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Predicting off-target effects for end-to-end CRISPR guide design

View ORCID ProfileJennifer Listgarten, Michael Weinstein, Melih Elibol, Luong Hoang, John Doench, Nicolo Fusi
doi: https://doi.org/10.1101/078253
Jennifer Listgarten
1Microsoft Research, Cambridge, MA
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  • ORCID record for Jennifer Listgarten
  • For correspondence: jennl@microsoft.com fusi@microsoft.com michael.weinstein@ucla.edu
Michael Weinstein
2Molecular, Cell, and Developmental Biology, and Quantitative and Computational Biosciences Institute, University of California Los Angeles, Los Angeles, CA
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  • For correspondence: jennl@microsoft.com fusi@microsoft.com michael.weinstein@ucla.edu
Melih Elibol
1Microsoft Research, Cambridge, MA
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Luong Hoang
1Microsoft Research, Cambridge, MA
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John Doench
3Broad Institute of MIT and Harvard, Cambridge, MA
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Nicolo Fusi
1Microsoft Research, Cambridge, MA
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  • For correspondence: jennl@microsoft.com fusi@microsoft.com michael.weinstein@ucla.edu
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Abstract

To enable more effective guide design we have developed the first machine learning-based approach to assess CRISPR/Cas9 off-target effects. Our approach consistently and substantially outperformed the state-of the-art over multiple, independent data sets, yielding up to a 6-fold improvement in accuracy. Because of the large computational demands of the task, we also developed a cloud-based service for end-to-end guide design which incorporates our previously reported on-target model, Azimuth, as well as our new off-target model, Elevation (https://www.microsoft.com/en-us/research/project/crispr).

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 05, 2016.
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Predicting off-target effects for end-to-end CRISPR guide design
Jennifer Listgarten, Michael Weinstein, Melih Elibol, Luong Hoang, John Doench, Nicolo Fusi
bioRxiv 078253; doi: https://doi.org/10.1101/078253
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Predicting off-target effects for end-to-end CRISPR guide design
Jennifer Listgarten, Michael Weinstein, Melih Elibol, Luong Hoang, John Doench, Nicolo Fusi
bioRxiv 078253; doi: https://doi.org/10.1101/078253

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