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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
          S17N        Neutral    1        0.457   PhD-SNP:  F[S]=100% F[N]=0%   Nali=5
          S17N   Unclassified   NA           NA   PANTHER:  F[S]=NA   F[N]=NA
          S17N        Neutral    8        0.078   SNPs&GO

          G88E        Neutral    1        0.471   PhD-SNP:  F[G]=45%  F[E]=9%   Nali=10
          G88E   Unclassified   NA           NA   PANTHER:  F[G]=NA   F[E]=NA
          G88E        Neutral    7        0.136   SNPs&GO

         E243K        Neutral    2        0.388   PhD-SNP:  F[E]=21%  F[K]=6%   Nali=355
         E243K        Neutral    4        0.323   PANTHER:  F[E]=27%  F[K]=6%  
         E243K        Neutral    5        0.253   SNPs&GO

         I366V        Neutral    6        0.182   PhD-SNP:  F[I]=28%  F[V]=13%  Nali=317
         I366V        Neutral    6        0.188   PANTHER:  F[I]=32%  F[V]=18% 
         I366V        Neutral    9        0.051   SNPs&GO

         R440C        Disease    2        0.585   PhD-SNP:  F[R]=25%  F[C]=1%   Nali=218
         R440C        Disease    8        0.891   PANTHER:  F[R]=29%  F[C]=0%  
         R440C        Neutral    1        0.473   SNPs&GO

         Q530R        Neutral    3        0.359   PhD-SNP:  F[Q]=41%  F[R]=3%   Nali=33
         Q530R   Unclassified   NA           NA   PANTHER:  F[Q]=NA   F[R]=NA
         Q530R        Neutral    7        0.147   SNPs&GO

         A891V        Disease    0        0.503   PhD-SNP:  F[A]=45%  F[V]=1%   Nali=200
         A891V        Disease    3        0.661   PANTHER:  F[A]=43%  F[V]=2%  
         A891V        Neutral    1        0.461   SNPs&GO

        Y1018C        Disease    7        0.860   PhD-SNP:  F[Y]=96%  F[C]=0%   Nali=236
        Y1018C        Disease    2        0.596   PANTHER:  F[Y]=28%  F[C]=1%  
        Y1018C        Neutral    0        0.480   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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