offTargetAnalysis: Design target-specific guide RNAs for CRISPR-Cas9 system in...

In LihuaJulieZhu/CRISPRseek: Design of target-specific guide RNAs in CRISPR-Cas9, genome-editing systems

Design target-specific guide RNAs for CRISPR-Cas9 system in one function

Description

Design target-specific guide RNAs (gRNAs) and predict relative indel fequencies for CRISPR-Cas9 system by automatically calling findgRNAs, filtergRNAs, searchHits, buildFeatureVectorForScoring, getOfftargetScore, filterOfftarget, calculating gRNA cleavage efficiency, and predict gRNA efficacy, indels and their frequencies.

Usage

offTargetAnalysis(
  inputFilePath,
  format = "fasta",
  header = FALSE,
  gRNAoutputName,
  findgRNAs = TRUE,
  exportAllgRNAs = c("all", "fasta", "genbank", "no"),
  findgRNAsWithREcutOnly = FALSE,
  REpatternFile = system.file("extdata", "NEBenzymes.fa", package = "CRISPRseek"),
  minREpatternSize = 4,
  overlap.gRNA.positions = c(17, 18),
  findPairedgRNAOnly = FALSE,
  annotatePaired = TRUE,
  paired.orientation = c("PAMout", "PAMin"),
  enable.multicore = FALSE,
  n.cores.max = 6,
  min.gap = 0,
  max.gap = 20,
  gRNA.name.prefix = "",
  PAM.size = 3,
  gRNA.size = 20,
  PAM = "NGG",
  BSgenomeName,
  chromToSearch = "all",
  chromToExclude = c("chr17_ctg5_hap1", "chr4_ctg9_hap1", "chr6_apd_hap1",
    "chr6_cox_hap2", "chr6_dbb_hap3", "chr6_mann_hap4", "chr6_mcf_hap5", "chr6_qbl_hap6",
    "chr6_ssto_hap7"),
  max.mismatch = 3,
  PAM.pattern = "NNG$|NGN$",
  allowed.mismatch.PAM = 1,
  gRNA.pattern = "",
  baseEditing = FALSE,
  targetBase = "C",
  editingWindow = 4:8,
  editingWindow.offtargets = 4:8,
  primeEditing = FALSE,
  PBS.length = 13L,
  RT.template.length = 8:28,
  RT.template.pattern = "D$",
  corrected.seq,
  targeted.seq.length.change,
  bp.after.target.end = 15L,
  target.start,
  target.end,
  primeEditingPaired.output = "pairedgRNAsForPE.xls",
  min.score = 0,
  topN = 1000,
  topN.OfftargetTotalScore = 10,
  annotateExon = TRUE,
  txdb,
  orgAnn,
  ignore.strand = TRUE,
  outputDir,
  fetchSequence = TRUE,
  upstream = 200,
  downstream = 200,
  upstream.search = 0,
  downstream.search = 0,
  weights = c(0, 0, 0.014, 0, 0, 0.395, 0.317, 0, 0.389, 0.079, 0.445, 0.508, 0.613,
    0.851, 0.732, 0.828, 0.615, 0.804, 0.685, 0.583),
  baseBeforegRNA = 4,
  baseAfterPAM = 3,
  featureWeightMatrixFile = system.file("extdata", "DoenchNBT2014.csv", package =
    "CRISPRseek"),
  useScore = TRUE,
  useEfficacyFromInputSeq = FALSE,
  outputUniqueREs = TRUE,
  foldgRNAs = FALSE,
 
    gRNA.backbone = "GUUUUAGAGCUAGAAAUAGCAAGUUAAAAUAAGGCUAGUCCGUUAUCAACUUGAAAAAGUGGCACCGAGUCGGUGCUUUUUU",
  temperature = 37,
  overwrite = FALSE,
  scoring.method = c("Hsu-Zhang", "CFDscore"),
  subPAM.activity = hash(AA = 0, AC = 0, AG = 0.259259259, AT = 0, CA = 0, CC = 0, CG =
    0.107142857, CT = 0, GA = 0.069444444, GC = 0.022222222, GG = 1, GT = 0.016129032, TA
    = 0, TC = 0, TG = 0.038961039, TT = 0),
  subPAM.position = c(22, 23),
  PAM.location = "3prime",
  rule.set = c("Root_RuleSet1_2014", "Root_RuleSet2_2016", "CRISPRscan", "DeepCpf1"),
  chrom_acc,
  calculategRNAefficacyForOfftargets = TRUE,
  mismatch.activity.file = system.file("extdata",
    "NatureBiot2016SuppTable19DoenchRoot.csv", package = "CRISPRseek"),
  predIndelFreq = FALSE,
  predictIndelFreq.onTargetOnly = TRUE,
  method.indelFreq = "Lindel",
  baseBeforegRNA.indelFreq = 13,
  baseAfterPAM.indelFreq = 24
)

Arguments

inputFilePath	Sequence input file path or a DNAStringSet object that contains sequences to be searched for potential gRNAs
format	Format of the input file, fasta, fastq and bed are supported, default fasta
header	Indicate whether the input file contains header, default FALSE, only applies to bed format
gRNAoutputName	Specify the name of the gRNA outupt file when inputFilePath is DNAStringSet object instead of file path
findgRNAs	Indicate whether to find gRNAs from the sequences in the input file or skip the step of finding gRNAs, default TRUE. Set it to FALSE if the input file contains user selected gRNAs plus PAM already.
exportAllgRNAs	Indicate whether to output all potential gRNAs to a file in fasta format, genbank format or both. Default to both.
findgRNAsWithREcutOnly	Indicate whether to find gRNAs overlap with restriction enzyme recognition pattern
REpatternFile	File path containing restriction enzyme cut patterns
minREpatternSize	Minimum restriction enzyme recognition pattern length required for the enzyme pattern to be searched for, default 4
overlap.gRNA.positions	The required overlap positions of gRNA and restriction enzyme cut site, default 17 and 18. For Cpf1, you can set it to 19 and 23.
findPairedgRNAOnly	Choose whether to only search for paired gRNAs in such an orientation that the first one is on minus strand called reverse gRNA and the second one is on plus strand called forward gRNA. TRUE or FALSE, default FALSE
annotatePaired	Indicate whether to output paired information, default TRUE
paired.orientation	PAMin orientation means the two adjacent PAMs on the sense and antisense strands face inwards towards each other like N21GG and CCN21 whereas PAMout orientation means they face away from each other like CCN21 and N21GG
enable.multicore	Indicate whether enable parallel processing, default FALSE. For super long sequences with lots of gRNAs, suggest set it to TRUE
n.cores.max	Indicating maximum number of cores to use in multi core mode, i.e., parallel processing, default 6. Please set it to 1 to disable multicore processing for small dataset.
min.gap	Minimum distance between two oppositely oriented gRNAs to be valid paired gRNAs. Default 0
max.gap	Maximum distance between two oppositely oriented gRNAs to be valid paired gRNAs. Default 20
gRNA.name.prefix	The prefix used when assign name to found gRNAs, default gRNA, short for guided RNA.
PAM.size	PAM length, default 3
gRNA.size	The size of the gRNA, default 20
PAM	PAM sequence after the gRNA, default NGG
BSgenomeName	BSgenome object. Please refer to available.genomes in BSgenome package. For example, BSgenome.Hsapiens.UCSC.hg19 - for hg19, BSgenome.Mmusculus.UCSC.mm10 - for mm10 BSgenome.Celegans.UCSC.ce6 - for ce6 BSgenome.Rnorvegicus.UCSC.rn5 - for rn5 BSgenome.Drerio.UCSC.danRer7 - for Zv9 BSgenome.Dmelanogaster.UCSC.dm3 - for dm3
chromToSearch	Specify the chromosome to search, default to all, meaning search all chromosomes. For example, chrX indicates searching for matching in chromosome X only
chromToExclude	Specify the chromosome not to search. If specified as "", meaning to search chromosomes specified by chromToSearch. By default, to exclude haplotype blocks from offtarget search in hg19, i.e., chromToExclude = c("chr17_ctg5_hap1","chr4_ctg9_hap1", "chr6_apd_hap1", "chr6_cox_hap2", "chr6_dbb_hap3", "chr6_mann_hap4", "chr6_mcf_hap5","chr6_qbl_hap6", "chr6_ssto_hap7")
max.mismatch	Maximum mismatch allowed in off target search, default 3. Warning: will be considerably slower if set > 3
PAM.pattern	Regular expression of protospacer-adjacent motif (PAM), default NNG$|NGN$ for spCas9. For cpf1, ^TTTN since it is a 5 prime PAM sequence
allowed.mismatch.PAM	Maximum number of mismatches allowed in the PAM sequence for offtarget search, default to 1 to allow NGN and NNG PAM pattern for offtarget identification.
gRNA.pattern	Regular expression or IUPAC Extended Genetic Alphabet to represent gRNA pattern, default is no restriction. To specify that the gRNA must start with GG for example, then set it to ^GG. Please see help(translatePattern) for a list of IUPAC Extended Genetic Alphabet.
baseEditing	Indicate whether to design gRNAs for base editing. Default to FALSE If TRUE, please set baseEditing = TRUE, targetBase and editingWidow accordingly.
targetBase	Applicable only when baseEditing is set to TRUE. It is used to indicate the target base for base editing systems, default to C for converting C to T in the CBE system. Please change it to A if you intend to use the ABE system.
editingWindow	Applicable only when baseEditing is set to TRUE. It is used to indicate the effective editing window, default to 4 to 8 which is for the original CBE system. Please change it accordingly if the system you use have a different editing window.
editingWindow.offtargets	Applicable only when baseEditing is set to TRUE. It is used to indicate the effective editing window to consider for the offtargets search only, default to 4 to 8 (1 means the most distal site from the 3' PAM, the most proximla site from the 5' PAM), which is for the original CBE system. Please change it accordingly if the system you use have a different editing window, or you would like to include offtargets with the target base in a larger editing window.
primeEditing	Indicate whether to design gRNAs for prime editing. Default to FALSE. If true, please set PBS.length, RT.template.length, RT.template.pattern, targeted.seq.length.change, bp.after.target.end, target.start, and target.end accordingly
PBS.length	Applicable only when primeEditing is set to TRUE. It is used to specify the number of bases to ouput for primer binding site.
RT.template.length	Applicable only when primeEditing is set to TRUE. It is used to specify the number of bases required for RT template, default to 8 to 18. Please increase the length if the edit is large insertion. Only gRNAs with calculated RT.template.length falling into the specified range will be in the output. It is calculated as the following. RT.template.length = target.start – cut.start + (target.end - target.start) + targeted.seq.length.change + bp.after.target.end
RT.template.pattern	Applicable only when primeEditing is set to TRUE. It is used to specify the RT template sequence pattern, default to not ending with C according to https://doi.org/10.1038/s41586-019-1711-4
corrected.seq	Applicable only when primeEditing is set to TRUE. It is used to specify the mutated or inserted sequences after successful editing.
targeted.seq.length.change	Applicable only when primeEditing is set to TRUE. It is used to specify the number of targeted sequence length change. Please set it to 0 for base changes, positive numbers for insersion, and negative number for deletion. For example, 10 means that the corrected sequence will have 10bp insertion, -10 means that the corrected sequence will have 10bp deletion, and 0 means only bases have been changed and the sequence length remains the same
bp.after.target.end	Applicable only when primeEditing is set to TRUE. It is used to specify the number of bases to add after the target change end site as part of RT template. Please refer to RT.template.length for how this parameter influences the RT.template.length calculation which is used as a filtering criteria in pregRNA selection.
target.start	Applicable only when primeEditing is set to TRUE. It is used to specify the start location in the input sequence to make changes, which will be used to obtain the RT template sequence. Please also refer to RT.template.length for how this parameter influences the RT.template.length calculation which is used as a filtering criteria in pregRNA selection.
target.end	Applicable only when primeEditing is set to TRUE. It is used to specify the end location in the input sequnence to make changes, which will be used to obtain the RT template sequence. Please also refer to RT.template.length for how this parameter influences the RT.template.length calculation which is used as a filtering criteria in pregRNA selection.
primeEditingPaired.output	Applicable only when primeEditing is set to TRUE. It is used to specify the file path to save pegRNA and the second gRNA with PBS, RT.template, gRNA sequences, default pairedgRNAsForPE.xls
min.score	minimum score of an off target to included in the final output, default 0
topN	top N off targets to be included in the final output, default 1000
topN.OfftargetTotalScore	top N off target used to calculate the total off target score, default 10
annotateExon	Choose whether or not to indicate whether the off target is inside an exon or not, default TRUE
txdb	TxDb object, for creating and using TxDb object, please refer to GenomicFeatures package. For a list of existing TxDb object, please search for annotation package starting with Txdb at http://www.bioconductor.org/packages/release/BiocViews.html#___AnnotationData, such as TxDb.Rnorvegicus.UCSC.rn5.refGene - for rat TxDb.Mmusculus.UCSC.mm10.knownGene - for mouse TxDb.Hsapiens.UCSC.hg19.knownGene - for human TxDb.Dmelanogaster.UCSC.dm3.ensGene - for Drosophila TxDb.Celegans.UCSC.ce6.ensGene - for C.elegans
orgAnn	organism annotation mapping such as org.Hs.egSYMBOL in org.Hs.eg.db package for human
ignore.strand	default to TRUE when annotating to gene
outputDir	the directory where the off target analysis and reports will be written to
fetchSequence	Fetch flank sequence of off target or not, default TRUE
upstream	upstream offset from the off target start, default 200
downstream	downstream offset from the off target end, default 200
upstream.search	upstream offset from the bed input starts to search for gRNAs, default 0
downstream.search	downstream offset from the bed input ends to search for gRNAs, default 0
weights	Applicable only when scoring.method is set to Hsu-Zhang a numeric vector size of gRNA length, default c(0, 0, 0.014, 0, 0, 0.395, 0.317, 0, 0.389, 0.079, 0.445, 0.508, 0.613, 0.851, 0.732, 0.828, 0.615, 0.804, 0.685, 0.583) which is used in Hsu et al., 2013 cited in the reference section
baseBeforegRNA	Number of bases before gRNA used for calculating gRNA efficiency, default 4 Please note, for PAM located on the 5 prime, need to specify the number of bases before the PAM sequence plus PAM size.
baseAfterPAM	Number of bases after PAM used for calculating gRNA efficiency, default 3 for spCas9 Please note, for PAM located on the 5 prime, need to include the length of the gRNA plus the extended sequence on the 3 prime
featureWeightMatrixFile	Feature weight matrix file used for calculating gRNA efficiency. By default DoenchNBT2014 weight matrix is used. To use alternative weight matrix file, please input a csv file with first column containing significant features and the second column containing the corresponding weights for the features. Please see Doench et al., 2014 for details.
useScore	Default TRUE, display in gray scale with the darkness indicating the gRNA efficacy. The taller bar shows the Cas9 cutting site. If set to False, efficacy will not show. Instead, gRNAs in plus strand will be colored red and gRNAs in negative strand will be colored green.
useEfficacyFromInputSeq	Default FALSE. If set to TRUE, summary file will contain gRNA efficacy calculated from input sequences instead of from off-target analysis. Set it to TRUE if the input sequence is from a different species than the one used for off-target analysis.
outputUniqueREs	Default TRUE. If set to TRUE, summary file will contain REs unique to the cleavage site within 100 or 200 bases surrounding the gRNA sequence.
foldgRNAs	Default FALSE. If set to TRUE, summary file will contain minimum free energy of the secondary structure of gRNA with gRNA backbone from GeneRfold package provided that GeneRfold package has been installed.
gRNA.backbone	gRNA backbone constant region sequence. Default to the sequence in Sp gRNA backbone.
temperature	temperature in celsius. Default to 37 celsius.
overwrite	overwrite the existing files in the output directory or not, default FALSE
scoring.method	Indicates which method to use for offtarget cleavage rate estimation, currently two methods are supported, Hsu-Zhang and CFDscore
subPAM.activity	Applicable only when scoring.method is set to CFDscore A hash to represent the cleavage rate for each alternative sub PAM sequence relative to preferred PAM sequence
subPAM.position	Applicable only when scoring.method is set to CFDscore The start and end positions of the sub PAM. Default to 22 and 23 for spCas9 with 20bp gRNA and NGG as preferred PAM. For Cpf1, it could be c(1,2).
PAM.location	PAM location relative to gRNA. For example, default to 3prime for spCas9 PAM. Please set to 5prime for cpf1 PAM since it's PAM is located on the 5 prime end
rule.set	Specify a rule set scoring system for calculating gRNA efficacy. Please note that Root_RuleSet2_2016 requires the following python packages with specified verion and python 2.7. 1. scikit-learn 0.16.1 2. pickle 3. pandas 4. numpy 5. scipy
chrom_acc	Optional binary variable indicating chromatin accessibility information with 1 indicating accessible and 0 not accessible.
calculategRNAefficacyForOfftargets	Default to TRUE to output gRNA efficacy for offtargets as well as ontargets. Set it to FALSE if only need gRNA efficacy calculated for ontargets only to speed up the analysis. Please refer to https://support.bioconductor.org/p/133538/#133661 for potential use cases of offtarget efficacies.
mismatch.activity.file	Applicable only when scoring.method is set to CFDscore A comma separated (csv) file containing the cleavage rates for all possible types of single nucleotide mismatche at each position of the gRNA. By default, using the supplemental Table 19 from Doench et al., Nature Biotechnology 2016
predIndelFreq	Default to FALSE. Set it to TRUE to output the predicted indels and their frequencies.
predictIndelFreq.onTargetOnly	Default to TRUE, indicating that indels and their frequencies will be predicted for ontargets only. Usually, researchers are only interested in predicting the editing outcome for the ontargets since any editing in the offtargets are unwanted. Set it to FALSE if you are interested in predicting indels and their frequencies for offtargets. It will take longer time to run if you set it to FALSE.
method.indelFreq	Currently only Lindel method has been implemented. Please let us know if you think additional methods should be made available. Lindel is compatible with both Python2.7 and Python3.5 or higher. Please type help(predictRelativeFreqIndels) to get more details.
baseBeforegRNA.indelFreq	Default to 13 for Lindel method.
baseAfterPAM.indelFreq	Default to 24 for Lindel method.

Value

Four tab delimited files are generated in the output directory:

OfftargetAnalysis.xls	- detailed information of off targets
Summary.xls	- summary of the gRNAs
REcutDetails.xls	- restriction enzyme cut sites of each gRNA
pairedgRNAs.xls	- potential paired gRNAs

Author(s)

Lihua Julie Zhu

References

Patrick D Hsu, David A Scott, Joshua A Weinstein, F Ann Ran, Silvana Konermann, Vineeta Agarwala, Yinqing Li, Eli J Fine, Xuebing Wu, Ophir Shalem, Thomas J Cradick, Luciano A Marraffini, Gang Bao & Feng Zhang (2013) DNA targeting specificity of rNA-guided Cas9 nucleases. Nature Biotechnology 31:827-834

Doench JG, Hartenian E, Graham DB, Tothova Z, Hegde M, Smith I, Sullender M, Ebert BL, Xavier RJ, Root DE. Rational design of highly active sgRNAs for CRISPR-Cas9-mediated gene inactivation. Nat Biotechnol. 2014 Sep 3. doi: 10.1038 nbt.3026

Lihua Julie Zhu, Benjamin R. Holmes, Neil Aronin and Michael Brodsky. CRISPRseek: a Bioconductor package to identify target-specific guide RNAs for CRISPR-Cas9 genome-editing systems. Plos One Sept 23rd 2014

Moreno-Mateos, M., Vejnar, C., Beaudoin, J. et al. CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo. Nat Methods 12, 982–988 (2015) doi:10.1038/nmeth.3543

Doench JG et al., Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nature Biotechnology Jan 18th 2016

Anzalone et al., Search-and-replace genome editing without double-strand breaks or donor DNA. Nature October 2019 https://www.nature.com/articles/s41586-019-1711-4

Wei Chen, Aaron McKenna, Jacob Schreiber et al., 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

Kim et al., Deep learning improves prediction of CRISPR–Cpf1 guide RNA activityNat Biotechnol 36, 239–241 (2018). https://doi.org/10.1038/nbt.4061

See Also

CRISPRseek

Examples

	library(CRISPRseek)
	library("BSgenome.Hsapiens.UCSC.hg19")
	library(TxDb.Hsapiens.UCSC.hg19.knownGene)
	library(org.Hs.eg.db)
	outputDir <- getwd()
	inputFilePath <- system.file("extdata", "inputseq.fa",
            package = "CRISPRseek")
	REpatternFile <- system.file("extdata", "NEBenzymes.fa",
            package = "CRISPRseek")
	results <- offTargetAnalysis(inputFilePath, findgRNAsWithREcutOnly = TRUE,
            REpatternFile = REpatternFile, findPairedgRNAOnly = FALSE,
            annotatePaired = FALSE,
            BSgenomeName = Hsapiens, chromToSearch = "chrX",
            txdb = TxDb.Hsapiens.UCSC.hg19.knownGene,
	    orgAnn = org.Hs.egSYMBOL, max.mismatch = 1,
            outputDir = outputDir, overwrite = TRUE)

       #### predict indels and their frequecies for target sites

       if (interactive())
       {
          results <- offTargetAnalysis(inputFilePath,findgRNAsWithREcutOnly = TRUE,
            findPairedgRNAOnly = FALSE,
            annotatePaired = FALSE,
            BSgenomeName = Hsapiens, chromToSearch = "chrX",
            txdb = TxDb.Hsapiens.UCSC.hg19.knownGene, 
	    orgAnn = org.Hs.egSYMBOL, max.mismatch = 1,
            outputDir = outputDir, overwrite = TRUE,
            predIndelFreq=TRUE, predictIndelFreq.onTargetOnly= TRUE)

          names(results$indelFreq)
          head(results$indelFreq[[1]])
          ### save the indel frequences to tab delimited files, one file for each target/offtarget site.
          mapply(write.table, results$indelFreq, file=paste0(names(results$indelFreq), '.xls'), sep = "\t", row.names = FALSE)

       #### predict gRNA efficacy using CRISPRscan
       featureWeightMatrixFile <- system.file("extdata", "Morenos-Mateo.csv",
            package = "CRISPRseek")

       results <- offTargetAnalysis(inputFilePath, findgRNAsWithREcutOnly = TRUE,
            REpatternFile = REpatternFile, findPairedgRNAOnly = FALSE,
            annotatePaired = FALSE,
            BSgenomeName = Hsapiens, chromToSearch = "chrX",
            txdb = TxDb.Hsapiens.UCSC.hg19.knownGene,
            orgAnn = org.Hs.egSYMBOL, max.mismatch = 1,
            rule.set = "CRISPRscan",
            baseBeforegRNA = 6, baseAfterPAM = 6,
            featureWeightMatrixFile = featureWeightMatrixFile,
            outputDir = outputDir, overwrite = TRUE)

       ######## PAM is on the 5 prime side, e.g., Cpf1
       results <- offTargetAnalysis(inputFilePath = system.file("extdata",
              "cpf1-2.fa", package = "CRISPRseek"), findgRNAsWithREcutOnly =  FALSE,
          findPairedgRNAOnly = FALSE,
          annotatePaired = FALSE,
          BSgenomeName = Hsapiens,
          chromToSearch = "chr8",
          txdb = TxDb.Hsapiens.UCSC.hg19.knownGene,
          orgAnn = org.Hs.egSYMBOL, max.mismatch = 4,
          baseBeforegRNA = 8, baseAfterPAM = 26,
          rule.set = "DeepCpf1",
          overlap.gRNA.positions = c(19, 23),
          useEfficacyFromInputSeq = FALSE,
          outputDir = getwd(),
          overwrite = TRUE, PAM.location = "5prime",PAM.size = 4,
          PAM = "TTTN", PAM.pattern = "^TNNN", allowed.mismatch.PAM = 2,
          subPAM.position = c(1,2))

        results1 <- offTargetAnalysis(inputFilePath, findgRNAsWithREcutOnly =  FALSE,
                 REpatternFile = REpatternFile, findPairedgRNAOnly = FALSE,
                 annotatePaired = FALSE,
                 BSgenomeName = Hsapiens, chromToSearch = "chrX",
                 txdb = TxDb.Hsapiens.UCSC.hg19.knownGene,
                 orgAnn = org.Hs.egSYMBOL, max.mismatch = 4,
                 outputDir = outputDir, overwrite = TRUE, PAM.location = "5prime",
                 PAM = "TGT", PAM.pattern = "^T[A|G]N", allowed.mismatch.PAM = 2,
                 subPAM.position = c(1,2), baseEditing = TRUE, editingWindow =20, targetBase = "G")

        results.testBE <- offTargetAnalysis(inputFilePath, findgRNAsWithREcutOnly =  FALSE,
                 REpatternFile = REpatternFile, findPairedgRNAOnly = FALSE,
                 annotatePaired = FALSE,
                 BSgenomeName = Hsapiens, chromToSearch = "chrX",
                 txdb = TxDb.Hsapiens.UCSC.hg19.knownGene,
                 orgAnn = org.Hs.egSYMBOL, max.mismatch = 4,
                 outputDir = outputDir, overwrite = TRUE, PAM.location = "5prime",
                 PAM = "TGT", PAM.pattern = "^T[A|G]N", allowed.mismatch.PAM = 2,
                 subPAM.position = c(1,2), baseEditing = TRUE,
                 editingWindow = 10:20, targetBase = "A")

        inputFilePath <- DNAStringSet(paste(
"CCAGTTTGTGGATCCTGCTCTGGTGTCCTCCACACCAGAATCAGGGATCGAAAA",
"CTCATCAGTCGATGCGAGTCATCTAAATTCCGATCAATTTCACACTTTAAACG", sep =""))
        names(inputFilePath) <- "testPE"
        results3 <- offTargetAnalysis(inputFilePath,
            gRNAoutputName = "testPEgRNAs",
            BSgenomeName = Hsapiens, chromToSearch = "chrX",
            txdb = TxDb.Hsapiens.UCSC.hg19.knownGene,
            orgAnn = org.Hs.egSYMBOL, max.mismatch = 1,
            outputDir = outputDir, overwrite = TRUE,
            PAM.size = 3L,
            gRNA.size = 20L,
            overlap.gRNA.positions = c(17L,18L),
            PBS.length = 15,
            corrected.seq = "T",
            RT.template.pattern = "D$",
            RT.template.length = 8:30,
            targeted.seq.length.change = 0,
            bp.after.target.end = 15,
            target.start = 20,
            target.end = 20,
            paired.orientation = "PAMin", min.gap = 20, max.gap = 90,
            primeEditing = TRUE, findPairedgRNAOnly = TRUE)
       }

LihuaJulieZhu/CRISPRseek documentation built on Feb. 3, 2024, 2:44 p.m.