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J. Keith Joung

Researcher at Harvard University

Publications -  211
Citations -  47900

J. Keith Joung is an academic researcher from Harvard University. The author has contributed to research in topics: CRISPR & Genome editing. The author has an hindex of 80, co-authored 197 publications receiving 40261 citations. Previous affiliations of J. Keith Joung include Howard Hughes Medical Institute & California Institute of Technology.

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High-frequency off-target mutagenesis induced by CRISPR-Cas nucleases in human cells.

TL;DR: It is found that single and double mismatches are tolerated to varying degrees depending on their position along the guide RNA (gRNA)-DNA interface, and off-target cleavage of CRISPR-associated (Cas)9-based RGNs is characterized.
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CRISPR-Cas systems for editing, regulating and targeting genomes

TL;DR: A modified version of the CRISPR-Cas9 system has been developed to recruit heterologous domains that can regulate endogenous gene expression or label specific genomic loci in living cells, which will undoubtedly transform biological research and spur the development of novel molecular therapeutics for human disease.
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Efficient genome editing in zebrafish using a CRISPR-Cas system

TL;DR: It is shown that the CRISPR-Cas system functions in vivo to induce targeted genetic modifications in zebrafish embryos with efficiencies similar to those obtained using zinc finger nucleases and transcription activator-like effector nucleases.
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High-fidelity CRISPR–Cas9 nucleases with no detectable genome-wide off-target effects

TL;DR: With its exceptional precision, SpCas9-HF1 provides an alternative to wild-type Sp Cas9 for research and therapeutic applications and suggests a general strategy for optimizing genome-wide specificities of other CRISPR-RNA-guided nucleases.
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Improving CRISPR-Cas nuclease specificity using truncated guide RNAs

TL;DR: It is reported that truncated gRNAs, with shorter regions of target complementarity <20 nucleotides in length, can decrease undesired mutagenesis at some off-target sites by 5,000-fold or more without sacrificing on-target genome editing efficiencies.