. Author manuscript; available in PMC: 2018 Jul 9.

Published in final edited form as: Nat Biomed Eng. 2018 Jan 10;2(1):38–47. doi: 10.1038/s41551-017-0178-6

Table 1.

Summary of CRISPR gRNA design services which include off-target scoring

Shorthand	On-target scoring	Off-target scoring	Off-target aggregator	On-target interface	Off-target interface
Elevation (this work) & Azimuth¹	new machine-learning based models	new machine-learning based models	Yes, machine-learning based	human exome targets pre-computed; cloud API for re-use in code and Excel; source code	human exome targets pre-computed web site; source code for any target
MIT server²¹	new hand-crafted rules	new hand-crafted rules	Yes, hand-crafted rules	web site	web site
CRISPR-DO¹⁸	re-uses rules from Xu et al.²⁵	re-uses rules from MIT server²¹	Yes, as in MIT server	web site; source code	web site; source code
CRISPOR¹⁵	re-uses rules from multiple papers^1,25–30	re-uses rules from MIT server²¹	Yes, as in MIT server	web site; source code	web site; source code
Broad GPP¹	new machine-learning based models	newly developed rules based on data	Not genome-wide (only relative within-gene scores)	web site; source code	web site; source code
E-CRISPR¹⁷	new hand-crafted rules, and rules from ref.^18,25,27	new hand-crafted rules	Yes, hand-crafted rules	web site	web site
CHOP-CHOP¹⁶	re-uses rules from Xu et al.^{25 by default, and ref.1,26,27}	counts # of off-targets but does not score them	No	web site	web site