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

View ORCID ProfileFrancesco Iorio, Fiona M Behan, View ORCID ProfileEmanuel Goncalves, Charlotte Beaver, Rizwan Ansari, Rachel Pooley, Piers Wilkinson, Sarah Harper, Euan Stronach, View ORCID ProfileJulio Saez-Rodriguez, Kosuke Yusa, Mathew J Garnett
doi: https://doi.org/10.1101/228189
Francesco Iorio
European Molecular Biology Laboratory;
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  • For correspondence: iorio@ebi.ac.uk
Fiona M Behan
Wellcome Trust Sanger Institute;
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Emanuel Goncalves
Wellcome Trust Sanger Institute;
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Charlotte Beaver
Wellcome Trust Sanger Institute;
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Rizwan Ansari
Wellcome Trust Sanger Institute;
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Rachel Pooley
Wellcome Trust Sanger Institute;
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Piers Wilkinson
Wellcome Trust Sanger Institute;
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Sarah Harper
Wellcome Trust Sanger Institute;
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Euan Stronach
GlaxoSmithKline;
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Julio Saez-Rodriguez
JRC-COMBINE, RWTH Aachen University
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Kosuke Yusa
Wellcome Trust Sanger Institute;
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Mathew J Garnett
Wellcome Trust Sanger Institute;
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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, particularly for those that are 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 (sgRNA) fold change values across the genome, without making any assumption on the copy number status of the targeted genes. Applying our method to 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 sgRNA read counts, and 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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted December 03, 2017.
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Unsupervised correction of gene-independent cell responses to CRISPR-Cas9 targeting
Francesco Iorio, Fiona M Behan, Emanuel Goncalves, Charlotte Beaver, Rizwan Ansari, Rachel Pooley, Piers Wilkinson, Sarah Harper, Euan Stronach, Julio Saez-Rodriguez, Kosuke Yusa, Mathew J Garnett
bioRxiv 228189; doi: https://doi.org/10.1101/228189
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Unsupervised correction of gene-independent cell responses to CRISPR-Cas9 targeting
Francesco Iorio, Fiona M Behan, Emanuel Goncalves, Charlotte Beaver, Rizwan Ansari, Rachel Pooley, Piers Wilkinson, Sarah Harper, Euan Stronach, Julio Saez-Rodriguez, Kosuke Yusa, Mathew J Garnett
bioRxiv 228189; doi: https://doi.org/10.1101/228189

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