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Emerging Topics in Life Sciences
Article . 2019 . Peer-reviewed
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Quantifying CRISPR off-target effects

Authors: Soragia Athina Gkazi;

Quantifying CRISPR off-target effects

Abstract

Abstract Recent advances in the era of genetic engineering have significantly improved our ability to make precise changes in the genomes of human cells. Throughout the years, clinical trials based on gene therapies have led to the cure of diseases such as X-linked severe combined immunodeficiency (SCID-X1), adenosine deaminase deficiency (ADA-SCID) and Wiskott–Aldrich syndrome. Despite the success gene therapy has had, there is still the risk of genotoxicity due to the potential oncogenesis introduced by utilising viral vectors. Research has focused on alternative strategies like genome editing without viral vectors as a means to reduce genotoxicity introduced by the viral vectors. Although there is an extensive use of RNA-guided genome editing via the clustered regularly interspaced short palindromic repeats (CRISPR) and associated protein-9 (Cas9) technology for biomedical research, its genome-wide target specificity and its genotoxic side effects remain controversial. There have been reports of on- and off-target effects created by CRISPR–Cas9 that can include small and large indels and inversions, highlighting the potential risk of insertional mutagenesis. In the last few years, a plethora of in silico, in vitro and in vivo genome-wide assays have been introduced with the sole purpose of profiling these effects. Here, we are going to discuss the genotoxic obstacles in gene therapies and give an up-to-date overview of methodologies for quantifying CRISPR–Cas9 effects.

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    16
    popularity
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    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
16
Top 10%
Average
Top 10%
bronze