## 2 Snapshot of Bioinformatic Resources for Glycoslation
This is why glyco-informatics exists as multiple disconnected and incompatible islands of experimental data, data resources and specific applications, managed by various independent consortia, institutions and research groups [43,62] and no comprehensive database for carbohydrate structures exists. Such isolated islands of information make it difficult for the user to access and derive required information. The difficulty encountered by the users is that they have to visit different database portals to retrieve information on a specific carbohydrate structure and they need to acclimize themselves with the different local query options, which demand prior knowledge on encoding of the residues in that respective database. In 2005, a comprehensive index of all available structures was initiated by translating the freely available databases to the GlycoCT sequence format [61] and storing in a new database known as GlycomeDB [63]. A web interface was developed as a single query point for all carbohydrate structure databases [64]. A novel bioinformatics method called GlycoMinestruct has been developed. This improves the prediction of human N- and O-linked glycosylation sites by combining sequence and structural features in an integrated computational framework with a two-step feature-selection strategy [65]. _GlycoMinestruct_ is established to outperform NGlycPred, which is the only predictor that incorporated both sequence and structure features, achieving high AUC values (in the field of pharmacokinetics, the area under the curve (AUC) defines the variation of a drug concentration in blood plasma as a function of time) for N- and O-linked glycosylation. _GlycoMinestruct_ has been used to screen the human proteome and obtain high-confidence predictions for N- and O-linked glycosylation sites and expedited discovery of glycosylation events and substrates facilitating hypothesis-driven experimental studies. There are other glycosylation online predictors available too, which predict N-Glycosylation, O-glycosylation and C-mannosylation sites in the sequence of interest. For example NetNGlyc [66] used for N-Glycosylation site prediction, NetOGlyc [67], YinOYang [68] for O-Glycosylation sites prediction, GPP [69] for N-Glycosylation and O-Glycosylation sites prediction likewise NetCGlyc [70] for C-mannosylation sites prediction) and GlycoMine [71] used for N-, C- and O-linked glycosylation sites prediction. These servers are designed based on different machine learning algorithms like SVM (Support Vector Machine), ANN (Artificial Neural Networks) and RFA (Random Forest Algorithm). OGlycBase [72] is the database of O- and C-glycosated proteins, contains the experimentally verified O- and C-glycosated proteins. Whereas the tools like GPI-SOM [73], PredGPI [74] and BIG-P [75] are used for Glycoslphosphatidylinositol modification site (GPI-anchors) prediction. These tools are designed based on SVM and Hidden Markov Model (HMM) algorithms. GlycoFragment and Distance Mapping are also used for glycosylation 3D structure prediction [76]. Bioinformatic tools have also been able to enhance the prediction of glycosylation sites. Glycoslation is able to affect the function of the modified protein and so methods have been developed to predict the glycosylation sites based on the amino acid sequences. The statistical analysis of amino acids surrounding the glycosylation binding sites is dealt with by the being investigated by German Cancer Research Center. One of their tools GlySeq [77], statistically analyzes the amino acids surrounding the glycosylation sites based on protein sequences acquired from Swiss-Prot and Protein Data Bank (PDB), available on the GlySeqDB database. In addition to analyzing the surround sequence, a tool called GlyViciinity performs a statistical analysis of a PDB entry. At Johns Hopkins University, a model to mathematically formulate N-glycosylation was developed [78] whereby, given a set of expressed genes, the list of possible glycans synthesized by the input can be predicted.