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

      Mutation     Prediction   RI  Probability   Method
          C61Y        Disease    8        0.880   PhD-SNP:  F[C]=98%  F[Y]=0%   Nali=52
          C61Y   Unclassified   NA           NA   PANTHER:  F[C]=NA   F[Y]=NA
          C61Y        Disease    2        0.604   SNPs&GO

          W99R        Disease    9        0.932   PhD-SNP:  F[W]=93%  F[R]=0%   Nali=55
          W99R        Neutral    8        0.124   PANTHER:  F[W]=16%  F[R]=7%  
          W99R        Disease    1        0.541   SNPs&GO

         L135P        Disease    4        0.705   PhD-SNP:  F[L]=22%  F[P]=0%   Nali=62
         L135P        Disease    6        0.791   PANTHER:  F[L]=64%  F[P]=0%  
         L135P        Disease    3        0.654   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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