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Unsupervised correction of gene-independent cell responses to CRISPR-Cas9 targeting

Overview of attention for article published in BMC Genomics, August 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

Mentioned by

twitter
18 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
22 Dimensions

Readers on

mendeley
59 Mendeley
citeulike
2 CiteULike
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Title
Unsupervised correction of gene-independent cell responses to CRISPR-Cas9 targeting
Published in
BMC Genomics, August 2018
DOI 10.1186/s12864-018-4989-y
Pubmed ID
Authors

Francesco Iorio, Fiona M. Behan, Emanuel Gonçalves, Shriram G. Bhosle, Elisabeth Chen, Rebecca Shepherd, Charlotte Beaver, Rizwan Ansari, Rachel Pooley, Piers Wilkinson, Sarah Harper, Adam P. Butler, Euan A. Stronach, Julio Saez-Rodriguez, Kosuke Yusa, Mathew J. Garnett

Abstract

Genome editing by CRISPR-Cas9 technology allows large-scale screening of gene essentiality in cancer. A confounding factor when interpreting CRISPR-Cas9 screens is the high false-positive rate in detecting essential genes within copy number amplified regions of the genome. We have developed the computational tool CRISPRcleanR which is capable of identifying and correcting gene-independent responses to CRISPR-Cas9 targeting. CRISPRcleanR uses an unsupervised approach based on the segmentation of single-guide RNA fold change values across the genome, without making any assumption about the copy number status of the targeted genes. Applying our method to existing and newly generated genome-wide essentiality profiles from 15 cancer cell lines, we demonstrate that CRISPRcleanR reduces false positives when calling essential genes, correcting biases within and outside of amplified regions, while maintaining true positive rates. Established cancer dependencies and essentiality signals of amplified cancer driver genes are detectable post-correction. CRISPRcleanR reports sgRNA fold changes and normalised read counts, is therefore compatible with downstream analysis tools, and works with multiple sgRNA libraries. CRISPRcleanR is a versatile open-source tool for the analysis of CRISPR-Cas9 knockout screens to identify essential genes.

Twitter Demographics

The data shown below were collected from the profiles of 18 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 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 59 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 32%
Researcher 13 22%
Student > Bachelor 7 12%
Student > Master 5 8%
Other 4 7%
Other 2 3%
Unknown 9 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 22 37%
Agricultural and Biological Sciences 14 24%
Computer Science 5 8%
Medicine and Dentistry 3 5%
Immunology and Microbiology 2 3%
Other 4 7%
Unknown 9 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 01 June 2020.
All research outputs
#1,717,019
of 15,364,275 outputs
Outputs from BMC Genomics
#761
of 8,701 outputs
Outputs of similar age
#49,044
of 276,425 outputs
Outputs of similar age from BMC Genomics
#2
of 15 outputs
Altmetric has tracked 15,364,275 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,701 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done particularly well, scoring higher than 91% 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 276,425 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 82% of its contemporaries.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.