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GENOME EDITING

Machine learning finds Cas9-edited genotypes

Nature Biomedical Engineering volume 2, pages 892–893 (2018)Cite this article

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A machine-learning algorithm reliably predicts Cas9-edited genotypes arising from the repair of DNA double-strand breaks in mouse cells and human cells.

Cas9 RNA-guided endonucleases, repurposed from the type-II clustered regularly interspaced short palindromic repeats (CRISPR) innate immune system in bacteria and archaea, are revolutionary genetic tools for reprogramming DNA sequences in almost any cell and organism in a targeted manner. The process of DNA editing by Cas9 nucleases involves cutting chromosomal DNA at user-defined target sites in the genome, thus producing site-specific DNA double-strand breaks (DSBs). These can be sealed efficiently by endogenous repair mechanisms such as non-homologous end joining (NHEJ) repair and microhomology (MH)-mediated end joining (MMEJ) repair in the absence of a homologous DNA template, or homology-directed repair (HDR) in the presence of a single-stranded or double-stranded DNA template. Because NHEJ and MMEJ operate independently of a template, they are error-prone processes, inducing small insertions or deletions (indels) at the cleavage sites; conversely, by relying on a template to repair DSBs, HDR is error-free, enabling precise gene correction. Unfortunately, because HDR depends on factors associated with active cell cycling, its efficiency in non-dividing cells, such as neurons and muscle cells, is low. And even in dividing cells, such as stem cells, HDR is much less efficient than the two error-prone end-joining mechanisms, limiting its utility in therapeutic gene correction.

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Fig. 1: The major editing outcomes after Cas9-mediated DNA cleavage can be predicted by machine learning.

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Authors and Affiliations

  1. Department of Chemistry, Hanyang University, Seoul, South Korea

    Sangsu Bae

  2. Department of Chemistry, Seoul National University and Center for Genome Engineering, Institute for Basic Science, Seoul, South Korea

    Jin-Soo Kim

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  1. Sangsu Bae
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  2. Jin-Soo Kim
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Correspondence to Jin-Soo Kim.

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Competing interests

J.-S.K. is a co-founder of and holds stock in ToolGen, Inc., a company focused on therapeutic genome editing.

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Bae, S., Kim, JS. Machine learning finds Cas9-edited genotypes. Nat Biomed Eng 2, 892–893 (2018). https://doi.org/10.1038/s41551-018-0327-6

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