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**                                                                                          **
**                                         SNPs&GO                                          **
**                  Predicting disease associated variation using GO terms                  **
**                                                                                          **
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      Sequence File: PRKCD_HUMAN.seq

      Mutation     Prediction   RI  Probability   Method
           R6H        Disease    2        0.581   PhD-SNP:  F[R]=100% F[H]=0%   Nali=35
           R6H   Unclassified   NA           NA   PANTHER:  F[R]=NA   F[H]=NA
           R6H        Disease    0        0.500   SNPs&GO

         Y187C        Disease    8        0.883   PhD-SNP:  F[Y]=58%  F[C]=0%   Nali=214
         Y187C   Unclassified   NA           NA   PANTHER:  F[Y]=NA   F[C]=NA
         Y187C        Disease    3        0.675   SNPs&GO

         C189R        Disease    9        0.928   PhD-SNP:  F[C]=100% F[R]=0%   Nali=216
         C189R   Unclassified   NA           NA   PANTHER:  F[C]=NA   F[R]=NA
         C189R        Disease    8        0.915   SNPs&GO

         C189Y        Disease    9        0.955   PhD-SNP:  F[C]=100% F[Y]=0%   Nali=216
         C189Y   Unclassified   NA           NA   PANTHER:  F[C]=NA   F[Y]=NA
         C189Y        Disease    9        0.925   SNPs&GO

         G281S        Disease    6        0.822   PhD-SNP:  F[G]=87%  F[S]=1%   Nali=278
         G281S        Disease    3        0.641   PANTHER:  F[G]=60%  F[S]=4%  
         G281S        Disease    4        0.722   SNPs&GO

         G361R        Disease    8        0.905   PhD-SNP:  F[G]=89%  F[R]=0%   Nali=1007
         G361R        Disease    8        0.923   PANTHER:  F[G]=65%  F[R]=0%  
         G361R        Disease    7        0.830   SNPs&GO

         V426E        Disease    8        0.895   PhD-SNP:  F[V]=78%  F[E]=0%   Nali=1054
         V426E        Disease    9        0.928   PANTHER:  F[V]=70%  F[E]=0%  
         V426E        Disease    5        0.770   SNPs&GO

         G432R        Disease    9        0.936   PhD-SNP:  F[G]=100% F[R]=0%   Nali=1058
         G432R        Disease   10        0.998   PANTHER:  F[G]=99%  F[R]=0%  
         G432R        Disease    6        0.784   SNPs&GO

         G432W        Disease    8        0.922   PhD-SNP:  F[G]=100% F[W]=0%   Nali=1058
         G432W        Disease   10        0.999   PANTHER:  F[G]=99%  F[W]=0%  
         G432W        Disease    5        0.726   SNPs&GO

         R449C        Disease    8        0.894   PhD-SNP:  F[R]=48%  F[C]=0%   Nali=1058
         R449C        Disease    8        0.909   PANTHER:  F[R]=33%  F[C]=0%  
         R449C        Disease    6        0.784   SNPs&GO

         R449P        Disease    9        0.933   PhD-SNP:  F[R]=48%  F[P]=0%   Nali=1058
         R449P        Disease    6        0.792   PANTHER:  F[R]=33%  F[P]=1%  
         R449P        Disease    8        0.877   SNPs&GO

         D491N        Disease    8        0.916   PhD-SNP:  F[D]=100% F[N]=0%   Nali=1061
         D491N        Disease   10        1.000   PANTHER:  F[D]=99%  F[N]=0%  
         D491N        Disease    7        0.859   SNPs&GO

         G510D        Disease    7        0.836   PhD-SNP:  F[G]=93%  F[D]=0%   Nali=1060
         G510D        Disease   10        1.000   PANTHER:  F[G]=99%  F[D]=0%  
         G510D        Disease    6        0.802   SNPs&GO

         G510S        Disease    6        0.791   PhD-SNP:  F[G]=93%  F[S]=0%   Nali=1060
         G510S        Disease   10        1.000   PANTHER:  F[G]=99%  F[S]=0%  
         G510S        Disease    6        0.786   SNPs&GO

         T511P        Disease    5        0.756   PhD-SNP:  F[T]=98%  F[P]=0%   Nali=1059
         T511P        Disease   10        0.998   PANTHER:  F[T]=98%  F[P]=0%  
         T511P        Disease    4        0.681   SNPs&GO

         F602S        Disease    8        0.886   PhD-SNP:  F[F]=81%  F[S]=0%   Nali=992
         F602S        Disease    8        0.922   PANTHER:  F[F]=73%  F[S]=0%  
         F602S        Disease    5        0.749   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

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**                                                                                          **
**     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.   **
**                                                                                          **
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