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X Demographics
Mendeley readers
Attention Score in Context
| 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. |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | 33% |
| France | 1 | 17% |
| Unknown | 3 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 83% |
| Scientists | 1 | 17% |
Mendeley readers
The data shown below were compiled from readership statistics for 133 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Austria | 1 | <1% |
| Unknown | 132 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 27 | 20% |
| Student > Ph. D. Student | 25 | 19% |
| Researcher | 20 | 15% |
| Student > Bachelor | 17 | 13% |
| Other | 7 | 5% |
| Other | 14 | 11% |
| Unknown | 23 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 52 | 39% |
| Agricultural and Biological Sciences | 31 | 23% |
| Pharmacology, Toxicology and Pharmaceutical Science | 7 | 5% |
| Chemistry | 7 | 5% |
| Chemical Engineering | 5 | 4% |
| Other | 8 | 6% |
| Unknown | 23 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 22 February 2022.
All research outputs
#3,419,366
of 23,567,572 outputs
Outputs from Methods in molecular biology
#834
of 13,353 outputs
Outputs of similar age
#49,542
of 355,843 outputs
Outputs of similar age from Methods in molecular biology
#48
of 991 outputs
Altmetric has tracked 23,567,572 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,353 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done particularly well, scoring higher than 93% 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 355,843 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 85% of its contemporaries.
We're also able to compare this research output to 991 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.