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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
High-Throughput RNAi Screening
|
|---|---|
| Chapter number | 15 |
| Book title |
High-Throughput RNAi Screening
|
| Published in |
Methods in molecular biology, January 2016
|
| DOI | 10.1007/978-1-4939-6337-9_15 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-6335-5, 978-1-4939-6337-9
|
| Authors |
Si, Tong, Zhao, Huimin, Tong Si, Huimin Zhao |
| Editors |
David O. Azorsa, Shilpi Arora |
| Abstract |
RNA interference (RNAi)-assisted genome evolution (RAGE) applies directed evolution principles to engineer Saccharomyces cerevisiae genomes. Here, we use acetic acid tolerance as a target trait to describe the key steps of RAGE. Briefly, iterative cycles of RNAi screening are performed to accumulate multiplex knockdown modifications, enabling directed evolution of the yeast genome and continuous improvement of a target phenotype. Detailed protocols are provided on the reconstitution of RNAi machinery, creation of genome-wide RNAi libraries, identification and integration of beneficial knockdown cassettes, and repeated RAGE cycles. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 9 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 2 | 22% |
| Other | 1 | 11% |
| Student > Ph. D. Student | 1 | 11% |
| Professor | 1 | 11% |
| Student > Master | 1 | 11% |
| Other | 1 | 11% |
| Unknown | 2 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 2 | 22% |
| Biochemistry, Genetics and Molecular Biology | 2 | 22% |
| Chemical Engineering | 1 | 11% |
| Immunology and Microbiology | 1 | 11% |
| Neuroscience | 1 | 11% |
| Other | 0 | 0% |
| Unknown | 2 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 30 December 2017.
All research outputs
#7,729,323
of 23,498,099 outputs
Outputs from Methods in molecular biology
#2,422
of 13,368 outputs
Outputs of similar age
#125,000
of 396,908 outputs
Outputs of similar age from Methods in molecular biology
#272
of 1,472 outputs
Altmetric has tracked 23,498,099 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,368 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 75% 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 396,908 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 55% of its contemporaries.
We're also able to compare this research output to 1,472 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.