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DeepCRISPR: optimized CRISPR guide RNA design by deep learning

Overview of attention for article published in Genome Biology, June 2018
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Citations

dimensions_citation
308 Dimensions

Readers on

mendeley
377 Mendeley
citeulike
2 CiteULike
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Title
DeepCRISPR: optimized CRISPR guide RNA design by deep learning
Published in
Genome Biology, June 2018
DOI 10.1186/s13059-018-1459-4
Pubmed ID
Authors

Guohui Chuai, Hanhui Ma, Jifang Yan, Ming Chen, Nanfang Hong, Dongyu Xue, Chi Zhou, Chenyu Zhu, Ke Chen, Bin Duan, Feng Gu, Sheng Qu, Deshuang Huang, Jia Wei, Qi Liu

Abstract

A major challenge for effective application of CRISPR systems is to accurately predict the single guide RNA (sgRNA) on-target knockout efficacy and off-target profile, which would facilitate the optimized design of sgRNAs with high sensitivity and specificity. Here we present DeepCRISPR, a comprehensive computational platform to unify sgRNA on-target and off-target site prediction into one framework with deep learning, surpassing available state-of-the-art in silico tools. In addition, DeepCRISPR fully automates the identification of sequence and epigenetic features that may affect sgRNA knockout efficacy in a data-driven manner. DeepCRISPR is available at http://www.deepcrispr.net/ .

X Demographics

X Demographics

The data shown below were collected from the profiles of 35 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 377 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 377 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 70 19%
Student > Ph. D. Student 63 17%
Student > Master 32 8%
Student > Bachelor 29 8%
Student > Doctoral Student 19 5%
Other 46 12%
Unknown 118 31%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 89 24%
Agricultural and Biological Sciences 55 15%
Computer Science 33 9%
Medicine and Dentistry 10 3%
Engineering 9 2%
Other 50 13%
Unknown 131 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 37. 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
#1,098,096
of 25,584,565 outputs
Outputs from Genome Biology
#789
of 4,492 outputs
Outputs of similar age
#23,395
of 343,079 outputs
Outputs of similar age from Genome Biology
#10
of 48 outputs
Altmetric has tracked 25,584,565 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,492 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has done well, scoring higher than 82% 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 343,079 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 48 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.