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Analysis and comparison of genome editing using CRISPResso2
View ORCID ProfileKendell Clement, Holly Rees, Matthew C. Canver, Jason M. Gehrke, Rick Farouni, Jonathan Y Hsu, Mitchel Cole, David R. Liu, J. Keith Joung, Daniel E. Bauer, View ORCID ProfileLuca Pinello
doi: https://doi.org/10.1101/392217
Kendell Clement
1Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02141, USA
2Molecular Pathology Unit, Center for Cancer Research, and Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, Massachusetts 02129, USA
3Department of Pathology, Harvard Medical School, Boston, Massachusetts 02115, USA
Holly Rees
1Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02141, USA
4Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, USA
5Division of Hematology/Oncology, Boston Children’s Hospital; Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
Matthew C. Canver
1Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02141, USA
2Molecular Pathology Unit, Center for Cancer Research, and Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, Massachusetts 02129, USA
3Department of Pathology, Harvard Medical School, Boston, Massachusetts 02115, USA
Jason M. Gehrke
2Molecular Pathology Unit, Center for Cancer Research, and Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, Massachusetts 02129, USA
3Department of Pathology, Harvard Medical School, Boston, Massachusetts 02115, USA
Rick Farouni
1Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02141, USA
2Molecular Pathology Unit, Center for Cancer Research, and Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, Massachusetts 02129, USA
3Department of Pathology, Harvard Medical School, Boston, Massachusetts 02115, USA
Jonathan Y Hsu
1Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02141, USA
2Molecular Pathology Unit, Center for Cancer Research, and Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, Massachusetts 02129, USA
3Department of Pathology, Harvard Medical School, Boston, Massachusetts 02115, USA
Mitchel Cole
5Division of Hematology/Oncology, Boston Children’s Hospital; Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
6Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
7Harvard Stem Cell Institute, Cambridge, MA 02138, USA
David R. Liu
1Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02141, USA
4Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, USA
5Division of Hematology/Oncology, Boston Children’s Hospital; Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
J. Keith Joung
2Molecular Pathology Unit, Center for Cancer Research, and Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, Massachusetts 02129, USA
3Department of Pathology, Harvard Medical School, Boston, Massachusetts 02115, USA
Daniel E. Bauer
5Division of Hematology/Oncology, Boston Children’s Hospital; Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
6Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
7Harvard Stem Cell Institute, Cambridge, MA 02138, USA
Luca Pinello
1Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02141, USA
2Molecular Pathology Unit, Center for Cancer Research, and Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, Massachusetts 02129, USA
3Department of Pathology, Harvard Medical School, Boston, Massachusetts 02115, USA
Abstract
Genome editing technologies are rapidly evolving, and analysis of deep sequencing data from target or off-target regions is necessary for measuring editing efficiency and evaluating safety. However, no software exists to analyze base editors, perform allele-specific quantification or that incorporates biologically-informed and scalable alignment approaches. Here, we present CRISPResso2 to fill this gap and illustrate its functionality by experimentally measuring and analyzing the editing properties of six genome editing agents.
Copyright
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.
Posted August 15, 2018.
Analysis and comparison of genome editing using CRISPResso2
Kendell Clement, Holly Rees, Matthew C. Canver, Jason M. Gehrke, Rick Farouni, Jonathan Y Hsu, Mitchel Cole, David R. Liu, J. Keith Joung, Daniel E. Bauer, Luca Pinello
bioRxiv 392217; doi: https://doi.org/10.1101/392217
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