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DeepCRISPR: optimized CRISPR guide RNA design by deep learning

Overview of attention for article published in Genome Biology (Online Edition), 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 (92nd percentile)

Citations

dimensions_citation
142 Dimensions

Readers on

mendeley
257 Mendeley
citeulike
2 CiteULike
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Title
DeepCRISPR: optimized CRISPR guide RNA design by deep learning
Published in
Genome Biology (Online Edition), 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/ .

Twitter Demographics

The data shown below were collected from the profiles of 37 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 257 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 257 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 52 20%
Student > Ph. D. Student 44 17%
Student > Master 26 10%
Student > Bachelor 23 9%
Student > Doctoral Student 18 7%
Other 37 14%
Unknown 57 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 68 26%
Agricultural and Biological Sciences 51 20%
Computer Science 25 10%
Medicine and Dentistry 8 3%
Engineering 5 2%
Other 32 12%
Unknown 68 26%

Attention Score in Context

This research output has an Altmetric Attention Score of 35. 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 12 August 2021.
All research outputs
#818,943
of 19,577,414 outputs
Outputs from Genome Biology (Online Edition)
#700
of 3,849 outputs
Outputs of similar age
#21,268
of 293,450 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 1 outputs
Altmetric has tracked 19,577,414 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 3,849 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.9. 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 293,450 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 92% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them