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Computational Systems Biology

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Cover of 'Computational Systems Biology'

Table of Contents

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    Book Overview
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    Chapter 1 DNA Sequencing Data Analysis
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    Chapter 2 Transcriptome Sequencing: RNA-Seq
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    Chapter 3 Capture Hybridization of Long-Range DNA Fragments for High-Throughput Sequencing
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    Chapter 4 The Introduction and Clinical Application of Cell-Free Tumor DNA
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    Chapter 5 Bioinformatics Analysis for Cell-Free Tumor DNA Sequencing Data
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    Chapter 6 An Overview of Genome-Wide Association Studies
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    Chapter 7 Integrative Analysis of Omics Big Data
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    Chapter 8 The Reconstruction and Analysis of Gene Regulatory Networks
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    Chapter 9 Differential Coexpression Network Analysis for Gene Expression Data
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    Chapter 10 iSeq: Web-Based RNA-seq Data Analysis and Visualization
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    Chapter 11 Revisit of Machine Learning Supported Biological and Biomedical Studies
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    Chapter 12 Identifying Interactions Between Long Noncoding RNAs and Diseases Based on Computational Methods
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    Chapter 13 Survey of Computational Approaches for Prediction of DNA-Binding Residues on Protein Surfaces
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    Chapter 14 Computational Prediction of Protein O-GlcNAc Modification
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    Chapter 15 Machine Learning-Based Modeling of Drug Toxicity
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    Chapter 16 Metabolomics: A High-Throughput Platform for Metabolite Profile Exploration
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    Chapter 17 Single-Cell Protein Assays: A Review
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    Chapter 18 Data Analysis in Single-Cell Transcriptome Sequencing
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    Chapter 19 Applications of Single-Cell Sequencing for Multiomics
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    Chapter 20 Progress on Diagnosis of Tuberculous Meningitis
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    Chapter 21 Insights of Acute Lymphoblastic Leukemia with Development of Genomic Investigation
Attention for Chapter 14: Computational Prediction of Protein O-GlcNAc Modification
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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

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
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%