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
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See how to installReview the instructions the agent will receive before you install.
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.
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.
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.
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.
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.
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.
getRuleSet1Scores, getRuleSet3Scores, getDeepHFScores, getCRISPRscanScores, or getCRISPRaterScores.getEnPAMGBScores.getCasRxRFScores.getMITScores and getCFDScores.getLindelScores.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.getRuleSet3Scores (e.g., "Hsu2013" or "Chen2013").getDeepHFScores ("WT", "HF", or "ESP").getDeepHFScores.getCasRxRFScores.condaEnv.getRuleSet3Scores or getDeepHFScores instead of the deprecated Azimuth algorithm.getEnPAMGBScores instead of the deprecated DeepCpf1 algorithm.buildingCondaEnvironments.sh script and pass the path to condaEnv.getRuleSet3Scores or getEnPAMGBScores.crisprScore.No reviews yet. Tell others what you thought.
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