Chapter title |
Glycan array data management at consortium for functional glycomics.
|
---|---|
Chapter number | 13 |
Book title |
Glycoinformatics
|
Published in |
Methods in molecular biology, February 2015
|
DOI | 10.1007/978-1-4939-2343-4_13 |
Pubmed ID | |
Book ISBNs |
978-1-4939-2342-7, 978-1-4939-2343-4
|
Authors |
Maha Venkataraman, Ram Sasisekharan, Rahul Raman, Venkataraman, Maha, Sasisekharan, Ram, Raman, Rahul |
Editors |
Thomas Lütteke, Martin Frank |
Abstract |
Glycomics or the study of structure-function relationships of complex glycans has reshaped post-genomics biology. Glycans mediate fundamental biological functions via their specific interactions with a variety of proteins. Recognizing the importance of glycomics, large-scale research initiatives such as the Consortium for Functional Glycomics (CFG) were established to address these challenges. Over the past decade, the Consortium for Functional Glycomics (CFG) has generated novel reagents and technologies for glycomics analyses, which in turn have led to generation of diverse datasets. These datasets have contributed to understanding glycan diversity and structure-function relationships at molecular (glycan-protein interactions), cellular (gene expression and glycan analysis), and whole organism (mouse phenotyping) levels. Among these analyses and datasets, screening of glycan-protein interactions on glycan array platforms has gained much prominence and has contributed to cross-disciplinary realization of the importance of glycomics in areas such as immunology, infectious diseases, cancer biomarkers, etc. This manuscript outlines methodologies for capturing data from glycan array experiments and online tools to access and visualize glycan array data implemented at the CFG. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 14 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Doctoral Student | 2 | 14% |
Professor | 2 | 14% |
Researcher | 2 | 14% |
Student > Ph. D. Student | 2 | 14% |
Student > Bachelor | 1 | 7% |
Other | 2 | 14% |
Unknown | 3 | 21% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 3 | 21% |
Agricultural and Biological Sciences | 3 | 21% |
Engineering | 2 | 14% |
Computer Science | 1 | 7% |
Chemistry | 1 | 7% |
Other | 1 | 7% |
Unknown | 3 | 21% |