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Jin-Soo Kim

Researcher at Seoul National University

Publications -  308
Citations -  26566

Jin-Soo Kim is an academic researcher from Seoul National University. The author has contributed to research in topics: CRISPR & Genome editing. The author has an hindex of 66, co-authored 264 publications receiving 21375 citations. Previous affiliations of Jin-Soo Kim include UPRRP College of Natural Sciences & Korea University of Science and Technology.

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Targeted genome engineering in human cells with the Cas9 RNA-guided endonuclease

TL;DR: It is shown that complexes of the Cas9 protein and artificial chimeric RNAs efficiently cleave two genomic sites and induce indels with frequencies of up to 33% in human cells.
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Highly efficient RNA-guided genome editing in human cells via delivery of purified Cas9 ribonucleoproteins

TL;DR: Delivery of purified recombinant Cas9 protein and guide RNA into cultured human cells including hard-to-transfect fibroblasts and pluripotent stem cells is delivered and RGEN ribonucleoproteins (RNPs) induce site-specific mutations at frequencies of up to 79%, while reducing off- target mutations associated with plasmid transfection at off-target sites.
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Cas-OFFinder: a fast and versatile algorithm that searches for potential off-target sites of Cas9 RNA-guided endonucleases

TL;DR: A novel algorithm termed Cas-OFFinder that searches for potential off-target sites in a given genome or user-defined sequences and allows variations in protospacer-adjacent motif sequences recognized by Cas9, the essential protein component in RGENs.
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Analysis of off-target effects of CRISPR/Cas-derived RNA-guided endonucleases and nickases

TL;DR: Off-target effects of RGENs can be reduced below the detection limits of deep sequencing by choosing unique target sequences in the genome and modifying both guide RNA and Cas9, and paired nickases induced chromosomal deletions in a targeted manner without causing unwanted translocations.
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A guide to genome engineering with programmable nucleases

TL;DR: Known nuclease-specific features are essential for researchers to choose the most appropriate tool for a range of applications, including their composition, targetable sites, specificities and mutation signatures, among other characteristics.