Package 'crisprScore' reference manual

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, Azimuth, DeepHF, DeepCpf1, 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, DeepCpf1 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:	1.9.3
Built:	2024-10-04 03:26:26 UTC
Source:	https://github.com/bioc/crisprScore

Calculate on-target sgRNA activity scores for Cas9 using Azimuth

Description

Calculate on-target sgRNA activity scores for CRISPR/Cas9-induced knockout using the Azimuth scoring method. The Azimuth algorithm is an improvement upon the commonly-used 'Rule Set 2', also developed by the Doench lab.

Usage

getAzimuthScores(sequences, fork = FALSE)
getAzimuthScores(sequences, fork = FALSE)

Arguments

`sequences`	Character vector of 30bp sequences needed for Azimuth scoring, see details below.
`fork`	Set to `TRUE` to preserve changes to the R configuration within the session.

Details

The input sequences for Azimuth 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 Azimuth.

Value

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

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

if (interactive()){
flank5 <- "ACCT" #4bp
spacer <- "ATCGATGCTGATGCTAGATA" #20bp
pam    <- "AGG" #3bp 
flank3 <- "TTG" #3bp
input  <- paste0(flank5, spacer, pam, flank3) 
results <- getAzimuthScores(input)
}

if (interactive()){
flank5 <- "ACCT" #4bp
spacer <- "ATCGATGCTGATGCTAGATA" #20bp
pam    <- "AGG" #3bp 
flank3 <- "TTG" #3bp
input  <- paste0(flank5, spacer, pam, flank3) 
results <- getAzimuthScores(input)
}

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
)
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)
}

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"))
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

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")
)

# 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 CRISPRa and CRISPRi

Description

Use the Weissman lab scoring method (library design v2) to calculate on-target sgRNA activity scores for Cas9-based CRISPR activation (CRISPRa) and CRISPR inactivation (CRISPRi) gene perturbation studies. The algorithm incorporates chromatin features, transcription start site, and sequence to predict gRNA activity scores. Only sgRNAs designed for the human genome (hg38 build) using Cas9 are supported at the moment, and only spacers of length 19 are supported at the moment.

Usage

getCrispraiScores(
  tss_df,
  sgrna_df,
  verbose = FALSE,
  modality = c("CRISPRa", "CRISPRi"),
  fastaFile = NULL,
  chromatinFiles = NULL
)
getCrispraiScores(
  tss_df,
  sgrna_df,
  verbose = FALSE,
  modality = c("CRISPRa", "CRISPRi"),
  fastaFile = NULL,
  chromatinFiles = NULL
)

Arguments

`tss_df`	A `data.frame` specifying coordinates of transcription start site (TSS) of the targeted promoter regions. Must have the following columns: `gene_symbol`, `promoter`, `transcripts`, `position`, `strand`, and `chr`. See details section below for more information.
`sgrna_df`	A `A data.frame` specifying coordinates and spacer sequences of the sgRNAs to score. Must have the following columns: `grna_id`, `tss_id`, `pam_site`, `strand`, and `spacer_19mer`. See details section below for more information.
`verbose`	Should messages be printed to the console? TRUE by default.
`modality`	Which mode of perturbation is being used? Must be a `string` specifying either `CRISPRa` or `CRISPRi`.
`fastaFile`	String specifying fasta file of the hg38 genome.
`chromatinFiles`	Named character vector of length 3 specifying BigWig files containing chromatin accessibility data.

Details

tss_df details: This must be a data.frame that contains the following columns: * tss_id: string specifying name of the TSS. * gene_symbol: string specifying sHGNC/HUGO gene identifier. * promoter: string specifying promoter ID (e.g. "P1" or "P2"). * transcripts: Ensembl transcript identifier. * position: start position of TSS in hg38 coordinates. * strand: strand of the gene/TSS. Must be either + or -. * chr: string specifying chromosome (e.g. "chr1").

sgrna_df details: This must be a data.frame that contains the following columns: * grna_id: string specifying a unique sgRNA identifier. * tss_id: string specifying name of the TSS. * pam_site: genomic ccoordinate of the N in the NGG PAM sequence. * strand: strand fo the sgRNA. Must be either + or -. * spacer_19mer: string specifying sgRNA 19mer spacer sequence.

Value

getCrispraiScores returns a data.frame with grna_id and score columns. The Weissman score takes on a value between 0 and 1. A higher score indicates higher sgRNA efficiency.

Author(s)

Pirunthan Perampalam, Jean-Philippe Fortin

References

Horlbeck et al. Compact and highly active next-generation libraries for CRISPR-mediated gene repression and activation eLife 2016;5:e19760. https://doi.org/10.7554/eLife.19760.

Examples

## Not run: 
results <- getCrispraiScores(tss_df=tssExampleCrispra,
                             sgrna_df=sgrnaExampleCrispra,
                             modality="CRISPRa")

results <- getCrispraiScores(tss_df=tssExampleCrispri,
                             sgrna_df=sgrnaExampleCrispri,
                             modality="CRISPRi")

## End(Not run)

## Not run: 
results <- getCrispraiScores(tss_df=tssExampleCrispra,
                             sgrna_df=sgrnaExampleCrispra,
                             modality="CRISPRa")

results <- getCrispraiScores(tss_df=tssExampleCrispri,
                             sgrna_df=sgrnaExampleCrispri,
                             modality="CRISPRi")

## End(Not run)

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)
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)

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)
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)

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 Cas12a using DeepCpf1

Description

Calculate on-target sgRNA activity scores for CRISPR/Cas12a-induced knockout using the DeepCpf1 scoring method. Currently not supported on Windows machines.

Usage

getDeepCpf1Scores(sequences, convertPAM = TRUE, fork = FALSE)
getDeepCpf1Scores(sequences, convertPAM = TRUE, fork = FALSE)

Arguments

`sequences`	Character vector of 34bp sequences needed for DeepCpf1 scoring, see details below.
`convertPAM`	Should non-canonical PAM sequences be converted to TTTC? TRUE by default.
`fork`	Set to `TRUE` to preserve changes to the R configuration within the session.

Details

The input sequences for DeepCpf1 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. If convertPAM is set to TRUE, any non-canonical PAM sequence will be convert to TTTC for scoring purposes.

Value

getDeepCpf1Scores returns a data.frame with sequence and score columns. The DeepCpf1 score takes on a value between 0 and 1. A higher score indicates higher knockout efficiency.

Author(s)

Jean-Philippe Fortin

References

Kim, H., Min, S., Song, M. et al. Deep learning improves prediction of CRISPR–Cpf1 guide RNA activity. Nat Biotechnol 36, 239–241 (2018). https://doi.org/10.1038/nbt.4061.

Examples

if (interactive()){
flank5 <- "ACCG" #4bp
pam    <- "TTTT" #4bp
spacer <- "AATCGATGCTGATGCTAGATATT" #23bp
flank3 <- "AAG" #4bp
input  <- paste0(flank5, pam, spacer, flank3) 
results <- getDeepCpf1Scores(input)
}

if (interactive()){
flank5 <- "ACCG" #4bp
pam    <- "TTTT" #4bp
spacer <- "AATCGATGCTGATGCTAGATATT" #23bp
flank3 <- "AAG" #4bp
input  <- paste0(flank5, pam, spacer, flank3) 
results <- getDeepCpf1Scores(input)
}

Calculate on-target sgRNA activity scores for Cas9 using DeepHF

Description

Usage

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

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.
`fork`	Set to `TRUE` to preserve changes to the R configuration within the session.

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) 

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

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

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

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

if (interactive()){
spacer  <- "ATCGATGCTGATGCTAGATA" #20bp
pam     <- "AGG" #3bp 
input   <- paste0(spacer, pam) 

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

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

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

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

Calculate on-target sgRNA activity scores for SpCas9 using DeepSpCas9

Description

Calculate on-target sgRNA activity scores for CRISPR/Cas9-induced knockout using the DeepSpCas9 scoring method.

Usage

getDeepSpCas9Scores(sequences, fork = FALSE)
getDeepSpCas9Scores(sequences, fork = FALSE)

Arguments

`sequences`	Character vector of 30bp sequences needed for DeepSpCas9 scoring, see details below.
`fork`	Set to `TRUE` to preserve changes to the R configuration within the session.

Details

The input sequences for DeepSpCas9 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

getDeepSpCas9Scores returns a data.frame with sequence and score columns. The getDeepSpCas9Scores score takes on a value between 0 and 1. A higher score indicates higher knockout efficiency.

Author(s)

Jean-Philippe Fortin

References

Kim HK, Kim Y, Lee S, et al. SpCas9 activity prediction by DeepSpCas9, a deep learning–base model with high generalization performance. Science advances. 2019 Nov 6;5(11):eaax9249.

Examples

if (interactive()){
flank5 <- "ACCG" #4bp
spacer <- "AATCGATGCTGATGCTAGAT" #20bp
pam    <- "AGG" #3bp
flank3 <- "AAT" #3bp
input  <- paste0(flank5, spacer, pam, flank3) 
results <- getDeepSpCas9Scores(input)
}
if (interactive()){
flank5 <- "ACCG" #4bp
spacer <- "AATCGATGCTGATGCTAGAT" #20bp
pam    <- "AGG" #3bp
flank3 <- "AAT" #3bp
input  <- paste0(flank5, spacer, pam, flank3) 
results <- getDeepSpCas9Scores(input)
}

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, fork = FALSE)
getEnPAMGBScores(sequences, fork = FALSE)

Arguments

`sequences`	Character vector of 34bp sequences needed for enPAM+GB scoring, see details below.
`fork`	Set to `TRUE` to preserve changes to the R configuration within the session.

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 nucleootides 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) 
results <- getEnPAMGBScores(input)
}
if (interactive()){
flank5 <- "CATG" #4bp
pam    <- "TTTT" #4bp
spacer <- "TTTGGGAACCAATCGATAATCAC" #23bp
flank3 <- "ATT" #3bp
input  <- paste0(flank5, pam, spacer, flank3) 
results <- getEnPAMGBScores(input)
}

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, fork = FALSE)
getLindelScores(sequences, fork = FALSE)

Arguments

`sequences`	Character vector of 65bp sequences needed for Lindel scoring, see details below.
`fork`	Set to `TRUE` to preserve changes to the R configuration within the session.

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)
results <- getLindelScores(input)
}
if (interactive()){
flank5 <- "ACCTTTTAATCGA" #13bp
spacer <- "TGCTGATGCTAGATATTAAG" #20bp
pam    <- "TGG" #3bp
flank3 <- "CTTTTAATCGATGCTGATGCTAGATATTA" #29bp
input <- paste0(flank5, spacer, pam, flank3)
results <- getLindelScores(input)
}

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)
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
)

# 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)
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)

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

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".

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) 
results <- getRuleSet3Scores(input)
}

if (interactive()){
flank5 <- "ACCG" #4bp
spacer <- "AATCGATGCTGATGCTAGAT" #20bp
pam    <- "AGG" #3bp
flank3 <- "AAT" #3bp
input  <- paste0(flank5, spacer, pam, flank3) 
results <- getRuleSet3Scores(input)
}

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)
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

Example CRISPRa gRNAs data.frame for the getCrispraiScores function

Description

Example CRISPRa gRNAs data.frame for the getCrispraiScores function. The targeted TSSs are described in the object tssExampleCrispra.

Usage

data(sgrnaExampleCrispra)
data(sgrnaExampleCrispra)

Format

A data.frame with 5 columns:

grna_id: String specifying gRNA unique identifier.
tss_id: String specifying the targeted TSS id.
pam_site: Genomic coordinate specifying the first nucleotide of PAM sequence.
strand: Strand of the gRNA strand. Must be "+" or "-".
spacer_19mer: String specifying the nucleotide sequence of the 19mer spacer sequence.

Example CRISPRi gRNAs data.frame for the getCrispraiScores function

Description

Example CRISPRi gRNAs data.frame for the getCrispraiScores function. The targeted TSSs are described in the object tssExampleCrispri.

Usage

data(sgrnaExampleCrispri)
data(sgrnaExampleCrispri)

Format

A data.frame with 5 columns:

grna_id: String specifying gRNA unique identifier.
tss_id: String specifying the targeted TSS id.
pam_site: Genomic coordinate specifying the first nucleotide of PAM sequence.
strand: Strand of the gRNA strand. Must be "+" or "-".
spacer_19mer: String specifying the nucleotide sequence of the 19mer spacer sequence.

Example TSS data.frame for the getCrispraiScores function

Description

Example TSS data for gRNAs stored in sgrnaExampleCrispra.

Usage

data(tssExampleCrispra)
data(tssExampleCrispra)

Format

A data.frame with 7 columns:

tss_id: String specifying the targeted TSS id.
gene_symbol: String specifying gene symbol.
promoter: String specifying the promoter suffix to add to the gene symbol columns to obtain the unique TSS id.
transcripts: Ensembl IDs of the targeted transcript.
position: Integer specifying genomic coordinate of the TSS.
strand: Strand of TSS. Must be "+" or "-".
chr: String specifying the chromosome name.

Example TSS data.frame for the getCrispraiScores function

Description

Example TSS data for gRNAs stored in sgrnaExampleCrispri.

Usage

data(tssExampleCrispri)
data(tssExampleCrispri)

Format

A data.frame with 7 columns:

tss_id: String specifying the targeted TSS id.
gene_symbol: String specifying gene symbol.
promoter: String specifying the promoter suffix to add to the gene symbol columns to obtain the unique TSS id.
transcripts: Ensembl IDs of the targeted transcript.
position: Integer specifying genomic coordinate of the TSS.
strand: Strand of TSS. Must be "+" or "-".
chr: String specifying the chromosome name.

Package 'crisprScore'

Help Index

Calculate on-target sgRNA activity scores for Cas9 using Azimuth

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

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

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Calculate CFD off-target specificity scores

Description

Usage

Arguments

Value

Author(s)

References

Examples

Calculate on-target sgRNA activity scores for CRISPRa and CRISPRi

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Calculate on-target sgRNA activity scores for Cas9 using CRISPRater

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Calculate on-target sgRNA activity scores for Cas9 using CRISPRscan

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Calculate on-target sgRNA activity scores for Cas12a using DeepCpf1

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Calculate on-target sgRNA activity scores for Cas9 using DeepHF

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Calculate on-target sgRNA activity scores for SpCas9 using DeepSpCas9

Description

Usage

Arguments

Details

Value

Author(s)