Large dataset enables prediction of repair after CRISPR–Cas9 editing in primary T cells

Ryan T. Leenay, Amirali Aghazadeh, Joseph Hiatt, David Tse, Theodore L. Roth, Ryan Apathy, Eric Shifrut, Judd F. Hultquist, Nevan Krogan, Zhenqin Wu, Giana Cirolia, Hera Canaj, Manuel D. Leonetti, Alexander Marson*, Andrew P. May, James Zou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Understanding of repair outcomes after Cas9-induced DNA cleavage is still limited, especially in primary human cells. We sequence repair outcomes at 1,656 on-target genomic sites in primary human T cells and use these data to train a machine learning model, which we have called CRISPR Repair Outcome (SPROUT). SPROUT accurately predicts the length, probability and sequence of nucleotide insertions and deletions, and will facilitate design of SpCas9 guide RNAs in therapeutically important primary human cells.

Original languageEnglish (US)
Pages (from-to)1034-1037
Number of pages4
JournalNature biotechnology
Volume37
Issue number9
DOIs
StatePublished - Sep 1 2019

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Applied Microbiology and Biotechnology
  • Molecular Medicine
  • Biomedical Engineering

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