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

      Mutation     Prediction   RI  Probability   Method
          R63H        Neutral    1        0.449   PhD-SNP:  F[R]=17%  F[H]=1%   Nali=885
          R63H        Neutral    0        0.488   PANTHER:  F[R]=41%  F[H]=2%  
          R63H        Neutral    3        0.368   SNPs&GO

         R146W        Disease    6        0.810   PhD-SNP:  F[R]=38%  F[W]=4%   Nali=944
         R146W        Disease    7        0.841   PANTHER:  F[R]=45%  F[W]=0%  
         R146W        Disease    3        0.668   SNPs&GO

         R186L        Disease    0        0.522   PhD-SNP:  F[R]=2%   F[L]=0%   Nali=972
         R186L        Neutral    4        0.319   PANTHER:  F[R]=25%  F[L]=2%  
         R186L        Neutral    4        0.296   SNPs&GO

         I289M        Disease    1        0.526   PhD-SNP:  F[I]=13%  F[M]=4%   Nali=965
         I289M        Neutral    1        0.461   PANTHER:  F[I]=47%  F[M]=2%  
         I289M        Neutral    1        0.453   SNPs&GO

         A299V        Disease    5        0.747   PhD-SNP:  F[A]=90%  F[V]=0%   Nali=979
         A299V        Neutral    0        0.488   PANTHER:  F[A]=62%  F[V]=3%  
         A299V        Neutral    1        0.454   SNPs&GO

         E322K        Disease    1        0.572   PhD-SNP:  F[E]=6%   F[K]=1%   Nali=981
         E322K        Neutral    1        0.470   PANTHER:  F[E]=62%  F[K]=3%  
         E322K        Disease    3        0.663   SNPs&GO

         R337Q        Disease    2        0.605   PhD-SNP:  F[R]=72%  F[Q]=9%   Nali=939
         R337Q        Disease    1        0.529   PANTHER:  F[R]=65%  F[Q]=2%  
         R337Q        Disease    0        0.522   SNPs&GO

         M350I        Disease    2        0.612   PhD-SNP:  F[M]=8%   F[I]=2%   Nali=983
         M350I        Neutral    6        0.178   PANTHER:  F[M]=32%  F[I]=8%  
         M350I        Neutral    4        0.289   SNPs&GO

         A352S        Disease    6        0.810   PhD-SNP:  F[A]=97%  F[S]=2%   Nali=984
         A352S        Disease    4        0.725   PANTHER:  F[A]=82%  F[S]=3%  
         A352S        Disease    3        0.661   SNPs&GO

         R374Q        Neutral    4        0.319   PhD-SNP:  F[R]=25%  F[Q]=11%  Nali=981
         R374Q        Neutral    8        0.084   PANTHER:  F[R]=28%  F[Q]=20% 
         R374Q        Neutral    9        0.052   SNPs&GO

         G439V        Disease    8        0.902   PhD-SNP:  F[G]=100% F[V]=0%   Nali=947
         G439V        Disease   10        0.987   PANTHER:  F[G]=96%  F[V]=0%  
         G439V        Disease    7        0.836   SNPs&GO

         D470N        Disease    1        0.530   PhD-SNP:  F[D]=55%  F[N]=7%   Nali=739
         D470N   Unclassified   NA           NA   PANTHER:  F[D]=NA   F[N]=NA
         D470N        Neutral    6        0.186   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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