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. Author manuscript; available in PMC: 2021 Apr 15.
Published in final edited form as: Nat Protoc. 2020 Dec 7;16(1):10–26. doi: 10.1038/s41596-020-00431-y

Table 1 |.

Comparison of in silico off-target site search tools for ZFNs and TALENs

Name Screening approach or algorithm Off-target ranking/scoring basis Strengths Weaknesses

ZFN tools
 PROGNOS78 (website) Base by base Weighted homology, conserved G score and polarity effects Allows user-defined spacing, ZF homodimerization and ambiguous bases. Analyzes ZF subunits. Relatively low false-positive ratios Limited training set
 ZFN-site128 (website) TagScan129 Sequence homology Allows user-defined spacing, ZF homodimerization and ambiguous bases No scoring algorithm
 Zinc finger tools130 (website) Search in <10-kb user-defined sequence N/A Screens ZF targets in user-supplied DNA sequences Screens limited to 49 triplets with validated ZF domains
TALEN tools
 PROGNOS78 (website) Base by base Modular position weight matrix, binding energy compensation, polarity effects Allows up to 20 mismatches. Relatively low false-positive ratios (∼11:1)a Limited training set
 CHOPCHOP62 (website) Bowtie Weighed off-target site number Allows up to two mismatches Potentially misses off-target sites
 TALENgetter/, TALENoffer76 (website, command line) Base by base with threshold-based speed-up strategy Machine-learning-based modular position weight matrix Allows up to ten mismatches; allows the use of rare RVDs Scoring algorithm not as accurate as PROGNOS78
 TALE-NT 2.0 (ref. 75; website) Base by base Modular position weight matrix The first scoring tool for TALEN off-target analysis Potential worse performance if using custom RVD designs
a

The false-positive ratios are defined as ‘the number of screened sites with no detectable activity to the number with detectable activity measured by experimental prediction methods’78.