Abstract

First, we constructed a reaction pattern library consisting of bond-formation patterns of GT reactions and investigated the co-occurrence frequencies of all reaction patterns in the glycan database. This was followed by the prediction of glycan structures using this library and a co-occurrence score. A penalty score was also implemented in the prediction method. Then we examined the performance of prediction by the leave-one-out cross validation method using individual reaction pattern profiles in the KEGG GLYCAN database as virtual expression profiles. The accuracy of prediction was 81%. Finally, we applied the prediction method to real expression data. Using expression profiles from the human carcinoma cell, glycan structures with sialic acid and sialyl Lewis X epitope were predicted, which corresponded well with experimental results.

Keywords

GlycosyltransferaseGlycanGeneComputational biologyGene expressionComputer scienceBiologyGeneticsGlycoprotein

Affiliated Institutions

Related Publications

The KEGG resource for deciphering the genome

M. Kanehisa

A grand challenge in the post-genomic era is a complete computer representation of the cell and the organism, which will enable computational prediction of higher-level complexi...

2003 Nucleic Acids Research 5098 citations

Publication Info

Year
2005
Type
article
Volume
21
Issue
21
Pages
3976-3982
Citations
75
Access
Closed

External Links

View on DOI.org

Social Impact

Altmetric
PlumX Metrics

Social media, news, blog, policy document mentions

Citation Metrics

75
OpenAlex

Cite This

Shin Kawano, Kosuke Hashimoto, Toshihito Miyama et al. (2005). Prediction of glycan structures from gene expression data based on glycosyltransferase reactions. Computer applications in the biosciences , 21 (21) , 3976-3982. https://doi.org/10.1093/bioinformatics/bti666

Identifiers

DOI
10.1093/bioinformatics/bti666