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ACS Synth Biol. 2017 May 19;6(5):902-904. doi: 10.1021/acssynbio.6b00343. Epub 2017 Feb 10.

sgRNA Scorer 2.0: A Species-Independent Model To Predict CRISPR/Cas9 Activity.

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Department of Genetics, Harvard Medical School , Boston, Massachusetts 02115, United States.
Wyss Institute for Biologically Inspired Engineering, Harvard University , Boston, Massachusetts 02115, United States.


It has been possible to create tools to predict single guide RNA (sgRNA) activity in the CRISPR/Cas9 system derived from Streptococcus pyogenes due to the large amount of data that has been generated in sgRNA library screens. However, with the discovery of additional CRISPR systems from different bacteria, which show potent activity in eukaryotic cells, the approach of generating large data sets for each of these systems to predict their activity is not tractable. Here, we present a new guide RNA tool that can predict sgRNA activity across multiple CRISPR systems. In addition to predicting activity for Cas9 from S. pyogenes and Streptococcus thermophilus CRISPR1, we experimentally demonstrate that our algorithm can predict activity for Cas9 from Staphylococcus aureus and S. thermophilus CRISPR3. We also have made available a new version of our software, sgRNA Scorer 2.0, which will allow users to identify sgRNA sites for any PAM sequence of interest.


CRISPR; Cas9; genome engineering; sgRNA activity prediction; sgRNA scorer

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