********************************************************************************************** ** ** ** SNPs&GO ** ** Predicting disease associated variation using GO terms ** ** ** ********************************************************************************************** Sequence File: SP.seq Mutation Prediction RI Probability Method E450G Neutral 2 0.384 PhD-SNP: F[E]=14% F[G]=1% Nali=994 E450G Disease 1 0.572 PANTHER: F[E]=64% F[G]=2% E450G Neutral 0 0.479 SNPs&GO V457I Neutral 7 0.165 PhD-SNP: F[V]=13% F[I]=15% Nali=995 V457I Neutral 5 0.238 PANTHER: F[V]=58% F[I]=11% V457I Neutral 8 0.114 SNPs&GO E458D Neutral 4 0.281 PhD-SNP: F[E]=7% F[D]=5% Nali=995 E458D Neutral 5 0.227 PANTHER: F[E]=42% F[D]=8% E458D Neutral 6 0.182 SNPs&GO V460I Neutral 6 0.202 PhD-SNP: F[V]=14% F[I]=3% Nali=995 V460I Neutral 8 0.124 PANTHER: F[V]=41% F[I]=19% V460I Neutral 9 0.048 SNPs&GO D467E Neutral 4 0.319 PhD-SNP: F[D]=20% F[E]=7% Nali=993 D467E Neutral 5 0.263 PANTHER: F[D]=49% F[E]=8% D467E Neutral 6 0.198 SNPs&GO R471L Disease 8 0.913 PhD-SNP: F[R]=100% F[L]=0% Nali=995 R471L Disease 8 0.890 PANTHER: F[R]=84% F[L]=0% R471L Disease 7 0.853 SNPs&GO R471Q Disease 7 0.872 PhD-SNP: F[R]=100% F[Q]=0% Nali=995 R471Q Disease 7 0.841 PANTHER: F[R]=84% F[Q]=1% R471Q Disease 6 0.796 SNPs&GO R471W Disease 8 0.912 PhD-SNP: F[R]=100% F[W]=0% Nali=995 R471W Disease 9 0.973 PANTHER: F[R]=84% F[W]=0% R471W Disease 7 0.827 SNPs&GO T472I Neutral 3 0.352 PhD-SNP: F[T]=12% F[I]=1% Nali=994 T472I Neutral 6 0.200 PANTHER: F[T]=10% F[I]=2% T472I Neutral 9 0.066 SNPs&GO I475F Disease 3 0.645 PhD-SNP: F[I]=20% F[F]=0% Nali=994 I475F Disease 0 0.524 PANTHER: F[I]=43% F[F]=2% I475F Disease 1 0.525 SNPs&GO I475V Neutral 4 0.324 PhD-SNP: F[I]=20% F[V]=3% Nali=994 I475V Neutral 8 0.101 PANTHER: F[I]=43% F[V]=26% I475V Neutral 9 0.031 SNPs&GO E476D Neutral 7 0.139 PhD-SNP: F[E]=13% F[D]=8% Nali=995 E476D Neutral 7 0.125 PANTHER: F[E]=25% F[D]=11% E476D Neutral 9 0.042 SNPs&GO A477V Neutral 4 0.304 PhD-SNP: F[A]=13% F[V]=0% Nali=995 A477V Neutral 6 0.215 PANTHER: F[A]=24% F[V]=5% A477V Neutral 8 0.087 SNPs&GO R479Q Disease 6 0.777 PhD-SNP: F[R]=43% F[Q]=0% Nali=994 R479Q Neutral 4 0.309 PANTHER: F[R]=40% F[Q]=5% R479Q Neutral 2 0.381 SNPs&GO R479W Disease 8 0.900 PhD-SNP: F[R]=43% F[W]=0% Nali=994 R479W Disease 7 0.838 PANTHER: F[R]=40% F[W]=0% R479W Disease 5 0.741 SNPs&GO E480G Neutral 3 0.360 PhD-SNP: F[E]=7% F[G]=1% Nali=995 E480G Neutral 6 0.217 PANTHER: F[E]=13% F[G]=3% E480G Neutral 9 0.071 SNPs&GO E480K Neutral 2 0.420 PhD-SNP: F[E]=7% F[K]=5% Nali=995 E480K Neutral 8 0.079 PANTHER: F[E]=13% F[K]=11% E480K Neutral 9 0.065 SNPs&GO G481A Disease 2 0.594 PhD-SNP: F[G]=85% F[A]=2% Nali=991 G481A Neutral 5 0.248 PANTHER: F[G]=29% F[A]=5% G481A Neutral 5 0.267 SNPs&GO G481S Disease 6 0.797 PhD-SNP: F[G]=85% F[S]=1% Nali=991 G481S Neutral 5 0.248 PANTHER: F[G]=29% F[S]=5% G481S Neutral 0 0.482 SNPs&GO T489A Disease 3 0.674 PhD-SNP: F[T]=97% F[A]=0% Nali=995 T489A Disease 10 0.989 PANTHER: F[T]=98% F[A]=0% T489A Disease 5 0.735 SNPs&GO V491F Disease 8 0.886 PhD-SNP: F[V]=82% F[F]=0% Nali=994 V491F Disease 2 0.617 PANTHER: F[V]=58% F[F]=1% V491F Disease 5 0.737 SNPs&GO V491I Neutral 5 0.263 PhD-SNP: F[V]=82% F[I]=15% Nali=994 V491I Neutral 5 0.238 PANTHER: F[V]=58% F[I]=11% V491I Neutral 8 0.121 SNPs&GO A492T Disease 6 0.823 PhD-SNP: F[A]=97% F[T]=0% Nali=994 A492T Neutral 1 0.461 PANTHER: F[A]=63% F[T]=3% A492T Disease 3 0.674 SNPs&GO F498L Neutral 0 0.500 PhD-SNP: F[F]=7% F[L]=1% Nali=994 F498L Neutral 3 0.374 PANTHER: F[F]=65% F[L]=6% F498L Disease 2 0.582 SNPs&GO P499L Disease 3 0.626 PhD-SNP: F[P]=35% F[L]=0% Nali=994 P499L Neutral 1 0.449 PANTHER: F[P]=38% F[L]=2% P499L Neutral 2 0.404 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. ** ** ** **********************************************************************************************