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In Silico Predictive Modeling of CRISPR/Cas9 guide efficiency

Nicolo Fusi, Ian Smith, John Doench, Jennifer Listgarten
doi: https://doi.org/10.1101/021568
Nicolo Fusi
1Microsoft Research New England, Cambridge, MA
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  • For correspondence: fusi@microsoft.com jennl@microsoft.com
Ian Smith
2Broad Institute of MIT and Harvard, Cambridge, MA
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John Doench
2Broad Institute of MIT and Harvard, Cambridge, MA
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Jennifer Listgarten
1Microsoft Research New England, Cambridge, MA
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  • For correspondence: fusi@microsoft.com jennl@microsoft.com
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ABSTRACT

The CRISPR/Cas9 system provides unprecedented genome editing capabilities; however, several facets of this system are under investigation for further characterization and optimization, including the choice of guide RNA that directs Cas9 to target DNA. In particular, given that one would like to target the protein-coding region of a gene, hundreds of guides satisfy the basic constraints of the CRISPR/Cas9 Protospacer Adjacent Motif sequence (PAM); however, not all of these guides actually generate gene knockouts with equal efficiency. Leveraging a broad set of experimental measurements of guide knockout efficiency, we introduce a state-of-the art in silico modeling approach to identify guides that will lead to more effective gene knockout. We first investigated which guide and gene features are critical for prediction (e.g., single- and di-nucleotide identity of the gene target), which are helpful (e.g., thermodynamics), and which are predictive but redundant (e.g., microhomology). We also investigated evaluation measures for comparing predictive models in the present context, suggesting that Area Under the Receiver Operating Curve is not ideal. Finally, we explored a variety of different model classes and found that use of gradient-boosted regression trees produced the best predictive performance. Pointers to our open-source software, code, and prediction server will be available at http://research.microsoft.com/en-us/projects/azimuth.

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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 June 26, 2015.
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In Silico Predictive Modeling of CRISPR/Cas9 guide efficiency
Nicolo Fusi, Ian Smith, John Doench, Jennifer Listgarten
bioRxiv 021568; doi: https://doi.org/10.1101/021568
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In Silico Predictive Modeling of CRISPR/Cas9 guide efficiency
Nicolo Fusi, Ian Smith, John Doench, Jennifer Listgarten
bioRxiv 021568; doi: https://doi.org/10.1101/021568

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