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Glyco-Engineering

Overview of attention for book
Cover of 'Glyco-Engineering'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Current Approaches to Engineering N -Linked Protein Glycosylation in Bacteria
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    Chapter 2 Inverse Metabolic Engineering for Enhanced Glycoprotein Production in Escherichia coli
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    Chapter 3 GlycoSNAP: A High-Throughput Screening Methodology for Engineering Designer Glycosylation Enzymes
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    Chapter 4 Production of Glycoproteins with Asparagine-Linked N -Acetylglucosamine in Escherichia coli
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    Chapter 5 Glyco-engineering O-Antigen-Based Vaccines and Diagnostics in E. coli
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    Chapter 6 Progress in Yeast Glycosylation Engineering.
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    Chapter 7 Protein Production with a Pichia pastoris OCH1 Knockout Strain in Fed-Batch Mode.
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    Chapter 8 Engineering the Pichia pastoris N-Glycosylation Pathway Using the GlycoSwitch Technology
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    Chapter 9 Development of a Valuable Yeast Strain Using a Novel Mutagenesis Technique for the Effective Production of Therapeutic Glycoproteins.
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    Chapter 10 An Overview and History of Glyco-Engineering in Insect Expression Systems.
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    Chapter 11 Glyco-Engineering
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    Chapter 12 Glyco-Engineering
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    Chapter 13 Engineering N-Glycosylation Pathway in Insect Cells: Suppression of β-N-Acetylglucosaminidase and Expression of β-1,4-Galactosyltransferase.
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    Chapter 14 N-Glyco-Engineering in Plants: Update on Strategies and Major Achievements
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    Chapter 15 Glyco-Engineering
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    Chapter 16 Im“plant”ing of Mammalian Glycosyltransferase Gene into Plant Suspension-Cultured Cells Using Agrobacterium-Mediated Transformation
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    Chapter 17 Transient Glyco-Engineering of N. benthamiana Aiming at the Synthesis of Multi-antennary Sialylated Proteins
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    Chapter 18 Subcellular Targeting of Proteins Involved in Modification of Plant N- and O-Glycosylation
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    Chapter 19 Assembly of Multigene Constructs Using Golden Gate Cloning.
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    Chapter 20 Strategies for Engineering Protein N-Glycosylation Pathways in Mammalian Cells
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    Chapter 21 Glycan Remodeling with Processing Inhibitors and Lectin-Resistant Eukaryotic Cells
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    Chapter 22 Production of Highly Sialylated Recombinant Glycoproteins Using Ricinus communis Agglutinin-I-Resistant CHO Glycosylation Mutants
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    Chapter 23 Metabolic Glyco-Engineering in Eukaryotic Cells and Selected Applications
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    Chapter 24 Evaluation of Quenching and Extraction Methods for Nucleotide/Nucleotide Sugar Analysis
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    Chapter 25 Chemoenzymatic Glyco-engineering of Monoclonal Antibodies
  27. Altmetric Badge
    Chapter 26 Chemical Polysialylation of Recombinant Human Proteins
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    Chapter 27 Site-Specific Glycosylation Profiling Using Liquid Chromatography-Tandem Mass Spectrometry (LC-MS)
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    Chapter 28 Mass Spectrometric Analysis of Oligo- and Polysialic Acids
  30. Altmetric Badge
    Chapter 29 Isomer-Specific Analysis of Released N-Glycans by LC-ESI MS/MS with Porous Graphitized Carbon
Overall attention for this book and its chapters
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

Mentioned by

twitter
7 tweeters
wikipedia
1 Wikipedia page

Citations

dimensions_citation
3 Dimensions

Readers on

mendeley
105 Mendeley
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Title
Glyco-Engineering
Published by
Methods in molecular biology, January 2015
DOI 10.1007/978-1-4939-2760-9
Pubmed ID
ISBNs
978-1-4939-2759-3, 978-1-4939-2760-9
Authors

Alexandra Castilho, Jaffé, Stephen R P, Strutton, Benjamin, Pandhal, Jagroop, Wright, Phillip C

Editors

Alexandra Castilho

Abstract

Inverse metabolic engineering (IME) provides a strategy to rapidly identify the genetic elements responsible for the desired phenotype of a chosen target organism. This methodology has been successfully applied towards enhancing the N-linked glycosylation efficiency of Escherichia coli. Here, we describe the generation of differentially sized libraries from the E. coli W3110 genome followed by high-throughput semiquantitative glycan specific screening. DNA sequenced targets demonstrating increased levels of glycan production were selected for forward engineering, protein overexpression, and absolute quantification of glycoproteins.

Twitter Demographics

The data shown below were collected from the profiles of 7 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 105 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Austria 1 <1%
Unknown 104 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 26 25%
Student > Ph. D. Student 19 18%
Researcher 18 17%
Student > Bachelor 11 10%
Other 6 6%
Other 10 10%
Unknown 15 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 39 37%
Agricultural and Biological Sciences 28 27%
Pharmacology, Toxicology and Pharmaceutical Science 7 7%
Chemistry 6 6%
Chemical Engineering 5 5%
Other 5 5%
Unknown 15 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 April 2017.
All research outputs
#1,885,055
of 9,741,572 outputs
Outputs from Methods in molecular biology
#673
of 7,458 outputs
Outputs of similar age
#76,826
of 342,896 outputs
Outputs of similar age from Methods in molecular biology
#116
of 1,123 outputs
Altmetric has tracked 9,741,572 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,458 research outputs from this source. They receive a mean Attention Score of 2.0. This one has done particularly well, scoring higher than 90% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 342,896 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 1,123 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.