CRISPECTOR provides accurate estimation of genome editing translocation and off-target activity from comparative NGS data

Ido Amit; Ortal Iancu; Alona Levy-Jurgenson; Gavin Kurgan; Matthew S. McNeill; Garrett R. Rettig; Daniel Allen; Dor Breier; Nimrod Ben Haim; Yu Wang; Leon Anavy; Ayal Hendel; Zohar Yakhini

doi:10.1038/s41467-021-22417-4

CRISPECTOR provides accurate estimation of genome editing translocation and off-target activity from comparative NGS data

Ido Amit, Ortal Iancu, Alona Levy-Jurgenson, Gavin Kurgan, Matthew S. McNeill, Garrett R. Rettig, Daniel Allen, Dor Breier, Nimrod Ben Haim, Yu Wang, Leon Anavy, Ayal Hendel, Zohar Yakhini

The Mina and Everard Goodman Faculty of Life Sciences

Research output: Contribution to journal › Article › peer-review

23 Scopus citations

Abstract

Controlling off-target editing activity is one of the central challenges in making CRISPR technology accurate and applicable in medical practice. Current algorithms for analyzing off-target activity do not provide statistical quantification, are not sufficiently sensitive in separating signal from noise in experiments with low editing rates, and do not address the detection of translocations. Here we present CRISPECTOR, a software tool that supports the detection and quantification of on- and off-target genome-editing activity from NGS data using paired treatment/control CRISPR experiments. In particular, CRISPECTOR facilitates the statistical analysis of NGS data from multiplex-PCR comparative experiments to detect and quantify adverse translocation events. We validate the observed results and show independent evidence of the occurrence of translocations in human cell lines, after genome editing. Our methodology is based on a statistical model comparison approach leading to better false-negative rates in sites with weak yet significant off-target activity.

Original language	English
Article number	3042
Journal	Nature Communications
Volume	12
Issue number	1
DOIs	https://doi.org/10.1038/s41467-021-22417-4
State	Published - 1 Dec 2021

Bibliographical note

Publisher Copyright:
© 2021, The Author(s).

Funding

The authors thank Chihong Choi, from IDC Herzliya, for leading the development of the html report. We also thank Mark Behlke from IDT for useful early discussions. We thank the Yakhini Research Group, at IDC and the Technion, and the Hendel Lab, in BIU, for numerous important discussions and comments. The Hendel Lab gratefully acknowledges the funding support from the European Research Council (ERC) under the Horizon 2020 research and innovation program (Grant agreement No. 755758). L. Anavy is supported by the Adams Fellowships Program of the Israel Academy of Sciences and Humanities.

Funders	Funder number
Horizon 2020 Framework Programme
European Commission
Israel Academy of Sciences and Humanities
Horizon 2020	755758

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Amit, I., Iancu, O., Levy-Jurgenson, A., Kurgan, G., McNeill, M. S., Rettig, G. R., Allen, D., Breier, D., Ben Haim, N., Wang, Y., Anavy, L., Hendel, A., & Yakhini, Z. (2021). CRISPECTOR provides accurate estimation of genome editing translocation and off-target activity from comparative NGS data. Nature Communications, 12(1), Article 3042. https://doi.org/10.1038/s41467-021-22417-4

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abstract = "Controlling off-target editing activity is one of the central challenges in making CRISPR technology accurate and applicable in medical practice. Current algorithms for analyzing off-target activity do not provide statistical quantification, are not sufficiently sensitive in separating signal from noise in experiments with low editing rates, and do not address the detection of translocations. Here we present CRISPECTOR, a software tool that supports the detection and quantification of on- and off-target genome-editing activity from NGS data using paired treatment/control CRISPR experiments. In particular, CRISPECTOR facilitates the statistical analysis of NGS data from multiplex-PCR comparative experiments to detect and quantify adverse translocation events. We validate the observed results and show independent evidence of the occurrence of translocations in human cell lines, after genome editing. Our methodology is based on a statistical model comparison approach leading to better false-negative rates in sites with weak yet significant off-target activity.",

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CRISPECTOR provides accurate estimation of genome editing translocation and off-target activity from comparative NGS data

Abstract

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