**********************************************************************************************
**                                                                                          **
**                                         SNPs&GO                                          **
**                  Predicting disease associated variation using GO terms                  **
**                                                                                          **
**********************************************************************************************

      Sequence File: PRKCD_HUMAN.seq

      Mutation     Prediction   RI  Probability   Method
         V114G        Disease    4        0.700   PhD-SNP:  F[V]=38%  F[G]=0%   Nali=134
         V114G   Unclassified   NA           NA   PANTHER:  F[V]=NA   F[G]=NA
         V114G        Disease    2        0.623   SNPs&GO

         Y187C        Disease    8        0.883   PhD-SNP:  F[Y]=58%  F[C]=0%   Nali=214
         Y187C   Unclassified   NA           NA   PANTHER:  F[Y]=NA   F[C]=NA
         Y187C        Disease    8        0.885   SNPs&GO

         C189R        Disease    9        0.928   PhD-SNP:  F[C]=100% F[R]=0%   Nali=216
         C189R   Unclassified   NA           NA   PANTHER:  F[C]=NA   F[R]=NA
         C189R        Disease   10        0.981   SNPs&GO

         C189Y        Disease    9        0.955   PhD-SNP:  F[C]=100% F[Y]=0%   Nali=216
         C189Y   Unclassified   NA           NA   PANTHER:  F[C]=NA   F[Y]=NA
         C189Y        Disease   10        0.985   SNPs&GO

         D228Y        Disease    6        0.804   PhD-SNP:  F[D]=33%  F[Y]=0%   Nali=178
         D228Y   Unclassified   NA           NA   PANTHER:  F[D]=NA   F[Y]=NA
         D228Y        Disease    5        0.726   SNPs&GO

         G248D        Disease    6        0.799   PhD-SNP:  F[G]=84%  F[D]=0%   Nali=268
         G248D        Disease    3        0.645   PANTHER:  F[G]=47%  F[D]=3%  
         G248D        Disease    8        0.915   SNPs&GO

         G361R        Disease    8        0.905   PhD-SNP:  F[G]=89%  F[R]=0%   Nali=1007
         G361R        Disease    8        0.923   PANTHER:  F[G]=65%  F[R]=0%  
         G361R        Disease    9        0.950   SNPs&GO

         T511P        Disease    5        0.756   PhD-SNP:  F[T]=98%  F[P]=0%   Nali=1059
         T511P        Disease   10        0.998   PANTHER:  F[T]=98%  F[P]=0%  
         T511P        Disease    7        0.864   SNPs&GO

         F602S        Disease    8        0.886   PhD-SNP:  F[F]=81%  F[S]=0%   Nali=992
         F602S        Disease    8        0.922   PANTHER:  F[F]=73%  F[S]=0%  
         F602S        Disease    8        0.901   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

**********************************************************************************************
**                                                                                          **
**     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.   **
**                                                                                          **
**********************************************************************************************