## 2 Snapshot of Bioinformatic Resources for Glycoslation  
Bioinformatic resources have become popular in glycan research for automation of analytical outputs and for high throughput processing of data. The analysis of glycans is challenging, this is why glycomics is lagging behind genomic and proteomic research. The robotic N-glycan release and labelling combined with UPLC/HPLC analytical platforms with other bioinformatics tools have enabled high-throughput glycop profiling. High resolution NMR spectroscopy and mass spectrometry have been reported for glycop profiling [48]. The intervention of bioinformatics resources in glycan research has impacted the successful characterization of biological samples and resulted in progress in biomarker discovery, and genome-wide association studies. Automation technology is a valuable asset when it comes to analysis, when combined with bioinformatics several orders of magnitude higher high throughput processing has been reported [43]. Figure 3 presents the workflow of glycosylation, beginning from wet lab analysis to analytical instrumentation to computation (bioinformatics). Bioinformatics offers a variety of databases to the glycosentist [30; 42; 49; 50; 51]. Glycan informatics has provided varied methods for glycosylation analysis, glycomics and glycan biomarker prediction. Tools are there for prediction of glycosylation binding sites, for statistical analysis of amino acids of a glycoprotein and mathematical modeling of glycosylation. Tools for annotation of mass spectra and for glycan structure analysis and Glyco-Gene Expression Analysis are available for users. Bioinformatics resources for glycoresearch can be grouped as: (i) databases containing information on proteins, (ii) databases storing information on enzymes and pathways building glycans, and (iii) carbohydrate structural databases [42; 43]. Not many databases provide information on glycoforms of glycoproteins. The complex carbohydrate structure database (CCSD)/CarbBank was developed and maintained by the Complex Carbohydrate Research Center of the University of Georgia (USA) [52; 53]. It is a reservoir of carbohydrate structures gathered through manual retrospective extraction from existing literature and all the publications in which complex carbohydrate structures were cataloged. Owing to lack of funding, the database has been no longer updated. However, holding 50,000 records from 14,000 publications and 23,500 different carbohydrate sequences, it remains as the largest carbohydrate-related repository. Offshoots of CCSD data have resulted in open access databases, such as GLYCOSCIENCES.de [54], KEGG Glycan database [55], CFG Glycan Database [56], Bacterial Carbohydrate Structure Database (BCSDB) [57] and GlycoBase (currently inactive replaced by GlyStore) [58]. The Japan Consortium for Glycobiology and Glycotechnology DataBase (JCGGDB),  
Figure 3: Work-flow of glycosylation research, beginning from wet lab analysis—instrumentational analysis culmination in bioinformatics based computation (data analysis). which assembles CabosDB, Galaxy, LipidBank, GlycoEpitope, LfDB, and Structural Glycomics Calculation (SGCAL) (1490 carbohydrate structures) and GlycoSuiteDB (3300 unique carbohydrate structures) [59] are freely accessible as well. The EUROCarbDB project aims at developing a new infrastructure, where scientists themselves can upload carbohydrate structure-related data. A prototype that can store carbohydrate structures, has been developed. Primary experimental data (MS, HPLC, and NMR) that serves as reference data for the carbohydrate structure in question can also be uploaded. Mutual exchange between carbohydrate databases is reported to be a challenging task [60], since various databases use different sequence formats to encode carbohydrate structures [61].