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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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Article Information

doi 
https://doi.org/10.1101/021568
History 
  • June 26, 2015.
Copyright 
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.

Author Information

  1. Nicolo Fusi1,*,
  2. Ian Smith2,
  3. John Doench2 and
  4. Jennifer Listgarten1,*
  1. 1Microsoft Research New England, Cambridge, MA
  2. 2Broad Institute of MIT and Harvard, Cambridge, MA
  1. ↵*Address correspondence to fusi{at}microsoft.com, jennl{at}microsoft.com
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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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