1 Overview

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.

2 Installation and getting started

2.1 Software requirements

2.1.1 OS Requirements

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.

2.2 Installation from Bioconductor

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")

2.3 Installation from GitHub

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)

3 Python scoring algorithms

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:

  1. 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. While crisprScore does not implement any CRISPRa or CRISPRi-specific scoring algorithms, we have found that the DeepHF algorithm works well at predicting CRISPRa and CRISPRi gRNAs.

  2. 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.

4 Installing conda environments

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

5 Getting started

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.

6 On-targeting efficiency scores

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.

6.1 Cas9 methods

Different algorithms require different input nucleotide sequences to predict cutting efficiency as illustrated in the figure below.

Sequence inputs for Cas9 scoring methods

Figure 1: Sequence inputs for Cas9 scoring methods

6.1.1 Rule Set 1

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)

6.1.2 Rule Set 3

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.

6.1.3 DeepHF

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.

6.1.4 CRISPRscan

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)

6.1.5 CRISPRater

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)

6.2 Cas12a methods

Different algorithms require different input nucleotide sequences to predict cutting efficiency as illustrated in the figure below.

Sequence inputs for Cas12a scoring methods

Figure 2: Sequence inputs for Cas12a scoring methods

6.2.1 enPAM+GB score

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)

6.3 Cas13d methods

6.3.1 CasRxRF

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 package
  • RNAplfold: available as part of the ViennaRNA package
  • RNAhybrid: 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.

7 Off-target specificity scores

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).

7.1 MIT score

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

\[\text{MIT} = \biggl(\prod_{p \in M}{w_p}\biggr)\times\frac{1}{\frac{19-d}{19}\times4+1}\times\frac{1}{m^2}\]

where \(M\) is the set of positions for which there is a mismatch between the sgRNA spacer sequence and the off-target sequence, \(w_p\) is an experimentally-derived mismatch tolerance weight at position \(p\), \(d\) is the average distance between mismatches, and \(m\) is the total number of mismatches. As the number of mismatches increases, the cutting likelihood decreases. In addition, off-targets with more adjacent mismatches will have a lower cutting likelihood.

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

7.2 CFD score

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

8 Indel prediction score

8.1 Lindel score (Cas9)

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)

9 License

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.

10 Reproducibility

sessionInfo()
## R Under development (unstable) (2025-10-20 r88955)
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## Running under: Ubuntu 24.04.3 LTS
## 
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## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0  LAPACK version 3.12.0
## 
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## 
## time zone: America/New_York
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] crisprScore_1.15.2     crisprScoreData_1.15.0 ExperimentHub_3.1.0   
## [4] AnnotationHub_4.1.0    BiocFileCache_3.1.0    dbplyr_2.5.1          
## [7] BiocGenerics_0.57.0    generics_0.1.4         BiocStyle_2.39.0      
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##  [1] KEGGREST_1.51.1      xfun_0.54            bslib_0.9.0         
##  [4] httr2_1.2.1          Biobase_2.71.0       lattice_0.22-7      
##  [7] vctrs_0.6.5          tools_4.6.0          stats4_4.6.0        
## [10] curl_7.0.0           tibble_3.3.0         AnnotationDbi_1.73.0
## [13] RSQLite_2.4.4        blob_1.2.4           pkgconfig_2.0.3     
## [16] Matrix_1.7-4         S4Vectors_0.49.0     lifecycle_1.0.4     
## [19] compiler_4.6.0       stringr_1.6.0        Biostrings_2.79.2   
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## [43] bit64_4.6.0-1        rmarkdown_2.30       XVector_0.51.0      
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## [52] knitr_1.50           IRanges_2.45.0       rlang_1.1.6         
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## [58] BiocManager_1.30.27  jsonlite_2.0.0       R6_2.6.1

References

Chen, Wei, Aaron McKenna, Jacob Schreiber, Maximilian Haeussler, Yi Yin, Vikram Agarwal, William Stafford Noble, and Jay Shendure. 2019. “Massively Parallel Profiling and Predictive Modeling of the Outcomes of Crispr/Cas9-Mediated Double-Strand Break Repair.” Nucleic Acids Research 47 (15): 7989–8003.

DeWeirdt, Peter C, Abby V McGee, Fengyi Zheng, Ifunanya Nwolah, Mudra Hegde, and John G Doench. 2022. “Accounting for Small Variations in the tracrRNA Sequence Improves sgRNA Activity Predictions for Crispr Screening.” bioRxiv. https://doi.org/10.1101/2022.06.27.497780.

DeWeirdt, Peter C, Kendall R Sanson, Annabel K Sangree, Mudra Hegde, Ruth E Hanna, Marissa N Feeley, Audrey L Griffith, et al. 2020. “Optimization of Ascas12a for Combinatorial Genetic Screens in Human Cells.” Nature Biotechnology, 1–11.

Doench, John G, Nicolo Fusi, Meagan Sullender, Mudra Hegde, Emma W Vaimberg, Katherine F Donovan, Ian Smith, et al. 2016. “Optimized sgRNA Design to Maximize Activity and Minimize Off-Target Effects of Crispr-Cas9.” Nature Biotechnology 34 (2): 184.

Doench, John G, Ella Hartenian, Daniel B Graham, Zuzana Tothova, Mudra Hegde, Ian Smith, Meagan Sullender, Benjamin L Ebert, Ramnik J Xavier, and David E Root. 2014. “Rational Design of Highly Active sgRNAs for Crispr-Cas9–Mediated Gene Inactivation.” Nature Biotechnology 32 (12): 1262–7.

Hsu, Patrick D, David A Scott, Joshua A Weinstein, F Ann Ran, Silvana Konermann, Vineeta Agarwala, Yinqing Li, et al. 2013. “DNA Targeting Specificity of Rna-Guided Cas9 Nucleases.” Nature Biotechnology 31 (9): 827.

Kleinstiver, Benjamin P, Alexander A Sousa, Russell T Walton, Y Esther Tak, Jonathan Y Hsu, Kendell Clement, Moira M Welch, et al. 2019. “Engineered Crispr–Cas12a Variants with Increased Activities and Improved Targeting Ranges for Gene, Epigenetic and Base Editing.” Nature Biotechnology 37 (3): 276–82.

Labuhn, Maurice, Felix F Adams, Michelle Ng, Sabine Knoess, Axel Schambach, Emmanuelle M Charpentier, Adrian Schwarzer, Juan L Mateo, Jan-Henning Klusmann, and Dirk Heckl. 2018. “Refined sgRNA Efficacy Prediction Improves Large-and Small-Scale Crispr–Cas9 Applications.” Nucleic Acids Research 46 (3): 1375–85.

Moreno-Mateos, Miguel A, Charles E Vejnar, Jean-Denis Beaudoin, Juan P Fernandez, Emily K Mis, Mustafa K Khokha, and Antonio J Giraldez. 2015. “CRISPRscan: Designing Highly Efficient sgRNAs for Crispr-Cas9 Targeting in Vivo.” Nature Methods 12 (10): 982–88.

Wang, Daqi, Chengdong Zhang, Bei Wang, Bin Li, Qiang Wang, Dong Liu, Hongyan Wang, et al. 2019. “Optimized Crispr Guide Rna Design for Two High-Fidelity Cas9 Variants by Deep Learning.” Nature Communications 10 (1): 1–14.

Wessels, Hans-Hermann, Alejandro Méndez-Mancilla, Xinyi Guo, Mateusz Legut, Zharko Daniloski, and Neville E Sanjana. 2020. “Massively Parallel Cas13 Screens Reveal Principles for Guide Rna Design.” Nature Biotechnology 38 (6): 722–27.