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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A condition-specific codon optimization approach for improved heterologous gene expression in Saccharomyces cerevisiae
|
|---|---|
| Published in |
BMC Systems Biology, March 2014
|
| DOI | 10.1186/1752-0509-8-33 |
| Pubmed ID | |
| Authors |
Amanda M Lanza, Kathleen A Curran, Lindsey G Rey, Hal S Alper |
| Abstract |
Heterologous gene expression is an important tool for synthetic biology that enables metabolic engineering and the production of non-natural biologics in a variety of host organisms. The translational efficiency of heterologous genes can often be improved by optimizing synonymous codon usage to better match the host organism. However, traditional approaches for optimization neglect to take into account many factors known to influence synonymous codon distributions. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 219 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 3 | 1% |
| United States | 3 | 1% |
| China | 2 | <1% |
| Australia | 1 | <1% |
| Canada | 1 | <1% |
| Lithuania | 1 | <1% |
| Germany | 1 | <1% |
| Philippines | 1 | <1% |
| Unknown | 206 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 54 | 25% |
| Researcher | 32 | 15% |
| Student > Master | 31 | 14% |
| Student > Bachelor | 25 | 11% |
| Professor > Associate Professor | 10 | 5% |
| Other | 21 | 10% |
| Unknown | 46 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 68 | 31% |
| Biochemistry, Genetics and Molecular Biology | 50 | 23% |
| Engineering | 15 | 7% |
| Computer Science | 6 | 3% |
| Chemical Engineering | 5 | 2% |
| Other | 21 | 10% |
| Unknown | 54 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 12. 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 12 November 2020.
All research outputs
#2,542,168
of 22,749,166 outputs
Outputs from BMC Systems Biology
#63
of 1,142 outputs
Outputs of similar age
#29,220
of 243,429 outputs
Outputs of similar age from BMC Systems Biology
#2
of 20 outputs
Altmetric has tracked 22,749,166 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,142 research outputs from this source. They receive a mean Attention Score of 3.6. This one has done particularly well, scoring higher than 94% 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 243,429 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 87% of its contemporaries.
We're also able to compare this research output to 20 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 90% of its contemporaries.