Package 'crisprScore'

Title: On-Target and Off-Target Scoring Algorithms for CRISPR gRNAs
Description: 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, and CRISPRscan. 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.
Authors: Jean-Philippe Fortin [aut, cre, cph], Aaron Lun [aut], Luke Hoberecht [ctb], Pirunthan Perampalam [ctb]
Maintainer: Jean-Philippe Fortin <[email protected]>
License: MIT + file LICENSE
Version: 1.17.0
Built: 2026-06-06 06:08:24 UTC
Source: https://github.com/bioc/crisprScore

Help Index

Calculate on-target sgRNA activity scores for CasRx using CasRx-RF

Description

Calculate on-target sgRNA activity scores for CasRx (RfxCas13d) using the CasRx-RF algorithm.

Usage

getCasRxRFScores(
  mrnaSequence,
  directRepeat = "aacccctaccaactggtcggggtttgaaac",
  binaries = NULL,
  sort = FALSE,
  verbose = TRUE
)

Arguments

mrnaSequence

A DNAStringSet represeting the mRNA sequence for which to extract spacer sequences and calculate scores.
directRepeat

String specifying the direct repeat used in the CasRx construct.
binaries

Named list of paths for binaries needed for CasRx-RF. Names of the list must be "RNAfold", "RNAhybrid", and "RNAplfold". Each list element is a string specifying the path of the binary. If NULL (default), binaries must be available on the PATH.
sort

Should spacers be sorted by score? FALSE by default.
verbose

Should messages be printed to console? TRUE by default.

Details

The function first extracts all 23mer spacer sequences targeting the mRNA sequence, and scores them for on-target activity.

Value

A data.frame with the following columns:

  • ID Character vector specifying spacer ID.

  • spacer 23-mer spacer sequence.

  • pfs_site coordinate of the protospacer flanking sequence (PFS).

  • protospacer 23-mer protospacer sequence (reverse complement of the spacer sequence).

  • PFS PFS nucleotide.

  • score Raw score (not standardized).

  • standardizedScore Score standardized between 0 and 1.

  • quartile Quartile score (1 to 4, with 4 being the best quartile.

A scores closer to 1 indicates higher predicted on-target activity.

Author(s)

Jean-Philippe Fortin

References

Wessels HH, Méndez-Mancilla A, Guo X, et al. Massively parallel Cas13 screens reveal principles for guide RNA design. Nat biotechnol. 2020 Jun;38(6):722-7.

Examples

if (interactive()){
    fasta <- file.path(system.file(package="crisprScore"),
        "casrxrf/test.fa")
    mrnaSequence <- Biostrings::readDNAStringSet(filepath=fasta,
        format="fasta",
        use.names=TRUE)
    results <- getCasRxRFScores(mrnaSequence)
}

Calculate CFD off-target specificity scores

Description

Calculate cutting frequency determination (CFD) off-target specificity scores for CRISPR/Cas9 or CRISPR/CasRX.

Usage

getCFDScores(spacers, protospacers, pams, nuclease = c("SpCas9", "CasRx"))

Arguments

spacers

Character vector of 20bp spacer sequences. Must be in 5' to 3' direction. For SpCas9, must be of length 20bp. For CasRx, must be at most of length 27bp.
protospacers

Character vector of 20bp protospacer sequences (target sequences). Must be in 5' to 3' direction.
pams

Character vector of PAM sequences.
nuclease

String specifying the nuclease. Either "SpCas9" (default) or "CasRx".

Value

getCFDScores returns a data.frame with spacer, protospacer, and score columns. The CFD score takes on a value between 0 and 1. For a given pair (on-target, off-target), a higher CFD score indicates a higher likelihood for the nuclease to cut at the off-target. Non-canonical PAM sequences are taken into account by the CFD algorithm.

Author(s)

Jean-Philippe Fortin

References

Doench, J., Fusi, N., Sullender, M. et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat Biotechnol 34, 184–191 (2016). https://doi.org/10.1038/nbt.3437.

Examples

# Calculating MIT scores for two off-targets with respect to
# one spacer sequence:
spacer <- "AGGTGTAGTGTGTGTGATAA"
protospacer1 <- "CGGTGTAGTGTGTGTGATAA"
protospacer2 <- "CGGTGTCGTGTGTGTGATAA"
results <- getCFDScores(spacers=spacer,
    protospacers=c(protospacer1, protospacer2),
    pams=c("AGG", "CGG")
)

Calculate on-target sgRNA activity scores for Cas9 using CRISPRater

Description

Calculate on-target sgRNA activity scores for CRISPR/Cas9-induced knockout using the DeepHF scoring method. Both U6 and T7 promoters are supported. Three different versions of the SpCas9 nuclease are supported: wildtype (WT-SpCas9), high-fidelity Cas9 (SpCas9-HF1) and enhanced Cas9 (eSpCas9). Currently not supported on Windows machines.

Usage

getCRISPRaterScores(sequences)

Arguments

sequences

Character vector of 20bp protospacer sequences.

Details

Input sequences for CRISPRater scoring must be 20 spacer sequences.

Value

getCrisprRaterScores returns a data.frame with sequence and score columns. The CRISPRater score takes on a value between 0 and 1. A higher score indicates higher knockout efficiency.

Author(s)

Jean-Philippe Fortin

References

Labuhn M, Adams FF, Ng M, et al. Refined sgRNA efficacy prediction improves large-and small-scale CRISPR–Cas9 applications. Nucleic acids research. 2018 Feb 16;46(3):1375-85.

Examples

spacer  <- "ATCGATGCTGATGCTAGATA" #20bp
results <- getCRISPRaterScores(spacer)

Calculate on-target sgRNA activity scores for Cas9 using CRISPRscan

Description

Calculate on-target sgRNA activity scores for CRISPR/Cas9-induced knockout using the CRISPRscan scoring method. The method is also know as the Moreno-Mateos score. The CRISPRscan algorithm was trained using in vitro transcription of sgRNAs using a T7 promoter, and might therefore be suboptimal to predict sgRNA activity when expressed from U6 promoter.

Usage

getCRISPRscanScores(sequences)

Arguments

sequences

Character vector of 35bp sequences needed for CRISPRscan scoring, see details below.

Details

The input sequences for Rule Set 1 scoring require 6 nucleotides upstream of the protospacer sequence, the protospacer sequence itself (23 nucleotides) and 6 nucleootides downstream of the protospacer sequence, for a total of 35 nucleotides: [6nt][20nt-spacer][NGG][6nt]. Note that a canonical PAM sequence (NGG) is required for CRISPRscan.

Value

getCRISPRscanScores returns a data.frame with sequence and score columns. The CRISPRscan score takes on a value between 0 and 1. A higher score indicates higher knockout efficiency.

Author(s)

Jean-Philippe Fortin

References

Moreno-Mateos MA, et al. CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo. Nature methods. 2015 Oct;12(10):982-8.

Examples

flank5 <- "ACCTGG" #6bp
spacer <- "ATCGATGCTGATGCTAGATA" #20bp
pam    <- "AGG" #3bp 
flank3 <- "TTGAGC" #6bp
input  <- paste0(flank5, spacer, pam, flank3) 
results <- getCRISPRscanScores(input)

Calculate on-target sgRNA activity scores for Cas9 using DeepHF

Description

Calculate on-target sgRNA activity scores for CRISPR/Cas9-induced knockout using the DeepHF scoring method. Both U6 and T7 promoters are supported. Three different versions of the SpCas9 nuclease are supported: wildtype (WT-SpCas9), high-fidelity Cas9 (SpCas9-HF1) and enhanced Cas9 (eSpCas9). Currently not supported on Windows machines.

Usage

getDeepHFScores(
  sequences,
  enzyme = c("WT", "ESP", "HF"),
  promoter = c("U6", "T7"),
  condaEnv
)

Arguments

sequences

Character vector of 23bp protospacer sequences.
enzyme

Character string specifying the Cas9 variant. Wildtype Cas9 (WT) by default, see details below.
promoter

Character string speciyfing promoter used for expressing sgRNAs for wildtype Cas9 (must be either "U6" or "T7"). "U6" by default.
condaEnv

String specifying the path of the conda environment needed to run the DeepHF calculations. See the crisprScore vignette for instructions on how to build the environment.

Details

Input sequences for DeepHF scoring must be 23bpprotospacer sequences (20bp spacer sequences + 3bp PAM sequences). Only canonical PAM sequences (NGG) are allowed. 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 enhanced Cas9 (eSpCas9). For wildtype Cas9, users can also specify the promoter used for expressing sgRNAs using the argument promoter ("U6" by default).

Value

getDeepHFScores returns a data.frame with sequence and score columns. The DeepHF score takes on a value between 0 and 1. A higher score indicates higher knockout efficiency.

Author(s)

Jean-Philippe Fortin

References

Wang, D., Zhang, C., Wang, B. et al. Optimized CRISPR guide RNA design for two high-fidelity Cas9 variants by deep learning. Nat Commun 10, 4284 (2019). https://doi.org/10.1038/s41467-019-12281-8

Examples

if (interactive()){
spacer  <- "ATCGATGCTGATGCTAGATA" #20bp
pam     <- "AGG" #3bp 
input   <- paste0(spacer, pam) 
condaEnv <- "/Users/fortin946/miniforge3/envs/deephf-env"

# Wiltype Cas9 using U6 promoter:
results <- getDeepHFScores(input, condaEnv=condaEnv)

# Wiltype Cas9 using T7 promoter:
results <- getDeepHFScores(input, promoter="T7", condaEnv=condaEnv)

#' High-fidelity Cas9:
results <- getDeepHFScores(input, enzyme="HF",condaEnv=condaEnv)

#' Enhanced Cas9:
results <- getDeepHFScores(input, enzyme="ESP",condaEnv=condaEnv)
}

Calculate on-target sgRNA activity scores for enCas12a using enPAM+GB

Description

Calculate on-target sgRNA activity scores for CRISPR/Cas12a-induced knockout using the enPAM+GB scoring method. Currently not supported on Windows machines.

Usage

getEnPAMGBScores(sequences, condaEnv)

Arguments

sequences

Character vector of 34bp sequences needed for enPAM+GB scoring, see details below.
condaEnv

String specifying the path of the conda environment needed to run the enPAM+GB calculations. See the crisprScore vignette for instructions on how to build the environment.

Details

The input sequences for enPAM+GB scoring require 4 nucleotides upstream of the protospacer sequence, the protospacer sequence itself (4bp PAM sequence + 23bp spacer sequence) and 3 nucleotides downstream of the protospacer sequence, for a total of 34 nucleotides. Both canonical and non-canonical PAM sequences can be provided.

Value

getEnPAMGBScores returns a data.frame with sequence and score columns.

Author(s)

Jean-Philippe Fortin

References

DeWeirdt, P.C., Sanson, K.R., Sangree, A.K. et al. Optimization of AsCas12a for combinatorial genetic screens in human cells. Nat Biotechnol 39, 94–104 (2021). https://doi.org/10.1038/s41587-020-0600-6.

Examples

if (interactive()){
flank5 <- "CATG" #4bp
pam    <- "TTTT" #4bp
spacer <- "TTTGGGAACCAATCGATAATCAC" #23bp
flank3 <- "ATT" #3bp
input  <- paste0(flank5, pam, spacer, flank3) 
condaEnv <- "/Users/fortin946/miniforge3/envs/enpamgb-env"
results <- getEnPAMGBScores(input, condaEnv)
}

Predict frameshift ratios from CRISPR/Cas9 indel prediction using Lindel

Description

Predict frameshift ratios from CRISPR/Cas9 indel prediction using the Lindel prediction algorithm.

Usage

getLindelScores(sequences, condaEnv)

Arguments

sequences

Character vector of 65bp sequences needed for Lindel scoring, see details below.
condaEnv

String specifying the path of the conda environment needed to run the DeepHF calculations. See the crisprScore vignette for instructions on how to build the environment.

Details

The input sequences for Lindel scoring require 13 nucleotides upstream of the protospacer sequence, the protospacer sequence itself (23 nucleotides) and 29 nucleootides downstream of the protospacer sequence, for a total of 65 nucleotides. Note that only canonical PAM sequences (NGG) are accepted by Lindel.

Value

A data.frame with predicted frameshift ratio (between 0 and 1). A higher ratio indicates a greater chance of a frameshift indel introduced by CRISPR/Cas9-induced double-strand breaks.

Author(s)

Jean-Philippe Fortin

References

Wei Chen, Aaron McKenna, Jacob Schreiber, Maximilian Haeussler, Yi Yin, Vikram Agarwal, William Stafford Noble, Jay Shendure, Massively parallel profiling and predictive modeling of the outcomes of CRISPR/Cas9-mediated double-strand break repair, Nucleic Acids Research, Volume 47, Issue 15, 05 September 2019, Pages 7989–8003, https://doi.org/10.1093/nar/gkz487.

Examples

if (interactive()){
flank5 <- "ACCTTTTAATCGA" #13bp
spacer <- "TGCTGATGCTAGATATTAAG" #20bp
pam    <- "TGG" #3bp
flank3 <- "CTTTTAATCGATGCTGATGCTAGATATTA" #29bp
input <- paste0(flank5, spacer, pam, flank3)
condaEnv <- "/Users/fortin946/miniforge3/envs/deephf-env"
results <- getLindelScores(input, condaEnv=condaEnv)
}

Calculate MIT off-target specificity scores for CRISPR/Cas9

Description

Calculate MIT off-target specificity scores for CRISPR/Cas9.

Usage

getMITScores(spacers, protospacers, pams, includeDistance = TRUE)

Arguments

spacers

Character vector of 20bp spacer sequences.
protospacers

Character vector of 20bp protospacer sequences for off-targets.
pams

Character vector of 3nt PAM sequences.
includeDistance

Should distance between mismatches be considered during scoring? TRUE by default.

Value

getMITScores returns a data.frame with spacer, protospacer, and score columns. The MIT score takes on a value between 0 and 1. For a given pair (on-target, off-target), a higher MIT score indicates a higher likelihood for the Cas9 nuclease to cut at the off-target. Non-canonical PAM sequences are taken into account by the MIT algorithm.

Author(s)

Jean-Philippe Fortin

References

Hsu, P., Scott, D., Weinstein, J. et al. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat Biotechnol 31, 827–832 (2013). https://doi.org/10.1038/nbt.2647.

Examples

# Calculating MIT scores for two off-targets with respect to
# one spacer sequence:
spacer <- "AGGTGTAGTGTGTGTGATAA"
protospacer1 <- "CGGTGTAGTGTGTGTGATAA"
protospacer2 <- "CGGTGTCGTGTGTGTGATAA"
pams <- c("AGG", "CGG")
results <- getMITScores(spacers=spacer,
    protospacers=c(protospacer1, protospacer2),
    pams=pams
)

Calculate on-target sgRNA activity scores for Cas9 using Rule Set 1

Description

Calculate on-target sgRNA activity scores for CRISPR/Cas9-induced knockout using the Rule Set 1 scoring method. The Rule Set 1 algorithm was an early on-target efficiency method developed by the Doench lab.

Usage

getRuleSet1Scores(sequences)

Arguments

sequences

Character vector of 30bp sequences needed for Rule Set 1 scoring, see details below.

Details

The input sequences for Rule Set 1 scoring require 4 nucleotides upstream of the protospacer sequence, the protospacer sequence itself (23 nucleotides) and 3 nucleootides downstream of the protospacer sequence, for a total of 30 nucleotides: [4nt][20nt-spacer][NGG][3nt]. Note that a canonical PAM sequence (NGG) is required for Rule Set 1.

Value

getRuleSet1Scores returns a data.frame with sequence and score columns. The Rule Set 1 score takes on a value between 0 and 1. A higher score indicates higher knockout efficiency.

Author(s)

Jean-Philippe Fortin

References

Doench, John G., et al. Rational design of highly active sgRNAs for CRISPR-Cas9–mediated gene inactivation. Nat Biotech 32, 1262-1267 (2014). https://doi.org/10.1038/nbt.3026.

Examples

flank5 <- "ACCT" #4bp
spacer <- "ATCGATGCTGATGCTAGATA" #20bp
pam    <- "AGG" #3bp 
flank3 <- "TTG" #3bp
input  <- paste0(flank5, spacer, pam, flank3) 
results <- getRuleSet1Scores(input)

Calculate on-target sgRNA activity scores for SpCas9 using Rule Set 3

Description

Calculate on-target sgRNA activity scores for CRISPR/Cas9-induced knockout using the Rule Set 3 scoring method.

Usage

getRuleSet3Scores(
  sequences,
  tracrRNA = c("Hsu2013", "Chen2013"),
  mode = c("sequence", "target"),
  condaEnv
)

Arguments

sequences

Character vector of 30bp sequences needed for Rule Set 3 scoring, see details below.
tracrRNA

String specifying which tracrRNA is used. Must be either "Hsu2013" (default) or "Chen2013".
mode

String specifying which prediction mode is used. Must be either "sequence" (default) or "target".
condaEnv

String specifying the path of the conda environment needed to run the DeepHF calculations. See the crisprScore vignette for instructions on how to build the environment.

Details

The input sequences for Rule Set 3 scoring require 4 nucleotides upstream of the protospacer sequence, the protospacer sequence itself (20bp spacer sequence + 3bp PAM sequence ) and 3 nucleootides downstream of the protospacer sequence, for a total of 30 nucleotides.

Value

getRuleSet3Scores returns a data.frame with sequence and score columns. The getRuleSet3Scores score is similar to a Z-score.A higher score indicates higher knockout efficiency.

Author(s)

Jean-Philippe Fortin

References

doi: https://doi.org/10.1101/2022.06.27.497780

Examples

if (interactive()){
flank5 <- "ACCG" #4bp
spacer <- "AATCGATGCTGATGCTAGAT" #20bp
pam    <- "AGG" #3bp
flank3 <- "AAT" #3bp
input  <- paste0(flank5, spacer, pam, flank3) 
condaEnv <- "/Users/fortin946/miniforge3/envs/rs3-env"
results <- getRuleSet3Scores(input, condaEnv=condaEnv, tracrRNA="Chen2013")
}

data.frame detailing available scoring methods

Description

data.frame detailing available scoring methods with information needed to extract nucleotide sequences needed by each scoring algorithm.

Usage

data(scoringMethodsInfo)

Format

A data frame with 6 columns:

method

name of the scoring method

nuclease

nuclease compatible with the scoring method

left

upstream offset (relative to PAM site) to extract nucleotide sequence needed for scoring

right

downstream offset (relative to PAM site) to extract nucleotide sequence needed for scoring

type

type of the scoring algorithm (on-target or off-target)

label

proper case-sensitive method name for labeling

len

length of the nucleotide sequence needed for scoring