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Sequence Submission

Sequence Name:-This is fully users ninformation only,users can write only letters or numbers. All other charecters are not allowed.

Input Sequence:-Our server provides two options for submitting thew query sequences. The first option user can paste their sequence in the given inbox. The other option user can upload theis sequence files.

Sequence Format:-The server can accept both the formatted or unformatted raw antigenic sequences.The server uses ReadSeq routine to parse the input.The user should choose whether the sequence uploaded or pasted is plain or formatted before running prediction.The results of the prediction will be wrong if the format choosen is wrong.

Prediction Approach:-

The method allow the prediction on the basis of two different appraoches.

  • Composition of amino acids:-

    The amino acid composition provided the information of protein in 20 dimensions vector. The amino acid composition is the fraction of each amino acid in protein.The composition based SVM module was able to predict with overall accuracy of 81.33%.

  • Dipeptide composition:-

    The dipeptide composition provided the information of protein in the form of a vector of 400 dimensions. The dipeptide composition encapsulates the information about fraction of amino acids as well as their local order of amino acid. The overall dipeptide composition prediction accuracy was 80.81%.

Prediction Results:-

The prediction results are presented in very user friendly format. The results are consist of mainly two parts.

 

  • Summary of query sequence:-

    This part provides the information about the submitted sequence like the sequence, length of sequence and date of scanning. This part also provides the information about the choosen prediction approach.

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    Prediction Result

    Table 1. The detailed results obtained from oxy with non-oxy protein using different SVM-based modules


    Table 2. The detailed aminoacid composition results obtained different classes using SVM-based modules



    Table 3. The detailed dipeptide composition results obtained using different SVM-based modules



    Table 4. Confisional Matrix prediction of all classes