Chapter title |
Computational Prediction of Protein O-GlcNAc Modification
|
---|---|
Chapter number | 14 |
Book title |
Computational Systems Biology
|
Published in |
Methods in molecular biology, January 2018
|
DOI | 10.1007/978-1-4939-7717-8_14 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7716-1, 978-1-4939-7717-8
|
Authors |
Cangzhi Jia, Yun Zuo |
Abstract |
Protein O-GlcNAcylation on serine and threonine residues is a significant posttranslational modification. Experimental techniques can uncover only a small portion of O-GlcNAcylation sites. Several computational algorithms have been proposed as necessary auxiliary tools to identify potential O-GlcNAcylation sites. This chapter discusses the metrics and procedures used to assess prediction tools and surveys six computational tools for the prediction of protein O-GlcNAcylation sites. Analyses of these tools using an independent test dataset indicated the advantages and disadvantages of the six existing prediction methods. We also discuss the challenges that may be faced while developing novel predictors in the future. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 8 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 2 | 25% |
Professor | 1 | 13% |
Student > Doctoral Student | 1 | 13% |
Student > Master | 1 | 13% |
Student > Ph. D. Student | 1 | 13% |
Other | 0 | 0% |
Unknown | 2 | 25% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 3 | 38% |
Biochemistry, Genetics and Molecular Biology | 2 | 25% |
Unknown | 3 | 38% |