Keywords:
Bioinformatics.
;
Glycoconjugates.
;
Electronic books.
Description / Table of Contents:
Introduction to Glycobiology Roles of carbohydrates Glycan structures Glycan classes Glycan biosynthesis Glycan motifs Potential for drug discovery Background Glycan nomenclature Carbohydrate-carbohydrate interactions Databases Glycan structure databases Glyco-gene databases Lipid databases Lectin databases Others Glycome Informatics Terminology and notations Algorithmic techniques Bioinformatic methods Data mining techniques Glycomics tools Potential Research Projects Sequence and structural analyses Databases and techniques to integrate heterogeneous data sets Automated characterization of glycan structures from MS spectra Prediction of glycan structures from data other than MS spectra Biomarker prediction Systems analyses Drug discovery Appendix A: Sequence Analysis Methods Pairwise sequence alignment (dynamic programming) Amino acid score matrix BLOSUM (BLOcks Substitution Matrix) Appendix B: Machine Learning Methods Kernel methods and SVMs Hidden Markov models Appendix C: Glycomics Technologies Mass spectrometry (MS) Nuclear magnetic resonance (NMR).
Type of Medium:
Online Resource
Pages:
1 online resource (263 pages)
Edition:
1st ed.
ISBN:
9781420083361
Series Statement:
Chapman and Hall/CRC Mathematical and Computational Biology Series
URL:
https://ebookcentral.proquest.com/lib/geomar/detail.action?docID=589861
DDC:
572/.567
Language:
English
Note:
Cover -- Half Title -- Series Page -- Published Titles -- Title Page -- Copyright Page -- Contents -- List of Tables -- List of Figures -- About the Author -- 1 Introduction to Glycobiology -- 1.1 Roles of carbohydrates -- 1.2 Glycan structures -- 1.3 Glycan classes -- 1.4 Glycan biosynthesis -- 1.4.1 N-linked glycans -- 1.4.2 O-linked glycans -- 1.4.3 Glycosaminoglycans (GAGs) -- 1.4.4 Glycosphingolipids (GSLs) -- 1.4.5 GPI anchors -- 1.4.6 LPS -- 1.5 Glycan motifs -- 1.6 Potential for drug discovery -- 2 Background -- 2.1 Glycan nomenclature -- 2.1.1 InChI™ -- 2.1.2 (Extended) IUPAC format -- 2.1.3 CarbBank format -- 2.1.4 KCF format -- 2.1.5 LINUCS format -- 2.1.6 BCSDB format -- 2.1.7 Linear Code® -- 2.1.8 GlycoCT format -- 2.1.9 XML representations -- 2.2 Lectin-glycan interactions -- 2.2.1 Families and types of lectins -- 2.2.2 Carbohydrate-binding mechanism of lectins -- 2.3 Carbohydrate-carbohydrate interactions -- 3 Databases -- 3.1 Glycan structure databases -- 3.1.1 KEGG GLYCAN -- 3.1.2 GLYCOSCIENCES.de -- 3.1.3 CFG -- 3.1.4 BCSDB -- 3.1.5 GLYCO3D -- 3.1.6 MonoSaccharideDB -- 3.1.7 GlycomeDB -- 3.2 Glyco-gene databases -- 3.2.1 KEGG BRITE -- 3.2.2 CFG -- 3.2.3 GGDB -- 3.2.4 CAZy -- 3.3 Lipid databases -- 3.3.1 SphingoMAP© -- 3.3.2 LipidBank -- 3.3.3 LMSD -- 3.4 Lectin databases -- 3.4.1 Lectines -- 3.4.2 Animal Lectin DB -- 3.5 Others -- 3.5.1 GlycoEpitopeDB -- 3.5.2 ECODAB -- 3.5.3 SugarBindDB -- 4 Glycome Informatics -- 4.1 Terminology and notations -- 4.2 Algorithmic techniques -- 4.2.1 Tree structure alignment -- 4.2.2 Linkage analysis using score matrices -- 4.2.3 Glycan variation map -- 4.3 Bioinformatic methods -- 4.3.1 Glycan structure prediction from glycogene microarrays -- 4.3.2 Glyco-gene sequence and structure analysis -- 4.3.3 Glyco-related pathway analysis -- 4.3.4 Mass spectral data annotation.
,
4.4 Data mining techniques -- 4.4.1 Kernel methods -- 4.4.2 Frequent subtree mining -- 4.4.3 Probabilistic models -- 4.5 Glycomics tools -- 4.5.1 Visualization tools -- 4.5.2 Pathway analysis tools -- 4.5.3 PDB data analysis -- 4.5.4 3D analysis tools -- 4.5.5 Molecular dynamics -- 4.5.6 Spectroscopic tools -- 4.5.7 NMR tools -- 5 Potential Research Projects -- 5.1 Sequence and structural analyses -- 5.1.1 Glycan score matrix -- 5.1.2 Visualization -- 5.2 Databases and techniques to integrate heterogeneous data sets -- 5.3 Automated characterization of glycans from MS data -- 5.4 Prediction of glycans from data other than MS -- 5.5 Biomarker prediction -- 5.6 Systems analyses -- 5.7 Drug discovery -- A Sequence Analysis Methods -- A.1 Pairwise sequence alignment (dynamic programming) -- A.1.1 Dynamic programming -- A.1.2 Sequence alignment -- A.2 BLOSUM (BLOcks Substitution Matrix) -- B Machine Learning Methods -- B.1 Kernel methods and SVMs -- B.2 Hidden Markov models -- B.2.1 The three problems of interest for HMMs -- B.2.2 Expectation-Maximization (EM) algorithm -- B.2.3 Hidden tree Markov models -- B.2.4 Profile Hidden Markov models (profile HMMs) -- C Glycomics Technologies -- C.1 Mass spectrometry (MS) -- C.1.1 MALDI-MS -- C.1.2 FT-ICR -- C.1.3 LC-MS (HPLC) -- C.1.4 Tandem MS -- C.2 Nuclear magnetic resonance (NMR) -- References -- Index.
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