SNPs&GO
 
cornerL
home
methods
benchmark
contact
cornerR
 
 

SNPs&GO
Predicting disease associated variations using GO terms


Required Inputs
SNPs&GO and SNPs&GO3d have been optimized to predict if a given single point protein variation can be classified as disease associated or neutral. SNPs&GO requires in input:
  • Protein Sequence: Protein Sequence: sequence-based SNPs&GO server requires the protein sequence that can be provided in three different ways: 1) raw format, 2) Swiss-Prot code; 3) uploading a text file containing the protein sequence. The server first checks the Protein Sequence box, if no sequence is provided it then checks if a text file has been uploaded. If both options have not been selected the server expects to receive in input the SwissProt code of the protein.

  • GO terms: although this input is optional functional information significantly increases the accuracy of the method. If GO terms are not provided the expected accuracy of the method is comparable with PhD-SNP. GO terms used for the prediction are automatically retrieved only if the input sequence is a Swiss-Prot code. If not, user has to manually provide the GO terms and the server will automatically find all their parents

  • Mutation: the server requires a list of comma separated mutations in the XPOSY format where X and Y are the wild-type and mutant residues respectively, and POS is the number of the mutated position in the sequence.

  • All Methods:with this option the server will performs predictions using PhD-SNP and SNPs&GO methods. If not checked only the prediction from SNPs&GO will be returned. If not checked only prediction from SNPs&GO will be returned;

The structure based algorithm SNPs&GO3d takes in input:
  • Protein Structure: the protein structure can be provided using the PDB code or uploading your own PDB file.

  • GO terms: although this input is optional functional information significantly increase the accuracy of the method. If GO terms are not provided the expected accuracy of the method is comparable with S3D-PROF. GO terms used for the prediction are automatically retrieved only if the input sequence is a Swiss-Prot code. If not, user has to manually provide the GO terms and the server will automatically find all their parents.

  • Mutation:the server requires a list of comma separated mutations in the XPOSY format where X and Y are the wild-type and mutant residues respectively, and POS is the number of the mutated position in the sequence.

  • All Methods: with this option the server will performs predictions using S3D-PROF and both SNPs&GO and SNPs&GO3d methods. If not checked only prediction from SNPs&GO3d will be returned.
The results of both algorithms can be sent to your e-mail address filling the appropriate box or displayed interactively if you do not enter any e-mail address.


Output
The output consists of a table listing the number of the mutated position in the protein sequence, the wild-type residue, the new residue and if the related mutation is predicted as disease-related (Disease or as neutral polymorphism Neutral). The RI value (Reliability Index) is evaluated from the output of the support vector machine O as

RI=20*abs(O-0.5).

More details about the output of the server are reported below.

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
    S3D-PROF: SVM input is the structure and profile at the mutated position
    SNPs&GO: SVM input is all the input in PhD-SNP, PANTHER and GO terms features
    S3Ds&GO: SVM input is all the input in S3D-PROF, PANTHER and GO terms features

F[X]: Frequency of residue X in the sequence profile
Nali: Number of aligned sequences in the mutated site


 
 
cornerL
cornerR