A Novel Linear Code Nomenclature for Complex Carbohydrates.

Banin, Ehud; Neuberger, Yael; Altshuler, Yaniv; Halevi, Asaf; Inbar, Ori; Dotan, Nir; Dukler, A.; 献一, 笠井

doi:10.4052/tigg.14.127

Cited by 82 publications

(45 citation statements)

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“…The glycan binding profile for MAb MG96 was analyzed by applying a sample of the antibody (234 ng/ ml) to 46 mono-and oligosaccharides (Table 1) expressed in condensed form, following the rules of the new linear syntax for carbohydrates (Banin et al, 2002). Although the antibody binding profile exhibited wide cross-reactivity, only a few of the glycans showed statistically significant binding (po0:05) (Fig.…”

Section: Mg96 Recognizes a Related Set Of Mono-and Oligosaccharides Omentioning

confidence: 99%

Sugar epitopes as potential universal disease transmission blocking targets

Dinglasan

Valenzuela

Azad

2005

Insect Biochemistry and Molecular Biology

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Section: Mg96 Recognizes a Related Set Of Mono-and Oligosaccharides Omentioning

confidence: 99%

Sugar epitopes as potential universal disease transmission blocking targets

Dinglasan

Valenzuela

Azad

2005

Insect Biochemistry and Molecular Biology

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“…KEGG GLYCAN has implemented the KCF (KEGG Chemical Function) format (Hattori et al, 2003), which is currently the main format used by RINGS. GLYCOSCIENCES.de uses their own LINUCS (Linear Notation for Unique Carbohydrate Sequences) format (BohneLang et al, 2001), and the CFG uses the Linear Codeª format (Banin et al, 2002). XML (extensible markup language) standards have also been developed for carbohydrate structure data exchange; GlycoCT and GLYDE-II.…”

Section: Introductionmentioning

confidence: 99%

The RINGS Resource for Glycome Informatics Analysis and Data Mining on the Web

Akune

Hosoda²,

Kaiya³

et al. 2010

OMICS: A Journal of Integrative Biology

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In the bioinformatics field, many computer algorithmic and data mining technologies have been developed for gene prediction, protein-protein interaction analysis, sequence analysis, and protein folding predictions, to name a few. This kind of research has branched off from the genomics field, creating the transcriptomics, proteomics, metabolomics, and glycomics research areas in the postgenomic age. In the glycomics field, given the complexity of glycan structures with their branches of monosaccharides in various conformations, new data mining and algorithmic methods have been developed in an attempt to gain a better understanding of glycans. However, these methods have not all been implemented as tools such that the glycobiology community may utilize them in their research. Thus, we have developed RINGS (Resource for INformatics of Glycomes at Soka) as a freely available Web resource for glycobiologists to analyze their data using the latest data mining and algorithmic techniques. It provides a number of tools including a 2D glycan drawing and querying interface called DrawRINGS, a Glycan Pathway Predictor (GPP) tool for dynamically computing the N-glycan biosynthesis pathway from a given glycan structure, and data mining tools Glycan Miner Tool and Profile PSTMM. These tools and other utilities provided by RINGS will be described. The URL for RINGS is http://rings.t.soka.ac.jp/.

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“…To overcome this limitation, linear notation for unique description of carbohydrate sequences (LINUCS) was developed to create a linear representation of the glycan by extending IUPAC description along with the glycosidic linkage information [7]. Another available format is Glycominds' Linear Code TM , which exploits a special lookup table for determining the order of branching [8]. The monosaccharide units and linkages are represented by oneto two-letters in this representation.…”

Section: Introductionmentioning

confidence: 99%

“…1 A comparison of current formats to depict N-linked glycan structures using a symbols adopted from the nomenclature proposed by the Oxford Glycobiology Institute (UK) to represent a structure pictorially. The same structure is then described using b a full word representation, c the LINUCS format [7], and d Linear Code TM [8] pathways. Towards this end, we propose a novel alphanumeric code, GlycoDigit, for the description of N-linked glycan structures that are commonly observed in secreted glycoproteins from engineered mammalian cell lines such as Chinese hamster ovary (CHO) cells.…”

Section: Introductionmentioning

confidence: 99%

An alpha-numeric code for representing N-linked glycan structures in secreted glycoproteins

Yusufi

Park

Lee

2008

Bioprocess Biosyst Eng

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Advances in high-throughput techniques have led to the creation of increasing amounts of glycome data. The storage and analysis of this data would benefit greatly from a compact notation for describing glycan structures that can be easily stored and interpreted by computers. Towards this end, we propose a fixed-length alpha-numeric code for representing N-linked glycan structures commonly found in secreted glycoproteins from mammalian cell cultures. This code, GlycoDigit, employs a pre-assigned alpha-numeric index to represent the monosaccharides attached in different branches to the core glycan structure. The present branch-centric representation allows us to visualize the structure while the numerical nature of the code makes it machine readable. In addition, a difference operator can be defined to quantitatively differentiate between glycan structures for further analysis. The usefulness and applicability of GlycoDigit were demonstrated by constructing and visualizing an N-linked glycosylation network.

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A Novel Linear Code Nomenclature for Complex Carbohydrates.

Cited by 82 publications

References 0 publications

Sugar epitopes as potential universal disease transmission blocking targets

Sugar epitopes as potential universal disease transmission blocking targets

The RINGS Resource for Glycome Informatics Analysis and Data Mining on the Web

An alpha-numeric code for representing N-linked glycan structures in secreted glycoproteins

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