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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
         R121W        Disease    8        0.916   PhD-SNP:  F[R]=98%  F[W]=0%   Nali=1009
         R121W   Unclassified   NA           NA   PANTHER:  F[R]=NA   F[W]=NA
         R121W        Disease    7        0.849   SNPs&GO

         R225W        Disease    9        0.933   PhD-SNP:  F[R]=97%  F[W]=0%   Nali=1247
         R225W        Disease   10        0.977   PANTHER:  F[R]=86%  F[W]=0%  
         R225W        Disease    6        0.813   SNPs&GO

         A226D        Disease    9        0.927   PhD-SNP:  F[A]=23%  F[D]=0%   Nali=1247
         A226D        Disease    7        0.870   PANTHER:  F[A]=78%  F[D]=0%  
         A226D        Disease    9        0.930   SNPs&GO

         A226V        Disease    5        0.756   PhD-SNP:  F[A]=23%  F[V]=5%   Nali=1247
         A226V        Disease    6        0.786   PANTHER:  F[A]=78%  F[V]=1%  
         A226V        Disease    6        0.785   SNPs&GO

         D356N        Disease    7        0.862   PhD-SNP:  F[D]=98%  F[N]=1%   Nali=1026
         D356N        Disease    7        0.839   PANTHER:  F[D]=86%  F[N]=1%  
         D356N        Disease    5        0.775   SNPs&GO

         D356Y        Disease    9        0.938   PhD-SNP:  F[D]=98%  F[Y]=0%   Nali=1026
         D356Y        Disease    9        0.961   PANTHER:  F[D]=86%  F[Y]=0%  
         D356Y        Disease    7        0.853   SNPs&GO

         L404Q        Disease    5        0.774   PhD-SNP:  F[L]=33%  F[Q]=0%   Nali=1002
         L404Q        Disease    5        0.761   PANTHER:  F[L]=65%  F[Q]=0%  
         L404Q        Disease    4        0.684   SNPs&GO

        V1201M        Disease    3        0.670   PhD-SNP:  F[V]=61%  F[M]=0%   Nali=1383
        V1201M        Disease    6        0.793   PANTHER:  F[V]=70%  F[M]=1%  
        V1201M        Disease    1        0.546   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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