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

      Mutation     Prediction   RI  Probability   Method
          P66S        Disease    4        0.723   PhD-SNP:  F[P]=99%  F[S]=0%   Nali=74
          P66S   Unclassified   NA           NA   PANTHER:  F[P]=NA   F[S]=NA
          P66S        Neutral    0        0.494   SNPs&GO

         G200D        Disease    8        0.879   PhD-SNP:  F[G]=98%  F[D]=0%   Nali=213
         G200D   Unclassified   NA           NA   PANTHER:  F[G]=NA   F[D]=NA
         G200D        Disease    8        0.900   SNPs&GO

         G200V        Disease    8        0.901   PhD-SNP:  F[G]=98%  F[V]=0%   Nali=213
         G200V   Unclassified   NA           NA   PANTHER:  F[G]=NA   F[V]=NA
         G200V        Disease    8        0.913   SNPs&GO

         C262S        Disease    8        0.882   PhD-SNP:  F[C]=97%  F[S]=0%   Nali=272
         C262S        Disease    8        0.923   PANTHER:  F[C]=92%  F[S]=0%  
         C262S        Disease    6        0.799   SNPs&GO

         G362R        Disease    9        0.936   PhD-SNP:  F[G]=100% F[R]=0%   Nali=1037
         G362R        Disease   10        0.998   PANTHER:  F[G]=99%  F[R]=0%  
         G362R        Disease    7        0.828   SNPs&GO

         G367R        Disease    8        0.904   PhD-SNP:  F[G]=87%  F[R]=0%   Nali=1039
         G367R        Disease    8        0.923   PANTHER:  F[G]=65%  F[R]=0%  
         G367R        Disease    6        0.816   SNPs&GO

         E403G        Disease    7        0.835   PhD-SNP:  F[E]=99%  F[G]=0%   Nali=1030
         E403G        Disease   10        0.999   PANTHER:  F[E]=99%  F[G]=0%  
         E403G        Disease    7        0.834   SNPs&GO

         L486P        Disease    7        0.869   PhD-SNP:  F[L]=84%  F[P]=0%   Nali=1067
         L486P        Disease    9        0.957   PANTHER:  F[L]=59%  F[P]=0%  
         L486P        Disease    5        0.769   SNPs&GO

         C501G        Disease    8        0.907   PhD-SNP:  F[C]=56%  F[G]=0%   Nali=1064
         C501G        Disease    6        0.780   PANTHER:  F[C]=71%  F[G]=2%  
         C501G        Disease    8        0.879   SNPs&GO

         R596C        Disease    7        0.849   PhD-SNP:  F[R]=100% F[C]=0%   Nali=1017
         R596C        Disease   10        1.000   PANTHER:  F[R]=99%  F[C]=0%  
         R596C        Disease    5        0.765   SNPs&GO

         R596H        Disease    6        0.783   PhD-SNP:  F[R]=100% F[H]=0%   Nali=1017
         R596H        Disease   10        1.000   PANTHER:  F[R]=99%  F[H]=0%  
         R596H        Disease    4        0.703   SNPs&GO

         F614C        Disease    8        0.879   PhD-SNP:  F[F]=95%  F[C]=0%   Nali=985
         F614C        Disease   10        0.986   PANTHER:  F[F]=91%  F[C]=0%  
         F614C        Disease    6        0.784   SNPs&GO

         F644S        Disease    8        0.875   PhD-SNP:  F[F]=94%  F[S]=0%   Nali=955
         F644S        Disease    9        0.970   PANTHER:  F[F]=91%  F[S]=0%  
         F644S        Disease    6        0.815   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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