Using Databases and Web Resources for Glycomics Research
2013; Elsevier BV; Volume: 12; Issue: 4 Linguagem: Inglês
10.1074/mcp.r112.026252
ISSN1535-9484
Autores Tópico(s)Genomics and Phylogenetic Studies
ResumoMany databases of carbohydrate structures and related information can be found on the World Wide Web. This review covers the major carbohydrate databases that have potential utility for glycoscientists and researchers entering the glycosciences. The first half provides a brief overview of carbohydrate databases and web resources (including a history of carbohydrate databases and carbohydrate notations used in these databases), and the second half provides a guide that can be used as an index to determine which resources provide the data of most interest to the user. Many databases of carbohydrate structures and related information can be found on the World Wide Web. This review covers the major carbohydrate databases that have potential utility for glycoscientists and researchers entering the glycosciences. The first half provides a brief overview of carbohydrate databases and web resources (including a history of carbohydrate databases and carbohydrate notations used in these databases), and the second half provides a guide that can be used as an index to determine which resources provide the data of most interest to the user. It was not until the 1990s, when CarbBank was developed, that a carbohydrate structure database was made available to the public. When the CarbBank project ended, many researchers found the need to somehow continue the development of carbohydrate-related databases, and so GLYCOSCIENCES.de, KEGG GLYCAN, and the Consortium for Functional Glycomics databases emerged. Since the development of these databases, many more carbohydrate-related web resources have been developed, to the point where it is becoming difficult to keep track of them all. Moreover, the use of different carbohydrate structure notations to represent the data in each database makes it difficult for users to utilize them all effectively. Therefore, in addition to a description of various carbohydrate-related databases and web resources, we also present briefly in this review each carbohydrate structure representation, along with suggestions of tools that can be used to convert one to another. In the main section of this review, we summarize many of the well-known carbohydrate-related databases and web resources that are now publicly available. Because many of these resources have already been published in detail elsewhere, only a summary is presented here. The final section presents carbohydrate-related data from the researcher's perspective; that is, various categories of data are listed (e.g. three-dimensional structures, taxonomy, experimental data, etc.), and the pertinent databases and web resources are listed within each category such that they can be assessed and compared with one another. The very first carbohydrate database was called CCSD, for Complex Carbohydrate Structure Database (1Doubet S. Bock K. Smith D. Darvill A. Albersheim P. The Complex Carbohydrate Structure Database.Trends Biochem. Sci. 1989; 14: 475-477Abstract Full Text PDF PubMed Scopus (141) Google Scholar, 2Doubet S. Albersheim P. CarbBank.Glycobiology. 1992; 2: 505Crossref PubMed Scopus (117) Google Scholar). However, because of the tool used to access this database, called CarbBank, it is more commonly known as CarbBank. This database was initially developed by the Complex Carbohydrate Research Center at the University of Georgia. It was the first attempt to accumulate such data from the research community, and it thus became a valuable resource for current carbohydrate databases. Unfortunately, funding for CCSD ceased in 1996, but the data, consisting of almost 45,000 entries of glycan structures, annotation, and literature information, were still made available to the public. There has been some criticism of the quality of the data in CarbBank (3Egorova K.S. Toukach P.V. Critical analysis of CCSD data quality.J. Chem. Inf. Model. 2012; 52: 2812-2814Crossref PubMed Scopus (18) Google Scholar). This can be attributed to the fact that there was no curation of the accumulated data, which resulted in many inconsistencies in terms of duplicated carbohydrate structures and inconsistent notations to represent carbohydrates (or, more specifically, monosaccharides) in the database. However, the same can be said of GenBank (4Harris D.J. Can you bank on GenBank?.Trends Ecol. Evol. 2003; 18: 317-319Abstract Full Text Full Text PDF Scopus (169) Google Scholar), and users should be aware of the potential errors in all publicly available databases. As many glycan structure databases were developed in the absence of a standard notation for representing glycan structures, many database providers developed their own notations. A list of these formats and corresponding examples for representing the N-linked glycan core structure are given in Table I. Detailed explanations of these formats have been presented elsewhere (5Aoki-Kinoshita K.F. Glycome Informatics: Methods and Applications. CRC Press, Boca Raton, FL2003Google Scholar), but one can see from this table that there are a wide variety of carbohydrate structure formats now in use, each with its own advantages and disadvantages. Although many databases provide different ways to input glycan structures as queries using innovative user interfaces, not all databases return the results of these queries in multiple formats. That is, a query might be returned in a single format that will need to be converted to another format in order for it to be used as a query for another database, for example.Table IGlycan structure representation formats and examples of notations of the N-linked glycan core structureNameExampleCarbBankIUPACMan a1–3(Man a1–6) Man b1–4 GlcNAc b1–4 GlcNAc bLINUCS[][b-d-GlcpNAc]{[(4 + 1)][b-d-GlcpNAc]{[(4 + 1)][b-d-Manp]{[(3 + 1)][a-d-Manp]{}[(6 + 1)][a-d-Manp]{}}}}GlycoMinds LinearCodeMa3(Ma6)Mb4GNb4GNbKCFENTRYGlycanNODE51GlcNAc10.07.02GlcNAc3.07.03Man−4.07.04Man−11.012.05Man−11.02.0EDGE412:b11:423:b12:435:a13:344:a13:6///GlycoCTcondensedRES1b:b-dglc-HEX-1:52s:n-acetyl3b:b-dglc-HEX-1:54s:n-acetyl5b:b-dman-HEX-1:56b:a-dman-HEX-1:57b:a-dman-HEX-1:5LIN1:1d(2 + 1)2n2:1o(4 + 1)3d3:3d(2 + 1)4n4:3o(4 + 1)5d5:5o(3 + 1)6d6:5o(6 + 1)7dGlycoCTXML<?xml version = "1.0" encoding = "UTF-8"?> GLYDE-II<?xml version = "1.0" encoding = "UTF-8"?> Open table in a new tab Therefore, it has become necessary to be able to convert a glycan structure from one format to another. The RINGS resource (6Akune Y. Hosoda M. Kaiya S. Shinmachi D. Aoki-Kinoshita K.F. The RINGS resource for glycome informatics analysis and data mining on the Web.OMICS. 2010; 14: 475-486Crossref PubMed Scopus (44) Google Scholar) of data mining and analytical tools for glycomics analysis provides a set of web-based utilities for structure conversion between many of these formats. Also, the GlycanBuilder tool (7Damerell D. Ceroni A. Maass K. Ranzinger R. Dell A. Haslam S.M. The GlycanBuilder and GlycoWorkbench glycoinformatics tools: updates and new developments.Biol. Chem. 2012; 393: 1357-1362Crossref PubMed Scopus (112) Google Scholar, 8Ceroni A. Dell A. Haslam S.M. The GlycanBuilder: a fast, intuitive and flexible software tool for building and displaying glycan structures.Source Code Biol. Med. 2007; 2: 3Crossref PubMed Scopus (128) Google Scholar), developed as a part of the EuroCarbDB project (9von der Lieth C.W. Freire A.A. Blank D. Campbell M.P. Ceroni A. Damerell D.R. Dell A. Dwek R.A. Ernst B. Fogh R. Frank M. Geyer H. Geyer R. Harrison M.J. Henrick K. Herget S. Hull W.E. Ionides J. Joshi H.J. Kamerling J.P. Leeflang B.R. Lütteke T. Lundborg M. Maass K. Merry A. Ranzinger R. Rosen J. Royle L. Rudd P.M. Schloissnig S. Stenutz R. Vranken W.F. Widmalm G. Haslam S.M. EUROCarbDB: an open-access platform for glycoinformatics.Glycobiology. 2011; 21: 493-502Crossref PubMed Scopus (100) Google Scholar), provides a graphical user interface through which carbohydrate structures can be imported in one format and then exported in another. In the future, however, it is expected that databases will be able to mutually exchange or link their data to one another, such that such format conversions will be unnecessary. Moreover, glycan structure analysis tools should provide ways to input glycan structures in any of the available formats, such that users need not take heed of such specifics. GLYCOSCIENCES.de is one of the oldest web portals for glycomics research; it was originally developed at the German Cancer Research Center and now is maintained by Justus-Liebig University Giessen (10Lütteke T. Bohne-Lang A. Loss A. Goetz T. Frank M. von der Lieth C. GLYCOSCIENCES.de: an Internet portal to support glycomics and glycobiology research.Glycobiology. 2006; 16: 71R-81RCrossref PubMed Scopus (219) Google Scholar). It includes glycan structure references to CarbBank, automatically generated three-dimensional coordinates, 1H and 13C NMR spectra including 1H and 13C NMR-shift lists, masses of theoretically calculated glycan fragments, ligand data from the Protein Data Bank (PDB), 1The abbreviations used are:BCSDBBacterial Carbohydrate Structure Data BaseCFGConsortium for Functional GlycomicsGBPglycan-binding proteinJCGGDBJapan Consortium for Glycobiology and Glycotechnology DataBaseKEGGKyoto Encyclopedia of Genes and GenomesPDBProtein Data Bank. and three-dimensional conformational maps for glycosidic linkages. Recently, a Glyco-CD database has also been incorporated that consists of cluster-of-differentiation antigens to aid in the classification of various cell surface macromolecules. Bacterial Carbohydrate Structure Data Base Consortium for Functional Glycomics glycan-binding protein Japan Consortium for Glycobiology and Glycotechnology DataBase Kyoto Encyclopedia of Genes and Genomes Protein Data Bank. In addition to databases, GLYCOSCIENCES.de also provides a number of tools to aid in carbohydrate structure analysis. The available tools are categorized into three major groups: three-dimensional-structure-related tools, tools related to structure representation, and mass spectrometry tools. The three-dimensional-structure-related tools are further categorized into the following three groups: detection/validation of carbohydrates in PDB files, statistical analysis of carbohydrates in PDB, and modeling. Table II provides references and brief descriptions of each tool.Table IITable of tools available in GLYCOSCIENCES.deCategorySub-categoryTool nameDescriptionThree-dimensional-structure relatedDetection and validation of PDB filespdb-care (31Lütteke T. von der Lieth C.W. pdb-care (PDB carbohydrate residue check): a program to support annotation of complex carbohydrate structures in PDB files.BMC Bioinformatics. 2004; 5: 69Crossref PubMed Scopus (140) Google Scholar)Given a PDB ID or file containing carbohydrate information, this tool verifies the accuracy of the carbohydrate conformations, including bond length, valency, and carbohydrate residue nomenclature.pdb2linucs (32Lütteke T. Frank M. von der Lieth C.W. Data mining the protein data bank: automatic detection and assignment of carbohydrate structures.Carbohydr. Res. 2004; 339: 1015-1020Crossref PubMed Scopus (100) Google Scholar)Given a PDB ID or file containing carbohydrate information, this tool extracts the carbohydrate structures and returns them in LINUCS or IUPAC format.CARP (33Lütteke T. Frank M. von der Lieth C.W. Carbohydrate Structure Suite (CSS): analysis of carbohydrate 3D structures derived from the PDB.Nucleic Acids Res. 2005; 33: D242-D246Crossref PubMed Scopus (166) Google Scholar)Given a PDB ID or file containing carbohydrate information, this tool finds each glycosidic linkage and generates a Ramachandran plot of the backbone torsion angles that can be compared with existing knowledge as found in PDB or GlycoMapsDB.Statistical analysis of carbohydrate properties in PDBGlyVicinity (33Lütteke T. Frank M. von der Lieth C.W. Carbohydrate Structure Suite (CSS): analysis of carbohydrate 3D structures derived from the PDB.Nucleic Acids Res. 2005; 33: D242-D246Crossref PubMed Scopus (166) Google Scholar)Plots of the frequency of appearance of amino acids found surrounding particular carbohydrate residues can be generated from this tool, based on the latest PDB data.GlyTorsion (33Lütteke T. Frank M. von der Lieth C.W. Carbohydrate Structure Suite (CSS): analysis of carbohydrate 3D structures derived from the PDB.Nucleic Acids Res. 2005; 33: D242-D246Crossref PubMed Scopus (166) Google Scholar)Plots of the frequency of ranges of torsion angles of carbohydrate components, as found in the latest PDB data, can be generated.GlySeq (33Lütteke T. Frank M. von der Lieth C.W. Carbohydrate Structure Suite (CSS): analysis of carbohydrate 3D structures derived from the PDB.Nucleic Acids Res. 2005; 33: D242-D246Crossref PubMed Scopus (166) Google Scholar)Plots of the frequency of amino acid compositions around glycosylation sites can be generated, based on the data derived from PDB or SwissProt.ModelingSweet-IIWhen the user inputs a glycan structure using the customized input form, three-dimensional structures of the glycan can be generated in a variety of formats, such as a PDB file, using JMol, VRML, Tinker, and Babel.GlyProt (34Bohne-Lang A. von der Lieth C. GlyProt: in silico glycosylation of proteins.Nucleic Acids Res. 2005; 33: W214-W219Crossref PubMed Scopus (183) Google Scholar)Given a PDB ID or file, potential glycosylation sites, their accessibility, and an in silico generation of the glycosylated protein in three dimensions can be generated.GlycoMapsDB (35Frank M. Lütteke T. von der Lieth C. GlycoMapsDB: a database of the accessible conformational space of glycosidic linkages.Nucleic Acids Res. 2007; 35: 287-290Crossref PubMed Scopus (69) Google Scholar)This is a database of pre-calculated conformational maps of various oligosaccharides found in N- and O-linked glycans.Carbohydrate-notation relatedLINUCS (36Bohne-Lang A. Lang E. Förster T. von der Lieth C.W. LINUCS: linear notation for unique description of carbohydrate sequences.Carbohydr. Res. 2001; 336: 1-11Crossref PubMed Scopus (100) Google Scholar)Given a carbohydrate structure in CarbBank format, this tool generates the LINUCS code format.LiGraphGiven a carbohydrate structure in CarbBank format, this tool generates two-dimensional images of the structure.sumoGiven a carbohydrate structure in LINUCS or IUPAC format, this tool extracts known structural motifs, such as Lewis antigens or core structures.Mass spectrometryGlycofragmentGiven a carbohydrate structure in CarbBank format, this tool attempts to find the fragments that can be expected to occur in MS spectra for the structure. Open table in a new tab KEGG GLYCAN is a part of the Kyoto Encyclopedia of Genes and Genomes (KEGG) resource developed by Kanehisa Laboratories in Japan. Details regarding each of the data sets available in KEGG GLYCAN are available elsewhere (11Aoki-Kinoshita K.F. Kanehisa M. Bioinformatics analysis of glycan structures from a genomic perspective.in: von der Lieth C.-W. Luetteke T. Frank M. Bioinformatics for Glycobiology and Glycomics: An Introduction. Wiley, Chichester, West Sussex UK.2010: 125-141Google Scholar); a brief summary is described in this section. KEGG GLYCAN contains glycan structures extracted from CarbBank and manually curated glycan structures from the literature. The Composite Structure Map (12Hashimoto K. Kawano S. Goto S. Aoki-Kinoshita K. Kawashima M. Kanehisa M. A global representation of the carbohydrate structures: a tool for the analysis of glycan.Genome Inform. 2005; 16: 214-222PubMed Google Scholar) was built from these structures to provide an overview of the structures in the database and the relationships among them. This map provides links to the glyco-gene and to glycan structure data in KEGG. Many of the structures in KEGG GLYCAN are linked to KEGG PATHWAY, which includes many reference pathways for glycan biosynthesis and metabolism. Genomic information regarding glycosyltransferases also has been summarized in the KEGG Orthology system and classified in KEGG BRITE. The KEGG Orthology system groups orthologous groups of genes according to sequence and function, and KEGG BRITE is a hierarchically organized database of information in KEGG. Moreover, a number of tools for glycan structure similarity searches (13Aoki K. Yamaguchi A. Ueda N. Akutsu T. Mamitsuka H. Goto S. Kanehisa M. KCaM (KEGG Carbohydrate Matcher): a software tool for analyzing the structures of carbohydrate sugar chains.Nucleic Acids Res. 2004; 32: W267-W272Crossref PubMed Scopus (90) Google Scholar), a two-dimensional glycan drawing and search tool (KegDraw, available for download from the KEGG web site), and tools for linking genomic/transcriptomic data of glycosyltransferases to glycan structures (14Suga A. Yamanishi Y. Hashimoto K. Goto S. Kanehisa M. An improved scoring scheme for predicting glycan structures from gene expression data.Genome Inform. 2007; 18: 237-246PubMed Google Scholar, 15Kawano S. Hashimoto K. Miyama T. Goto S. Kanehisa M. Prediction of glycan structures from gene expression data based on glycosyltransferase reactions.Bioinformatics. 2005; 21: 3976-3982Crossref PubMed Scopus (70) Google Scholar) are available. The Consortium for Functional Glycomics (CFG) was a large international research initiative started in 2001 with funding from a National Institute of General Medical Sciences Large-Scale Collaborative Project award. The CFG database (16Raman R. Venkataraman M. Ramakrishnan S. Lang W. Raguram S. Sasisekharan R. Advancing glycomics: implementation strategies at the Consortium for Functional Glycomics.Glycobiology. 2006; 16: 82R-90RCrossref PubMed Scopus (186) Google Scholar) consists of glycan structures, glycan-binding proteins, and glycosyltransferase data (in their CFG Molecule Pages) that are supplemented by the CFG-derived information from their glycan arrays, glycan profiling data from MALDI-MS experiments, gene microarrays of glyco-gene expression, and mouse phenotyping data (CFG Data). The glycan structures in CFG have been accumulated from CarbBank, a database developed by GlycoMinds Ltd., and from their own CFG Data resources. The Glycan Binding Protein data resource provides detailed information about glycan-binding proteins (GBPs), including DNA and protein sequences, binding specificities, biological functions, etc. Experts in various fields of GBP research have provided the summary information and references for each GBP. The Glycosyltransferase resource serves as a portal to relevant CFG data and other glycan-related databases. Each enzyme is categorized according to the carbohydrate structure with which it is involved. A graphic display of a combined glycan structure is also provided so that users can click directly on the glycosidic linkage to which the enzyme of interest is related. The glycan array data resource provides all the results that have been obtained by the CFG from screening samples for glycan binding specificity (17Blixt O. Head S. Mondala T. Scanlan C. Huflejt M.E. Alvarez R. Bryan M.C. Fazio F. Calarese D. Stevens J. Razi N. Stevens D.J. Skehel J.J. van Die I. Burton D.R. Wilson I.A. Cummings R. Bovin N. Wong C.H. Paulson J.C. Printed covalent glycan array for ligand profiling of diverse glycan binding proteins.Proc. Natl. Acad. Sci. U.S.A. 2004; 101: 17033-17038Crossref PubMed Scopus (967) Google Scholar). A large variety of GBPs such as lectins, antibodies, pathogens, and cells have been screened, and each experimental result can be viewed graphically in a web browser. The glycan profiling resource provides all the results that have been obtained by the CFG from MALDI-MS and other analyses to identify and characterize the glycans in human and mouse tissues and cells (18North S.J. Hitchen P.G. Haslam S.M. Dell A. Mass spectrometry in the analysis of N-linked and O-linked glycans.Curr. Opin. Struct. Biol. 2009; 19: 498-506Crossref PubMed Scopus (176) Google Scholar, 19Jang-Lee J. North S.J. Sutton-Smith M. Goldberg D. Panico M. Morris H. Haslam S. Dell A. Glycomic profiling of cells and tissues by mass spectrometry: fingerprinting and sequencing methodologies.Methods Enzymol. 2006; 415: 59-86Crossref PubMed Scopus (134) Google Scholar). Each of the GBPs and glycans in the results from these experiments is also linked to other data resources and external databases. The microarray data resource provides gene expression data from experiments using human and mouse samples on the glycogene microarray chip developed by the CFG (20Comelli E.M. Head S.R. Gilmartin T. Whisenant T. Haslam S.M. North S.J. Wong N.K. Kudo T. Narimatsu H. Esko J.D. Drickamer K. Dell A. Paulson J.C. A focused microarray approach to functional glycomics: transcriptional regulation of the glycome.Glycobiology. 2006; 16: 117-131Crossref PubMed Scopus (139) Google Scholar), as well as some other commercial gene chips. Each experimental result has been processed to provide signals, present/absent calls, and p values for each gene. The mouse phenotyping resource (21Orr S.L. Le D. Long J.M. Sobieszczuk P. Ma B. Tian H. Fang X. Paulson J.C. Marth J.D. Varki N. A phenotype survey of thirty-six mutant mouse strains with gene targeted defects in glycosyltransferases or glycan-binding proteins.Glycobiology. 2012; 23: 363-380Crossref PubMed Scopus (37) Google Scholar) provides the data obtained from knockout gene experiments on a variety of mouse strains. The details regarding each experimental protocol, the raw data obtained, and processed data, including a summary of the experimental results, are all provided. The CFG also provides Paradigm Pages, which is presented in a wiki format and describes detailed information about exemplary GBPs that are considered "paradigm GBPs." CFG investigators volunteer to make contributions to these pages, which can also be updated as needed. The Japan Consortium for Glycobiology and Glycotechnology DataBase (JCGGDB) is a database of glycoscience data accumulated in Japan. It includes an integrated search function for many glycoscience databases throughout Japan. Table III lists the representative databases that are accessible from JCGGDB. In addition to a keyword search function that works across all the integrated databases, JCGGDB also provides many data resources of interest to glycobiologists, such as glycan-related diseases and experimental protocols. Many data have also been accumulated from the large amount of experimental data obtained at the National Institute of Advanced Industrial Science and Technology, which houses JCGGDB. These include mass spectrometry data, lectin affinity data, glycoprotein data, and glyco-gene information, as described in Table III.Table IIIThe representative data resources that are accessible from JCGGDBNameDescriptionGGDB (GlycoGene DataBase)GlycoGene is a database that includes genes associated with glycan synthesis such as glycosyltransferases, sugar nucleotide synthases, sugar-nucleotide transporters, and sulfotransferases. All of the nearly 200 human glycogenes have been identified, cloned, and characterized (28Narimatsu H. Construction of a human glycogene library and comprehensive functional analysis.Glycoconj. J. 2004; 21: 17-24Crossref PubMed Scopus (67) Google Scholar). It also includes substrate specificity information.LfDB (Lectin Frontier DataBase)LfDB provides basic information on lectins, as well as interaction data obtained via the frontal affinity chromatography–fluorescence detection system (37Tateno H. Nakamura-Tsuruta S. Hirabayashi J. Frontal affinity chromatography: sugar-protein interactions.Nat. Protoc. 2007; 2: 2529-2537Crossref PubMed Scopus (113) Google Scholar, 38Hirabayashi J. Arata Y. Kasai K. Frontal affinity chromatography as a tool for elucidation of sugar recognition properties of lectins.Methods Enzymol. 2003; 362: 353-368Crossref PubMed Scopus (96) Google Scholar).GlycoProtDB (GlycoProtein Database) (39Kaji H. Shikanai T. Sasaki-Sawa A. Wen H. Fujita M. Suzuki Y. Sugahara D. Sawaki H. Yamauchi Y. Shinkawa T. Taoka M. Takahashi N. Isobe T. Narimatsu H. Large-scale identification of N-glycosylated proteins of mouse tissues and construction of a glycoprotein database, GlycoProtDB.J. Proteome Res. 2012; 11: 4553-4566Crossref PubMed Scopus (67) Google Scholar)GlycoProtDB is a database of N-glycoproteins that have been experimentally identified in C. elegans N2 and mouse tissues (strain C52BL/6J, male).GMDB (Glycan Mass Spectral DataBase)GMDB currently stores MS2, MS3, and MS4 spectra of N- and O-linked glycans and glycolipid glycans and their fragments (40Kameyama A. Kikuchi N. Nakaya S. Ito H. Sato T. Shikanai T. Takahashi Y. Takahashi K. Narimatsu H. A strategy for identification of oligosaccharide structures using observational multistage mass spectral library.Anal. Chem. 2005; 77: 4719-4725Crossref PubMed Scopus (128) Google Scholar).LipidBankLipidBank (41Watanabe K. Yasugi E. Oshima M. How to search the glycolipid data in LIPIDBANK for Web: the newly developed lipid database.Trends Glycosci. Glycotechnol. 2000; 12: 175-184Crossref Scopus (55) Google Scholar) is a freely available database of natural lipids including fatty acids, glycerolipids, sphingolipids, steroids, and various vitamins. It is the official database of the Japanese Conference on the Biochemistry of Lipids.GlycoEpitopeThe GlycoEpitope database provides information on carbohydrate antigens such as glycoproteins that express carbohydrate antigens, glycolipids of which the partial structure is a carbohydrate epitope, enzymes that take part in the synthesis and degradation of glycoepitopes, the time and site of expression of carbohydrate epitopes, diseases to which carbohydrate epitopes are related, etc.GALAXY (Glycoanalysis by the three axes of MS and chromatography)GALAXY contains data on approximately 500 different pyridylamino-glycans, including the structures, HPLC elution positions expressed in glucose units on ODS and amide-silica columns, relative molecular mass, code numbers, sources of samples, and references (42Tomiya N. Awaya J. Kurono M. Endo S. Arata Y. Takahashi N. Analyses of N-linked oligosaccharides using a two-dimensional mapping technique.Anal. Biochem. 1988; 171: 73-90Crossref PubMed Scopus (390) Google Scholar).GlycoPOD (GlycoScience Protocol Online Database)GlycoPOD is a collection of experimental protoc
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