********************************************************************************************** ** ** ** SNPs&GO ** ** Predicting disease associated variation using GO terms ** ** ** ********************************************************************************************** Sequence File: SP.seq Mutation Prediction RI Probability Method G10S Neutral 4 0.279 PhD-SNP: F[G]=61% F[S]=0% Nali=17 G10S Unclassified NA NA PANTHER: F[G]=NA F[S]=NA G10S Neutral 9 0.056 SNPs&GO R127H Disease 4 0.681 PhD-SNP: F[R]=65% F[H]=0% Nali=191 R127H Unclassified NA NA PANTHER: F[R]=NA F[H]=NA R127H Neutral 5 0.237 SNPs&GO M129I Neutral 3 0.336 PhD-SNP: F[M]=20% F[I]=7% Nali=193 M129I Unclassified NA NA PANTHER: F[M]=NA F[I]=NA M129I Neutral 8 0.101 SNPs&GO L138P Disease 0 0.511 PhD-SNP: F[L]=32% F[P]=27% Nali=193 L138P Unclassified NA NA PANTHER: F[L]=NA F[P]=NA L138P Neutral 7 0.135 SNPs&GO F148L Disease 2 0.619 PhD-SNP: F[F]=35% F[L]=5% Nali=189 F148L Unclassified NA NA PANTHER: F[F]=NA F[L]=NA F148L Neutral 5 0.244 SNPs&GO L181I Neutral 0 0.496 PhD-SNP: F[L]=76% F[I]=2% Nali=192 L181I Unclassified NA NA PANTHER: F[L]=NA F[I]=NA L181I Neutral 5 0.226 SNPs&GO P291T Neutral 7 0.166 PhD-SNP: F[P]=37% F[T]=2% Nali=179 P291T Unclassified NA NA PANTHER: F[P]=NA F[T]=NA P291T Neutral 9 0.043 SNPs&GO A293T Neutral 8 0.107 PhD-SNP: F[A]=8% F[T]=8% Nali=171 A293T Unclassified NA NA PANTHER: F[A]=NA F[T]=NA A293T Neutral 9 0.052 SNPs&GO R296G Neutral 3 0.345 PhD-SNP: F[R]=16% F[G]=2% Nali=179 R296G Unclassified NA NA PANTHER: F[R]=NA F[G]=NA R296G Neutral 6 0.197 SNPs&GO G297D Disease 1 0.566 PhD-SNP: F[G]=37% F[D]=8% Nali=178 G297D Unclassified NA NA PANTHER: F[G]=NA F[D]=NA G297D Neutral 6 0.222 SNPs&GO T333I Disease 0 0.501 PhD-SNP: F[T]=38% F[I]=5% Nali=181 T333I Unclassified NA NA PANTHER: F[T]=NA F[I]=NA T333I Neutral 5 0.228 SNPs&GO Y334N Disease 2 0.619 PhD-SNP: F[Y]=47% F[N]=9% Nali=184 Y334N Unclassified NA NA PANTHER: F[Y]=NA F[N]=NA Y334N Neutral 5 0.236 SNPs&GO L364S Neutral 7 0.174 PhD-SNP: F[L]=34% F[S]=29% Nali=172 L364S Unclassified NA NA PANTHER: F[L]=NA F[S]=NA L364S Neutral 9 0.031 SNPs&GO T371P Disease 7 0.863 PhD-SNP: F[T]=36% F[P]=2% Nali=176 T371P Unclassified NA NA PANTHER: F[T]=NA F[P]=NA T371P Neutral 1 0.432 SNPs&GO I377T Neutral 2 0.377 PhD-SNP: F[I]=34% F[T]=10% Nali=173 I377T Unclassified NA NA PANTHER: F[I]=NA F[T]=NA I377T Neutral 8 0.099 SNPs&GO S379P Disease 5 0.731 PhD-SNP: F[S]=24% F[P]=0% Nali=164 S379P Unclassified NA NA PANTHER: F[S]=NA F[P]=NA S379P Neutral 2 0.388 SNPs&GO R511T Neutral 5 0.260 PhD-SNP: F[R]=32% F[T]=3% Nali=144 R511T Unclassified NA NA PANTHER: F[R]=NA F[T]=NA R511T Neutral 8 0.089 SNPs&GO R523K Neutral 5 0.228 PhD-SNP: F[R]=69% F[K]=6% Nali=111 R523K Unclassified NA NA PANTHER: F[R]=NA F[K]=NA R523K Neutral 9 0.056 SNPs&GO D524N Neutral 9 0.057 PhD-SNP: F[D]=19% F[N]=5% Nali=92 D524N Unclassified NA NA PANTHER: F[D]=NA F[N]=NA D524N Neutral 10 0.016 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. ** ** ** **********************************************************************************************