Chapman and Hall/CRC
256 pages | 93 B/W Illus.
A Focused, State-of-the-Art Overview of This Evolving Field
Presents Various Techniques for Glycoinformatics
The development and use of informatics tools and databases for glycobiology and glycomics research have increased considerably in recent years. In addition to accumulating well-structured glyco-related data, researchers have now developed semi-automated methods for the annotation of mass spectral data and algorithms for capturing patterns in glycan structure data. These techniques have enabled researchers to gain a better understanding of how these complex structures affect protein function and other biological processes, including cancer.
One of the few up-to-date books available in this important area, Glycome Informatics: Methods and Applications covers all known informatics methods pertaining to the study of glycans. It discusses the current status of carbohydrate databases, the latest analytical techniques, and the informatics needed for rapid progress in glycomics research.
Providing an overall understanding of glycobiology, this self-contained guide focuses on the development of glycome informatics methods and current problems faced by researchers. It explains how to implement informatics methods in glycobiology. The author includes the required background material on glycobiology as well as the mathematical concepts needed to understand advanced mining and algorithmic techniques. She also suggests project themes for readers looking to begin research in the field.
Introduction to Glycobiology
Roles of carbohydrates
Potential for drug discovery
Glycan structure databases
Terminology and notations
Data mining techniques
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
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)