Home    Standard Predictor Advanced Predictor Sequon Scanner Help      Downloads    Team    Contact  

OSDDlinux for Standalone, Galaxy & Local version
******* If you are using this server, please cite -> Chauhan JS, Rao A, Raghava GPS (2013) In silico Platform for Prediction of N-, O- and C-Glycosites in Eukaryotic Protein Sequences. PLoS ONE 8(6): e67008. ********

Standard Predictor: The standard predictor method is developed using unique glycosite patterns extracted from glycoprotein which have less
than 40% similarity. (i.e. sequence based redundancy reduction). The training datasets contains 2604 N-linked , 456 O-linked
and 48 C-linked glycosites. The datasets obtained from Swiss-Prot release June 2011.

SUBMIT your sequence(s) here

Sequence Name: [optional]    
E-mail Address: [optional]      


Type/paste protein sequence in FASTA format:       


                          Prediction of N-linked Glycosylation
                            Prediction based on Binary profile of patterns (BPP)
                            Prediction based on Composition profile of patterns (CPP)
                            Prediction based on PSSM profile of patterns (PPP)
                            Prediction based on Average Surface Accessibility (ASA+BPP)
                          Prediction of O-linked Glycosylation
                            Prediction based on Binary profile of patterns (BPP)
                            Prediction based on Composition profile of patterns (CPP)
                            Prediction based on PSSM profile of patterns (PPP)
                            Prediction based on Average Surface Accessibility (ASA+CPP)
                          Prediction of C-linked Glycosylation
                            Prediction based on Binary profile of patterns (BPP)
                            Prediction based on Composition profile of patterns (CPP)
                            Prediction based on PSSM profile of patterns (PPP)
                            Prediction based on Average Surface Accessibility (ASA+BPP)




            SVM threshold: