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A modular toolbox for gRNA–Cas9 genome engineering in plants based on the GoldenBraid standard

Overview of attention for article published in Plant Methods, February 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

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15 X users

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279 Mendeley
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1 CiteULike
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Title
A modular toolbox for gRNA–Cas9 genome engineering in plants based on the GoldenBraid standard
Published in
Plant Methods, February 2016
DOI 10.1186/s13007-016-0101-2
Pubmed ID
Authors

Marta Vazquez-Vilar, Joan Miquel Bernabé-Orts, Asun Fernandez-del-Carmen, Pello Ziarsolo, Jose Blanca, Antonio Granell, Diego Orzaez

Abstract

The efficiency, versatility and multiplexing capacity of RNA-guided genome engineering using the CRISPR/Cas9 technology enables a variety of applications in plants, ranging from gene editing to the construction of transcriptional gene circuits, many of which depend on the technical ability to compose and transfer complex synthetic instructions into the plant cell. The engineering principles of standardization and modularity applied to DNA cloning are impacting plant genetic engineering, by increasing multigene assembly efficiency and by fostering the exchange of well-defined physical DNA parts with precise functional information. Here we describe the adaptation of the RNA-guided Cas9 system to GoldenBraid (GB), a modular DNA construction framework being increasingly used in Plant Synthetic Biology. In this work, the genetic elements required for CRISPRs-based editing and transcriptional regulation were adapted to GB, and a workflow for gRNAs construction was designed and optimized. New software tools specific for CRISPRs assembly were created and incorporated to the public GB resources site. The functionality and the efficiency of gRNA-Cas9 GB tools were demonstrated in Nicotiana benthamiana using transient expression assays both for gene targeted mutations and for transcriptional regulation. The availability of gRNA-Cas9 GB toolbox will facilitate the application of CRISPR/Cas9 technology to plant genome engineering.

X Demographics

X Demographics

The data shown below were collected from the profiles of 15 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 279 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 2 <1%
Switzerland 1 <1%
Portugal 1 <1%
Argentina 1 <1%
United Kingdom 1 <1%
Unknown 273 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 69 25%
Student > Ph. D. Student 48 17%
Student > Master 35 13%
Student > Bachelor 20 7%
Student > Doctoral Student 17 6%
Other 36 13%
Unknown 54 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 136 49%
Biochemistry, Genetics and Molecular Biology 62 22%
Engineering 4 1%
Computer Science 2 <1%
Medicine and Dentistry 2 <1%
Other 9 3%
Unknown 64 23%
Attention Score in Context

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 30 March 2016.
All research outputs
#3,696,029
of 22,842,950 outputs
Outputs from Plant Methods
#196
of 1,082 outputs
Outputs of similar age
#66,179
of 397,369 outputs
Outputs of similar age from Plant Methods
#6
of 23 outputs
Altmetric has tracked 22,842,950 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,082 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one has done well, scoring higher than 81% 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 397,369 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 83% of its contemporaries.
We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.