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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
          R88Q        Disease    7        0.832   PhD-SNP:  F[R]=88%  F[Q]=3%   Nali=67
          R88Q        Disease    4        0.711   PANTHER:  F[R]=35%  F[Q]=1%  
          R88Q        Disease    1        0.574   SNPs&GO

         R140Q        Disease    8        0.917   PhD-SNP:  F[R]=99%  F[Q]=0%   Nali=77
         R140Q        Disease    2        0.609   PANTHER:  F[R]=37%  F[Q]=3%  
         R140Q        Disease    4        0.716   SNPs&GO

         L156V        Neutral    7        0.165   PhD-SNP:  F[L]=11%  F[V]=7%   Nali=71
         L156V        Neutral    6        0.186   PANTHER:  F[L]=5%   F[V]=4%  
         L156V        Neutral    9        0.036   SNPs&GO

         D350G        Disease    4        0.675   PhD-SNP:  F[D]=22%  F[G]=2%   Nali=120
         D350G        Neutral    1        0.425   PANTHER:  F[D]=9%   F[G]=2%  
         D350G        Neutral    2        0.383   SNPs&GO

         K440T        Neutral    4        0.280   PhD-SNP:  F[K]=14%  F[T]=9%   Nali=170
         K440T        Neutral    3        0.366   PANTHER:  F[K]=11%  F[T]=3%  
         K440T        Neutral    7        0.148   SNPs&GO

         E545A        Neutral    6        0.215   PhD-SNP:  F[E]=28%  F[A]=8%   Nali=272
         E545A        Neutral    4        0.298   PANTHER:  F[E]=29%  F[A]=13% 
         E545A        Neutral    8        0.084   SNPs&GO

         E545K        Neutral    3        0.343   PhD-SNP:  F[E]=28%  F[K]=5%   Nali=272
         E545K        Neutral    3        0.341   PANTHER:  F[E]=29%  F[K]=11% 
         E545K        Neutral    5        0.251   SNPs&GO

         W824C        Disease    8        0.905   PhD-SNP:  F[W]=49%  F[C]=0%   Nali=610
         W824C        Disease   10        0.996   PANTHER:  F[W]=97%  F[C]=0%  
         W824C        Disease    7        0.872   SNPs&GO

         Q825L        Disease    4        0.704   PhD-SNP:  F[Q]=22%  F[L]=12%  Nali=610
         Q825L        Disease    3        0.649   PANTHER:  F[Q]=13%  F[L]=1%  
         Q825L        Neutral    0        0.476   SNPs&GO

         Q827L        Disease    5        0.727   PhD-SNP:  F[Q]=4%   F[L]=0%   Nali=609
         Q827L        Disease    2        0.579   PANTHER:  F[Q]=14%  F[L]=1%  
         Q827L        Disease    1        0.529   SNPs&GO

        N1044K        Disease    0        0.504   PhD-SNP:  F[N]=21%  F[K]=7%   Nali=433
        N1044K        Neutral    1        0.469   PANTHER:  F[N]=20%  F[K]=4%  
        N1044K        Neutral    0        0.490   SNPs&GO

        H1047L        Disease    0        0.514   PhD-SNP:  F[H]=5%   F[L]=24%  Nali=371
        H1047L        Disease    0        0.501   PANTHER:  F[H]=6%   F[L]=33% 
        H1047L        Neutral    5        0.267   SNPs&GO

        H1047R        Disease    4        0.701   PhD-SNP:  F[H]=5%   F[R]=3%   Nali=371
        H1047R        Neutral    4        0.296   PANTHER:  F[H]=6%   F[R]=12% 
        H1047R        Neutral    1        0.472   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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