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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Parts Characterization for Tunable Protein Expression
|
|---|---|
| Chapter number | 1 |
| Book title |
Synthetic Metabolic Pathways
|
| Published in |
Methods in molecular biology, January 2018
|
| DOI | 10.1007/978-1-4939-7295-1_1 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7294-4, 978-1-4939-7295-1
|
| Authors |
Michael S. Klausen, Morten O. A. Sommer, Klausen, Michael S., Sommer, Morten O. A. |
| Abstract |
Flow-seq combines flexible genome engineering methods with flow cytometry-based cell sorting and deep DNA sequencing to enable comprehensive interrogation of genotype to phenotype relationships. One application is to study the effect of specific regulatory elements on protein expression. Constructing targeted genomic variation around genomically integrated fluorescent marker genes enables rapid elucidation of the contribution of specific sequence variants to protein expression. Such an approach can be used to characterize the impact of modifications to the Shine-Dalgarno sequence in Escherichia coli. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 7 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 2 | 29% |
| Student > Ph. D. Student | 1 | 14% |
| Student > Doctoral Student | 1 | 14% |
| Student > Master | 1 | 14% |
| Professor > Associate Professor | 1 | 14% |
| Other | 0 | 0% |
| Unknown | 1 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 2 | 29% |
| Agricultural and Biological Sciences | 2 | 29% |
| Chemical Engineering | 1 | 14% |
| Immunology and Microbiology | 1 | 14% |
| Unknown | 1 | 14% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 01 October 2020.
All research outputs
#6,488,834
of 23,008,860 outputs
Outputs from Methods in molecular biology
#1,970
of 13,157 outputs
Outputs of similar age
#132,188
of 442,295 outputs
Outputs of similar age from Methods in molecular biology
#180
of 1,498 outputs
Altmetric has tracked 23,008,860 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 13,157 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 84% 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 442,295 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 1,498 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.