Glycome Informatics: Methods and Applications
by Aoki-Kinoshita; Kiyoko F.Edition: 1st
Format: Hardcover
Pub. Date: 2009-11-19
Publisher(s): Chapman & Hall/
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Summary
The four essential molecular building blocks of cells are collectively referred to as glycans. The development of informatics tools for glycobiology and glycomics research has increased in recent years. This book provides an understanding of the glycobiology field, existing glycome informatics methods, and current problems faced by researchers. It presents an overview of available database resources, algorithms, and techniques. The author also introduces the role of carbohydrates as the third class of information-containing biological macromolecules and explains the fundamental characteristics of carbohydrate structures, which require specific encoding schemata, and algorithmic developments.
Table of Contents
| List of Tables | p. xi |
| List of Figures | p. xiii |
| About the Author | p. xvii |
| Introduction to Glycobiology | p. 1 |
| Roles of carbohydrates | p. 1 |
| Glycan structures | p. 2 |
| Glycan classes | p. 6 |
| Glycan biosynthesis | p. 13 |
| N-linked glycans | p. 13 |
| O-linked glycans | p. 16 |
| Glycosaminoglycans (GAGs) | p. 16 |
| Glycosphingolipids (GSLs) | p. 17 |
| GPI anchors | p. 19 |
| LPS | p. 19 |
| Glycan motifs | p. 20 |
| Potential for drug discovery | p. 22 |
| Background | p. 25 |
| Glycan nomenclature | p. 25 |
| InChIÖ | p. 25 |
| (Extended) IUPAC format | p. 27 |
| CarbBank format | p. 30 |
| KCF format | p. 31 |
| LINUCS format | p. 32 |
| BCSDB format | p. 34 |
| Linear Code“ | p. 37 |
| GlycoCT format | p. 40 |
| XML representations | p. 46 |
| Lectin-glycan interactions | p. 48 |
| Families and types of lectins | p. 50 |
| Carbohydrate-binding mechanism of lectins | p. 57 |
| Carbohydrate-carbohydrate interactions | p. 58 |
| Databases | p. 61 |
| Glycan structure databases | p. 61 |
| KEGG GLYCAN | p. 62 |
| GLYCOSCIENCES.de | p. 68 |
| CFG | p. 74 |
| BCSDB | p. 82 |
| GLYCO3D | p. 85 |
| MonoSaccharideDB | p. 86 |
| GlycomeDB | p. 89 |
| Glyco-gene databases | p. 90 |
| KEGG BRITE | p. 91 |
| CFG | p. 91 |
| GGDB | p. 94 |
| CAZy | p. 94 |
| Lipid databases | p. 96 |
| SphingoMAP© | p. 96 |
| LipidBank | p. 97 |
| LMSD | p. 98 |
| Lectin databases | p. 101 |
| Lectines | p. 101 |
| Animal Lectin DB | p. 101 |
| Others | p. 101 |
| GlycoEpitopeDB | p. 101 |
| ECODAB | p. 102 |
| SugarBindDB | p. 106 |
| Glycome Informatics | p. 107 |
| Terminology and notations | p. 107 |
| Algorithmic techniques | p. 108 |
| Tree structure alignment | p. 108 |
| Linkage analysis using score matrices | p. 110 |
| Glycan variation map | p. 112 |
| Bioinformatic methods | p. 114 |
| Glycan structure prediction from glycogene microarrays | p. 114 |
| Glyco-gene sequence and structure analysis | p. 116 |
| Glyco-related pathway analysis | p. 119 |
| Mass spectral data annotation | p. 124 |
| Data mining techniques | p. 130 |
| Kernel methods | p. 131 |
| Frequent subtree mining | p. 138 |
| Probabilistic models | p. 142 |
| Glycomics tools | p. 173 |
| Visualization tools | p. 173 |
| Pathway analysis tools | p. 177 |
| PDB data analysis | p. 178 |
| 3D analysis tools | p. 179 |
| Molecular dynamics | p. 182 |
| Spectroscopic tools | p. 186 |
| NMR tools | p. 189 |
| Potential Research Projects | p. 193 |
| Sequence and structural analyses | p. 193 |
| Glycan score matrix | p. 194 |
| Visualization | p. 194 |
| Databases and techniques to integrate heterogeneous data sets | p. 195 |
| Automated characterization of glycans from MS data | p. 196 |
| Prediction of glycans from data other than MS | p. 196 |
| Biomarker prediction | p. 197 |
| Systems analyses | p. 197 |
| Drug discovery | p. 198 |
| Sequence Analysis Methods | p. 199 |
| Pairwise sequence alignment (dynamic programming) | p. 199 |
| Dynamic programming | p. 199 |
| Sequence alignment | p. 202 |
| BLOSUM (BLOcks Substitution Matrix) | p. 205 |
| Machine Learning Methods | p. 207 |
| Kernel methods and SVMs | p. 207 |
| Hidden Markov models | p. 211 |
| The three problems of interest for HMMs | p. 213 |
| Expectation-Maximization (EM) algorithm | p. 215 |
| Hidden tree Markov models | p. 216 |
| Profile Hidden Markov models (profile HMMs) | p. 218 |
| Glycomics Technologies | p. 221 |
| Mass spectrometry (MS) | p. 221 |
| MALDI-MS | p. 222 |
| FT-ICR | p. 223 |
| LC-MS (HPLC) | p. 224 |
| Tandem MS | p. 224 |
| Nuclear magnetic resonance (NMR) | p. 225 |
| References | p. 227 |
| Index | p. 241 |
| Table of Contents provided by Ingram. All Rights Reserved. |
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