- Overview
- Installation and getting started
- Getting started
- On-targeting efficiency scores
- Off-target specificity scores
- Indel prediction score
- License
- Reproducibility
- References
Authors: Jean-Philippe Fortin, Aaron Lun, Luke Hoberecht, Pirunthan Perampalan
Date: July 25, 2024
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, Azimuth, DeepHF, DeepSpCas9, DeepCpf1, 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, DeepCpf1 and enPAM+GB are not available on Windows machines.
Our work is described in our recent papera recent bioRxiv preprint: “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.
This package is supported for macOS, Linux and Windows machines. Some functionalities are not supported for Windows machines. Packages require R version 4.4 or higher.
crisprScore can be installed from Bioconductor using the following
commands in a fresh R session:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(version="devel")
BiocManager::install("crisprScore")To install the release version, use this instead:
BiocManager::install(version="release")Under the hood, crisprScore uses the basilisk package to install
Python environments needed to run some of the prediction algorithms.
When calling one of the scoring methods for the first time after package
installation, the underlying python module and conda environments will
be automatically downloaded and installed without the need for user
intervention. This may take several minutes, but this is a one-time
installation.
We made recent changes to install Python packages only from the public
Bioconda and conda-forge channels to be compliant with the latest
Anaconda licensing policies. Setting the environment variable
BASILISK_USE_MINIFORGE=1 will ensure that crisprScore will use
miniforge to install the Python packages. See the documentation of the
basilisk package (link
here)
for more information re. different settings for the installation of the
underlying Python packages
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)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 azimuth SpCas9 -24 5 On-target Azimuth 30
## 3 deephf SpCas9 -20 2 On-target DeepHF 23
## 4 lindel SpCas9 -33 31 On-target Lindel 65
## 5 mit SpCas9 -20 2 Off-target MIT 23
## 6 cfd SpCas9 -20 2 Off-target CFD 23
## 7 deepcpf1 AsCas12a -4 29 On-target DeepCpf1 34
## 8 enpamgb enAsCas12a -4 29 On-target EnPAMGB 34
## 9 crisprscan SpCas9 -26 8 On-target CRISPRscan 35
## 10 casrxrf CasRx NA NA On-target CasRx-RF NA
## 11 crisprai SpCas9 -19 2 On-target CRISPRai 22
## 12 crisprater SpCas9 -20 -1 On-target CRISPRater 20
## 13 deepspcas9 SpCas9 -24 5 On-target DeepSpCas9 30
## 14 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.
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.
Different algorithms require different input nucleotide sequences to predict cutting efficiency as illustrated in the figure below.
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)The Azimuth score described below is an improvement over Rule Set 1 from the same lab.
The Azimuth algorithm is an improved version of the popular Rule Set 2 score for the Cas9 nuclease (Doench et al. 2016). 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 <- getAzimuthScores(input)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)
results <- getRuleSet3Scores(input, tracrRNA="Hsu2013")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.
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)
results <- getDeepHFScores(input)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.
The DeepSpCas9 algorithm is an on-target cutting efficiency prediction algorithm for the SpCas9 nuclease (Kim et al. 2019). Similar to the Azimuth score, it generates a probability of cutting at the intended on-target. 4 nucleotides upstream of the protospacer sequence, and 3 nucleotides downstream of the PAM sequence are needed in top of the protospacer sequence for scoring:
flank5 <- "ACCT" #4bp
spacer <- "ATCGATGCTGATGCTAGATA" #20bp
pam <- "AGG" #3bp
flank3 <- "TTG" #3bp
input <- paste0(flank5, spacer, pam, flank3)
results <- getDeepSpCas9Scores(input)spacer <- "ATCGATGCTGATGCTAGATA" #20bp
pam <- "AGG" #3bp
input <- paste0(spacer, pam)
results <- getDeepHFScores(input)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.
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)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)The CRISPRai algorithm was developed by the Weissman lab to score SpCas9
gRNAs for CRISPRa and CRISPRi applications (Horlbeck et al. 2016), for
the human genome. The function getCrispraiScores requires several
inputs.
First, it requires a data.frame specifying the genomic coordinates of the transcription starting sites (TSSs). An example of such a data.frame is provided in the crisprScore package:
head(tssExampleCrispri)## tss_id gene_symbol promoter transcripts position strand chr
## 1 A1BG_P1 A1BG P1 ENST00000596924 58347625 - chr19
## 2 A1BG_P2 A1BG P2 ENST00000263100 58353463 - chr19
## 3 KRAS_P1 KRAS P1 ENST00000311936 25250929 - chr12
## 4 SMARCA2_P1 SMARCA2 P1 ENST00000357248 2015347 + chr9
## 5 SMARCA2_P2 SMARCA2 P2 ENST00000382194 2017615 + chr9
## 6 SMARCA2_P3 SMARCA2 P3 ENST00000635133 2158470 + chr9
It also requires a data.frame specifying the genomic coordinates of the gRNA sequences to score. An example of such a data.frame is provided in the crisprScore package:
head(sgrnaExampleCrispri)## grna_id tss_id pam_site strand spacer_19mer
## 1 A1BG_P1_1 A1BG_P1 58347601 - CTCCGGGCGACGTGGAGTG
## 2 A1BG_P1_2 A1BG_P1 58347421 - GGGCACCCAGGAGCGGTAG
## 3 A1BG_P1_3 A1BG_P1 58347624 - TCCACGTCGCCCGGAGCTG
## 4 A1BG_P1_4 A1BG_P1 58347583 - GCAGCGCAGGACGGCATCT
## 5 A1BG_P1_5 A1BG_P1 58347548 - AGCAGCTCGAAGGTGACGT
## 6 A1BG_P2_1 A1BG_P2 58353455 - ATGATGGTCGCGCTCACTC
All columns present in tssExampleCrispri and sgrnaExampleCrispri are
mandatory for getCrispraiScores to work.
Two additional arguments are required: fastaFile, to specify the path
of the fasta file of the human reference genome, and chromatinFiles,
which is a list of length 3 specifying the path of files containing the
chromatin accessibility data needed for the algorithm in hg38
coordinates. The chromatin files can be downloaded from Zenodo
here. The fasta file
for the human genome (hg38) can be downloaded directly from here:
https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz
One can obtain the CRISPRai scores using the following command:
results <- getCrispraiScores(tss_df=tssExampleCrispri,
sgrna_df=sgrnaExampleCrispri,
modality="CRISPRi",
fastaFile="your/path/hg38.fa",
chromatinFiles=list(mnase="path/to/mnaseFile.bw",
dnase="path/to/dnaseFile.bw",
faire="oath/to/faireFile.bw"))The function works identically for CRISPRa applications, with modality
replaced by CRISPRa.
Different algorithms require different input nucleotide sequences to predict cutting efficiency as illustrated in the figure below.
The DeepCpf1 algorithm is an on-target cutting efficiency prediction algorithm for the Cas12a nuclease (Kim et al. 2018) using a convolutional neural network (CNN) framework. It generates a score between 0 and 1 to quantify the likelihood of Cas12a to cut for a given sgRNA. 3 nucleotides upstream and 4 nucleotides downstream of the PAM sequence are needed for scoring:
flank5 <- "ACC" #3bp
pam <- "TTTT" #4bp
spacer <- "AATCGATGCTGATGCTAGATATT" #23bp
flank3 <- "AAGT" #4bp
input <- paste0(flank5, pam, spacer, flank3)
results <- getDeepCpf1Scores(input)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. 3 nucleotides upstream of the PAM sequence and 4 nucleotides downstream of the protospacer sequence are needed for scoring:
flank5 <- "ACC" #3bp
pam <- "TTTT" #4bp
spacer <- "AATCGATGCTGATGCTAGATATT" #23bp
flank3 <- "AAGT" #4bp
input <- paste0(flank5, pam, spacer, flank3)
results <- getEnPAMGBScores(input)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 packageRNAplfold: available as part of the ViennaRNA packageRNAhybrid: 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.
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).
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
where
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
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
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)
results <- getLindelScores(input)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.
sessionInfo()## R version 4.4.1 (2024-06-14)
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