Package 'CRISPRseek'

Design of target-specific guide RNAs (gRNAs) in CRISPR-Cas9, genome-editing systems

Description

Design of target-specific gRNAs for the CRISPR-Cas9 system by automatically finding potential gRNAs (paired/not paired), with/without restriction enzyme cut site(s) in a given sequence, searching for off targets with user defined maximum number of mismatches, calculating score of each off target based on mismatch positions in the off target and a penalty weight matrix, filtering off targets with user-defined criteria, and annotating off targets with flank sequences, whether located in exon or not. Summary report is also generated with gRNAs ranked by total topN off target score, annotated with restriction enzyme cut sites, gRNA efficacy and possible paired gRNAs. Detailed paired gRNAs information and restriction enzyme cut sites are stored in separate files in the output directory specified by the user. In total, four tab delimited files are generated in the output directory: OfftargetAnalysis.xls (off target details), Summary.xls (gRNA summary), REcutDetails.xls (restriction enzyme cut sites of each gRNA), and pairedgRNAs.xls (potential paired gRNAs).

Details

Package:	CRISPRseek
Type:	Package
Version:	1.0
Date:	2013-10-04
License:	GPL (>= 2)

Function offTargetAnalysis integrates all steps of off target analysis into one function call

Author(s)

Lihua Julie Zhu and Michael Brodsky Maintainer: [email protected]

References

Mali P, Aach J, Stranges PB, Esvelt KM, Moosburner M, Kosuri S, Yang L, Church GM.CAS9 transcriptional activators for target specificity screening and paired nickases for cooperative genome engineering. Nat Biotechnol. 2013. 31(9):833-8 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. DNA targeting specificity of rNA-guided Cas9 nucleases. Nat Biotechnol. 2013. 31:827-834 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 Doench JG et al., Optimized sgRNA design to maximize activity and minimize off-target effe cts of CRISPR-Cas9. Nature Biotechnology Jan 18th 2016

See Also

offTargetAnalysis

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")
######## Scenario 1. Target and off-target analysis for paired gRNAs with 
######## one of the pairs overlap RE sites
    results <- offTargetAnalysis(inputFilePath, findgRNAsWithREcutOnly=TRUE,
        REpatternFile =REpatternFile,findPairedgRNAOnly=TRUE, 
        BSgenomeName=Hsapiens, txdb=TxDb.Hsapiens.UCSC.hg19.knownGene, 
        orgAnn = org.Hs.egSYMBOL,max.mismatch = 1, chromToSearch = "chrX",
        outputDir = outputDir,overwrite = TRUE)

######## Scenario 2. Target and off-target analysis for paired gRNAs with or 
######## without RE sites
    results <- offTargetAnalysis(inputFilePath, findgRNAsWithREcutOnly = FALSE,
        REpatternFile = REpatternFile,findPairedgRNAOnly = TRUE,
        BSgenomeName = Hsapiens, txdb = TxDb.Hsapiens.UCSC.hg19.knownGene, 
        orgAnn = org.Hs.egSYMBOL,max.mismatch = 1, chromToSearch = "chrX",
        outputDir = outputDir, overwrite = TRUE)

######## Scenario 3. Target and off-target analysis for gRNAs overlap RE sites

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

######## Scenario 4. Off-target analysis for all potential gRNAs, this will 
########be the slowest among the aforementioned scenarios.

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

######## Scenario 5. Target and off-target analysis for gRNAs input by user.
    gRNAFilePath <- system.file("extdata", "testHsap_GATA1_ex2_gRNA1.fa",
        package="CRISPRseek")
    results <- offTargetAnalysis(inputFilePath = gRNAFilePath, findgRNAs = FALSE, 
        findgRNAsWithREcutOnly = FALSE, REpatternFile = REpatternFile,
        findPairedgRNAOnly = FALSE, BSgenomeName = Hsapiens, 
        txdb = TxDb.Hsapiens.UCSC.hg19.knownGene,
         orgAnn = org.Hs.egSYMBOL, max.mismatch = 1, chromToSearch = "chrX", 
        outputDir = outputDir, overwrite = TRUE)

####### Scenario 6. Quick gRNA finding  without target and off-target analysis 
    results <- offTargetAnalysis(inputFilePath, findgRNAsWithREcutOnly = TRUE,
        REpatternFile = REpatternFile,findPairedgRNAOnly = TRUE,  
        chromToSearch = "", outputDir = outputDir, overwrite = TRUE)

####### Scenario 7. Quick gRNA finding  with gRNA efficacy analysis
    results <- offTargetAnalysis(inputFilePath, findgRNAsWithREcutOnly = TRUE,
        REpatternFile = REpatternFile,findPairedgRNAOnly = TRUE,
	BSgenomeName = Hsapiens, annotateExon = FALSE,
        max.mismatch = 0, outputDir = outputDir, overwrite = TRUE)

---

annotate off targets

Description

Annotate Off targets to indicate whether each one (respectively) is inside an exon or intron, as well as the gene ID if inside the gene.

Usage

annotateOffTargets(scores, txdb, orgAnn, ignore.strand = TRUE)

Arguments

scores	a data frame output from getOfftargetScore or filterOfftarget. It contains strand - strand of the off target ((+) for plus and (-) for minus strand) chrom - chromosome of the off target chromStart - start position of the off target chromEnd - end position of the off target name - gRNA name gRNAPlusPAM - gRNA sequence with PAM sequence concatenated OffTargetSequence - the genomic sequence of the off target n.mismatch - number of mismatches between the off target and the gRNA forViewInUCSC - string for viewing in UCSC genome browser, e.g., chr14:31665685-31665707 score - score of the off target mismatch.distance2PAM - a comma separated distances of all mismatches to PAM, e.g., 14,11 means one mismatch is 14 bp away from PAM and the other mismatch is 11 bp away from PAM alignment - alignment between gRNA and off target, e.g., ......G..C.......... means that this off target aligns with gRNA except that G and C are mismatches NGG - this off target contains canonical PAM or not, 1 for yes and 0 for no mean.neighbor.distance.mismatch - mean distance between neighboring mismatches
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. Which lives in the org.Hs.eg.db package for humans.
ignore.strand	default to TRUE

Value

a Data Frame with Off Target annotation

Author(s)

Lihua Julie Zhu

References

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

See Also

offTargetAnalysis

Examples

library(CRISPRseek)
    #library("BSgenome.Hsapiens.UCSC.hg19")
    library(TxDb.Hsapiens.UCSC.hg19.knownGene)
    library(org.Hs.eg.db)
    hitsFile <-  system.file("extdata", "hits.txt", package="CRISPRseek")
    hits <- read.table(hitsFile, sep = "\t", header = TRUE, 
        stringsAsFactors = FALSE)
    featureVectors <- buildFeatureVectorForScoring(hits)
    scores <- getOfftargetScore(featureVectors)
    outputDir <- getwd() 
    results <- annotateOffTargets(scores, 
        txdb = TxDb.Hsapiens.UCSC.hg19.knownGene,
         orgAnn = org.Hs.egSYMBOL)
    results

---

Build feature vectors

Description

Build feature vectors for calculating scores of off targets

Usage

buildFeatureVectorForScoring(
  hits,
  gRNA.size = 20,
  canonical.PAM = "NGG",
  subPAM.position = c(22, 23),
  PAM.size = 3,
  PAM.location = "3prime"
)

Arguments

hits	A Data frame generated from searchHits, which contains IsMismatch.posX - Indicator variable indicating whether this position X is mismatch or not, (1 means yes and 0 means not). X takes on values from 1 to gRNA.size, representing all positions in the guide RNA (gRNA). strand - strand of the off target, + for plus and - for minus strand chrom - chromosome of the off target chromStart - start position of the off target chromEnd - end position of the off target name - gRNA name gRNAPlusPAM - gRNA sequence with PAM sequence concatenated OffTargetSequence - the genomic sequence of the off target n.mismatch - number of mismatches between the off target and the gRNA forViewInUCSC - string for viewing in UCSC genome browser, e.g., chr14:31665685-31665707 score - Set to 100, and will be calculated in getOfftargetScore
gRNA.size	gRNA size. The default is 20
canonical.PAM	Canonical PAM. The default is NGG for spCas9, TTTN for Cpf1
subPAM.position	The start and end positions of the sub PAM to fetch. Default to 22 and 23 for SP with 20bp gRNA and NGG as preferred PAM
PAM.size	Size of PAM, default to 3 for spCas9, 4 for Cpf1
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

Value

A data frame with hits plus features used for calculating scores and for generating report, including

• IsMismatch.posX - Indicator variable indicating whether this position X is mismatch or not, (1 means yes and 0 means not, X = 1 - gRNA.size), representing all positions in the gRNA

• strand - strand of the off target, + for plus and - for minus strand

• chrom - chromosome of the off target

• chromStart - start position of the off target

• chromEnd - end position of the off target

• name - gRNA name

• gRNAPlusPAM - gRNA sequence with PAM sequence concatenated

• OffTargetSequence - the genomic sequence of the off target

• n.mismatch - number of mismatches between the off target and the gRNA

• forViewInUCSC - string for viewing in UCSC genome browser, e.g., chr14:31665685-31665707

• score - score of the off target

• mismatche.distance2PAM - a comma separated distances of all mismatches to PAM, e.g., 14,11 means one mismatch is 14 bp away from PAM and the other mismatch is 11 bp away from PAM

• alignment - alignment between gRNA and off target, e.g., ......G..C.......... means that this off target aligns with gRNA except that G and C are mismatches

• NGG - this off target contains canonical PAM or not, 1 for yes and 0 for no

• mean.neighbor.distance.mismatch - mean distance between neighboring mismatches

Author(s)

Lihua Julie Zhu

See Also

offTargetAnalysis

Examples

hitsFile <-  system.file("extdata", "hits.txt", package = "CRISPRseek")
    hits <- read.table(hitsFile, sep= "\t", header = TRUE,
        stringsAsFactors = FALSE)
    buildFeatureVectorForScoring(hits)

---

Calculate gRNA Efficiency

Description

Calculate gRNA Efficiency for a given set of sequences and feature weight matrix

Usage

calculategRNAEfficiency(
  extendedSequence,
  baseBeforegRNA,
  featureWeightMatrix,
  gRNA.size = 20,
  enable.multicore = FALSE,
  n.cores.max = 6
)

Arguments

extendedSequence	Sequences containing gRNA plus PAM plus flanking sequences. Each sequence should be long enough for building features specified in the featureWeightMatrix
baseBeforegRNA	Number of bases before gRNA used for calculating gRNA efficiency, default 4
featureWeightMatrix	a data frame with the first column containing significant features and the second column containing the weight of corresponding features. In the following example, DoenchNBT2014 weight matrix is used. Briefly, features include INTERCEPT GC_LOW - penalty for low GC content in the gRNA sequence GC_HIGH - penalty for high GC content in the gRNA sequence G02 - means G at second position of the extendedSequence GT02 - means GT di-nucleotides starting at 2nd position of the extendedSequence To understand how is the feature weight matrix is identified, or how to use alternative feature weight matrix file, please see Doench et al., 2014 for details.
gRNA.size	The size of the gRNA, default 20
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.

Value

DNAStringSet consists of potential gRNAs that can be input to filtergRNAs function directly

Author(s)

Lihua Julie Zhu

References

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 http://www.broadinstitute.org/rnai/public/analysis-tools/sgrna-design

See Also

offTargetAnalysis

Examples

extendedSequence <- c("TGGATTGTATAATCAGCATGGATTTGGAAC",
		"TCAACGAGGATATTCTCAGGCTTCAGGTCC",
		"GTTACCTGAATTTGACCTGCTCGGAGGTAA",
		"CTTGGTGTGGCTTCCTTTAAGACATGGAGC",
		"CATACAGGCATTGAAGAAGAATTTAGGCCT",
		"AGTACTATACATTTGGCTTAGATTTGGCGG",
		"TTTTCCAGATAGCCGATCTTGGTGTGGCTT",
		"AAGAAGGGAACTATTCGCTGGTGATGGAGT"
	)
	featureWeightMatrixFile <- system.file("extdata", "DoenchNBT2014.csv", 
		package = "CRISPRseek")
	featureWeightMatrix <- read.csv(featureWeightMatrixFile, header=TRUE)
	calculategRNAEfficiency(extendedSequence, baseBeforegRNA = 4, 
		featureWeightMatrix, gRNA.size = 20)

---

Compare 2 input sequences/sequence sets for possible guide RNAs (gRNAs)

Description

Generate all possible guide RNAs (gRNAs) for two input sequences, or two sets of sequences, and generate scores for potential off-targets in the other sequence.

Usage

compare2Sequences(
  inputFile1Path,
  inputFile2Path,
  inputNames = c("Seq1", "Seq2"),
  format = c("fasta", "fasta"),
  header = FALSE,
  findgRNAsWithREcutOnly = FALSE,
  searchDirection = c("both", "1to2", "2to1"),
  BSgenomeName,
  baseEditing = FALSE,
  targetBase = "C",
  editingWindow = 4:8,
  editingWindow.offtargets = 4:8,
  REpatternFile = system.file("extdata", "NEBenzymes.fa", package = "CRISPRseek"),
  minREpatternSize = 6,
  findgRNAs = c(TRUE, TRUE),
  removegRNADetails = c(FALSE, FALSE),
  exportAllgRNAs = c("no", "all", "fasta", "genbank"),
  annotatePaired = FALSE,
  overlap.gRNA.positions = c(17, 18),
  findPairedgRNAOnly = FALSE,
  min.gap = 0,
  max.gap = 20,
  gRNA.name.prefix = "_gR",
  PAM.size = 3,
  gRNA.size = 20,
  PAM = "NGG",
  PAM.pattern = "NNG$|NGN$",
  allowed.mismatch.PAM = 1,
  max.mismatch = 3,
  outputDir,
  upstream = 0,
  downstream = 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),
  overwrite = FALSE,
  baseBeforegRNA = 4,
  baseAfterPAM = 3,
  featureWeightMatrixFile = system.file("extdata", "DoenchNBT2014.csv", package =
    "CRISPRseek"),
  foldgRNAs = FALSE,
 
    gRNA.backbone = "GUUUUAGAGCUAGAAAUAGCAAGUUAAAAUAAGGCUAGUCCGUUAUCAACUUGAAAAAGUGGCACCGAGUCGGUGCUUUUUU",
  temperature = 37,
  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"),
  mismatch.activity.file = system.file("extdata",
    "NatureBiot2016SuppTable19DoenchRoot.csv", package = "CRISPRseek")
)

Arguments

inputFile1Path	Sequence input file 1 path that contains one of the two sequences to be searched for potential gRNAs. It can also be a DNAStringSet object with names field set. Please see examples below.
inputFile2Path	Sequence input file 2 path that contains one of the two sequences to be searched for potential gRNAs. It can also be a DNAStringSet object with names field set. Please see examples below.
inputNames	Name of the input sequences when inputFile1Path and inputFile2Path are DNAStringSet instead of file path
format	Format of the input files, fasta, fastq and bed format are supported, default fasta
header	Indicate whether the input file contains header, default FALSE, only applies to bed format
findgRNAsWithREcutOnly	Indicate whether to find gRNAs overlap with restriction enzyme recognition pattern
searchDirection	Indicate whether perfrom gRNA in both sequences and off-target search against each other (both) or search gRNA in input1 and off-target analysis in input2 (1to2), or vice versa (2to1)
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, and BSgenome.Dmelanogaster.UCSC.dm3 for dm3
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 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.
REpatternFile	File path containing restriction enzyme cut patters
minREpatternSize	Minimum restriction enzyme recognition pattern length required for the enzyme pattern to be searched for, default 6
findgRNAs	Indicate whether to find gRNAs from the sequences in the input file or skip the step of finding gRNAs, default TRUE for both input sequences. Set it to FALSE if the input file contains user selected gRNAs plus PAM already.
removegRNADetails	Indicate whether to remove the detailed gRNA information such as efficacy file and restriction enzyme cut sites, default false for both input sequences. Set it to TRUE if the input file contains the 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 no.
annotatePaired	Indicate whether to output paired information, default to FALSE
overlap.gRNA.positions	The required overlap positions of gRNA and restriction enzyme cut site, default 17 and 18
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
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 _gR, 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
PAM.pattern	Regular expression of PAM, default NNG or NGN for spCas9. For cpf1, ^TTTN since it is a 5 prime PAM sequence
allowed.mismatch.PAM	Maximum number of mismatches allowed to the PAM sequence, default to 1 for PAM.pattern NNG or NGN PAM
max.mismatch	Maximum mismatch allowed to search the off targets in the other sequence, default 3
outputDir	the directory where the sequence comparison results will be written to
upstream	upstream offset from the bed input starts to search for gRNA and/or offtargets, default 0
downstream	downstream offset from the bed input ends to search for gRNA and/or offtargets, default 0
weights	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
overwrite	overwrite the existing files in the output directory or not, default TRUE
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.
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.
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 SP with 20bp gRNA and NGG as preferred PAM
PAM.location	PAM location relative to gRNA. For example, spCas9 PAM is located on the 3 prime (3prime) while cpf1 PAM is located on the 5 prime (5prime)
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
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

Value

Return a data frame with all potential gRNAs from both sequences. In addition, a tab delimited file scoresFor2InputSequences.xls is also saved in the outputDir, sorted by scoreDiff descending.

name	name of the gRNA
gRNAPlusPAM	gRNA plus PAM sequence
targetInSeq1	target/off-target sequence including PAM in the 1st input sequence file
targetInSeq2	target/off-target sequence incuding PAM in the 2nd input sequence file
guideAlignment2Offtarget	alignment of gRNA to the other input sequence (off-target sequence)
offTargetStrand	strand of the other sequence (off-target sequence) the gRNA align to
scoreForSeq1	score for the target sequence in the 1st input sequence file
scoreForSeq2	score for the target sequence in the 1st input sequence file
mismatch.distance2PAM	distances of mismatch to PAM, e.g., 14 means the mismatch is 14 bp away from PAM
n.mismatch	number of mismatches between the off-target and the gRNA
targetSeqName	the name of the input sequence where the target sequence is located
scoreDiff	scoreForSeq1 - scoreForSeq2
bracket.notation	folded gRNA in bracket notation
mfe.sgRNA	minimum free energy of sgRNA
mfe.diff	mfe.sgRNA-mfe.backbone
mfe.backbone	minimum free energy of the gRNA backbone by itself

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

See Also

CRISPRseek

Examples

library(CRISPRseek)
    inputFile1Path <- system.file("extdata", "rs362331T.fa",
            package = "CRISPRseek")
    inputFile2Path <- system.file("extdata", "rs362331C.fa",
            package = "CRISPRseek")
    REpatternFile <- system.file("extdata", "NEBenzymes.fa", 
            package = "CRISPRseek")
    seqs <- compare2Sequences(inputFile1Path, inputFile2Path,
        outputDir = getwd(), 
        REpatternFile = REpatternFile, overwrite = TRUE)

    seqs2 <- compare2Sequences(inputFile1Path, inputFile2Path,
               inputNames=c("Seq1", "Seq2"),
               scoring.method = "CFDscore",
               outputDir = getwd(), 
               overwrite = TRUE, baseEditing = TRUE)

    inputFile1Path <- 
DNAStringSet(
"TAATATTTTAAAATCGGTGACGTGGGCCCAAAACGAGTGCAGTTCCAAAGGCACCCACCTGTGGCAG"
)
    ## when set inputFile1Path to a DNAStringSet object, it is important
    ## to call names
    names(inputFile1Path) <- "seq1"
    
    inputFile2Path <- 
DNAStringSet(
"TAATATTTTAAAATCGGTGACGTGGGCCCAAAACGAGTGCAGTTCCAAAGGCACCCACCTGTGGCAG"
)
     ## when set inputFile2Path to a DNAStringSet object, it is important 
    ## to call names

    names(inputFile2Path) <- "seq2"

    seqs <- compare2Sequences(inputFile1Path, inputFile2Path,
          inputNames=c("Seq1", "Seq2"),
          scoring.method = "CFDscore",
          outputDir = getwd(), 
          overwrite = TRUE)

    seqs2 <- compare2Sequences(inputFile1Path, inputFile2Path,
               inputNames=c("Seq1", "Seq2"),
               scoring.method = "CFDscore",
               outputDir = getwd(), 
               overwrite = TRUE, baseEditing = TRUE)

---

DeepCpf1 Algorithm for predicting CRISPR-Cpf1 gRNA Efficacy

Description

DeepCpf1 algorithm from https://doi.org/10.1038/nbt.4061, which takes in 34 bp target sequences with/without chromatin accessibility information and returns predicted CRISPR-Cpf1 gRNA efficacy for each input sequence.

Usage

deepCpf1(extendedSequence, chrom_acc)

Arguments

extendedSequence	Sequences containing gRNA plus PAM plus flanking sequences. Each sequence should be 34 bp long as specified by http://deepcrispr.info/, i.e., 4bp before the 5' PAM, 4bp PAM, 20bp gRNA, and 6bp after 3' of gRNA.
chrom_acc	Optional binary variable indicating chromatin accessibility information with 1 indicating accessible and 0 not accessible.

Details

Having chromatin accessibility information will aid in the accuracy of the scores, but one can still get accurate scoring with only the 34 bp target sequences.

Value

a numeric vector with prediced CRISPR-Cpf1 gRNA efficacy taking into account chromatin accessibility information if accessibility information is provided

Author(s)

Paul Scemama and Lihua Julie Zhu

References

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

Examples

library(keras)
library(mltools)
library(dplyr)
library(data.table)

use_implementation("tensorflow")

extendedSequence <- c('GTTATTTGAGCAATGCCACTTAATAAACATGTAA',
 'TGACTTTGAATGGAGTCGTGAGCGCAAGAACGCT',
 'GTTATTTGAGCAATGCCACTTAATAAACATGTAA',
 'TGACTTTGAATGGAGTCGTGAGCGCAAGAACGCT')
chrom_acc <- c(0,1, 0, 1)

if (interactive()) {
 deepCpf1(extendedSequence = extendedSequence, chrom_acc = chrom_acc)
}

---

Filter gRNAs

Description

Filter gRNAs containing restriction enzyme cut site

Usage

filtergRNAs(
  all.gRNAs,
  pairOutputFile = "",
  findgRNAsWithREcutOnly = FALSE,
  REpatternFile = system.file("extdata", "NEBenzymes.fa", package = "CRISPRseek"),
  format = "fasta",
  minREpatternSize = 4,
  overlap.gRNA.positions = c(17, 18),
  overlap.allpos = TRUE
)

Arguments

all.gRNAs	gRNAs as DNAStringSet, such as the output from findgRNAs
pairOutputFile	File path with paired gRNAs
findgRNAsWithREcutOnly	Indicate whether to find gRNAs overlap with restriction enzyme recognition pattern
REpatternFile	File path containing restriction enzyme cut patters
format	Format of the REpatternFile, default as fasta
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
overlap.allpos	Default TRUE, meaning that only gRNAs overlap with all the positions are retained FALSE, meaning that gRNAs overlap with one or both of the positions are retained

Value

gRNAs.withRE	gRNAs as DNAStringSet that passed the filter criteria
gRNAREcutDetails	a data frame that contains a set of gRNAs annotated with restriction enzyme cut details

Author(s)

Lihua Julie Zhu

See Also

offTargetAnalysis

Examples

all.gRNAs <- findgRNAs(
        inputFilePath = system.file("extdata", "inputseq.fa", 
        package = "CRISPRseek"),
        pairOutputFile = "testpairedgRNAs.xls",
        findPairedgRNAOnly = TRUE)

    gRNAs.RE <- filtergRNAs(all.gRNAs = all.gRNAs,
        pairOutputFile = "testpairedgRNAs.xls",minREpatternSize = 6,
        REpatternFile = system.file("extdata", "NEBenzymes.fa", 
        package = "CRISPRseek"), overlap.allpos = TRUE)

    gRNAs  <- gRNAs.RE$gRNAs.withRE
    restriction.enzyme.cut.sites <- gRNAs.RE$gRNAREcutDetails

---

filter off targets and generate reports.

Description

filter off targets that meet the criteria set by users such as minimum score, topN. In addition, off target was annotated with flank sequence, gRNA cleavage efficiency and whether it is inside an exon or not if fetchSequence is set to TRUE and annotateExon is set to TRUE

Usage

filterOffTarget(
  scores,
  min.score = 0.01,
  topN = 200,
  topN.OfftargetTotalScore = 20,
  annotateExon = TRUE,
  txdb,
  orgAnn,
  ignore.strand = TRUE,
  outputDir,
  oneFilePergRNA = FALSE,
  fetchSequence = TRUE,
  upstream = 200,
  downstream = 200,
  BSgenomeName,
  baseBeforegRNA = 4,
  baseAfterPAM = 3,
  gRNA.size = 20,
  PAM.location = "3prime",
  PAM.size = 3,
  featureWeightMatrixFile = system.file("extdata", "DoenchNBT2014.csv", package =
    "CRISPRseek"),
  rule.set = c("Root_RuleSet1_2014", "Root_RuleSet2_2016", "CRISPRscan", "DeepCpf1"),
  chrom_acc,
  calculategRNAefficacyForOfftargets = TRUE
)

Arguments

scores	a data frame output from getOfftargetScore. It contains strand - strand of the off target, + for plus and - for minus strand chrom - chromosome of the off target chromStart - start position of the offtarget chromEnd - end position of the offtarget name - gRNA name gRNAPlusPAM - gRNA sequence with PAM sequence concatenated OffTargetSequence - the genomic sequence of the off target n.mismatch - number of mismatches between the off target and the gRNA forViewInUCSC - string for viewing in UCSC genome browser, e.g., chr14:31665685-31665707 score - score of the off target mismatch.distance2PAM - a comma separated distances of all mismatches to PAM, e.g., 14,11 means one mismatch is 14 bp away from PAM and the other mismatch is 11 bp away from PAM alignment - alignment between gRNA and off target, e.g., ......G..C.......... means that this off target aligns with gRNA except that G and C are mismatches NGG - this off target contains canonical PAM or not, 1 for yes and 0 for no) mean.neighbor.distance.mismatch - mean distance between neighboring mismatches
min.score	minimum score of an off target to included in the final output, default 0.5
topN	top N off targets to be included in the final output, default 100
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 and 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
outputDir	the directory where the off target analysis and reports will be written to
oneFilePergRNA	write to one file for each gRNA or not, default to FALSE
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
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.Dmelanogaster.UCSC.dm3 - for dm3
baseBeforegRNA	Number of bases before gRNA used for calculating gRNA efficiency, default 4
baseAfterPAM	Number of bases after PAM used for calculating gRNA efficiency, default 3
gRNA.size	The size of the gRNA, default 20 for spCas9
PAM.location	PAM location relative to gRNA. For example, spCas9 PAM is located on the 3 prime while cpf1 PAM is located on the 5 prime
PAM.size	PAM length, default 3 for spCas9
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.
rule.set	Specify a rule set scoring system for calculating gRNA efficacy.
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.

Value

offtargets	a data frame with off target analysis results
summary	a data frame with summary of the off target analysis results

Author(s)

Lihua Julie Zhu

References

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

See Also

offTargetAnalysis

Examples

library(CRISPRseek)
    library("BSgenome.Hsapiens.UCSC.hg19")
    library(TxDb.Hsapiens.UCSC.hg19.knownGene)
    library(org.Hs.eg.db)
    hitsFile <-  system.file("extdata", "hits.txt", package="CRISPRseek")
    hits <- read.table(hitsFile, sep = "\t", header = TRUE,
        stringsAsFactors = FALSE)
    featureVectors <- buildFeatureVectorForScoring(hits)
    scores <- getOfftargetScore(featureVectors)
    outputDir <- getwd()
    results <- filterOffTarget(scores, BSgenomeName = Hsapiens,
        txdb = TxDb.Hsapiens.UCSC.hg19.knownGene,
         orgAnn = org.Hs.egSYMBOL, outputDir = outputDir,
        min.score = 0.1, topN = 10, topN.OfftargetTotalScore = 5)
    results$offtargets
    results$summary

---

Find potential gRNAs

Description

Find potential gRNAs for an input file containing sequences in fasta format

Usage

findgRNAs(
  inputFilePath,
  baseEditing = FALSE,
  targetBase = "C",
  editingWindow = 4:8,
  format = "fasta",
  PAM = "NGG",
  PAM.size = 3,
  findPairedgRNAOnly = FALSE,
  annotatePaired = TRUE,
  paired.orientation = c("PAMout", "PAMin"),
  enable.multicore = FALSE,
  n.cores.max = 6,
  gRNA.pattern = "",
  gRNA.size = 20,
  overlap.gRNA.positions = c(17, 18),
  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.gap = 0,
  max.gap = 20,
  pairOutputFile,
  name.prefix = "",
  featureWeightMatrixFile = system.file("extdata", "DoenchNBT2014.csv", package =
    "CRISPRseek"),
  baseBeforegRNA = 4,
  baseAfterPAM = 3,
  calculategRNAEfficacy = FALSE,
  efficacyFile,
  PAM.location = "3prime",
  rule.set = c("Root_RuleSet1_2014", "Root_RuleSet2_2016", "CRISPRscan", "DeepCpf1"),
  chrom_acc
)

Arguments

inputFilePath	Sequence input file path or a DNAStringSet object that contains sequences to be searched for potential gRNAs
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.
format	Format of the input file, fasta and fastq are supported, default fasta
PAM	protospacer-adjacent motif (PAM) sequence after the gRNA, default NGG
PAM.size	PAM length, default 3
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.
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.
gRNA.size	The size of the gRNA, default 20
overlap.gRNA.positions	The required overlap positions of gRNA and restriction enzyme cut site, default 17 and 18. For Cpf1,you may set it to 19 and 23.
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.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
pairOutputFile	The output file for writing paired gRNA information to
name.prefix	The prefix used when assign name to found gRNAs, default gRNA, short for guided RNA.
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.
baseBeforegRNA	Number of bases before gRNA used for calculating gRNA efficiency, default 4 for spCas9 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
calculategRNAEfficacy	Default to FALSE, not to calculate gRNA efficacy
efficacyFile	File path to write gRNA efficacies
PAM.location	PAM location relative to gRNA. For example, spCas9 PAM is located on the 3 prime while cpf1 PAM is located on the 5 prime
rule.set	Specify a rule set scoring system for calculating gRNA efficacy. Please note that if specifying DeepCpf1, please specify other parameters accordingly for CRISPR-Cpf1 gRNAs.
chrom_acc	Optional binary variable indicating chromatin accessibility information with 1 indicating accessible and 0 not accessible.

Details

If users already has a fasta file that contains a set of potential gRNAs, then users can call filergRNAs directly although the easiest way is to call the one-stop-shopping function OffTargetAnalysis with findgRNAs set to FALSE.

Value

DNAStringSet consists of potential gRNAs that can be input to filtergRNAs function directly

Note

If the input sequence file contains multiple >300 bp sequences, suggest create one input file for each sequence and run the OffTargetAnalysis separately.

Author(s)

Lihua Julie Zhu

See Also

offTargetAnalysis

Examples

findgRNAs(inputFilePath = system.file("extdata",
        "inputseq.fa", package = "CRISPRseek"),
        pairOutputFile = "testpairedgRNAs.xls",
        findPairedgRNAOnly = TRUE)


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

    findgRNAs(inputFilePath = system.file("extdata",
        "inputseq.fa", package = "CRISPRseek"),
        pairOutputFile = "testpairedgRNAs.xls",
        findPairedgRNAOnly = FALSE,
        calculategRNAEfficacy= TRUE,
        rule.set = "CRISPRscan",
        baseBeforegRNA = 6, baseAfterPAM = 6,
        featureWeightMatrixFile = featureWeightMatrixFile,
        efficacyFile = "testCRISPRscanEfficacy.xls"
     )

     findgRNAs(inputFilePath = system.file("extdata",
        "testCRISPRscan.fa", package = "CRISPRseek"),
        pairOutputFile = "testpairedgRNAs.xls",
        findPairedgRNAOnly = FALSE,
        calculategRNAEfficacy= TRUE,
        rule.set = "CRISPRscan",
        baseBeforegRNA = 6, baseAfterPAM = 6,
        featureWeightMatrixFile = featureWeightMatrixFile,
        efficacyFile = "testCRISPRscanEfficacy.xls"
     )
   if (interactive()) {
     findgRNAs(inputFilePath = system.file("extdata",
        "cpf1.fa", package = "CRISPRseek"),
        findPairedgRNAOnly=FALSE,
        pairOutputFile = "testpairedgRNAs-cpf1.xls",
        PAM="TTTN", PAM.location = "5prime", PAM.size = 4,
        overlap.gRNA.positions = c(19, 23),
        baseBeforegRNA = 8, baseAfterPAM = 26,
        calculategRNAEfficacy= TRUE,
        rule.set = "DeepCpf1",
       efficacyFile = "testcpf1Efficacy.xls")

     findgRNAs(inputFilePath = system.file("extdata",
             "cpf1.fa", package = "CRISPRseek"),
             findPairedgRNAOnly=FALSE,
             pairOutputFile = "testpairedgRNAs-cpf1.xls",
             PAM="TTTN", PAM.location = "5prime", PAM.size = 4,
             overlap.gRNA.positions = c(19,23),
             baseBeforegRNA = 8, baseAfterPAM = 26,
             calculategRNAEfficacy= TRUE,
             rule.set = "DeepCpf1",
             efficacyFile = "testcpf1Efficacy.xls", baseEditing =  TRUE,
             editingWindow=20, targetBase = "X")

     findgRNAs(inputFilePath = system.file("extdata",
             "cpf1.fa", package = "CRISPRseek"),
             findPairedgRNAOnly=FALSE,
             pairOutputFile = "testpairedgRNAs-cpf1.xls",
             PAM="TTTN", PAM.location = "5prime", PAM.size = 4,
             overlap.gRNA.positions = c(19, 23),
             baseBeforegRNA = 8, baseAfterPAM = 26,
             calculategRNAEfficacy= TRUE,
             rule.set = "DeepCpf1",
             efficacyFile = "testcpf1Efficacy.xls", baseEditing =  TRUE,
             editingWindow=20, targetBase = "C")
   }

     inputSeq <-  DNAStringSet(paste(
"CCAGTTTGTGGATCCTGCTCTGGTGTCCTCCACACCAGAATCAGGGATCGAAAACTCA",
"TCAGTCGATGCGAGTCATCTAAATTCCGATCAATTTCACACTTTAAACG", sep =""))
     gRNAs <-  findgRNAs(inputFilePath =  inputSeq,
         pairOutputFile = "testpairedgRNAs1.xls",
         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 = 46,
         target.end = 46,
         paired.orientation = "PAMin", min.gap = 20, max.gap = 90,
         primeEditing = TRUE, findPairedgRNAOnly = TRUE)

---

Calculate score for each off target

Description

Calculate score for each off target with given feature vectors and weights vector

Usage

getOfftargetScore(
  featureVectors,
  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)
)

Arguments

featureVectors

a data frame generated from buildFeatureVectorForScoring. It contains IsMismatch.posX - Indicator variable indicating whether this position X is mismatch or not, (1 means yes and 0 means not). X takes on values from 1 to gRNA.size, representing all positions in the guide RNA (gRNA). strand - strand of the off target, + for plus and - for minus strand chrom - chromosome of the off target chromStart - start position of the off target chromEnd - end position of the off target name - gRNA name gRNAPlusPAM - gRNA sequence with PAM sequence concatenated OffTargetSequence - the genomic sequence of the off target n.mismatch - number of mismatches between the off target and the gRNA forViewInUCSC - string for viewing in UCSC genome browser, e.g., chr14:31665685-31665707 score - score of the off target mismatche.distance2PAM - a comma separated distances of all mismatches to PAM, e.g., 14,11 means one mismatch is 14 bp away from PAM and the other mismatch is 11 bp away from PAM alignment - alignment between gRNA and off target, e.g., ......G..C.......... means that this off target aligns with gRNA except that G and C are mismatches NGG - this off target contains canonical PAM or not, 1 for yes and 0 for no mean.neighbor.distance.mismatch - mean distance between neighboring mismatches

weights

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

Details

score is calculated using the weights and algorithm by Hsu et al., 2013 cited in the reference section

Value

a data frame containing

strand - strand of the match, + for plus and - for minus strand

• chrom - chromosome of the off target

• chromStart - start position of the off target

• chromEnd - end position of the off target

• name - gRNA name

• gRNAPlusPAM - gRNA sequence with PAM sequence concatenated

• OffTargetSequence - the genomic sequence of the off target

• n.mismatch - number of mismatches between the off target and the gRNA

• forViewInUCSC - string for viewing in UCSC genome browser, e.g., chr14:31665685-31665707

• score - score of the off target

• mismatch.distance2PAM - a comma separated distances of all mismatches to PAM, e.g., 14,11 means one mismatch is 14 bp away from PAM and the other mismatch is 11 bp away from PAM

• alignment - alignment between gRNA and off target, e.g., ......G..C.......... means that this off target aligns with gRNA except that G and C are mismatches

• NGG - this off target contains canonical PAM or not, 1 for yes and 0 for no

• mean.neighbor.distance.mismatch - mean distance between neighboring mismatches

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

See Also

offTargetAnalysis

Examples

hitsFile <-  system.file("extdata", "hits.txt", 
        package = "CRISPRseek")
    hits <- read.table(hitsFile, sep = "\t", header = TRUE,
        stringsAsFactors = FALSE)
    featureVectors <- buildFeatureVectorForScoring(hits)
    getOfftargetScore(featureVectors)

---

Output whether the input patterns occurs only once in the sequence

Description

Input a sequence and a list of patterns and determine if the patterns occurs only once in the sequence. Used for determining whether an RE site in gRNA also occurs in the flanking region.

Usage

isPatternUnique(seq, patterns)

Arguments

seq	flanking sequence of a gRNA
patterns	patterns as DNAStringSet, such as a list of RE sites

Value

returns a character vectors containing the uniqueness of each pattern/RE site

Author(s)

Lihua Julie Zhu

Examples

seq <- "TGGATTGTATAATCAGCATGGATTTGGAAC"
    patterns <- DNAStringSet(c("TGG", "TGGA", "TGGATA", "TTGGAAC", ""))
    isPatternUnique(seq, patterns)
    isPatternUnique(seq)
    isPatternUnique(patterns)

---

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)
       }

---

Design of target-specific guide RNAs for CRISPR-Cas9 system in one function without BSgenome

Description

Design of target-specific guide RNAs (gRNAs) for CRISPR-Cas9 system without BSgenome by automatically calling findgRNAs, filtergRNAs, searchHits, buildFeatureVectorForScoring, getOfftargetScore, filterOfftarget, calculating gRNA cleavage efficiency and generate reports.

Usage

offTargetAnalysisWithoutBSgenome(
  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,
  mismatch.activity.file = system.file("extdata",
    "NatureBiot2016SuppTable19DoenchRoot.csv", package = "CRISPRseek"),
  useBSgenome = FALSE,
  genomeSeqFile,
  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
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 for NNG or NGN PAM pattern for offtarget finding.
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.
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
useBSgenome	Specify whether BSgenome is avaialbe for searching for gRNA and offtargets, default to FALSE. If set it to TRUE, the results should be the same as when using offtargetAnalysis function.
genomeSeqFile	Specify the genome sequence file in fasta format. It is only applicable and required when useBSgenome is set to FALSE.
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

See Also

CRISPRseek

Examples

library(CRISPRseek)
	inputFilePath <- system.file("extdata", "inputseqWithoutBSgenome.fa",
            package = "CRISPRseek")
########### genomeSeq.fasta contains the genomic sequence in fasta format for gRNA and offtarget search###############
        genomeSeqFile <- system.file("extdata", "genomeSeq.fasta",
             package = "CRISPRseek")
        library(hash)
        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)
        featureWeightMatrixFile <- system.file("extdata", "Morenos-Mateo.csv",
            package = "CRISPRseek")
	results <- offTargetAnalysisWithoutBSgenome(inputFilePath = inputFilePath, 
            format = "fasta",
            useBSgenome = FALSE,
            genomeSeqFile = genomeSeqFile,
            findgRNAs = TRUE,
            exportAllgRNAs = "fasta",
            fetchSequence = FALSE, 
            findgRNAsWithREcutOnly = FALSE, 
            findPairedgRNAOnly = FALSE, 
            annotatePaired = FALSE,
	    max.mismatch = 1, 
            annotateExon = FALSE,            
            scoring.method = "CFDscore",
            min.score = 0.01,
            PAM = "NGG",
            PAM.pattern <- "NNN",
            rule.set = "CRISPRscan",
            featureWeightMatrixFile = featureWeightMatrixFile,
            subPAM.activity  = subPAM.activity,
            outputDir = "gRNAoutput-CRISPRseek-CRISPRscan-CFDscore", overwrite = TRUE)

---

Predict insertions and deletions induced by CRISPR/Cas9 editing

Description

Predict insertions and deletions, and associated reletive frequecies induced by CRISPR/Cas9 editing

Usage

predictRelativeFreqIndels(extendedSequence, method = "Lindel")

Arguments

extendedSequence	A vector of DNA sequences of length 60bp. It consists 30bp before the cut site and 30bp after the cut site.
method	the prediction method. default to Lindel. Currently only Lindel method are implemented.

Details

Predict relative indel frequency around target sites of CRISPR/Cas9 system. Currently only Lindel method using logistic regression is implemented in CRISPRseek.

Lindel is compatible with both Python2.7 and Python3.5 or higher.

By default, reticulate uses the version of Python found on your PATH (i.e. Sys.which("python")).

Use the function use_python in reticulate library to set the python path to a specific version. For example, use_python('/opt/anaconda3/lib/python3.7')

This function implements the Lindel method

Value

A list with the same length as the input extendedSequence.

Each list item either contains a warning message, or a predicted fraction of frameshift in the mutational outcomes plus a data frame with three columns.

The three columns are the alignment of predicted indel sequence to the original unedited sequence, predicted indel frequency, and the location of the predicted indels. The warning message for the Lindel method is as follows.

Warning: No PAM sequence is identified. Please check your sequence and try again.

A list with the same length as the input extendedSequence.

Each list item either contains a warning message, or a predicted fraction of frameshift in the mutational outcomes plus a data frame with three columns.

The three columns are the alignment of predicted indel sequence to the original unedited sequence, predicted indel frequency, and the location of the predicted indels. The warning message for the Lindel method is as follows.

Warning: No PAM sequence is identified. Please check your sequence and try again.

Author(s)

Hui Mao and Lihua Julie Zhu Predict insertions and deletions induced by CRISPR/Cas9 editing

Predict insertions and deletions, and associated reletive frequecies induced by CRISPR/Cas9 editing

Predict relative indel frequency around target sites of CRISPR/Cas9 system. Currently only Lindel method using logistic regression is implemented in CRISPRseek.

Lindel is compatible with both Python2.7 and Python3.5 or higher.

By default, reticulate uses the version of Python found on your PATH (i.e. Sys.which("python")).

Use the function use_python in reticulate library to set the python path to a specific version. For example, use_python('/opt/anaconda3/lib/python3.7')

This function implements the Lindel method

Hui Mao and Lihua Julie Zhu

References

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

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

Examples

extendedSequence <- c("AAA", "TAACGTTATCAACGCCTATATTAAAGCGACCGTCGGTTGAACTGCGTGGATCAATGCGTC")
if (interactive())
    indelFreq <- predictRelativeFreqIndels(extendedSequence, method = "Lindel") 



extendedSequence <- c("AAA", "TAACGTTATCAACGCCTATATTAAAGCGACCGTCGGTTGAACTGCGTGGATCAATGCGTC")
if (interactive())
    indelFreq <- predictRelativeFreqIndels(extendedSequence, method = "Lindel")

---

Search for off targets in a sequence as DNAString

Description

Search for off targets for given gRNAs, sequence and maximum mismatches

Usage

searchHits(
  gRNAs,
  seqs,
  seqname,
  max.mismatch = 3,
  PAM.size = 3,
  gRNA.size = 20,
  PAM = "NGG",
  PAM.pattern = "NNN$",
  allowed.mismatch.PAM = 2,
  PAM.location = "3prime",
  outfile,
  baseEditing = FALSE,
  targetBase = "C",
  editingWindow = 4:8
)

Arguments

gRNAs	DNAStringSet object containing a set of gRNAs. Please note the sequences must contain PAM appended after gRNAs, e.g., ATCGAAATTCGAGCCAATCCCGG where ATCGAAATTCGAGCCAATCC is the gRNA and CGG is the PAM
seqs	DNAString object containing a DNA sequence.
seqname	Specify the name of the sequence
max.mismatch	Maximum mismatch allowed in off target search, default 3. Warning: will be considerably slower if it is set to greater than 3
PAM.size	Size of PAM, default 3
gRNA.size	Size of gRNA, default 20
PAM	PAM as regular expression for appending to the gRNA, default NGG for SpCas9, change to TTTN for cpf1.
PAM.pattern	Regular expression of PAM, default N[A|G]G$ for spCas9. For cpf1, ^TTTN since it is a 5 prime PAM sequence
allowed.mismatch.PAM	Maximum number of mismatches allowed in the offtargets comparing to the PAM sequence. Default to 2 for NGG PAM
PAM.location	PAM location relative to gRNA. For example, spCas9 PAM is located on the 3 prime while cpf1 PAM is located on the 5 prime
outfile	File path to temporarily store the search results
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 to consider for the offtargets search only, 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, or you would like to include offtargets with the target base in a larger editing window.

Value

a data frame contains

• IsMismatch.posX - indicator variable indicating whether this position X is mismatch or not, (1 means yes and 0 means not). X takes on values from 1 to gRNA.size, representing all positions in the guide RNA (gRNA).

• strand - strand of the match, + for plus and - for minus strand

• chrom - chromosome of the off target

• chromStart - start position of the off target

• chromEnd - end position of the off target

• name - gRNA name

• gRNAPlusPAM - gRNA sequence with PAM sequence concatenated

• OffTargetSequence - the genomic sequence of the off target

• n.mismatch - number of mismatches between the off target and the gRNA

• forViewInUCSC - string for viewing in UCSC genome browser, e.g., chr14:31665685-31665707

• score - set to 100, and will be updated in getOfftargetScore

Author(s)

Lihua Julie Zhu

See Also

offTargetAnalysis

Examples

all.gRNAs <- findgRNAs(inputFilePath =
       system.file("extdata", "inputseq.fa", package = "CRISPRseek"),
       pairOutputFile = "pairedgRNAs.xls",
       findPairedgRNAOnly = TRUE)
  hits <- searchHits(all.gRNAs[1], 
      seqs = DNAString(
           "TAATATTTTAAAATCGGTGACGTGGGCCCAAAACGAGTGCAGTTCCAAAGGCACCCACCTGTGGCAG"), 
      seqname = "myseq", max.mismatch = 10, outfile = "test_searchHits")
  colnames(hits)
  all.gRNAs <- findgRNAs(inputFilePath =
          DNAStringSet(
              "TAATATTTTAAAATCGGTGACGTGGGCCCAAAACGAGTGCAGTTCCAAAGGCACCCACCTGTGGCAG"),
          pairOutputFile = "pairedgRNAs.xls",
          findPairedgRNAOnly = FALSE,
          PAM = "TTTN", PAM.location = "5prime")
   hits <- searchHits(all.gRNAs[1], seqs = DNAString(
       "TAATATTTTAAAATCGGTGACGTGGGCCCAAAACGAGTGCAGTTCCAAAGGCACCCACCTGTGGCAG"), 
       seqname = "myseq", 
       max.mismatch = 0, 
       outfile = "test_searchHits", PAM.location = "5prime",
       PAM.pattern = "^T[A|T]NN", allowed.mismatch.PAM = 0, PAM = "TTTN")
   colnames(hits)

---

Search for off targets

Description

Search for off targets for given gRNAs, BSgenome and maximum mismatches

Usage

searchHits2(
  gRNAs,
  BSgenomeName,
  chromToSearch = "all",
  chromToExclude = "",
  max.mismatch = 3,
  PAM.size = 3,
  gRNA.size = 20,
  PAM = "NGG",
  PAM.pattern = "N[A|G]G$",
  allowed.mismatch.PAM = 1,
  PAM.location = "3prime",
  baseEditing = FALSE,
  targetBase = "C",
  editingWindow = 4:8
)

Arguments

gRNAs	DNAStringSet object containing a set of gRNAs. Please note the sequences must contain PAM appended after gRNAs, e.g., ATCGAAATTCGAGCCAATCCCGG where ATCGAAATTCGAGCCAATCC is the gRNA and CGG is the PAM
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, default to none, meaning to search chromosomes specified by chromToSearch. For example, to exclude haplotype blocks from offtarget search in hg19, set chromToExclude to 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 it is set to greater than 3
PAM.size	Size of PAM, default 3
gRNA.size	Size of gRNA, default 20
PAM	Regular expression of protospacer-adjacent motif (PAM), default NGG for spCas9. For cpf1, ^TTTN
PAM.pattern	Regular expression of PAM, default N[A|G]G$ for spCas9. For cpf1, ^TTTN since it is a 5 prime PAM sequence
allowed.mismatch.PAM	Number of degenerative bases in the PAM sequence, default to 1 for N[A|G]G PAM
PAM.location	PAM location relative to gRNA. For example, spCas9 PAM is located on the 3 prime while cpf1 PAM is located on the 5 prime
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 to consider for the offtargets search only, 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, or you would like to include offtargets with the target base in a larger editing window.

Value

a data frame contains

• IsMismatch.posX - indicator variable indicating whether this position X is mismatch or not, (1 means yes and 0 means not). X takes on values from 1 to gRNA.size, representing all positions in the guide RNA (gRNA).

• strand - strand of the match, + for plus and - for minus strand

• chrom - chromosome of the off target

• chromStart - start position of the off target

• chromEnd - end position of the off target

• name - gRNA name

• gRNAPlusPAM - gRNA sequence with PAM sequence concatenated

• OffTargetSequence - the genomic sequence of the off target

• n.mismatch - number of mismatches between the off target and the gRNA

• forViewInUCSC - string for viewing in UCSC genome browser, e.g., chr14:31665685-31665707

• score - set to 100, and will be updated in getOfftargetScore

Author(s)

Lihua Julie Zhu

See Also

offTargetAnalysis

Examples

all.gRNAs <- findgRNAs(inputFilePath = 
        system.file("extdata", "inputseq.fa", package = "CRISPRseek"),
        pairOutputFile = "pairedgRNAs.xls",
	findPairedgRNAOnly = TRUE)

    library("BSgenome.Hsapiens.UCSC.hg19")
    ### for speed reason, use max.mismatch = 0 for finding all targets with 
    ### all variants of PAM
    hits <- searchHits2(all.gRNAs[1], BSgenomeName = Hsapiens,
        max.mismatch = 0, chromToSearch = "chrX")
    colnames(hits)

    ### test PAM located at 5 prime
    all.gRNAs <- findgRNAs(inputFilePath = 
             system.file("extdata", "inputseq.fa", package = "CRISPRseek"),
             pairOutputFile = "pairedgRNAs.xls",
             findPairedgRNAOnly = FALSE,
             PAM = "TGT", PAM.location = "5prime")
     
    library("BSgenome.Hsapiens.UCSC.hg19")
         ### for speed reason, use max.mismatch = 0 for finding all targets with 
         ### all variants of PAM
    hits <- searchHits2(all.gRNAs[1], BSgenomeName = Hsapiens, PAM.size = 3,
        max.mismatch = 0, chromToSearch = "chrX", PAM.location = "5prime",
        PAM = "NGG", 
        PAM.pattern = "^T[A|G]N", allowed.mismatch.PAM = 2)
    colnames(hits)

---

translate pattern from IUPAC Extended Genetic Alphabet to regular expression

Description

translate pattern containing the IUPAC nucleotide ambiguity codes to regular expression. For example,Y->[C|T], R-> [A|G], S-> [G|C], W-> [A|T], K-> [T|U|G], M-> [A|C], B-> [C|G|T], D-> [A|G|T], H-> [A|C|T], V-> [A|C|G] and N-> [A|C|T|G].

Usage

translatePattern(pattern)

Arguments

pattern

a character vector with the IUPAC nucleotide ambiguity codes

Value

a character vector with the pattern represented as regular expression

Author(s)

Lihua Julie Zhu

Examples

pattern1 <- "AACCNWMK"
	  translatePattern(pattern1)

---

Output restriction enzymes that recognize only the gRNA cleavage sites

Description

For each identified gRNA, output restriction enzymes that recognize only the gRNA cleavage sites.

Usage

uniqueREs(
  REcutDetails,
  summary,
  offTargets,
  scanUpstream = 100,
  scanDownstream = 100,
  BSgenomeName
)

Arguments

REcutDetails	REcutDetails stored in the REcutDetails.xls
summary	summary stored in the summary.xls
offTargets	offTargets stored in the offTargets.xls
scanUpstream	upstream offset from the gRNA start, default 100
scanDownstream	downstream offset from the gRNA end, default 100
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

Value

returns the RE sites that recognize only the gRNA cleavage sites for each gRNA.

Author(s)

Lihua Julie Zhu

Examples

library("BSgenome.Hsapiens.UCSC.hg19")
    load(system.file("extdata", "ForTestinguniqueREs.RData",
            package = "CRISPRseek"))
    uniqueREs(results$REcutDetails, results$summary, results$offtarget,
	scanUpstream = 50,
        scanDownstream = 50, BSgenomeName = Hsapiens)

---

Write the hits of sequence search from a sequence to a file

Description

write the hits of sequence search from a sequence instead of BSgenome to a file, internal function used by searchHits

Usage

writeHits(
  gRNA,
  seqname,
  matches,
  strand,
  file,
  gRNA.size = 20L,
  PAM = "NGG",
  PAM.pattern = "N[A|G]G$",
  max.mismatch = 4L,
  chrom.len,
  append = FALSE,
  PAM.location = "3prime",
  PAM.size = 3L,
  allowed.mismatch.PAM = 1L,
  seqs,
  baseEditing = FALSE,
  targetBase = "C",
  editingWindow = 4:8
)

Arguments

gRNA	DNAString object with gRNA sequence with PAM appended immediately after,e.g., ACGTACGTACGTACTGACGTCGG with 20bp gRNA sequence plus 3bp PAM sequence CGG
seqname	sequence name as character
matches	XStringViews object storing matched chromosome locations
strand	strand of the match, + for plus strand and - for minus strand
file	file path where the hits is written to
gRNA.size	gRNA size, default 20
PAM	PAM as regular expression for appending to the gRNA, default NGG for SpCas9, change to TTTN for cpf1.
PAM.pattern	PAM as regular expression for filtering the hits, default N[A|G]G$ for spCas9. For cpf1, ^TTTN since it is a 5 prime PAM sequence.
max.mismatch	maximum mismatch allowed within the gRNA (excluding PAM portion) for filtering the hits, default 4
chrom.len	length of the matched chromosome
append	TRUE if append to existing file, false if start a new file
PAM.location	PAM location relative to gRNA. For example, spCas9 PAM is located on the 3 prime while cpf1 PAM is located on the 5 prime
PAM.size	Size of PAM, default 3
allowed.mismatch.PAM	Maximum number of mismatches allowed in the offtargets comparing to the PAM sequence. Default to 1 for NGG PAM
seqs	DNAString object containing a DNA sequence.
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 to consider for the offtargets search only, 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, or you would like to include offtargets with the target base in a larger editing window.

Value

results are saved in the file specified by file

Author(s)

Lihua Julie Zhu

References

http://bioconductor.org/packages/2.8/bioc/vignettes/BSgenome/inst/doc/ GenomeSearching.pdf

See Also

offTargetAnalysis

Examples

if(interactive())
 {
    gRNAPlusPAM <- DNAString("ACGTACGTACGTACTGACGTCGG")
    x <- DNAString("AAGCGCGATATGACGTACGTACGTACTGACGTCGG")
    chrom.len <- nchar(as.character(x))
    m <- matchPattern(gRNAPlusPAM, x)
    names(m) <- "testing"
    writeHits(gRNA = gRNAPlusPAM, seqname = "chr1", 
        matches = m, strand = "+", file = "exampleWriteHits.txt", 
        chrom.len = chrom.len, append = FALSE, seqs = x)
 }

---

Write the hits of sequence search to a file

Description

write the hits of sequence search to a file, internal function used by searchHits

Usage

writeHits2(
  gRNA,
  seqname,
  matches,
  strand,
  file,
  gRNA.size = 20L,
  PAM = "NGG",
  PAM.pattern = "N[A|G]G$",
  max.mismatch = 4L,
  chrom.len,
  append = FALSE,
  PAM.location = "3prime",
  PAM.size = 3L,
  allowed.mismatch.PAM = 1L,
  BSgenomeName,
  baseEditing = FALSE,
  targetBase = "C",
  editingWindow = 4:8
)

Arguments

gRNA	DNAString object with gRNA sequence with PAM appended immediately after,e.g., ACGTACGTACGTACTGACGTCGG with 20bp gRNA sequence plus 3bp PAM sequence CGG
seqname	chromosome name as character, e.g., chr1
matches	XStringViews object storing matched chromosome locations
strand	strand of the match, + for plus strand and - for minus strand
file	file path where the hits is written to
gRNA.size	gRNA size, default 20
PAM	PAM as regular expression for filtering the hits, default NGG for spCas9. For cpf1, TTTN.
PAM.pattern	Regular expression of protospacer-adjacent motif (PAM), default N[A|G]G$ for spCas9. For cpf1, ^TTTN since it is a 5 prime PAM sequence
max.mismatch	maximum mismatch allowed within the gRNA (excluding PAM portion) for filtering the hits, default 4
chrom.len	length of the matched chromosome
append	TRUE if append to existing file, false if start a new file
PAM.location	PAM location relative to gRNA. For example, spCas9 PAM is located on the 3 prime while cpf1 PAM is located on the 5 prime
PAM.size	Size of PAM, default 3
allowed.mismatch.PAM	Number of degenerative bases in the PAM sequence, default to 1 for N[A|G]G PAM
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
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 to consider for the offtargets search only, 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, or you would like to include offtargets with the target base in a larger editing window.

Value

results are saved in the file specified by file

Author(s)

Lihua Julie Zhu

References

http://bioconductor.org/packages/2.8/bioc/vignettes/BSgenome/inst/doc/ GenomeSearching.pdf

See Also

offTargetAnalysis

Examples

library("BSgenome.Hsapiens.UCSC.hg19")
    gRNAPlusPAM <- DNAString("ACGTACGTACGTACTGACGTCGG")
    x <- DNAString("AAGCGCGATATGACGTACGTACGTACTGACGTCGG")
    chrom.len <- nchar(as.character(x))
    m <- matchPattern(gRNAPlusPAM, x)
    names(m) <- "testing"
    writeHits2(gRNA = gRNAPlusPAM, seqname = "chr1", 
        PAM = "NGG", PAM.pattern = "NNN$", allowed.mismatch.PAM = 2,
        matches = m, strand = "+", file = "exampleWriteHits.txt", 
        chrom.len = chrom.len, append = FALSE, BSgenomeName = Hsapiens)

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