- Overview
- Installation and getting started
- Python scoring algorithms
- Installing conda environments
- Getting started
- On-targeting efficiency scores
- Off-target specificity scores
- Indel prediction score
- License
- Reproducibility
- References
Authors: Jean-Philippe Fortin, Aaron Lun, Luke Hoberecht, Pirunthan Perampalan
Date: Nov 20, 2025
The crisprScore package provides R wrappers of several on-target and
off-target scoring methods for CRISPR guide RNAs (gRNAs). The following
nucleases are supported: SpCas9, AsCas12a, enAsCas12a, and RfxCas13d
(CasRx). The available on-target cutting efficiency scoring methods are
RuleSet1, RuleSet3, DeepHF, enPAM+GB, CRISPRscan and CRISPRater. Both
the CFD and MIT scoring methods are available for off-target specificity
prediction. The package also provides a Lindel-derived score to predict
the probability of a gRNA to produce indels inducing a frameshift for
the Cas9 nuclease. Note that DeepHF and enPAM+GB are not available on
Windows machines.
Our work is described in a recent bioRxiv preprint: “The crisprVerse: A comprehensive Bioconductor ecosystem for the design of CRISPR guide RNAs across nucleases and technologies”
Our main gRNA design package
crisprDesign utilizes the
crisprScore package to add on- and off-target scores to user-designed
gRNAs; check out our Cas9 gRNA tutorial
page
to learn how to use crisprScore via crisprDesign.
This package is supported for macOS, Linux and Windows machines. Some functionalities are not supported for Windows machines. Packages were developed and tested on R version 4.2.
crisprScore can be installed from from the Bioconductor devel branch
using the following commands in a fresh R session:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(version="devel")
BiocManager::install("crisprScore")Alternatively, the development version of crisprScore and its
dependencies can be installed by typing the following commands inside of
an R session:
install.packages("devtools")
library(devtools)
install_github("crisprVerse/crisprScoreData")
install_github("crisprVerse/crisprScore")Note that RStudio users will need to add the following line to their
.Rprofile file in order for crisprScore to work properly:
options(reticulate.useImportHook=FALSE)crisprScore used to rely on the basilisk Bioconductor package to
maintain and install the conda environments necessary to run the
different scoring algorithms written in Python. However, due to recent
changes to basilisk and the reticulate package, the changes in the
conda licensing, the deprecation of Python 2, and the arrival of Apple
Silicon, it has become increasingly difficult for us to resolve those
environments automatically across machines and scoring algorithms.
As a result, we have made the following two decisions:
-
scoring algorithms written in Python 2 are no longer supported by
crisprScore; this includes Azimuth, DeepCpf1, DeepSpCas9, and CRISPRai. The more recent and more performant RuleSet3 and DeepHF scoring algorithms can be used instead of Azimuth, and the enPam+GB algorithm can be used instead of DeepCpf1. WhilecrisprScoredoes not implement any CRISPRa or CRISPRi-specific scoring algorithms, we have found that the DeepHF algorithm works well at predicting CRISPRa and CRISPRi gRNAs. -
users now have to build the conda environments themselves prior to using
crisprScore, and pass the conda environment path to the different scoring algorithms. See next section.
For each of the Python scoring algorithms implemented in crisprScore,
we provide in the crisprScore/inst/python folder a script to install the
conda environments: buildingCondaEnvironments.sh.
The script assumes that a conda instance is available on path. We recommend using miniforge: https://github.com/conda-forge/miniforge to install and manage conda environments.
We use the following conda channel specifications in the .condarc file:
channels:
- conda-forge
- bioconda
- defaults
channel_priority: strict
We load crisprScore in the usual way:
library(crisprScore)The scoringMethodsInfo data.frame contains a succinct summary of
scoring methods available in crisprScore:
data(scoringMethodsInfo)
print(scoringMethodsInfo)## method nuclease left right type label len
## 1 ruleset1 SpCas9 -24 5 On-target RuleSet1 30
## 2 deephf SpCas9 -20 2 On-target DeepHF 23
## 3 lindel SpCas9 -33 31 On-target Lindel 65
## 4 mit SpCas9 -20 2 Off-target MIT 23
## 5 cfd SpCas9 -20 2 Off-target CFD 23
## 6 enpamgb enAsCas12a -4 29 On-target EnPAMGB 34
## 7 crisprscan SpCas9 -26 8 On-target CRISPRscan 35
## 8 casrxrf CasRx NA NA On-target CasRx-RF NA
## 9 crisprater SpCas9 -20 -1 On-target CRISPRater 20
## 10 ruleset3 SpCas9 -24 5 On-target RuleSet3 30
Each scoring algorithm requires a different contextual nucleotide
sequence. The left and right columns indicates how many nucleotides
upstream and downstream of the first nucleotide of the PAM sequence are
needed for input, and the len column indicates the total number of
nucleotides needed for input. The crisprDesign (GitHub
link) package provides
user-friendly functionalities to extract and score those sequences
automatically via the addOnTargetScores function.
Predicting on-target cutting efficiency is an extensive area of
research, and we try to provide in crisprScore the latest
state-of-the-art algorithms as they become available.
Different algorithms require different input nucleotide sequences to predict cutting efficiency as illustrated in the figure below.
The Rule Set 1 algorithm is one of the first on-target efficiency methods developed for the Cas9 nuclease (Doench et al. 2014). It generates a probability (therefore a score between 0 and 1) that a given sgRNA will cut at its intended target. 4 nucleotides upstream and 3 nucleotides downstream of the PAM sequence are needed for scoring:
flank5 <- "ACCT" #4bp
spacer <- "ATCGATGCTGATGCTAGATA" #20bp
pam <- "AGG" #3bp
flank3 <- "TTG" #3bp
input <- paste0(flank5, spacer, pam, flank3)
results <- getRuleSet1Scores(input)The Rule Set 3 is an improvement over Rule Set 1 and Rule Set 2/Azimuth developed for the SpCas9 nuclease, taking into account the type of tracrRNAs (DeWeirdt et al. 2022). Two types of tracrRNAs are currently offered:
GTTTTAGAGCTA-----GAAA-----TAGCAAGTTAAAAT... --> Hsu2013 tracrRNA
GTTTAAGAGCTATGCTGGAAACAGCATAGCAAGTTTAAAT... --> Chen2013 tracrRNA
Similar to Rule Set 1 and Azimuth, the input sequence requires 4 nucleotides upstream of the protospacer sequence, the protospacer sequence itself (20nt spacersequence and PAM sequence), and 3 nucleotides downstream of the PAM sequence:
flank5 <- "ACCT" #4bp
spacer <- "ATCGATGCTGATGCTAGATA" #20bp
pam <- "AGG" #3bp
flank3 <- "TTG" #3bp
input <- paste0(flank5, spacer, pam, flank3) We specify the path of the conda environment necessary to run the python code and run the results:
condaEnv <- "/Users/fortin946/miniforge3/envs/rs3-env"
results <- getRuleSet3Scores(input, tracrRNA="Hsu2013", condaEnv=condaEnv)A more involved version of the algorithm takes into account gene context
of the target protospacer sequence (Rule Set 3 Target) and will be soon
implemented in crisprScore.
The DeepHF algorithm is an on-target cutting efficiency prediction algorithm for several variants of the Cas9 nuclease (Wang et al. 2019) using a recurrent neural network (RNN) framework. Similar to the Azimuth score, it generates a probability of cutting at the intended on-target. The algorithm only needs the protospacer and PAM sequences as inputs:
spacer <- "ATCGATGCTGATGCTAGATA" #20bp
pam <- "AGG" #3bp
input <- paste0(spacer, pam)
condaEnv <- "/Users/fortin946/miniforge3/envs/deephf-env"
results <- getDeepHFScores(input, condaEnv=condaEnv)Users can specify for which Cas9 they wish to score sgRNAs by using the
argument enzyme: “WT” for Wildtype Cas9 (WT-SpCas9), “HF” for
high-fidelity Cas9 (SpCas9-HF), or “ESP” for enhancedCas9 (eSpCas9). For
wildtype Cas9, users can also specify the promoter used for expressing
sgRNAs using the argument promoter (“U6” by default). See
?getDeepHFScores for more details.
The CRISPRscan algorithm, also known as the Moreno-Mateos score, is an on-target efficiency method for the SpCas9 nuclease developed for sgRNAs expressed from a T7 promoter, and trained on zebrafish data (Moreno-Mateos et al. 2015). It generates a probability (therefore a score between 0 and 1) that a given sgRNA will cut at its intended target. 6 nucleotides upstream of the protospacer sequence and 6 nucleotides downstream of the PAM sequence are needed for scoring:
flank5 <- "ACCTAA" #6bp
spacer <- "ATCGATGCTGATGCTAGATA" #20bp
pam <- "AGG" #3bp
flank3 <- "TTGAAT" #6bp
input <- paste0(flank5, spacer, pam, flank3)
results <- getCRISPRscanScores(input)The CRISPRater algorithm is an on-target efficiency method for the SpCas9 nuclease (Labuhn et al. 2018). It generates a probability (therefore a score between 0 and 1) that a given sgRNA will cut at its intended target. Only the 20bp spacer sequence is required.
spacer <- "ATCGATGCTGATGCTAGATA" #20bp
results <- getCRISPRaterScores(spacer)Different algorithms require different input nucleotide sequences to predict cutting efficiency as illustrated in the figure below.
The enPAM+GB algorithm is an on-target cutting efficiency prediction algorithm for the enhanced Cas12a (enCas12a) nuclease (DeWeirdt et al. 2020) using a gradient-booster (GB) model. The enCas12a nuclease as an extended set of active PAM sequences in comparison to the wildtype Cas12 nuclease (Kleinstiver et al. 2019), and the enPAM+GB algorithm takes PAM activity into account in the calculation of the final score. It generates a probability (therefore a score between 0 and 1) of a given sgRNA to cut at the intended target. 4 nucleotides upstream of the PAM sequence, and 3 nucleotides downstream of the protospacer sequence are needed for scoring:
flank5 <- "ACCG" #4bp
pam <- "TTTT" #4bp
spacer <- "AATCGATGCTGATGCTAGATATT" #23bp
flank3 <- "AAG" #3bp
input <- paste0(flank5, pam, spacer, flank3)
condaEnv <- "/Users/fortin946/miniforge3/envs/enpamgb-env"
results <- getEnPAMGBScores(input, condaEnv=condaEnv)The CasRxRF method was developed to characterize on-target efficiency of the RNA-targeting nuclease RfxCas13d, abbreviated as CasRx (Wessels et al. 2020).
It requires as an input the mRNA sequence targeted by the gRNAs, and returns as an output on-target efficiency scores for all gRNAs targeting the mRNA sequence.
As an example, we predict on-target efficiency for gRNAs targeting the
mRNA sequence stored in the file test.fa:
fasta <- file.path(system.file(package="crisprScore"),
"casrxrf/test.fa")
mrnaSequence <- Biostrings::readDNAStringSet(filepath=fasta
format="fasta",
use.names=TRUE)
results <- getCasRxRFScores(mrnaSequence)Note that the function has a default argument directRepeat set to
aacccctaccaactggtcggggtttgaaac, specifying the direct repeat used in
the CasRx construct (see (Wessels et al. 2020).) The function also has
an argument binaries that specifies the file path of the binaries for
three programs necessary by the CasRxRF algorithm:
RNAfold: available as part of the ViennaRNA packageRNAplfold: available as part of the ViennaRNA packageRNAhybrid: available as part of the RNAhybrid package
Those programs can be installed from their respective websites: VienneRNA and RNAhybrid.
If the argument is NULL, the binaries are assumed to be available on
the PATH.
For CRISPR knockout systems, off-targeting effects can occur when the CRISPR nuclease tolerates some levels of imperfect complementarity between gRNA spacer sequences and protospacer sequences of the targeted genome. Generally, a greater number of mismatches between spacer and protospacer sequences decreases the likelihood of cleavage by a nuclease, but the nature of the nucleotide substitution can module the likelihood as well. Several off-target specificity scores were developed to predict the likelihood of a nuclease to cut at an unintended off-target site given a position-specific set of nucleotide mismatches.
We provide in crisprScore two popular off-target specificity scoring
methods for CRISPR/Cas9 knockout systems: the MIT score
(Hsu et al. 2013) and the cutting frequency
determination (CFD) score (Doench et al.
2016).
The MIT score was an early off-target specificity prediction algorithm developed for the CRISPR/Cas9 system (Hsu et al. 2013). It predicts the likelihood that the Cas9 nuclease will cut at an off-target site using position-specific mismatch tolerance weights. It also takes into consideration the total number of mismatches, as well as the average distance between mismatches. However, it does not take into account the nature of the nucleotide substitutions. The exact formula used to estimate the cutting likelihood is
where
The getMITScores function takes as argument a character vector of 20bp
sequences specifying the spacer sequences of sgRNAs (spacers
argument), as well as a vector of 20bp sequences representing the
protospacer sequences of the putative off-targets in the targeted genome
(protospacers argument). PAM sequences (pams) must also be provided.
If only one spacer sequence is provided, it will reused for all provided
protospacers.
The following code will generate MIT scores for 3 off-targets with
respect to the sgRNA ATCGATGCTGATGCTAGATA:
spacer <- "ATCGATGCTGATGCTAGATA"
protospacers <- c("ACCGATGCTGATGCTAGATA",
"ATCGATGCTGATGCTAGATT",
"ATCGATGCTGATGCTAGATA")
pams <- c("AGG", "AGG", "AGA")
getMITScores(spacers=spacer,
protospacers=protospacers,
pams=pams)## spacer protospacer score
## 1 ATCGATGCTGATGCTAGATA ACCGATGCTGATGCTAGATA 1.00000000
## 2 ATCGATGCTGATGCTAGATA ATCGATGCTGATGCTAGATT 0.41700000
## 3 ATCGATGCTGATGCTAGATA ATCGATGCTGATGCTAGATA 0.06944444
The CFD off-target specificity prediction algorithm was initially developed for the CRISPR/Cas9 system, and was shown to be superior to the MIT score (Doench et al. 2016). Unlike the MIT score, position-specific mismatch weights vary according to the nature of the nucleotide substitution (e.g. an A->G mismatch at position 15 has a different weight than an A->T mismatch at position 15).
Similar to the getMITScores function, the getCFDScores function
takes as argument a character vector of 20bp sequences specifying the
spacer sequences of sgRNAs (spacers argument), as well as a vector of
20bp sequences representing the protospacer sequences of the putative
off-targets in the targeted genome (protospacers argument). pams
must also be provided. If only one spacer sequence is provided, it will
be used for all provided protospacers.
The following code will generate CFD scores for 3 off-targets with
respect to the sgRNA ATCGATGCTGATGCTAGATA:
spacer <- "ATCGATGCTGATGCTAGATA"
protospacers <- c("ACCGATGCTGATGCTAGATA",
"ATCGATGCTGATGCTAGATT",
"ATCGATGCTGATGCTAGATA")
pams <- c("AGG", "AGG", "AGA")
getCFDScores(spacers=spacer,
protospacers=protospacers,
pams=pams)## spacer protospacer score
## 1 ATCGATGCTGATGCTAGATA ACCGATGCTGATGCTAGATA 0.85714286
## 2 ATCGATGCTGATGCTAGATA ATCGATGCTGATGCTAGATT 0.60000000
## 3 ATCGATGCTGATGCTAGATA ATCGATGCTGATGCTAGATA 0.06944444
Non-homologous end-joining (NHEJ) plays an important role in double-strand break (DSB) repair of DNA. Error patterns of NHEJ can be strongly biased by sequence context, and several studies have shown that microhomology can be used to predict indels resulting from CRISPR/Cas9-mediated cleavage. Among other useful metrics, the frequency of frameshift-causing indels can be estimated for a given sgRNA.
Lindel (Chen et al. 2019) is a logistic regression model that was
trained to use local sequence context to predict the distribution of
mutational outcomes. In crisprScore, the function getLindelScores
return the proportion of “frameshifting” indels estimated by Lindel. By
chance, assuming a random distribution of indel lengths, frameshifting
proportions should be roughly around 0.66. A Lindel score higher than
0.66 indicates a higher than by chance probability that a sgRNA induces
a frameshift mutation.
The Lindel algorithm requires nucleotide context around the protospacer
sequence; the following full sequence is needed: [13bp upstream
flanking sequence][23bp protospacer sequence] [29bp downstream
flanking sequence], for a total of 65bp. The function getLindelScores
takes as inputs such 65bp sequences:
flank5 <- "ACCTTTTAATCGA" #13bp
spacer <- "TGCTGATGCTAGATATTAAG" #20bp
pam <- "TGG" #3bp
flank3 <- "CTTTTAATCGATGCTGATGCTAGATATTA" #29bp
input <- paste0(flank5, spacer, pam, flank3)
condaEnv <- "/Users/fortin946/miniforge3/envs/lindel-env"
results <- getLindelScores(input, condaEnv=condaEnv)The project as a whole is covered by the MIT license. The code for all
underlying Python packages, with their original licenses, can be found
in inst/python. We made sure that all licenses are compatible with the
MIT license and to indicate changes that we have made to the original
code.
sessionInfo()## R version 4.5.1 (2025-06-13)
## Platform: aarch64-apple-darwin20
## Running under: macOS Sequoia 15.6.1
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## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## [1] crisprScore_1.15.1 crisprScoreData_1.13.0 ExperimentHub_2.99.6
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## [7] BiocGenerics_0.55.4 generics_0.1.4
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## [4] lattice_0.22-7 RSQLite_2.4.3 digest_0.6.37
## [7] magrittr_2.0.4 grid_4.5.1 evaluate_1.0.5
## [10] fastmap_1.2.0 blob_1.2.4 jsonlite_2.0.0
## [13] Matrix_1.7-4 AnnotationDbi_1.71.2 DBI_1.2.3
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