| Title |
Review of CRISPR/Cas9 sgRNA Design Tools
|
|---|---|
| Published in |
Interdisciplinary Sciences: Computational Life Sciences, April 2018
|
| DOI | 10.1007/s12539-018-0298-z |
| Pubmed ID | |
| Authors |
Yingbo Cui, Jiaming Xu, Minxia Cheng, Xiangke Liao, Shaoliang Peng |
| Abstract |
The adaptive immunity system in bacteria and archaea, Clustered Regularly Interspaced Short Palindromic Repeats, CRISPR-associate (CRISPR/Cas), has been adapted as a powerful gene editing tool and got a broad application in genome research field due to its ease of use and cost-effectiveness. The performance of CRISPR/Cas relies on well-designed single-guide RNA (sgRNA), so a lot of bioinformatic tools have been developed to assist the design of highly active and specific sgRNA. These tools vary in design specifications, parameters, genomes and so on. To help researchers to choose their proper tools, we reviewed various sgRNA design tools, mainly focusing on their on-target efficiency prediction model and off-target detection algorithm. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 5 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Scientists | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 661 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 661 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 119 | 18% |
| Student > Ph. D. Student | 100 | 15% |
| Student > Master | 71 | 11% |
| Researcher | 69 | 10% |
| Student > Doctoral Student | 27 | 4% |
| Other | 61 | 9% |
| Unknown | 214 | 32% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 227 | 34% |
| Agricultural and Biological Sciences | 105 | 16% |
| Engineering | 14 | 2% |
| Medicine and Dentistry | 13 | 2% |
| Neuroscience | 12 | 2% |
| Other | 60 | 9% |
| Unknown | 230 | 35% |
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 19 March 2024.
All research outputs
#3,421,548
of 23,578,918 outputs
Outputs from Interdisciplinary Sciences: Computational Life Sciences
#18
of 286 outputs
Outputs of similar age
#70,671
of 330,122 outputs
Outputs of similar age from Interdisciplinary Sciences: Computational Life Sciences
#2
of 5 outputs
Altmetric has tracked 23,578,918 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 286 research outputs from this source. They receive a mean Attention Score of 3.0. 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 330,122 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 78% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.