| Chapter title |
Bioinformatics in glycomics: glycan characterization with mass spectrometric data using SimGlycan.
|
|---|---|
| Chapter number | 19 |
| Book title |
Functional Glycomics
|
| Published in |
Methods in molecular biology, January 2010
|
| DOI | 10.1007/978-1-60761-454-8_19 |
| Pubmed ID | |
| Book ISBNs |
978-1-60761-453-1, 978-1-60761-454-8
|
| Authors |
Arun Apte, Ningombam Sanjib Meitei, Apte, Arun, Meitei, Ningombam Sanjib |
| Abstract |
Mass spectrometry (MS), with its low sample requirement and high sensitivity, has been the predominantly used methodology for characterization and elucidation of glycan structures. However, manual interpretation of MS data is complex and tedious due to large number of product ions observed and also due to the variation in their m/z values under various experimental conditions. We present an automated tool, SimGlycan, for this purpose, which accepts raw/standard MS data files as input and characterizes the associated glycan structure with high accuracy using database searching and scoring techniques. Not only does it predict the glycan structure using an MS/MS database searching technique, but it also facilitates predicting novel glycans by drawing a glycan and mapping it onto an experimental spectrum to check the degree of proximity between the theoretical and the experimental glycans. It serves as a platform for developing advanced tools that may be used for glycopeptide identification using MS data and 3D structural analysis of glycans with a few improvements in the existing features. |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 8% |
| France | 1 | 4% |
| Unknown | 22 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 24% |
| Student > Ph. D. Student | 5 | 20% |
| Professor | 3 | 12% |
| Student > Bachelor | 2 | 8% |
| Student > Doctoral Student | 1 | 4% |
| Other | 4 | 16% |
| Unknown | 4 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 11 | 44% |
| Chemistry | 3 | 12% |
| Engineering | 2 | 8% |
| Unspecified | 1 | 4% |
| Nursing and Health Professions | 1 | 4% |
| Other | 3 | 12% |
| Unknown | 4 | 16% |