Volume 15 Supplement 1
Toolboxes for a standardised and systematic study of glycans
- Matthew P Campbell1,
- René Ranzinger2,
- Thomas Lütteke3,
- Julien Mariethoz4,
- Catherine A Hayes5,
- Jingyu Zhang1,
- Yukie Akune6,
- Kiyoko F Aoki-Kinoshita6,
- David Damerell7, 11,
- Giorgio Carta8,
- Will S York2,
- Stuart M Haslam7,
- Hisashi Narimatsu9,
- Pauline M Rudd8,
- Niclas G Karlsson4,
- Nicolle H Packer1 and
- Frédérique Lisacek4, 10Email author
© Campbell et al.; licensee BioMed Central Ltd. 2014
Published: 10 January 2014
Recent progress in method development for characterising the branched structures of complex carbohydrates has now enabled higher throughput technology. Automation of structure analysis then calls for software development since adding meaning to large data collections in reasonable time requires corresponding bioinformatics methods and tools. Current glycobioinformatics resources do cover information on the structure and function of glycans, their interaction with proteins or their enzymatic synthesis. However, this information is partial, scattered and often difficult to find to for non-glycobiologists.
Following our diagnosis of the causes of the slow development of glycobioinformatics, we review the "objective" difficulties encountered in defining adequate formats for representing complex entities and developing efficient analysis software.
Various solutions already implemented and strategies defined to bridge glycobiology with different fields and integrate the heterogeneous glyco-related information are presented.
Despite the initial stage of our integrative efforts, this paper highlights the rapid expansion of glycomics, the validity of existing resources and the bright future of glycobioinformatics.
Glycans or carbohydrates, both in the form of polysaccharides or glycoconjugates are known to partake in many biological processes and increasingly recognised as being implicated in human health. Glycosylation is probably the most important post-translational modification in terms of the number of proteins modified and the diversity generated. Since glycoproteins, glycolipids and glycan-binding proteins are frequently located on the cell's primary interface with the external environment many biologically significant events can be attributed to glycan recognition. In fact, glycans mediate many important cellular processes, such as cell adhesion, trafficking and signalling, through interactions with proteins. Protein-carbohydrate interactions are also involved in many disease processes including bacterial and viral infection, cancer metastasis, autoimmunity and inflammation [1–3].
In spite of such a central role in biological processes, the study of glycans remains isolated, protein-carbohydrate interactions are rarely reported in bioinformatics databases and glycomics is lagging behind other -omics. However, a key impetus in glycomics is now perceptible in the move toward large-scale analysis of the structure and function of glycans. A diverse range of technologies and strategies are being applied to address the technically difficult problems of glycan structural analysis and subsequently the investigation of their functional roles, ultimately to crack the glycocode.
Adding meaning to large data collections requires advances in software and database solutions, along with common platforms to allow data sharing. Current glycobioinformatics resources do cover information on the structure and function of glycans, their interaction with proteins or their enzymatic synthesis. However, this information is partial, scattered and often difficult to find for non-glycobiologists.
Several initiatives to catalogue and organise glycan-related information were launched in the past couple of decades starting with CarbBank [4, 5] in 1987. Regrettably, funding for this structural database was discontinued in 1997. Several projects have followed, among which EUROCarbDB  is the most recent, though now also unfunded since 2011. In many cases, these databases have remained confined to the realm of glycoscientists and their restricted popularity has often led to the withdrawal of funds. A similar fate is awaiting the databases created by the Consortium for Functional Glycomics (CFG)  despite twelve years of service but with limited connectivity to other leading bioinformatics resources such as those hosted at NCBI http://www.ncbi.nlm.nih.gov, EBI http://www.ebi.ac.uk or on the ExPASy server http://www.expasy.org to name only a few.
Even though a few stable initiatives such as GlycomeDB [8, 9], or KEGG-GLYCAN  have remained, the bleak prospect of producing yet another resource as part of yet another rescue plan likely to collapse a few years later, led our small but dedicated glycobioinformatics community to adopt cooperative strategies for enhancing the consistency of existing online services, and bridging with other -omics initiatives, thereby bringing glycomics to the fore. The development of compatible and complementary toolboxes for analysing glycomics data and cross-linking results with other -omics datasets appears as a solution to longer-term prospects and stability.
An obstacle in linking glycomics with other -omics is the independent accumulation of data regarding the constituents of glycoconjugates. Few protocols have been developed that produce data for the glycan, the glycoconjugates and their relationship to each other to allow the generation of datasets containing information from both perspectives. In fact, most glycan structures have been solved after being cleaved off their natural support (e.g., glycoproteins or glycolipids). Consequently, key information on the conjugate is lost. Conversely, protein glycosylation sites are studied and stored independently of the sugar structure  that is often not solved in the process. As a result, key information on the attached glycan structures is lost. The correlation between glycan structures and proteins can sometimes be partially restored manually through literature searches that are both labour and time consuming. Nonetheless, the expansion of systems biology that brings together multiple aspects of a biological phenomenon is steadily integrating glycomics data. Recently, this approach was followed in a study by Lauc and colleagues, to unveil the role of glycans in immunity .
This paper (1) reviews the extent of previously defined standards for representing glycan-related information and its consequences for automated analysis, (2) describes existing software for solving some of the issues raised in (1), (3) emphasises the means of cross-linking glycobioinformatics and other bioinformatics resources and (4) highlights collaborative efforts of integration within glycomics applications. Our report highlights the benefit of including glycomics to better understand biological processes and the necessary steps to achieve this goal.
Specific issues of glycan representation
Nomenclatures and formats
Nucleic acids and proteins can be represented (at least in their most basic forms) as simple character strings. In contrast, glycans are inherently more complex and involve significant degrees of branching. Moreover, the breadth of monosaccharide diversity (the building blocks of glycans) compared to the 4 nucleotides of nucleic acids and the 20 amino acids of proteins is substantially more extensive. Even though the mammalian glycome seems to arise from approximately 20 monosaccharides , bacterial glycans show more than ten-fold greater diversity at the monosaccharide level, and a nine-fold difference at the disaccharide unit space . Consequently, a simple textual representation of this complexity that should include monosaccharide anomericity, glycosidic linkages, residues modifications and substitutions, and account for structure ambiguity is difficult to capture in a format akin to those in proteomics and genomics.
Encoding of IUPAC monosaccharide α-D-Neup5Ac in databases
Protein Data Bank
Protein Data Bank
Adopting standards for representing glycan-related information
One of the common reasons for the development of new sequence encoding formats, instead of adopting existing efforts, is that few initiatives have provided published and documented application programming interfaces (APIs) for parsing and encoding glycan structures. Furthermore, not all formats cover the complete namespace of residues, and each has limitations in the encoding of specific structural features or annotations, such as repeating units or partially missing linkage information as a result of incomplete structure elucidation from acquired experimental data, or limitation of the experimental technique(s) used.
Current on-line resources
Glycan structures, References to reactions and pathways, Glyco-gene information
Glycan structures, MS profile, GlycanArray data, Glyco-gene expression data
Glycan structures, Cross references
Glycan structures, 3D structures, NMR data, Software tools
Repertoire of monosaccharides
Glycan structures, LC/MS-MS, HPLC data
Literature based curated glycan structures
Curated glycan structures
Glycan structures, HPLC profiles
Glycan structures, glyco-gene information, glycomics-related protocols, cross-references to other national life science resources
Bacterial glycan structures, NMR data,
Curated glycan structures
Accounting for existing software: parsers and translators
In the past, problems of inconsistent naming of monosaccharides and heterogeneous sequence formats in databases made it almost impossible for users and for bioinformatics software to access and reconcile data from several databases. Therefore several projects including GlycomeDB and RINGS  have started developing translation tools for parsing and translating sequence formats from different databases. These translators make it possible to use the databases for statistical analysis  and for the mash-up and comparison of data from different sources . It has also led to the creation of the GlycomeDB, a data warehouse for glycan structures that accesses the structural content of almost all publically available glycan structure databases and translates the sequences into a consistent representation creating an index of available glycan structures.
Available format translation across databases
GlycomeDB, Glycan Builder
GlycomeDB, UniCarbKB*, Glycan
GlycomeDB, Glycan Builder
As illustrated above, one of the basic problems for the sequence parsing and translation is the usage of different naming schemes for monosaccharides. For that purpose MonosaccharideDB http://www.monosaccharidedb.org was developed as a web portal and as a programming/lookup library that is used by several of the translation tools for the normalisation and translation of monosaccharide names.
Accounting for existing software: graphical structure input tools
To access the structural content captured in many databases, web interfaces can be used to search and retrieve information. Early databases such as CarbBank and GLYCOSCIENCES.de were using textual input tools for the structure input making it sometimes difficult for inexperienced users to enter a valid query.
In recent years, graphical input tools have been developed to allow the definition of structures by using the cartoon representation as previously described and illustrated in Figure 2. A majority of existing databases provide users with the tools to search for a defined structure and/or structures containing a substructure. A few databases also allow searching for structure based on well-known motifs or by structural similarity.
GlycO [31, 32] is a curated ontology that has been developed for representing glycan and glycoconjugates together with their components and their relationship. This ontology is used in combination with other ontologies to model the reactions and enzymes involved in the biosynthesis and modification of glycan structures, and the metabolic pathways in which they participate. Since the glycan structures in the ontology have been added by a multistep manual expert curation, the ontology is also used for the annotation of experimental data.
The GlycO schema relies on the web ontology language and description logic (OWL-DL) to place restrictions on relationships, thus making it suitable to classify new instance data. These logical restrictions are necessary due to the chemical nature of glycans, which have complex, branched structures that cannot be represented in any simple way. The structural knowledge in GlycO is modularized, in that larger structures are semantically composed of smaller canonical building blocks. In particular, glycan instances are modelled by linking together several instances of canonical monosaccharide residues, which embody knowledge of their chemical structure (e.g., β-D-Glcp NAc) and context (e.g., attached directly to an Asn residue of a protein). This bottom-up semantic modelling of large molecular structures using smaller building blocks allows structures in GlycO to be placed in a biochemical context by describing the specific interactions of its component parts with proteins, enzymes and other biochemical entities.
An important aspect of glycomics analysis is that very often a single type of experiment is not sufficient to fully define a glycan structure. Orthogonal strategies are employed to fully elucidate structures with a greater measure of confidence. Data acquired from different analytical methods such as NMR, HPLC, MS, glycan array, capillary electrophoresis, monosaccharide analysis or molecular dynamics simulations can be used in combination to characterise complex biological samples. Each experiment solves parts of the puzzle and by combining the derived information from the different experiments it is possible to improve the annotation accuracy. In those cases where the complete structure is not elucidated, due to limits in the experimental methods and acquired data, it is possible to infer some structural features from knowledge about biosynthetic pathways. This is a major difference to classical molecular biology fields, in that the proteome has a template and the genome can now be easily sequenced, whereas the glycome is indirectly encoded via the expression profile of glycosyltransferases, other enzymes involved in glycan synthesis and nucleotide sugar substrate concentrations. However, as in the other -omics initiatives, a concerted effort to define a standard spanning the Minimum Information Required for A Glycomics Experiment (MIRAGE) was initiated in 2011 by some of the authors . Currently only a few databases allow the storage and retrieval of experimental data. In addition most of these databases store only experiments generated by the research group or consortium providing the database. Example databases storing experimental data are EUROCarbDB (MS, NMR, HPLC), GlycoBase in Dublin, Ireland  (HPLC), GlycoBase in Lille, France  (NMR) and the CFG database (MS profile, Glycan Array). As mass spectrometry (MS) has now become the most common method for solving glycan structures and identifying glycopeptides, there is now an increasing range of software tools that are available for analysing MS data produced in glycomics . At this stage, there is still a low level of integration with other data that needs a joint effort to support workflow creation and integration of MS data analysis. The involvement of some of the authors in the development of UniCarb-DB , the first LC MS/MS data repository for glycans is a step in this direction.
Bridging with other fields
Adopting standards is a necessary but not a sufficient step towards automating the analysis of glycans. A critical feature/component in glycobioinformatics is the availability of standardised approaches to connect remote databases. The NAS (National Academy of Sciences) "Transforming Glycoscience: A Roadmap for the Future" report  exemplifies the hurdles and problems faced by the research community due to the disconnected and incomplete nature of existing databases. Several initiatives have commenced to bridge the information content available in the described databases.
Bridging chemistry and biology with data curation
GlycoSuiteDB [38, 39] contains glycan structures derived from glycoproteins of different biological sources that have been described in the literature, and free oligosaccharides isolated from biologically important fluids (e.g., milk, saliva, urine). The curated database provides contextual information for glycan structures attached to proteins and re-establishes the frequently lost connection between a glycan structure and the attached functional protein as annotated in the UniProtKB resource that is cross-referenced to GlycoSuiteDB. This database is forming the basis of the central glycan structural database in UniCarbKB, which is designed to incorporate information from other structural databases including EUROCarbDB, UniCarb-DB and GlycoBase. The content and manual curation principles of GlycoSuiteDB will form the basis of the central glycan structural database of UniCarbKB to maintain the quality of information stored in the knowledgebase. The links to UniProtKB will help to connect key information between glycosylated sites and specific structures.
Bridging glycobioinformatics and bioinformatics using web services
The development of a web services protocol enables searches across several databases. Such technologies have gained much attention in the field of life sciences as an open architecture that facilitates interoperability across heterogeneous platforms. An ongoing programme in the glycomics domain is the Working Group on Glycomics Database Standards (WGGDS) activity, which was initially supported by a CFG-bridging grant. A working draft of the protocols can be accessed at http://glycomics.ccrc.uga.edu/GlycomicsWiki/Informatics:Cross-Database_Search/Protocol_%28WGGDS%29. The WGGDS enabled developers from the CFG, EUROCarbDB/UniCarb-DB, GlycomeDB, GLYCOSCIENCES.de and RINGS to seed the beginnings of a communication interface, which provides access to the data contained in multiple, autonomous glycomics databases with an emphasis on structural data collections.
A complete suite of representational state transfer (REST) based tools has been developed by some of the authors with new and improved applications being built. Each service provides access to a (sub-)structure search that supports remote queries for complete or partial structure and allows for substructure/epitope matching. This can only be achieved with universal acceptance of structure encoding formats and access to accurate and complete glycan translators. Here, the sequence attribute of the XML-based message protocol conforms to the GlydeII format (see above), which can be readily converted into GlycoCT and/or KCF formats for executing database searches. In addition, individual databases have expanded this service to enable searching based on molecular mass, experimental evidences, e.g. mass spectrometry, and monosaccharide composition. To realise this goal it was imperative for the glycobioinformatics community to agree on encoding formats and ensure robustness in the frameworks.
Since the exchange interface (REST) and protocol are independent of the database backend, the WGGDS guidelines can be easily incorporated and extended by other databases. Web services enable researchers to access data and provide a framework for programmers to build applications without installing and maintaining the necessary databases.
Bridging glycobioinformatics and bioinformatics using RDF
Semantic Web approaches are based on common formats that enable the integration and aggregation of data from multiple resources, which potentially offers a means to solve data compatibility issue in the glycomics space. The Semantic Web is a growing area of active research and growth in the life sciences field, which has the ability to improve bioinformatics analyses by leveraging the vast stores of data accumulated in web-accessible resources (e.g., Bio2RDF ). A range of commonly accessed databases such as UniProtKB has adopted the resource description framework (RDF)  as a format to support data integration and more sophisticated queries.
Several database projects in Japan have been involved in adopting RDF such as PDBj  or JCGGDB  as a part of the Integrated Database Project http://lifesciencedb.jp that focuses on data integration of heterogeneous datasets to provide users with a comprehensive data resource that can be accessible from a single endpoint. In order to efficiently implement RDF solutions, the existing database providers must agree on a standard for representing glycan structure and annotation information. For that purpose, the developers of major glycomics databases including BCSDB , GlycomeDB, JCGGDB, GLYCOSCIENCES.de and UniCarbKB designed a draft standard and prototype implementation of the RDF generation during BioHackathon 2012 http://2012.biohackathon.org.
GlycoRDF is a future-thinking collaborative effort that is addressing the requirement for sophisticated data mashups that answer complex research questions. It also allows the integration of information across different -omics, a potential that is demonstrated by the adoption of Semantic Web technologies in other fields including proteomics and genomics. The GlycoRDF innovative solution requires the harvesting of knowledge from multiple resources. Here, initial activities have focused on providing normalised RDF documents sourced from the wealth of information provided by the partners spanning structural and experimental data collections. The developers involved in this project released the first version of GlycoRDF in 2013 .
In the last few years, small collaborative projects have started between international glycobioinformatics researchers, with very limited funding, but these are slowly transforming the way glyco-related data is shared and queried. Information is getting more centralised at a technology level. Cooperation started informally at the 1st Beilstein Symposium on Glyco-Bioinformatics (2009, Postdam, Germany, http://www.beilstein-institut.de/en/symposia/overview/glyco-bioinformatics, and became more structured during the 3rd Warren workshop (2010, Gothenburg, Sweden, http://www.biomedicine.gu.se/biomedicine/Charles_Warren_Workshop_III. The 2nd Beilstein symposium on Glyco-Bioinformatics (2011, Postdam, Germany, http://www.beilstein-institut.de/en/symposia/overview/2-glyco-bioinformatics provided an opportunity to reinforce the UniCarbKB consortium and led to the publication of a Viewpoint article  suggesting a roadmap for glycobioinformatics. At this stage, further input into WGGDS was achieved by common work on universal formats and guidelines that provide for easier integration and interoperability between glycobioinformatics applications and the data stored in the partner databases. At the 4th Warren workshop (2012, Athens, Georgia, USA, http://glycomics.ccrc.uga.edu/warren-workshop), definite steps towards adopting standards were manifest and implemented in the manuscript submission process of the journal of Molecular and Cellular Proteomics http://www.mcponline.org. These events undoubtedly strengthened the coherence of glycobioinformatics initiatives. We expect that projects like MIRAGE will help drive the adoption of data standards. The next step should focus on analytical formats along the lines of the widely used MS pepXML in proteomics.
GlycomeDB and UniCarbKB are examples of initiatives that can address the issues mentioned in the first section of this paper. GlycomeDB is currently the most comprehensive and unified resource for carbohydrate structures. It integrates the structural and taxonomic data of all major public carbohydrate databases including CarbBank, KEGG, CFG, GlycoBase, BCSDB, GLYCOSCIENCES.de as well as carbohydrates contained in the Protein Data Bank (PDB). GLYCOSCIENCES.de adds information on 3D structures of glycoproteins and protein-carbohydrate complexes from the PDB as well as tools to validate and statistically analyse these data [44, 45]. UniCarbKB is an informatics framework for the storage and the analysis of high-quality data collections on glycoconjugates, including informative meta-data and annotated experimental datasets. While it is still in the early development phases, this scalable web-friendly framework, at this stage, integrates curated glycan structural information and PubMed references from GlycoSuiteDB and EUROCarbDB, and experimental MS/MS data from UniCarb-DB. Information relevant to glycoproteins, notably the inclusion of glycosylated structures localised in different tissues and on different proteins, as sourced from literature mining, will bridge to the proteomics knowledgebases. Linking this information with curated data on structures recognised by bacteria and lectins as described for instance in SugarBind  or by glycoarray data (CFG) allows deeper mining of the functional role of glycans.
The usability of GlycomeDB and UniCarbKB sets the basis for tackling the second section of this paper as each -omics specialty comes with a bioinformatics toolbox for analysing high-throughput data. Furthermore, in the vast majority of cases, the interpretation of this data is related to gene sequences. Indeed, popular and established bioinformatics databases are sequence-centred, so that straight or translated DNA sequences constitute the fundamental piece of information around which all other useful properties or data types are organised (gene expression, protein structure, etc.). The recent move towards Systems Biology has confirmed the status of DNA/RNA/protein sequence as the element minimally shared by each -omics domain. In this context, the systematic investigation of glycan expression profiles obviously needs to be recorded with the associated glycoproteins and mapped onto amino acid sequences. This will enable further exploration of the subtle differences characterising pathological or any other specific conditions in which glycans are expressed and prevent, modulate or facilitate protein recognition and binding.
Overall, the international consortia involved in the cited projects are thereby attempting to bring together the many disconnected islands of glycobiological information in a standardised open access framework, aiming in the near future, to automatically mashup data from many resources - opening glycomics to the general scientific community.
In this paper originally introduced at NETTAB'12 , we have first diagnosed the causes of the slow development of glycobioinformatics and the "objective" difficulties encountered in defining adequate formats for representing complex entities. We have then suggested three directions for attending to the listed issues in relation to twenty years of mixed results in developing glycobioinformatics resources.
We first advocate setting, and complying with, standards as a minimum requirement for planning the future of automated processing and analysis of glycans. We secondly embark on several programmes for bridging glycomics with other -omics following different strategies. Finally, we show by co-authoring this paper and collaborating in consortia that these initiatives should be developed and supported by a cohesive community if we wish to successfully meet the goal of integration.
The overall aim of new or improved and integrated resources is to access, query and mine existing glycobioinformation in various and complementing ways. These tools, designed to connect with other -omics information, are destined to support research in analytical glycobiology in the context of whole systems biology. They should give rise to enhanced methods for the prediction of protein function and interactions and the continued development of these resources will enable the real understanding of biological processes.
API: Application Programming Interface;
CFG: Consortium for Functional Glycomics;
IUPAC: International Union of Pure and Applied Chemistry;
KCF: KEGG Chemical Function;
MS: Mass Spectrometry;
OWL-DL: web ontology language and description logic;
RDF: Resource Description Framework;
REST: Representational State Transfer;
WGGDS: Working Group on Glycomics Database Standards;
RR and WSY are supported by NIH/NIGMS funding the National Center for Glycomics and Glycoproteomics (8P41GM103490) and the Beilstein-Institut, a non-profit foundation located in Frankfurt am Main, Germany, funding the MIRAGE initiative.
UniCarbKB is supported by the Australian National eResearch Collaboration Tools and Resources project (NeCTAR).
GlycoSuiteDB and SugarBind are supported by the Swiss National Science Foundation (SNSF).
UniCarb-DB and GlycoBase are supported by STINT (Swedish Foundation for International Cooperation in Research and Higher Education).
RINGS was supported by Grant-In-Aid for Young Scientists (A), KAKENHI (20016025), the Japan Society for the Promotion of Science (JSPS) and the Ministry of Education, Culture, Sports, Science and Technology (MEXT).
The BioHackathon in Japan was sponsored by JST (Japan Science and Technology Agency) and NBDC (National Bioscience Database Center) Program for the Life Science Database Integration Project, and support for the glycan database developers at the BioHackathon was partly provided by AIST (National Institute of Advanced Industrial Science and Technology).
The publication costs for the article are covered by the Swiss National Science Foundation grant #31003A_141215.
This article has been published as part of BMC Bioinformatics Volume 15 Supplement 1, 2014: Integrated Bio-Search: Selected Works from the 12th International Workshop on Network Tools and Applications in Biology (NETTAB 2012). The full contents of the supplement are available online at http://www.biomedcentral.com/bmcbioinformatics/supplements/15/S1.
- Ohtsubo K, Marth JD: Glycosylation in cellular mechanisms of health and disease. Cell. 2006, 126 (5): 855-867. 10.1016/j.cell.2006.08.019.View ArticlePubMedGoogle Scholar
- Varki A, Cummings RD, Esko JD, Freeze HH, Stanley P, Bertozzi CR, Hart GW, Etzler ME: Essentials of Glycobiology. 2009, Plainview, NY: Cold Spring Harbor Laboratory Press, 2Google Scholar
- Walt D, Aoki-Kinoshita KF, Bendiak B, Bertozzi C, Boons G, Darvill A, Hart G, Kiessling L, Lowe J, Moon R: Transforming Glycoscience: A Roadmap for the Future. 2012, Washington, DC: National Academies PressGoogle Scholar
- Doubet S, Albersheim P: CarbBank. Glycobiology. 1992, 2 (6): 505-View ArticlePubMedGoogle Scholar
- Doubet S, Bock K, Smith D, Darvill A, Albersheim P: The Complex Carbohydrate Structure Database. Trends Biochem Sci. 1989, 14 (12): 475-477. 10.1016/0968-0004(89)90175-8.View ArticlePubMedGoogle Scholar
- von der Lieth CW, Freire AA, Blank D, Campbell MP, Ceroni A, Damerell DR, Dell A, Dwek RA, Ernst B, Fogh R, Frank M, Geyer H, Geyer R, Harrison MJ, Henrick K, Herget S, Hull WE, Ionides J, Joshi HJ, Kamerling JP, Leeflang BR, Lütteke T, Lundborg M, Maass K, Merry A, Ranzinger R, Rosen J, Royle L, Rudd PM, Schloissnig S: EUROCarbDB: An open-access platform for glycoinformatics. Glycobiology. 2011, 21 (4): 493-502. 10.1093/glycob/cwq188.PubMed CentralView ArticlePubMedGoogle Scholar
- Raman R, Venkataraman M, Ramakrishnan S, Lang W, Raguram S, Sasisekharan R: Advancing glycomics: implementation strategies at the consortium for functional glycomics. Glycobiology. 2006, 16 (5): 82R-90R. 10.1093/glycob/cwj080.View ArticlePubMedGoogle Scholar
- Ranzinger R, Frank M, von der Lieth CW, Herget S: Glycome-DB.org: A portal for querying across the digital world of carbohydrate sequences. Glycobiology. 2009, 19 (11): 1563-1567.View ArticlePubMedGoogle Scholar
- Ranzinger R, Herget S, Wetter T, von der Lieth CW: GlycomeDB - integration of open-access carbohydrate structure databases. BMC Bioinformatics. 2008, 9: 384-10.1186/1471-2105-9-384.PubMed CentralView ArticlePubMedGoogle Scholar
- Hashimoto K, Goto S, Kawano S, Aoki-Kinoshita KF, Ueda N, Hamajima M, Kawasaki T, Kanehisa M: KEGG as a glycome informatics resource. Glycobiology. 2006, 16 (5): 63R-70R. 10.1093/glycob/cwj010.View ArticlePubMedGoogle Scholar
- Zhang H, Loriaux P, Eng J, Campbell D, Keller A, Moss P, Bonneau R, Zhang N, Zhou Y, Wollscheid B, Cooke K, Yi EC, Lee H, Peskind ER, Zhang J, Smith RD, Aebersold R: UniPep - a database for human N-linked glycosites: a resource for biomarker discovery. Genome Biol. 2006, 7 (8): R73-10.1186/gb-2006-7-8-r73.PubMed CentralView ArticlePubMedGoogle Scholar
- Lauc G, Essafi A, Huffman JE, Hayward C, Knežević A, Kattla JJ, Polašek O, Gornik O, Vitart V, Abrahams JL, Pučić M, Novokmet M, Redžić I, Campbell S, Wild SH, Borovečki F, Wang W, Kolčić I, Zgaga L, Gyllensten U, Wilson JF, Wright AF, Hastie ND, Campbell H, Rudd PM, Rudan I: Genomics meets glycomics-the first GWAS study of human N-Glycome identifies HNF1alpha as a master regulator of plasma protein fucosylation. PLoS Genet. 2010, 6 (12): e1001256-10.1371/journal.pgen.1001256.PubMed CentralView ArticlePubMedGoogle Scholar
- Werz DB, Ranzinger R, Herget S, Adibekian A, von der Lieth CW, Seeberger PH: Exploring the structural diversity of mammalian carbohydrates ("glycospace") by statistical databank analysis. ACS Chem Biol. 2007, 2 (10): 685-691. 10.1021/cb700178s.View ArticlePubMedGoogle Scholar
- Herget S, Toukach P, Ranzinger R, Hull W, Knirel Y, von der Lieth CW: Statistical analysis of the Bacterial Carbohydrate Structure Data Base (BCSDB): Characteristics and diversity of bacterial carbohydrates in comparison with mammalian glycans. BMC Struct Biol. 2008, 8 (1): 35-10.1186/1472-6807-8-35.PubMed CentralView ArticlePubMedGoogle Scholar
- McNaught AD: Nomenclature of carbohydrates (recommendations 1996). Adv Carbohydr Chem Biochem. 1997, 52: 43-177.View ArticlePubMedGoogle Scholar
- Bohne-Lang A, Lang E, Förster T, von der Lieth CW: LINUCS: linear notation for unique description of carbohydrate sequences. Carbohydr Res. 2001, 336 (1): 1-11. 10.1016/S0008-6215(01)00230-0.View ArticlePubMedGoogle Scholar
- Toukach PV: Bacterial carbohydrate structure database 3: principles and realization. J Chem Inf Model. 2011, 51 (1): 159-170. 10.1021/ci100150d.View ArticlePubMedGoogle Scholar
- Banin E, Neuberger Y, Altshuler Y, Halevi A, Inbar O, Dotan N, Dukler A: A Novel LinearCode® Nomenclature for Complex Carbohydrates. Trends Glycosci Glycotechnol. 2002, 14 (77): 127-137. 10.4052/tigg.14.127.View ArticleGoogle Scholar
- Herget S, Ranzinger R, Maass K, von der Lieth CW: GlycoCT-a unifying sequence format for carbohydrates. Carbohydr Res. 2008, 343 (12): 2162-2171. 10.1016/j.carres.2008.03.011.View ArticlePubMedGoogle Scholar
- Aoki KF, 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 (Web Server): W267-W272. 10.1093/nar/gkh473.PubMed CentralView ArticlePubMedGoogle Scholar
- Kikuchi N, Kameyama A, Nakaya S, Ito H, Sato T, Shikanai T, Takahashi Y, Narimatsu H: The carbohydrate sequence markup language (CabosML): an XML description of carbohydrate structures. Bioinformatics. 2005, 21 (8): 1717-1718. 10.1093/bioinformatics/bti152.View ArticlePubMedGoogle Scholar
- Sahoo SS, Thomas C, Sheth A, Henson C, York WS: GLYDE-an expressive XML standard for the representation of glycan structure. Carbohydr Res. 2005, 340 (18): 2802-2807. 10.1016/j.carres.2005.09.019.View ArticlePubMedGoogle Scholar
- Harvey DJ, Merry AH, Royle L, Campbell MP, Rudd PM: Symbol nomenclature for representing glycan structures: Extension to cover different carbohydrate types. Proteomics. 2011, 11 (22): 4291-4295. 10.1002/pmic.201100300.View ArticlePubMedGoogle Scholar
- Harvey DJ, Merry AH, Royle L, Campbell MP, Dwek RA, Rudd PM: Proposal for a standard system for drawing structural diagrams of N- and O-linked carbohydrates and related compounds. Proteomics. 2009, 9 (15): 3796-3801. 10.1002/pmic.200900096.View ArticlePubMedGoogle Scholar
- Campbell MP, Hayes CA, Struwe WB, Wilkins MR, Aoki-Kinoshita KF, Harvey DJ, Rudd PM, Kolarich D, Lisacek F, Karlsson NG, Packer NH: UniCarbKB: putting the pieces together for glycomics research. Proteomics. 2011, 11 (21): 4117-4121. 10.1002/pmic.201100302.View ArticlePubMedGoogle Scholar
- Ceroni A, Dell A, Haslam SM: The GlycanBuilder: a fast, intuitive and flexible software tool for building and displaying glycan structures. Source Code Biol Med. 2007, 2: 3-10.1186/1751-0473-2-3.PubMed CentralView ArticlePubMedGoogle Scholar
- Damerell D, Ceroni A, Maass K, Ranzinger R, Dell A, Haslam SM: The GlycanBuilder and GlycoWorkbench glycoinformatics tools: updates and new developments. Biol Chem. 2012, 393 (11): 1357-1362.View ArticlePubMedGoogle Scholar
- Akune Y, Hosoda M, Kaiya S, Shinmachi D, Aoki-Kinoshita KF: The RINGS resource for glycome informatics analysis and data mining on the Web. Omics. 2010, 14 (4): 475-486. 10.1089/omi.2009.0129.View ArticlePubMedGoogle Scholar
- Lütteke T, Bohne-Lang A, Loss A, Goetz T, Frank M, von der Lieth CW: GLYCOSCIENCES.de: an Internet portal to support glycomics and glycobiology research. Glycobiology. 2006, 16 (5): 71R-81R. 10.1093/glycob/cwj049.View ArticlePubMedGoogle Scholar
- Ceroni A, Maass K, Geyer H, Geyer R, Dell A, Haslam SM: GlycoWorkbench: a tool for the computer-assisted annotation of mass spectra of glycans. J Proteome Res. 2008, 7 (4): 1650-1659. 10.1021/pr7008252.View ArticlePubMedGoogle Scholar
- Sahoo SS, Thomas C, Sheth A, York WS, Tartir S: Knowledge modeling and its application in life sciences: a tale of two ontologies. Proceedings of the 15th international conference on World Wide Web: 22-26 May 2006; Edinburgh, Scotland. 2006, 317-326.View ArticleGoogle Scholar
- Thomas CJ, Sheth AP, York WS: Modular Ontology Design Using Canonical Building Blocks in the Biochemistry Domain. Formal Ontology in Information Systems, Proceedings of the Fourth International Conference: 9-11 November 2006; Baltimore, MA. Edited by: Bennett B, Fellbaum C. 2006, 115-127.Google Scholar
- Kolarich D, Rapp E, Struwe WB, Haslam SM, Zaia J, McBride R, Agravat S, Campbell MP, Kato M, Ranzinger R, Kettner C, York WS: The minimum information required for a glycomics experiment (MIRAGE) project: improving the standards for reporting mass-spectrometry-based glycoanalytic data. Mol Cell Proteomics. 2013, 12 (4): 991-995. 10.1074/mcp.O112.026492.PubMed CentralView ArticlePubMedGoogle Scholar
- Campbell MP, Royle L, Radcliffe CM, Dwek RA, Rudd PM: GlycoBase and autoGU: tools for HPLC-based glycan analysis. Bioinformatics. 2008, 24 (9): 1214-1216. 10.1093/bioinformatics/btn090.View ArticlePubMedGoogle Scholar
- Maes E, Bonachera F, Strecker G, Guerardel Y: SOACS index: an easy NMR-based query for glycan retrieval. Carbohydr Res. 2009, 344 (3): 322-330. 10.1016/j.carres.2008.11.001.View ArticlePubMedGoogle Scholar
- Li F, Glinskii OV, Glinsky VV: Glycobioinformatics: current strategies and tools for data mining in MS-based glycoproteomics. Proteomics. 2013, 13 (2): 341-354. 10.1002/pmic.201200149.View ArticlePubMedGoogle Scholar
- Hayes CA, Karlsson NG, Struwe WB, Lisacek F, Rudd PM, Packer NH, Campbell MP: UniCarb-DB: a database resource for glycomic discovery. Bioinformatics. 2011, 27 (9): 1343-1344. 10.1093/bioinformatics/btr137.View ArticlePubMedGoogle Scholar
- Cooper CA, Joshi HJ, Harrison MJ, Wilkins MR, Packer NH: GlycoSuiteDB: a curated relational database of glycoprotein glycan structures and their biological sources. 2003 update. Nucleic Acids Res. 2003, 31 (1): 511-513. 10.1093/nar/gkg099.PubMed CentralView ArticlePubMedGoogle Scholar
- Cooper CA, Harrison MJ, Wilkins MR, Packer NH: GlycoSuiteDB: a new curated relational database of glycoprotein glycan structures and their biological sources. Nucleic Acids Res. 2001, 29 (1): 332-335. 10.1093/nar/29.1.332.PubMed CentralView ArticlePubMedGoogle Scholar
- Belleau F, Nolin MA, Tourigny N, Rigault P, Morissette J: Bio2RDF: towards a mashup to build bioinformatics knowledge systems. J Biomed Inform. 2008, 41 (5): 706-716. 10.1016/j.jbi.2008.03.004.View ArticlePubMedGoogle Scholar
- UniProt Consortium: Reorganizing the protein space at the Universal Protein Resource (UniProt). Nucleic Acids Res. 2012, 40 (Database): D71-75.View ArticleGoogle Scholar
- Kinjo AR, Suzuki H, Yamashita R, Ikegawa Y, Kudou T, Igarashi R, Kengaku Y, Cho H, Standley DM, Nakagawa A, Nakamura H: Protein Data Bank Japan (PDBj): maintaining a structural data archive and resource description framework format. Nucleic Acids Res. 2012, 40 (Database): D453-460.PubMed CentralView ArticlePubMedGoogle Scholar
- Aoki-Kinoshita KF, Sawaki H, An HJ, Cho JW, Hsu D, Kato M, Kawano S, Kawasaki T, Khoo KH, Kim J, Kim JD, Li X, Lütteke T, Okuda S, Packer NH, Paulson JC, Raman R, Ranzinger R, Shen H, Shikanai T, Yamada I, Yang P, Yamaguchi Y, Ying W, Yoo JS, Zhang Y, Narimatsu H: The Third ACGG-DB Meeting Report: Towards an international collaborative infrastructure for glycobioinformatics. Glycobiology. 2013, 23 (2): 144-146.View ArticlePubMedGoogle Scholar
- Lutteke T: Analysis and validation of carbohydrate three-dimensional structures. Acta Crystallogr D Biol Crystallogr. 2009, 65 (Pt 2): 156-168.PubMed CentralView ArticlePubMedGoogle Scholar
- Lutteke T, Frank M, von der Lieth CW: Carbohydrate Structure Suite (CSS): analysis of carbohydrate 3D structures derived from the PDB. Nucleic Acids Res. 2005, 33 (Database): D242-246.PubMed CentralPubMedGoogle Scholar
- Shakhsheer B, Anderson M, Khatib K, Tadoori L, Joshi L, Lisacek F, Hirschman L, Mullen E: SugarBind Database (SugarBindDB): A resource of pathogen lectins and corresponding glycan targets. J Mol Recognit. 2013, 26 (9): 426-431. 10.1002/jmr.2285.View ArticlePubMedGoogle Scholar
- Campbell MP, Mariethoz J, Hayes CM, Rudd PG, Karlsson NG, Packer NH, Lisacek F: Glycans, the forgotten biomolecular actors of the big picture. EMBnet journal. 2012, 18: B:84-85. 10.14806/ej.18.B.559.View ArticleGoogle Scholar
- Aoki-Kinoshita KF, Bolleman J, Campbell MP, Kawano S, Kim JD, Lütteke T, Matsubara M, Okuda S, Ranzinger R, Sawaki H, Shikanai T, Shinmachi T, Suzuki Y, Toukach P, Yamada Y, Packer YH, Narimatsu H: Introducing glycomics data into the Semantic Web. J Biomed Semantics . 2013, 4 (1): 39-10.1186/2041-1480-4-39.PubMed CentralView ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.