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CabosDB: Carbohydrate Sequencing Database

  • Norihiro Kikuchi

Abstract

High-throughput methods for sequencing biomolecules such as DNA, RNA and proteins, and for profiling their patterns of expression have accelerated genome-wide analyses in living organisms. Further, they have enabled molecular biologists and biochemists to investigate biological functions and disorders, the so-called -omics studies. Glycomics, which has become one of the most important fields in the post-genomic era, has lagged behind genomics and proteomics, mainly because of the inherent difficulties in the analysis of glycan structures and functions. However, developments in technologies and strategies for analyzing glycan structures should overcome many of these difficulties. In April 2003, the NEDO structural glycomics project (SG project) was initiated under the NEDO (New Energy and Industrial Technology Organization) framework supported by METI (the Ministry of Economy, Trade, and Industry), Japan; the project ran for 3 years. The purpose of this initiative was to develop novel methodologies for structural glycomics. In this project, we used a glycoproteomics method based on liquid chromatography coupled with mass spectrometry (LC-MS) and multistage tandem mass spectrometry, and a lectin profiling method using frontal affinity chromatography (FAC) and lectin arrays. These methods were applied to analyze glycosylation sites, the glycan sequences attached to glycoproteins, and to profile complex features of glycans expressed on cell surfaces. In addition to these methods, database and bioinformatics tools were developed to integrate data from the experiments, to support data analysis, and to provide glycobiologists with these data and with bioinformatics tools for structural glycomic studies. Here, we describe a database named CabosDB (CArBOhydrate Sequencing DataBase).

Keywords

Glycan Structure Oligosaccharide Structure Liquid Chromatography Couple With Mass Spectrometry Lectin Molecule Mass Spectrum Database 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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Copyright information

© Springer 2008

Authors and Affiliations

  • Norihiro Kikuchi
    • 1
  1. 1.TokyoJapan

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