********************************************************************************************** ** ** ** SNPs&GO ** ** Predicting disease associated variation using GO terms ** ** ** ********************************************************************************************** Sequence File: fileseq.seq Mutation Prediction RI Probability Method R782Q Neutral 5 0.239 PhD-SNP: F[R]=30% F[Q]=24% Nali=117 R782Q Neutral 8 0.110 PANTHER: F[R]=10% F[Q]=18% R782Q Neutral 9 0.038 SNPs&GO V830A Neutral 1 0.466 PhD-SNP: F[V]=38% F[A]=1% Nali=114 V830A Neutral 3 0.358 PANTHER: F[V]=29% F[A]=3% V830A Neutral 6 0.204 SNPs&GO E1021K Neutral 1 0.445 PhD-SNP: F[E]=22% F[K]=4% Nali=117 E1021K Neutral 6 0.179 PANTHER: F[E]=17% F[K]=5% E1021K Neutral 7 0.150 SNPs&GO E1491D Neutral 2 0.389 PhD-SNP: F[E]=44% F[D]=0% Nali=35 E1491D Neutral 7 0.153 PANTHER: F[E]=21% F[D]=6% E1491D Neutral 8 0.106 SNPs&GO L1516I Neutral 5 0.260 PhD-SNP: F[L]=55% F[I]=0% Nali=21 L1516I Neutral 8 0.098 PANTHER: F[L]=15% F[I]=8% L1516I Neutral 9 0.039 SNPs&GO R1561T Neutral 1 0.426 PhD-SNP: F[R]=59% F[T]=0% Nali=31 R1561T Unclassified NA NA PANTHER: F[R]=NA F[T]=NA R1561T Neutral 8 0.109 SNPs&GO K1562N Neutral 6 0.182 PhD-SNP: F[K]=32% F[N]=4% Nali=27 K1562N Unclassified NA NA PANTHER: F[K]=NA F[N]=NA K1562N Neutral 9 0.060 SNPs&GO K1565N Neutral 4 0.281 PhD-SNP: F[K]=50% F[N]=4% Nali=25 K1565N Unclassified NA NA PANTHER: F[K]=NA F[N]=NA K1565N Neutral 8 0.088 SNPs&GO E1566D Neutral 5 0.251 PhD-SNP: F[E]=46% F[D]=8% Nali=25 E1566D Unclassified NA NA PANTHER: F[E]=NA F[D]=NA E1566D Neutral 8 0.110 SNPs&GO E1566Q Neutral 7 0.162 PhD-SNP: F[E]=46% F[Q]=15% Nali=25 E1566Q Unclassified NA NA PANTHER: F[E]=NA F[Q]=NA E1566Q Neutral 9 0.041 SNPs&GO K1567N Neutral 1 0.446 PhD-SNP: F[K]=58% F[N]=0% Nali=25 K1567N Unclassified NA NA PANTHER: F[K]=NA F[N]=NA K1567N Neutral 7 0.153 SNPs&GO A1569P Disease 0 0.500 PhD-SNP: F[A]=32% F[P]=4% Nali=24 A1569P Unclassified NA NA PANTHER: F[A]=NA F[P]=NA A1569P Neutral 7 0.163 SNPs&GO A1611T Neutral 1 0.475 PhD-SNP: F[A]=37% F[T]=0% Nali=42 A1611T Neutral 8 0.117 PANTHER: F[A]=9% F[T]=4% A1611T Neutral 8 0.086 SNPs&GO V1641A Disease 6 0.809 PhD-SNP: F[V]=95% F[A]=0% Nali=41 V1641A Neutral 9 0.067 PANTHER: F[V]=9% F[A]=8% V1641A Neutral 4 0.281 SNPs&GO Y1653S Disease 8 0.894 PhD-SNP: F[Y]=98% F[S]=0% Nali=41 Y1653S Neutral 8 0.112 PANTHER: F[Y]=4% F[S]=11% Y1653S Neutral 2 0.388 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. ** ** ** **********************************************************************************************