Journal
NATURE BIOTECHNOLOGY
Volume 37, Issue 9, Pages 1034-+Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/s41587-019-0203-2
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Funding
- Chan-Zuckerberg Biohub
- Chan-Zuckerberg Investigator grant
- National Science Foundation [CRII 1657155]
- National Iinstitutes of Health (NIH)/NIDA Avenir New Innovator Award [DP2DA042423]
- NIH/NIGMS [P50 GM082250]
- Burroughs Wellcome Fund
- Innovative Genomics Institute (IGI)
- Parker Institute for Cancer Immunotherapy (PICI)
- NIH [7R01HG008164-04]
- Stanford data science initiative
- UCSF Medical Scientist Training Program
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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.
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