutral 1 0.429 PhD-SNP: F[Q]=100% F[H]=0% Nali=13 Q725H Unclassified NA NA PANTHER: F[Q]=NA F[H]=NA Q725H Neutral 8 0.121 SNPs&GO R847S Neutral 0 0.496 PhD-SNP: F[R]=92% F[S]=0% Nali=11 R847S Unclassified NA NA PANTHER: F[R]=NA F[S]=NA R847S Neutral 6 0.212 SNPs&GO N1002H Disease 4 0.678 PhD-SNP: F[N]=78% F[H]=0% Nali=8 N1002H Unclassified NA NA PANTHER: F[N]=NA F[H]=NA N1002H Neutral 7 0.156 SNPs&GO E1228D Neutral 4 0.291 PhD-SNP: F[E]=88% F[D]=0% Nali=7 E1228D Unclassified NA NA PANTHER: F[E]=NA F[D]=NA E1228D Neutral 8 0.082 SNPs&GO D1232N Neutral 7 0.173 PhD-SNP: F[D]=33% F[N]=0% Nali=8 D1232N Unclassified NA NA PANTHER: F[D]=NA F[N]=NA D1232N Neutral 9 0.032 SNPs&GO L1346P Neutral 4 0.301 PhD-SNP: F[L]=78% F[P]=11% Nali=8 L1346P Unclassified NA NA PANTHER: F[L]=NA F[P]=NA L1346P Neutral 8 0.084 SNPs&GO R1751H Neutral 3 0.343 PhD-SNP: F[R]=44% F[H]=4% Nali=24 R1751H Unclassified NA NA PANTHER: F[R]=NA F[H]=NA R1751H Neutral 8 0.110 SNPs&GO K2067R Neutral 7 0.144 PhD-SNP: F[K]=43% F[R]=3% Nali=34 K2067R Unclassified NA NA PANTHER: F[K]=NA F[R]=NA K2067R Neutral 9 0.043 SNPs&GO T2427A Neutral 7 0.175 PhD-SNP: F[T]=64% F[A]=0% Nali=13 T2427A Neutral 4 0.296 PANTHER: F[T]=21% F[A]=5% T2427A Neutral 9 0.053 SNPs&GO E2633K Neutral 4 0.287 PhD-SNP: F[E]=80% F[K]=0% Nali=9 E2633K Neutral 4 0.303 PANTHER: F[E]=18% F[K]=4% E2633K Neutral 8 0.113 SNPs&GO S2728Y Neutral 7 0.174 PhD-SNP: F[S]=33% F[Y]=0% Nali=14 S2728Y Disease 5 0.733 PANTHER: F[S]=32% F[Y]=0% S2728Y Neutral 7 0.142 SNPs&GO V2736G Neutral 2 0.405 PhD-SNP: F[V]=53% F[G]=0% Nali=14 V2736G Disease 6 0.820 PANTHER: F[V]=39% F[G]=0% V2736G Neutral 4 0.298 SNPs&GO S2892Y Disease 2 0.576 PhD-SNP: F[S]=83% F[Y]=0% Nali=17 S2892Y Disease 7 0.840 PANTHER: F[S]=41% F[Y]=0% S2892Y Neutral 1 0.453 SNPs&GO R3046W Neutral 2 0.394 PhD-SNP: F[R]=21% F[W]=0% Nali=27 R3046W Disease 6 0.810 PANTHER: F[R]=16% F[W]=0% R3046W Neutral 5 0.264 SNPs&GO Q3299R Neutral 5 0.258 PhD-SNP: F[Q]=86% F[R]=4% Nali=27 Q3299R Neutral 1 0.443 PANTHER: F[Q]=30% F[R]=3% Q3299R Neutral 8 0.079 SNPs&GO P3340S Neutral 9 0.053 PhD-SNP: F[P]=54% F[S]=8% Nali=23 P3340S Neutral 0 0.499 PANTHER: F[P]=31% F[S]=2% P3340S Neutral 9 0.048 SNPs&GO G3507S Neutral 2 0.403 PhD-SNP: F[G]=48% F[S]=0% Nali=20 G3507S Neutral 6 0.205 PANTHER: F[G]=20% F[S]=4% G3507S Neutral 7 0.134 SNPs&GO S3665Y Neutral 2 0.417 PhD-SNP: F[S]=55% F[Y]=0% Nali=37 S3665Y Disease 1 0.552 PANTHER: F[S]=14% F[Y]=1% S3665Y Neutral 8 0.124 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. ** ** ** **********************************************************************************************