Table 3 |.

Comparison of in silico off-target site ranking and scoring tools for CRISPR-Cas9

Name	Main features reported	Strengths	Weaknesses
E-CRISP65 (website); formula based	Mismatch numbers	An early approach for CRISPR-Cas9 off-target identifications	Rankings were outperformed by other algorithms
CCTOP84 (website); formula based	Mismatch positions and numbers	Web support	Scorings were outperformed by other algorithms
COSMID78 (website); formula based	Mismatch positions and numbers	Web support, bulges allowed	Scorings were outperformed by other algorithms
Cropit86 (website); formula based	Mismatch numbers and continuities (optional: chromatin states)	Web support. Better performance than other formula-based algorithms on ChIP-seq data	Scores did not correlate well with cleavage-based genome-wide experimental data
MIT23 (websitea); formula-based modular PWM (see Box 1)	Mismatch positions, numbers and mean distances	Web support. The most popular formula-based algorithm. Good ranking performance80	Scorings were outperformed by CFD88, no bulges allowed
Hsu score23 (command linea); normalized modular PWM	Mismatch positions and numbers	A simplified version of MIT	Scores did not correlate with experimental data as well as the MIT score
CFD88 (command line); modular PWM	Mismatch positions, numbers and identities	Based on the biggest cleavage dataset to date. Good ranking performance80	No bulges allowed
predictCRISPR90 (command line); machine learning	281 sequence-related features	All the machine-learning-based tools showed similar to better performances than the algorithms in other categories. However, because most of these models were trained by genome-wide experiment data, which were largely overlapped to most of the training sets, a potential over-fitting issue exists in the comparison (as described in detail in the ‘Performance comparisons’ section and shown in Supplementary Table 1). Elevation is recommended for in silico prediction, and CRISTA is the only option that allows bulges
CRISTA89 (website, command line); machine learning	Nucleotide identities, alignment, thermodynamics and genomic contents
Elevation91 (website, command line); Machine learning	gRNA spacer sequence and off-target sequence
CNN_std92 (command line); deep learning	gRNA spacer sequence and off-target sequence
deepCRISPR93 (command line); deep learning	gRNA spacer sequence and off-target sequence, cell-type-specific features
Synergizing CRISPR94 (command line); deep learning	Scores from five other algorithms (CFD, MIT Website, MIT score, Cropit, CCTop) and evolutionary conservation

^aImplemented in the CRISPOR website and reviewed in ref. ⁸⁰.