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With the recent publication of Transforming Glycoscience: A Roadmap for the Future by the US National Academies (see Jerry Hart's letter to GlycoForum for more details: Glycobiology22, 1399), numerous funding agencies and leaders have been exposed to the critical importance of Glycosciences. I want to encourage everyone in the Glycobiology community to take advantage of this document to educate other faculty and administrators at your institutions, administrators at funding agencies, elected officials, and anyone else you can think of about the importance of education and increased funding for this critical area. We have a unique opportunity right now to bring awareness of our field to the public, and we do not want to miss it.
One of the research tools that has brought awareness of the field to many non-Glycobiologists are glycan arrays like those provided through the Consortium for Functional Glycomics (see http://www.functionalglycomics.org/static/consortium/resources/resourcecoreh.shtml for details). Arrays allow investigators to determine whether their favorite protein has carbohydrate-binding activity and provide detailed information regarding the specificity of that binding with very little prior knowledge of Glycobiology. In honor of the tremendous impact the availability of these arrays has had on the field, and the number of new investigators brought into the field by their easy access, I have chosen an array for the cover this year. I hope that it encourages even more investigators to begin pursuing the importance of glycans in whatever system they are studying.
Wishing you a year rich in glycans,
Robert S. Haltiwanger
Editor-in-Chief
Glycobiology. 2013 Feb;23(2):143–146.
PolySac3DB: An Annotated Data Base of 3 Dimensional Structures of Polysaccharides
PolySac3DB is an annotated database that contains the 3D structural information of about 160 polysaccharide entries that have been collected from an extensive screening of scientific literature. They have been systematically organized using standard names in the field of carbohydrate research into 18 categories representing polysaccharide families. Structure-related information includes the saccharides making up the repeat unit(s) and their glycosidic linkages, the expanded 3D representation of the repeat unit, unit cell dimensions and space group, helix type, diffraction diagram(s) (when applicable), experimental and/or simulation methods used for structure description, link to the abstract of the publication, reference and the atomic coordinate files for visualization and download. The database is accompanied by a user-friendly graphical user interface (GUI). It features interactive displays of polysaccharide structures and customized search options for beginners and experts, respectively. The site also serves as an information portal for polysaccharide structure determination techniques. The web-interface also references external links where other carbohydrate-related resources are available. All the data and features are available via the web-interface utilizing the search engine and can be accessed at http://polysac3db.cermav.cnrs.fr.
Centre de Recherches sur les Macromolécules Végétales (CERMAV*) Centre National de la Recherche Scientifique, Grenoble Cedex 9 BP 53X, F-38041, France
ACGG (Asian Communications of Glycobiology and Glycotechnology), formed in 2009, is a communicative activity for encouraging and sharing glycan studies among researchers in Asia. Among the goals of ACGG, one is to develop a database (ACGG-DB) and to build collaborations among researchers in the informatics field. Thus, the Research Center for Medical Glycoscience at AIST (National Institute of Advanced Industrial Science and Technology) took the lead and held ACGG-DB meetings with researchers from each Asian country, first in Seoul, Korea in June, 2011, and also in Shanghai, China the following October. At least two representatives in both wet and dry research areas were invited from each country. The third ACGG-DB meeting was held at the Okinawa Industry Support Center in Naha-city, Okinawa, April 23-24, 2012, and this time, representatives from the glycoscience research community in Western countries were also invited. Thus, active discussions could take place regarding glyco-informatics and the need for collaborations in a global framework.
In recent informatics research, much work has been put into data exchange such that each database could provide links to other databases to obtain equivalent or related data entries. The Linked Open Data (LOD) cloud consists of a collection of online databases that provide links to one another (interlinks) through standardized representations of data. In particular, database providers provide their data using the Resource Description Framework (RDF), whose specifications have already been established by the W3C (http://www.w3.org/2001/sw/Specs.html). The LOD cloud initially consisted of just 12 datasets in 2007, but has grown to include almost 300 datasets from a large variety of fields such as media, government, geography and the life sciences (see the diagram at http://lod-cloud.net/). It has been shown that knowledge discovery can be simplified through the implementation of higher levels of querying methods through the LOD cloud. The online data in the LOD cloud is considered the Semantic Web, since RDF provides the means to add semantics to the links between the data. There are already a number of existing services that employ the Semantic Web. The publishing industry has a tradition of utilizing the latest web technologies and has been adhering to the Semantic Web from early on (Kurata 2012). Some major publishing companies have already incorporated semantic technologies in their services (Tutton 2008). The same applies to the life sciences in academic research (Post et al. 2007; Shirokizawa and Takagi 2011; Deus et al. 2012), and many databases have completed their transitions to the Semantic Web (see the LinkingOpenData project at http://www.w3.org/wiki/SweoIG/TaskForces/CommunityProjects/LinkingOpenData). Although the importance of developing databases is documented by the large amounts of data being produced by genome projects in the life sciences, for many research areas, results are not stored in databases and only provided as text. Thus it is difficult to convert such data uniformly into traditional relational databases. However, by utilizing semantic technologies, it is expected that it will become possible to accumulate experimental results from such data semi-automatically, whereas previously, researchers were required to manually read and understand numerous publications to sort through the results (Marshall et al. 2012).
Based on this situation, it is easy to see that semantic technologies will be needed in the life sciences in order to access and accumulate relevant experimental data. Despite this need, however, at the beginning of 2012, no glyco-related databases were applying semantic technologies. As a result, no glyco-related data could be found on the Semantic Web, which meant that among the life science databases that were linked on the Semantic Web, no information could be obtained about glycans. To address this gap, efforts to incorporate semantic technologies into glyco-informatics databases are urgently needed.
The third ACGG-DB Okinawa meeting was aimed at establishing a starting point for global collaborative relations to develop basic technologies to integrate semantic technologies into data resources that store glyco-related data. Thus, invitations were sent to the researchers involved in major glycan-related database projects. Jim Paulson of the Scripps Institute and Rahul Raman of MIT represented the US Consortium for Functional Glycomics (CFG) (Raman et al. 2006); René Ranzinger of the CCRC, University of Georgia, USA represented GlycomeDB (Ranzinger et al. 2009), Nicki Packer of Macquarie University, Australia, represented UniCarbKB (Campbell et al. 2011), and Thomas Lütteke (Justus-Liebig Univ. Gießen, Germany) represented MonosaccharideDB (http://www.monosaccharidedb.org) and GLYCOSCIENCES.de (Lutteke et al. 2006). From Japan, 11 glyco-researchers from AIST, Ritsumeikan University, DBCLS, RIKEN, the Noguchi Institute and Soka University attended. Another nine participants attended from the East Asian (Taiwan, Korea, China) region, totaling 25 attendees. During the two days of this meeting, the first half was used to review the major glycan-related database projects around the world. In the latter half, AIST and DBCLS researchers updated the participants on the Semantic Web and related technologies and explained the need for RDF-ication of glyco-related databases. Overall, everyone agreed and started discussing a plan of action, the first steps of which were initially undertaken at BioHackathon 2012 held in Toyama, Japan, that was attended by programmers involved in JCGGDB (http://jcggdb.jp), RINGS (Akune et al. 2010) GlycomeDB, MonosaccharideDB, UniCarbKB and BCSDB (Toukach 2011). As a result, it is expected that all of these databases will soon be made available on the Semantic Web to allow queries not only within the glyco field, but also linked with other life science databases.
Group photo in front of Okinawa Industry Support Center in Naha-city. 25 glyco-scientists from 19 research institutes discussed the potential for collaboration between glyco-science databases at the ACGG-DB meeting held April 23–24, 2012.
Acknowledgements
All participants appreciate Madoka Ishizaki for her help to manage this meeting. This meeting is a part of the JCGGDB project, which is supported by JST (Japan Science and Technology Agency) and NBDC (National Bioscience Database Center) Program for the Life Science Database Integration Project.
References
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):475–86. doi: 10.1089/omi.2009.0129. [DOI] [PubMed] [Google Scholar]
Campbell MP, Hayes CA, Struwe WB, Wilkins MR, Aoki-Kinoshita KF, Harvey DJ, Rudd PM, Kolarich D, Lisacek F, Karlsson NG, et al. UniCarbKB: putting the pieces together for glycomics research. Proteomics. 2011;11(21):4117–4121. doi: 10.1002/pmic.201100302. [DOI] [PubMed] [Google Scholar]
Deus HF, Veiga DF, Freire PR, Weinstein JN, Mills GB, Almeida JS. Exposing the cancer genome atlas as a SPARQL endpoint. J Biomed Inform. 2010;43:998–1008. doi: 10.1016/j.jbi.2010.09.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
Kurata K. Transformation of information resource: Electronic journal. Journal of Information Processing and Management. 2012;55:366–369. [Google Scholar]
Lutteke T, Bohne-Lang A, Loss A, Goetz T, Frank M, von der Lieth C-W. GLYCOSCIENCES.de: an Internet portal to support glycomics and glycobiology research. Glycobiology. 2006;16(5):71R–81R. doi: 10.1093/glycob/cwj049. [DOI] [PubMed] [Google Scholar]
Marshall MS, Boyce R, Deus HF, Zhao J, Willighagen EL, Samwald M, Pichler E, Hajagos J, Prud'hommeaux E, Stephens S. Emerging practices for mapping and linking life sciences data using RDF - A case series. J. Web Sem. 2012:2–13. [Google Scholar]
Post LJG, Roos M, Marshall MS, van Driel R, Breit TM. A semantic web approach applied to integrative bioinformatics experimentation: a biological use case with genomics data. Bioinformatics. 2007;23:3080–3087. doi: 10.1093/bioinformatics/btm461. [DOI] [PubMed] [Google 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. doi: 10.1093/glycob/cwj080. [DOI] [PubMed] [Google Scholar]
Ranzinger R, Frank M, von der Lieth C-W, Herget S. Glycome-DB.org. 2009. A portal for querying across the digital world of carbohydrate sequences. Glycobiology. 19(11):1563–1567. doi: 10.1093/glycob/cwp137. [DOI] [PubMed] [Google Scholar]
Shirokizawa Y, Takagi T. Towards the integration of databases in life science field: Establishment of National Bioscience Database Center (NBDC) Journal of Information Processing and Management. 2011;54:144–151. [Google Scholar]
Toukach PV. Bacterial carbohydrate structure database 3: principles and realization. Journal of Chemical Information and Modeling. 2011;51(1):159–170. doi: 10.1021/ci100150d. [DOI] [PubMed] [Google Scholar]
