Glycome Informatics: Methods and Applications

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)