crisprScore: on-target and off-target scoring for CRISPR gRNAs ================ - <a href="#overview" id="toc-overview">Overview</a> - <a href="#installation-and-getting-started" id="toc-installation-and-getting-started">Installation and getting started</a> - <a href="#software-requirements" id="toc-software-requirements">Software requirements</a> - <a href="#os-requirements" id="toc-os-requirements">OS Requirements</a> - <a href="#installation" id="toc-installation">Installation</a> - <a href="#getting-started" id="toc-getting-started">Getting started</a> - <a href="#on-targeting-efficiency-scores" id="toc-on-targeting-efficiency-scores">On-targeting efficiency scores</a> - <a href="#cas9-methods" id="toc-cas9-methods">Cas9 methods</a> - <a href="#rule-set-1" id="toc-rule-set-1">Rule Set 1</a> - <a href="#azimuth" id="toc-azimuth">Azimuth</a> - <a href="#rule-set-3" id="toc-rule-set-3">Rule Set 3</a> - <a href="#deephf" id="toc-deephf">DeepHF</a> - <a href="#deepspcas9" id="toc-deepspcas9">DeepSpCas9</a> - <a href="#crisprscan" id="toc-crisprscan">CRISPRscan</a> - <a href="#crisprater" id="toc-crisprater">CRISPRater</a> - <a href="#crisprai" id="toc-crisprai">CRISPRai</a> - <a href="#cas12a-methods" id="toc-cas12a-methods">Cas12a methods</a> - <a href="#deepcpf1-score" id="toc-deepcpf1-score">DeepCpf1 score</a> - <a href="#enpamgb-score" id="toc-enpamgb-score">enPAM+GB score</a> - <a href="#cas13d-methods" id="toc-cas13d-methods">Cas13d methods</a> - <a href="#casrxrf" id="toc-casrxrf">CasRxRF</a> - <a href="#off-target-specificity-scores" id="toc-off-target-specificity-scores">Off-target specificity scores</a> - <a href="#mit-score" id="toc-mit-score">MIT score</a> - <a href="#cfd-score" id="toc-cfd-score">CFD score</a> - <a href="#indel-prediction-score" id="toc-indel-prediction-score">Indel prediction score</a> - <a href="#lindel-score-cas9" id="toc-lindel-score-cas9">Lindel score (Cas9)</a> - <a href="#license" id="toc-license">License</a> - <a href="#reproducibility" id="toc-reproducibility">Reproducibility</a> - <a href="#references" id="toc-references">References</a> Authors: Jean-Philippe Fortin, Aaron Lun, Luke Hoberecht, Pirunthan Perampalan Date: July 25, 2024 # Overview 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”](https://www.nature.com/articles/s41467-022-34320-7) Our main gRNA design package [crisprDesign](https://github.com/crisprVerse/crisprDesign) utilizes the `crisprScore` package to add on- and off-target scores to user-designed gRNAs; check out our [Cas9 gRNA tutorial page](https://github.com/crisprVerse/Tutorials/tree/master/Design_CRISPRko_Cas9) to learn how to use `crisprScore` via `crisprDesign`. # Installation and getting started ## Software requirements ### OS Requirements 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. ## Installation `crisprScore` can be installed from Bioconductor using the following commands in a fresh R session: ``` r if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install(version="devel") BiocManager::install("crisprScore") ``` To install the release version, use this instead: ``` r 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)](https://www.bioconductor.org/packages/release/bioc/html/basilisk.html) 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: ``` r options(reticulate.useImportHook=FALSE) ``` # Getting started We load `crisprScore` in the usual way: ``` r library(crisprScore) ``` The `scoringMethodsInfo` data.frame contains a succinct summary of scoring methods available in `crisprScore`: ``` r 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)](https://github.com/Jfortin1/crisprDesign) package provides user-friendly functionalities to extract and score those sequences automatically via the `addOnTargetScores` function. # On-targeting efficiency scores 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. ## Cas9 methods Different algorithms require different input nucleotide sequences to predict cutting efficiency as illustrated in the figure below.  ### Rule Set 1 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: ``` r 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. ### Azimuth 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: ``` r flank5 <- "ACCT" #4bp spacer <- "ATCGATGCTGATGCTAGATA" #20bp pam <- "AGG" #3bp flank3 <- "TTG" #3bp input <- paste0(flank5, spacer, pam, flank3) results <- getAzimuthScores(input) ``` ### Rule Set 3 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: ``` r 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`. ### DeepHF 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: ``` r 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. ### DeepSpCas9 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: ``` r flank5 <- "ACCT" #4bp spacer <- "ATCGATGCTGATGCTAGATA" #20bp pam <- "AGG" #3bp flank3 <- "TTG" #3bp input <- paste0(flank5, spacer, pam, flank3) results <- getDeepSpCas9Scores(input) ``` ``` r 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. ### CRISPRscan 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: ``` r flank5 <- "ACCTAA" #6bp spacer <- "ATCGATGCTGATGCTAGATA" #20bp pam <- "AGG" #3bp flank3 <- "TTGAAT" #6bp input <- paste0(flank5, spacer, pam, flank3) results <- getCRISPRscanScores(input) ``` ### CRISPRater 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. ``` r spacer <- "ATCGATGCTGATGCTAGATA" #20bp results <- getCRISPRaterScores(spacer) ``` ### CRISPRai 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: ``` r 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: ``` r 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](https://zenodo.org/record/6716721#.YrzCfS-cY4d). 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: ``` r 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`. ## Cas12a methods Different algorithms require different input nucleotide sequences to predict cutting efficiency as illustrated in the figure below.  ### DeepCpf1 score 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: ``` r flank5 <- "ACC" #3bp pam <- "TTTT" #4bp spacer <- "AATCGATGCTGATGCTAGATATT" #23bp flank3 <- "AAGT" #4bp input <- paste0(flank5, pam, spacer, flank3) results <- getDeepCpf1Scores(input) ``` ### enPAM+GB score 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: ``` r flank5 <- "ACC" #3bp pam <- "TTTT" #4bp spacer <- "AATCGATGCTGATGCTAGATATT" #23bp flank3 <- "AAGT" #4bp input <- paste0(flank5, pam, spacer, flank3) results <- getEnPAMGBScores(input) ``` ## Cas13d methods ### CasRxRF 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`: ``` r 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 package - `RNAplfold`: available as part of the ViennaRNA package - `RNAhybrid`: available as part of the RNAhybrid package Those programs can be installed from their respective websites: [VienneRNA](https://www.tbi.univie.ac.at/RNA/) and [RNAhybrid](https://bibiserv.cebitec.uni-bielefeld.de/rnahybrid/). If the argument is `NULL`, the binaries are assumed to be available on the PATH. # Off-target specificity scores 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). ## MIT score 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 $$\text{MIT} = \biggl(\prod_{p \in M}{w_p}\biggr)\times\frac{1}{\frac{19-d}{19}\times4+1}\times\frac{1}{m^2}$$ where $M$ is the set of positions for which there is a mismatch between the sgRNA spacer sequence and the off-target sequence, $w_p$ is an experimentally-derived mismatch tolerance weight at position $p$, $d$ is the average distance between mismatches, and $m$ is the total number of mismatches. As the number of mismatches increases, the cutting likelihood decreases. In addition, off-targets with more adjacent mismatches will have a lower cutting likelihood. 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`: ``` r 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 ## CFD score 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`: ``` r 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 # Indel prediction score ## Lindel score (Cas9) 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: ``` r flank5 <- "ACCTTTTAATCGA" #13bp spacer <- "TGCTGATGCTAGATATTAAG" #20bp pam <- "TGG" #3bp flank3 <- "CTTTTAATCGATGCTGATGCTAGATATTA" #29bp input <- paste0(flank5, spacer, pam, flank3) results <- getLindelScores(input) ``` # License 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. # Reproducibility ``` r sessionInfo() ``` ## R version 4.4.1 (2024-06-14) ## Platform: x86_64-apple-darwin20 ## Running under: macOS Ventura 13.6.7 ## ## Matrix products: default ## BLAS: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRblas.0.dylib ## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0 ## ## locale: ## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 ## ## time zone: America/Los_Angeles ## tzcode source: internal ## ## attached base packages: ## [1] stats graphics grDevices utils datasets methods base ## ## other attached packages: ## [1] crisprScore_1.9.1 crisprScoreData_1.9.0 ExperimentHub_2.13.0 ## [4] AnnotationHub_3.13.0 BiocFileCache_2.13.0 dbplyr_2.5.0 ## [7] BiocGenerics_0.51.0 ## ## loaded via a namespace (and not attached): ## [1] KEGGREST_1.45.1 dir.expiry_1.13.0 xfun_0.46 ## [4] lattice_0.22-6 Biobase_2.65.0 vctrs_0.6.5 ## [7] tools_4.4.1 generics_0.1.3 stats4_4.4.1 ## [10] curl_5.2.1 parallel_4.4.1 tibble_3.2.1 ## [13] fansi_1.0.6 AnnotationDbi_1.67.0 RSQLite_2.3.7 ## [16] highr_0.11 blob_1.2.4 pkgconfig_2.0.3 ## [19] Matrix_1.7-0 S4Vectors_0.43.2 lifecycle_1.0.4 ## [22] GenomeInfoDbData_1.2.12 stringr_1.5.1 compiler_4.4.1 ## [25] Biostrings_2.73.1 GenomeInfoDb_1.41.1 htmltools_0.5.8.1 ## [28] yaml_2.3.9 pillar_1.9.0 crayon_1.5.3 ## [31] cachem_1.1.0 basilisk_1.17.0 tidyselect_1.2.1 ## [34] digest_0.6.36 stringi_1.8.4 dplyr_1.1.4 ## [37] BiocVersion_3.20.0 grid_4.4.1 fastmap_1.2.0 ## [40] cli_3.6.3 magrittr_2.0.3 randomForest_4.7-1.1 ## [43] utf8_1.2.4 filelock_1.0.3 UCSC.utils_1.1.0 ## [46] rappdirs_0.3.3 bit64_4.0.5 rmarkdown_2.27 ## [49] XVector_0.45.0 httr_1.4.7 bit_4.0.5 ## [52] reticulate_1.38.0 png_0.1-8 memoise_2.0.1 ## [55] evaluate_0.24.0 knitr_1.48 IRanges_2.39.2 ## [58] basilisk.utils_1.17.0 rlang_1.1.4 Rcpp_1.0.13 ## [61] glue_1.7.0 DBI_1.2.3 BiocManager_1.30.23 ## [64] rstudioapi_0.16.0 jsonlite_1.8.8 R6_2.5.1 ## [67] zlibbioc_1.51.1 # References <div id="refs" class="references csl-bib-body hanging-indent"> <div id="ref-lindel" class="csl-entry"> Chen, Wei, Aaron McKenna, Jacob Schreiber, Maximilian Haeussler, Yi Yin, Vikram Agarwal, William Stafford Noble, and Jay Shendure. 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. </div> <div id="ref-ruleset3" class="csl-entry"> DeWeirdt, Peter C, Abby V McGee, Fengyi Zheng, Ifunanya Nwolah, Mudra Hegde, and John G Doench. 2022. “Accounting for Small Variations in the tracrRNA Sequence Improves sgRNA Activity Predictions for CRISPR Screening.” *bioRxiv*. <https://doi.org/10.1101/2022.06.27.497780>. </div> <div id="ref-enpamgb" class="csl-entry"> DeWeirdt, Peter C, Kendall R Sanson, Annabel K Sangree, Mudra Hegde, Ruth E Hanna, Marissa N Feeley, Audrey L Griffith, et al. 2020. “Optimization of AsCas12a for Combinatorial Genetic Screens in Human Cells.” *Nature Biotechnology*, 1–11. </div> <div id="ref-azimuth" class="csl-entry"> Doench, John G, Nicolo Fusi, Meagan Sullender, Mudra Hegde, Emma W Vaimberg, Katherine F Donovan, Ian Smith, et al. 2016. “Optimized sgRNA Design to Maximize Activity and Minimize Off-Target Effects of CRISPR-Cas9.” *Nature Biotechnology* 34 (2): 184. </div> <div id="ref-ruleset1" class="csl-entry"> Doench, John G, Ella Hartenian, Daniel B Graham, Zuzana Tothova, Mudra Hegde, Ian Smith, Meagan Sullender, Benjamin L Ebert, Ramnik J Xavier, and David E Root. 2014. “Rational Design of Highly Active sgRNAs for CRISPR-Cas9–Mediated Gene Inactivation.” *Nature Biotechnology* 32 (12): 1262–67. </div> <div id="ref-crisprai" class="csl-entry"> Horlbeck, Max A, Luke A Gilbert, Jacqueline E Villalta, Britt Adamson, Ryan A Pak, Yuwen Chen, Alexander P Fields, et al. 2016. “Compact and Highly Active Next-Generation Libraries for CRISPR-Mediated Gene Repression and Activation.” *Elife* 5. </div> <div id="ref-mit" class="csl-entry"> Hsu, Patrick D, David A Scott, Joshua A Weinstein, F Ann Ran, Silvana Konermann, Vineeta Agarwala, Yinqing Li, et al. 2013. “DNA Targeting Specificity of RNA-Guided Cas9 Nucleases.” *Nature Biotechnology* 31 (9): 827. </div> <div id="ref-deepspcas9" class="csl-entry"> Kim, Hui Kwon, Younggwang Kim, Sungtae Lee, Seonwoo Min, Jung Yoon Bae, Jae Woo Choi, Jinman Park, Dongmin Jung, Sungroh Yoon, and Hyongbum Henry Kim. 2019. “SpCas9 Activity Prediction by DeepSpCas9, a Deep Learning–Based Model with High Generalization Performance.” *Science Advances* 5 (11): eaax9249. </div> <div id="ref-deepcpf1" class="csl-entry"> Kim, Hui Kwon, Seonwoo Min, Myungjae Song, Soobin Jung, Jae Woo Choi, Younggwang Kim, Sangeun Lee, Sungroh Yoon, and Hyongbum Henry Kim. 2018. “Deep Learning Improves Prediction of CRISPR–Cpf1 Guide RNA Activity.” *Nature Biotechnology* 36 (3): 239. </div> <div id="ref-encas12a" class="csl-entry"> Kleinstiver, Benjamin P, Alexander A Sousa, Russell T Walton, Y Esther Tak, Jonathan Y Hsu, Kendell Clement, Moira M Welch, et al. 2019. “Engineered CRISPR–Cas12a Variants with Increased Activities and Improved Targeting Ranges for Gene, Epigenetic and Base Editing.” *Nature Biotechnology* 37 (3): 276–82. </div> <div id="ref-crisprater" class="csl-entry"> Labuhn, Maurice, Felix F Adams, Michelle Ng, Sabine Knoess, Axel Schambach, Emmanuelle M Charpentier, Adrian Schwarzer, Juan L Mateo, Jan-Henning Klusmann, and Dirk Heckl. 2018. “Refined sgRNA Efficacy Prediction Improves Large-and Small-Scale CRISPR–Cas9 Applications.” *Nucleic Acids Research* 46 (3): 1375–85. </div> <div id="ref-crisprscan" class="csl-entry"> Moreno-Mateos, Miguel A, Charles E Vejnar, Jean-Denis Beaudoin, Juan P Fernandez, Emily K Mis, Mustafa K Khokha, and Antonio J Giraldez. 2015. “CRISPRscan: Designing Highly Efficient sgRNAs for CRISPR-Cas9 Targeting in Vivo.” *Nature Methods* 12 (10): 982–88. </div> <div id="ref-deepcas9" class="csl-entry"> Wang, Daqi, Chengdong Zhang, Bei Wang, Bin Li, Qiang Wang, Dong Liu, Hongyan Wang, et al. 2019. “Optimized CRISPR Guide RNA Design for Two High-Fidelity Cas9 Variants by Deep Learning.” *Nature Communications* 10 (1): 1–14. </div> <div id="ref-casrxrf" class="csl-entry"> Wessels, Hans-Hermann, Alejandro Méndez-Mancilla, Xinyi Guo, Mateusz Legut, Zharko Daniloski, and Neville E Sanjana. 2020. “Massively Parallel Cas13 Screens Reveal Principles for Guide RNA Design.” *Nature Biotechnology* 38 (6): 722–27. </div> </div>