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

TitleLarge dataset enables prediction of repair after CRISPR–Cas9 editing in primary T cells
Authors
Leenay, Ryan T.Aghazadeh, AmiraliHiatt, JosephTse, DavidRoth, Theodore L.Apathy, RyanShifrut, EricHultquist, Judd F.Krogan, NevanWu, ZhenqinCirolia, GianaCanaj, HeraLeonetti, Manuel D.Marson, AlexanderMay, Andrew P.Zou, James
Issue Date2019
Citation
Nature Biotechnology, 2019, v. 37, n. 9, p. 1034-1037 How to Cite?
DOI: http://dx.doi.org/10.1038/s41587-019-0203-2
AbstractUnderstanding 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.
Persistent Identifierhttp://hdl.handle.net/10722/354131
ISSN
1087-0156
2023 Impact Factor: 33.1
2023 SCImago Journal Rankings: 18.117
ISI Accession Number ID
WOS:000488532200018

 

DC FieldValueLanguage
dc.contributor.authorLeenay, Ryan T.-
dc.contributor.authorAghazadeh, Amirali-
dc.contributor.authorHiatt, Joseph-
dc.contributor.authorTse, David-
dc.contributor.authorRoth, Theodore L.-
dc.contributor.authorApathy, Ryan-
dc.contributor.authorShifrut, Eric-
dc.contributor.authorHultquist, Judd F.-
dc.contributor.authorKrogan, Nevan-
dc.contributor.authorWu, Zhenqin-
dc.contributor.authorCirolia, Giana-
dc.contributor.authorCanaj, Hera-
dc.contributor.authorLeonetti, Manuel D.-
dc.contributor.authorMarson, Alexander-
dc.contributor.authorMay, Andrew P.-
dc.contributor.authorZou, James-
dc.date.accessioned2025-02-07T08:46:39Z-
dc.date.available2025-02-07T08:46:39Z-
dc.date.issued2019-
dc.identifier.citationNature Biotechnology, 2019, v. 37, n. 9, p. 1034-1037-
dc.identifier.issn1087-0156-
dc.identifier.urihttp://hdl.handle.net/10722/354131-
dc.description.abstractUnderstanding 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.-
dc.languageeng-
dc.relation.ispartofNature Biotechnology-
dc.titleLarge dataset enables prediction of repair after CRISPR–Cas9 editing in primary T cells-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1038/s41587-019-0203-2-
dc.identifier.pmid31359007-
dc.identifier.scopuseid_2-s2.0-85069898509-
dc.identifier.volume37-
dc.identifier.issue9-
dc.identifier.spage1034-
dc.identifier.epage1037-
dc.identifier.eissn1546-1696-
dc.identifier.isiWOS:000488532200018-

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