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

Research output: Contribution to journalArticlepeer-review

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 languageEnglish
Article number3042
JournalNature Communications
Volume12
Issue number1
DOIs
StatePublished - Dec 2021
Externally publishedYes

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