Glycome informatics : methods and applications / Kiyoko F. Aoki-Kinoshita.

Format:

Book

Author/Creator:

Aoki-Kinoshita, Kiyoko F.

Series:

Chapman and Hall/CRC mathematical & computational biology series

Chapman & Hall/CRC mathematical and computational biology series

Language:

English

Subjects (All):

Glycoconjugates.

Glycomics.

Bioinformatics.

Medical Subjects:

Glycomics.

Physical Description:

xvii, 244 pages : illustrations ; 25 cm.

Place of Publication:

Boca Raton : CRC Press, [2010]

Contents:

1 Introduction to Glycobiology 1

1.1 Roles of carbohydrates 1

1.2 Glycan structures 2

1.3 Glycan classes 6

1.4 Glycan biosynthesis 13

1.4.1 N-linked glycans 13

1.4.2 O-linked glycans 16

1.4.3 Glycosaminoglycans (GAGs) 16

1.4.4 Glycosphingolipids (GSLs) 17

1.4.5 GPI anchors 19

1.4.6 LPS 19

1.5 Glycan motifs 20

1.6 Potential for drug discovery 22

2 Background 25

2.1 Glycan nomenclature 25

2.1.1 InChI™ 25

2.1.2 (Extended) IUPAC format 27

2.1.3 CarbBank format 30

2.1.4 KCF format 31

2.1.5 LINUCS format 32

2.1.6 BCSDB format 34

2.1.7 Linear Codeʼ 37

2.1.8 GlycoCT format 40

2.1.9 XML representations 46

2.2 Lectin-glycan interactions 48

2.2.1 Families and types of lectins 50

2.2.2 Carbohydrate-binding mechanism of lectins 57

2.3 Carbohydrate-carbohydrate interactions 58

3 Databases 61

3.1 Glycan structure databases 61

3.1.1 KEGG GLYCAN 62

3.1.2 GLYCOSCIENCES.de 68

3.1.3 CFG 74

3.1.4 BCSDB 82

3.1.5 GLYCO3D 85

3.1.6 MonoSaccharideDB 86

3.1.7 GlycomeDB 89

3.2 Glyco-gene databases 90

3.2.1 KEGG BRITE 91

3.2.2 CFG 91

3.2.3 GGDB 94

3.2.4 CAZy 94

3.3 Lipid databases 96

3.3.1 SphingoMAP♭ 96

3.3.2 LipidBank 97

3.3.3 LMSD 98

3.4 Lectin databases 101

3.4.1 Lectines 101

3.4.2 Animal Lectin DB 101

3.5 Others 101

3.5.1 GlycoEpitopeDB 101

3.5.2 ECODAB 102

3.5.3 SugarBindDB 106

4 Glycome Informatics 107

4.1 Terminology and notations 107

4.2 Algorithmic techniques 108

4.2.1 Tree structure alignment 108

4.2.2 Linkage analysis using score matrices 110

4.2.3 Glycan variation map 112

4.3 Bioinformatic methods 114

4.3.1 Glycan structure prediction from glycogene microarrays 114

4.3.2 Glyco-gene sequence and structure analysis 116

4.3.3 Glyco-related pathway analysis 119

4.3.4 Mass spectral data annotation 124

4.4 Data mining techniques 130

4.4.1 Kernel methods 131

4.4.2 Frequent subtree mining 138

4.4.3 Probabilistic models 142

4.5 Glycomics tools 173

4.5.1 Visualization tools 173

4.5.2 Pathway analysis tools 177

4.5.3 PDB data analysis 178

4.5.4 3D analysis tools 179

4.5.5 Molecular dynamics 182

4.5.6 Spectroscopic tools 186

4.5.7 NMR tools 189

5 Potential Research Projects 193

5.1 Sequence and structural analyses 193

5.1.1 Glycan score matrix 194

5.1.2 Visualization 194

5.2 Databases and techniques to integrate heterogeneous data sets 195

5.3 Automated characterization of glycans from MS data 196

5.4 Prediction of glycans from data other than MS 196

5.5 Biomarker prediction 197

5.6 Systems analyses 197

5.7 Drug discovery 198

A Sequence Analysis Methods 199

A.1 Pairwise sequence alignment (dynamic programming) 199

A.1.1 Dynamic programming 199

A.1.2 Sequence alignment 202

A.2 BLOSUM (BLOcks Substitution Matrix) 205

B Machine Learning Methods 207

B.1 Kernel methods and SVMs 207

B.2 Hidden Markov models 211

B.2.1 The three problems of interest for HMMs 213

B.2.2 Expectation-Maximization (EM) algorithm 215

B.2.3 Hidden tree Markov models 216

B.2.4 Profile Hidden Markov models (profile HMMs) 218

C Glycomics Technologies 221

C.1 Mass spectrometry (MS) 221

C.1.l MALDI-MS 222

C.1.2 FT-ICR 223

C.1.3 LC-MS (HPLC) 224

C.1.4 Tandem MS 224

C.2 Nuclear magnetic resonance (NMR) 225.

Notes:

Includes bibliographical references and index.

ISBN:

9781420083347

1420083341

OCLC:

226357297