bioconductor-crisprscore

name

bioconductor-crisprscore

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

crisprScore

Workflows

Standard Workflow

library(crisprScore)
spacer <- "ATCGATGCTGATGCTAGATA"
results <- getCRISPRaterScores(spacer)

Inputs: A character vector of 20bp spacer sequences. Outputs: A numeric vector of CRISPRater on-target efficiency scores.

Off Target Specificity Prediction

library(crisprScore)
spacer <- "ATCGATGCTGATGCTAGATA"
protospacers <- c("ACCGATGCTGATGCTAGATA", "ATCGATGCTGATGCTAGATT", "ATCGATGCTGATGCTAGATA")
pams <- c("AGG", "AGG", "AGA")

mit_scores <- getMITScores(spacers=spacer, protospacers=protospacers, pams=pams)
cfd_scores <- getCFDScores(spacers=spacer, protospacers=protospacers, pams=pams)

Inputs: Spacer sequence, potential off-target protospacer sequences, and PAM sequences. Outputs: Data frames containing MIT and CFD off-target specificity scores.

Indel Frameshift Prediction

library(crisprScore)
flank5 <- "ACCTTTTAATCGA"
spacer <- "TGCTGATGCTAGATATTAAG"
pam    <- "TGG"
flank3 <- "CTTTTAATCGATGCTGATGCTAGATATTA"
input <- paste0(flank5, spacer, pam, flank3)
# results <- getLindelScores(input, condaEnv="/path/to/lindel-env")

Inputs: A 65bp character vector containing flanking and protospacer sequences. Outputs: Predicted proportion of frameshifting indels.

Cas12A On Target Scoring

library(crisprScore)
flank5 <- "ACCG"
pam    <- "TTTT"
spacer <- "AATCGATGCTGATGCTAGATATT"
flank3 <- "AAG"
input  <- paste0(flank5, pam, spacer, flank3)
# results <- getEnPAMGBScores(input, condaEnv="/path/to/enpamgb-env")

Inputs: A 34bp character vector containing 4bp upstream, 4bp PAM, 23bp spacer, and 3bp downstream. Outputs: Predicted enPAM+GB on-target efficiency scores.

Cas13D On Target Scoring

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

Inputs: An mRNA sequence as a DNAStringSet object. Outputs: A data frame containing predicted CasRx-RF on-target efficiency scores.

Off Target Specificity Scoring

library(crisprScore)
spacer <- "ATCGATGCTGATGCTAGATA"
protospacers <- c("ACCGATGCTGATGCTAGATA", "ATCGATGCTGATGCTAGATT", "ATCGATGCTGATGCTAGATA")
pams <- c("AGG", "AGG", "AGA")

mit_res <- getMITScores(spacers=spacer, protospacers=protospacers, pams=pams)
cfd_res <- getCFDScores(spacers=spacer, protospacers=protospacers, pams=pams)

Inputs: Spacer, protospacer, and PAM sequences. Outputs: Data frames containing calculated MIT and CFD specificity scores.

When to Use

• Cas9 On-Target Scoring: Predicting on-target cutting efficiency for SpCas9 using getRuleSet1Scores, getRuleSet3Scores, getDeepHFScores, getCRISPRscanScores, or getCRISPRaterScores.
• Cas12a On-Target Scoring: Predicting on-target cutting efficiency for enAsCas12a using getEnPAMGBScores.
• Cas13d On-Target Scoring: Predicting on-target efficiency for RfxCas13d (CasRx) using getCasRxRFScores.
• Off-Target Specificity: Predicting off-target specificity using getMITScores and getCFDScores.
• Frameshift Prediction: Predicting frameshift-inducing indel probabilities using getLindelScores.

When NOT to Use

• Unsupported R/Bioconductor Versions: Do not use if R version is less than 4.1 or Bioconductor version is less than 3.16.
• Python 2 Algorithms: Do not use for running Python 2-based algorithms (like Azimuth, DeepCpf1, DeepSpCas9, and CRISPRai) which are no longer supported.

Data Requirements

• Sequence Lengths:
  • getRuleSet1Scores / getRuleSet3Scores: 30bp sequences (4bp upstream + 20bp spacer + 3bp PAM + 3bp downstream).
  • getCRISPRscanScores: 35bp sequences (6bp upstream + 20bp spacer + 3bp PAM + 6bp downstream).
  • getCRISPRaterScores: 20bp spacer sequences.
  • getLindelScores: 65bp sequences (13bp upstream + 20bp spacer + 3bp PAM + 29bp downstream).
  • getCasRxRFScores: mRNA sequences as a DNAStringSet object.

Key Parameters

• tracrRNA ("Hsu2013"): TracrRNA design type used in getRuleSet3Scores (e.g., "Hsu2013" or "Chen2013").
• condaEnv (NULL): Path to the conda environment containing Python dependencies for algorithms like RuleSet3, DeepHF, enPAM+GB, and Lindel.
• enzyme ("WT"): Cas9 variant for getDeepHFScores ("WT", "HF", or "ESP").
• promoter ("U6"): Promoter used for expressing sgRNAs in getDeepHFScores.
• directRepeat ("aacccctaccaactggtcggggtttgaaac"): Direct repeat sequence used in getCasRxRFScores.
• spacers (no default): Character vector of 20bp spacer sequences for off-target scoring.
• protospacers (no default): Character vector of 20bp target protospacer sequences for off-target scoring.
• pams (no default): Character vector of PAM sequences.

Best Practices

• Conda Environments: Build the required conda environments manually prior to using Python-based scoring algorithms and pass the path to condaEnv.
• Algorithm Upgrades: Use getRuleSet3Scores or getDeepHFScores instead of the deprecated Azimuth algorithm.
• Cas12a Upgrades: Use getEnPAMGBScores instead of the deprecated DeepCpf1 algorithm.

Common Pitfalls

• Missing Conda Environments: Attempting to run Python-based algorithms without specifying a valid conda environment path. Fix: Set up the environment using the provided buildingCondaEnvironments.sh script and pass the path to condaEnv.
• Deprecated Algorithms: Using Python 2-based algorithms like Azimuth or DeepCpf1. Fix: Use supported alternatives like getRuleSet3Scores or getEnPAMGBScores.

Alternatives

• crisprDesign: For full gRNA design workflows that internally call crisprScore.
• crisprBase: For defining CRISPR nucleases and base editors.

Citations

• Doench, John G, et al. 2014. "Rational Design of Highly Active sgRNAs for Crispr-Cas9–Mediated Gene Inactivation." Nature Biotechnology 32 (12): 1262–7.
• DeWeirdt, Peter C, et al. 2022. "Accounting for Small Variations in the tracrRNA Sequence Improves sgRNA Activity Predictions for Crispr Screening." bioRxiv.
• Wang, Daqi, et al. 2019. "Optimized Crispr Guide Rna Design for Two High-Fidelity Cas9 Variants by Deep Learning." Nature Communications 10 (1): 1–14.
• Chen, Wei, et al. 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.

References

• Homepage: bioconductor.org/packages/crisprScore
• Vignette: bioconductor.org/packages/release/bioc/vignettes/crisprScore/inst/doc/crisprScore.html