Package 'CRISPRseek'

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)

---

Default lengthy arguments

Description

This contains a list of long constant values used as defaults in many function.

Usage

chromToExclude_default

Format

A character string.

Examples

REpatternFile_default  # Display the default value for REpatternFile.

chromToExclude_default

---

Compare two 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 = NULL,
  inputFile2Path = NULL,
  inputNames = c("Seq1", "Seq2"),
  format = c("fasta", "fasta"),
  header = FALSE,
  findgRNAsWithREcutOnly = FALSE,
  searchDirection = c("both", "1to2", "2to1"),
  BSgenomeName = NULL,
  baseEditing = FALSE,
  targetBase = "C",
  editingWindow = 4:8,
  editingWindow.offtargets = 4:8,
  REpatternFile = REpatternFile_default(),
  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 = NULL,
  upstream = 0,
  downstream = 0,
  weights = weights_default,
  overwrite = FALSE,
  baseBeforegRNA = 4,
  baseAfterPAM = 3,
  featureWeightMatrixFile = featureWeightMatrixFile_default(),
  foldgRNAs = FALSE,
  gRNA.backbone = gRNA.backbone_default,
  temperature = 37,
  scoring.method = c("Hsu-Zhang", "CFDscore"),
  subPAM.activity = subPAM.activity_default,
  subPAM.position = c(22, 23),
  PAM.location = "3prime",
  rule.set = c("Root_RuleSet1_2014", "Root_RuleSet2_2016", "CRISPRscan", "DeepCpf1"),
  mismatch.activity.file = mismatch.activity.file_default()
)

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.xlsx 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")
    outputDir <- tempdir()
    seqs <- compare2Sequences(inputFile1Path, inputFile2Path,
        outputDir = outputDir, 
        REpatternFile = REpatternFile, overwrite = TRUE)

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

    seqs2 <- compare2Sequences(inputFile1Path, inputFile2Path,
               inputNames=c("Seq1", "Seq2"),
               scoring.method = "CFDscore",
               outputDir = outputDir, 
               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 = NULL, chrom_acc = NULL)

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)
}

---

featureWeightMatrixFile_default

Description

Default value for featureWeightMatrixFile, use featureWeightMatrixFile() to show its value.

Usage

featureWeightMatrixFile_default()

---

Filter gRNAs

Description

Filter gRNAs containing restriction enzyme cut site

Usage

filtergRNAs(
  all.gRNAs = NULL,
  pairOutputFile = NULL,
  findgRNAsWithREcutOnly = FALSE,
  REpatternFile = REpatternFile_default(),
  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.xlsx",
  findPairedgRNAOnly = TRUE)

gRNAs.RE <- filtergRNAs(
  all.gRNAs = all.gRNAs,
  pairOutputFile = "testpairedgRNAs.xlsx",
  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 = NULL,
  min.score = 0.01,
  topN = 200,
  topN.OfftargetTotalScore = 10,
  annotateExon = TRUE,
  txdb = NULL,
  orgAnn = NULL,
  ignore.strand = TRUE,
  outputDir = NULL,
  oneFilePergRNA = FALSE,
  fetchSequence = TRUE,
  upstream = 200,
  downstream = 200,
  BSgenomeName = NULL,
  genomeSeqFile = NULL,
  baseBeforegRNA = 4,
  baseAfterPAM = 3,
  gRNA.size = 20,
  PAM.location = "3prime",
  PAM.size = 3,
  featureWeightMatrixFile = featureWeightMatrixFile_default(),
  rule.set = c("Root_RuleSet1_2014", "Root_RuleSet2_2016", "CRISPRscan", "DeepCpf1"),
  chrom_acc = NULL,
  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
genomeSeqFile	Other than BSgenomeName, a custome FASTA file can be supplied, if set, overwrites BSgenomeName.
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 <- tempdir()
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 = NULL,
  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 = NULL,
  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 = NULL,
  targeted.seq.length.change = NULL,
  bp.after.target.end = 15L,
  target.start = NULL,
  target.end = NULL,
  primeEditingPaired.output = "pairedgRNAsForPE.xls",
  min.gap = 0,
  max.gap = 20,
  pairOutputFile = NULL,
  name.prefix = NULL,
  featureWeightMatrixFile = featureWeightMatrixFile_default(),
  baseBeforegRNA = 4,
  baseAfterPAM = 3,
  calculategRNAEfficacy = FALSE,
  efficacyFile = NULL,
  PAM.location = "3prime",
  rule.set = c("Root_RuleSet1_2014", "Root_RuleSet2_2016", "CRISPRscan", "DeepCpf1"),
  chrom_acc = NULL
)

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 near 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

# Example1: DNAStringSet as input, only output paired gRNAs
inputSeq <-  DNAStringSet(paste0("CCAGTTTGTGGATCCTGCTCTGGTGTC",
                                 "CTCCACACCAGAATCAGGGATCGAAAA",
                                 "CTCATCAGTCGATGCGAGTCATCTAAA",
                                 "TTCCGATCAATTTCACACTTTAAACG"))
findgRNAs(inputFilePath =  inputSeq,
          findPairedgRNAOnly = TRUE,
          pairOutputFile = "test_findgRNAs1.xlsx",
          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)

# Example2: FASTA as input, only output paired gRNAs
findgRNAs(inputFilePath = system.file("extdata",
                                      "inputseq.fa", 
                                      package = "CRISPRseek"),
          findPairedgRNAOnly = TRUE,
          pairOutputFile = "test_findgRNAs2.xlsx")

# Example3: predict gRNA efficacy using CRISPRscan
featureWeightMatrixFile <- system.file("extdata", 
                                       "Morenos-Mateo.csv",
                                       package = "CRISPRseek")
findgRNAs(inputFilePath = system.file("extdata",
                                      "testCRISPRscan.fa", 
                                      package = "CRISPRseek"),
          pairOutputFile = "test_findgRNAs3.xlsx",
          findPairedgRNAOnly = FALSE,
          calculategRNAEfficacy= TRUE,
          rule.set = "CRISPRscan",
          baseBeforegRNA = 6, 
          baseAfterPAM = 6,
          featureWeightMatrixFile = featureWeightMatrixFile,
          efficacyFile = "testCRISPRscanEfficacy.xlsx")

# Example 4: predict gRNA efficacy using DeepCpf1
# Note: that these examples may fail during build/check on Bioconductor when 
# running on MacOS Monterey due to compatibility issues with keras. To avoid 
# errors, wrap the code in `if (interactive)`.
if (interactive()) {
  findgRNAs(inputFilePath = system.file("extdata", 
                                        "cpf1.fa", 
                                        package = "CRISPRseek"),
            findPairedgRNAOnly = FALSE,
            pairOutputFile = "test_findgRNAs_cpf1.xlsx",
            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.xlsx")

  findgRNAs(inputFilePath = system.file("extdata", 
                                        "cpf1.fa", 
                                        package = "CRISPRseek"),
            findPairedgRNAOnly = FALSE,
            pairOutputFile = "test_findgRNAs_cpf1.xlsx",
            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.xlsx", 
            baseEditing = TRUE,
            editingWindow = 20, 
            targetBase = "X")

  findgRNAs(inputFilePath = system.file("extdata",
                                        "cpf1.fa", 
                                        package = "CRISPRseek"),
            findPairedgRNAOnly = FALSE,
            pairOutputFile = "test_findgRNAs_cpf1.xlsx",
            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.xlsx", 
            baseEditing = TRUE,
            editingWindow = 20, 
            targetBase = "C")
}

---

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)

---

Identify off-targets with bulges for target-specific gRNAs designed for CRISPR-Cas9 systems.

Description

This function extends the off-targets identified by offTargetAnalysis() by detecting off-targets that contain bulges. In gRNA design, "bulges" refer to insertions ("RNA bulges") or deletions ("DNA bulges") in the gRNA sequence relative to the target DNA sequence. Bulges can affect the binding affinity and specificity of the gRNA to its target. The function wraps around ['Cas-OFFinder'](http://www.rgenome.net/cas-offinder/) internally.

Usage

getOfftargetWithBulge(
  gRNA_PAM = NULL,
  output_csv_name = NULL,
  PAM.size = 3,
  PAM.pattern = "NNG$|NGN$",
  PAM.location = c("3prime", "5prime"),
  max.mismatch = 3,
  DNA_bulge = 2,
  RNA_bulge = 2,
  BSgenomeName = NULL,
  genomeSeqFile = NULL,
  chromToSearch = "all",
  chromToExclude = NULL,
  cas_offinder_version = c("2.4.1", "3.0.0b3")
)

Arguments

gRNA_PAM	A 'DNAStringSet' object returned by 'findgRNA()' that contains gRNA plus PAM sequences. Alternatively, you can supply the 'list' object returned by the 'offTargetAnalysis()' function.
output_csv_name	A string specifying the output CSV file name. Defaults to 'NULL', meaning that the output will be printed to the console.
PAM.size	See 'offTargetAnalysis()'.
PAM.pattern	See 'offTargetAnalysis()'.
PAM.location	See 'offTargetAnalysis()'.
max.mismatch	See 'offTargetAnalysis()'.
DNA_bulge	The maximum size of DNA bulges, specified in nucleotides. Defaults to 2.
RNA_bulge	The maximum size of RNA bulges, specified in nucleotides. Defaults to 2.
BSgenomeName	See 'offTargetAnalysis()'. Alternatively, use 'genomeSeqFile' to specify the file path to custom genome fasta file. Note, 'genomeSeqFile' overwrites 'BSgenomeName' if both set.
genomeSeqFile	If you are using a custom genome, specify the file path to the FASTA file using 'genomeSeqFile'.
chromToSearch	See 'offTargetAnalysis()'.
chromToExclude	See 'offTargetAnalysis()'.
cas_offinder_version	The version of "Cas-OFFinder" to use. Currently supported versions are "2.4.1" and "3.0.0b3". Defaults to "2.4.1".

Value

If 'output_csv_name' is not set, the function returns a data frame containing the output generated by 'Cas-OFFinder'. Otherwise, it saves the data frame to the CSV file specified by 'output_csv_name'. When 'cas_offinder_version == "2.4.1"', the following columns will be included: "bulge_type", "gRNA", "DNA", "chr", "start_0_based", "strand", "mismatches", "bulge_size". For 'cas_offinder_version == "3.0.0b3"', the included columns will be: "gRNA_id", "bulge_type", "gRNA", "DNA", "chr", "start_0_based", "strand", "mismatches", "bulge_size".

Author(s)

Kai Hu

References

1. Sangsu Bae, Jeongbin Park, Jin-Soo Kim, Cas-OFFinder: a fast and versatile algorithm that searches for potential off-target sites of Cas9 RNA-guided endonucleases, Bioinformatics, Volume 30, Issue 10, May 2014, Pages 1473–1475, https://doi.org/10.1093/bioinformatics/btu048

See Also

'offTargetAnalysis()' for off-targets analysis, 'Cas-OFFinder' (https://github.com/snugel/cas-offinder) for more on output format.

Examples

# Example with `DNAStringSet` as input
library(CRISPRseek)
library(BSgenome.Hsapiens.UCSC.hg19)

gRNA_PAM <- findgRNAs(inputFilePath = system.file("extdata", 
                                                  "inputseq.fa", 
                                                   package = "CRISPRseek"),
                      pairOutputFile = "testpairedgRNAs.xls",
                      findPairedgRNAOnly = TRUE)
df <- getOfftargetWithBulge(gRNA_PAM, PAM.pattern = "NNG$|NGN$",
                            DNA_bulge = 2, RNA_bulge = 2,
                            BSgenomeName = Hsapiens, chromToSearch = "chrX")

# Example with `list` output from `offTargetAnalysis` as input
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(org.Hs.eg.db)

inputFilePath <- system.file("extdata", "inputseq.fa", package = "CRISPRseek")
REpatternFile <- system.file("extdata", "NEBenzymes.fa", package = "CRISPRseek")
res <- 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 = tempdir(),
                         overwrite = TRUE)
df <- getOfftargetWithBulge(res, PAM.pattern = "NNG$|NGN$", DNA_bulge = 2, 
                            RNA_bulge = 2, BSgenomeName = Hsapiens, 
                            chromToSearch = "chrX")

---

gRNA.backbone_default

Description

gRNA.backbone_default

Usage

gRNA.backbone_default

Format

An object of class character of length 1.

---

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)

---

mismatch.activity.file_default

Description

Default value for mismatch.activity.file (csv), use mismatch.activity.file_default() to show its value

Usage

mismatch.activity.file_default()

---

mismatch.activity.file_default_xlsx

Description

Default value for mismatch.activity.file (xlsx), use mismatch.activity.file_default_xlsx() to show its value.

Usage

mismatch.activity.file_default_xlsx()

---

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 = NULL,
  format = c("fasta", "fastq", "bed"),
  header = FALSE,
  gRNAoutputName = "test",
  findgRNAs = TRUE,
  exportAllgRNAs = c("all", "fasta", "genbank", "no"),
  findgRNAsWithREcutOnly = FALSE,
  REpatternFile = REpatternFile_default(),
  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 = NULL,
  gRNA.size = 20,
  PAM = "NGG",
  PAM.size = width(PAM),
  PAM.pattern = "NNG$|NGN$",
  BSgenomeName = NULL,
  genomeSeqFile = NULL,
  chromToSearch = "all",
  chromToExclude = chromToExclude_default,
  max.mismatch = 3,
  allowed.mismatch.PAM = 1,
  gRNA.pattern = NULL,
  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 = NULL,
  targeted.seq.length.change = NULL,
  bp.after.target.end = 15L,
  target.start = NULL,
  target.end = NULL,
  primeEditingPaired.output = "pairedgRNAsForPE.xls",
  min.score = 0,
  topN = 1000,
  topN.OfftargetTotalScore = 10,
  annotateExon = TRUE,
  txdb = NULL,
  orgAnn = NULL,
  ignore.strand = TRUE,
  outputDir = getwd(),
  fetchSequence = TRUE,
  upstream = 200,
  downstream = 200,
  weights = weights_default,
  baseBeforegRNA = 4,
  baseAfterPAM = 3,
  featureWeightMatrixFile = featureWeightMatrixFile_default(),
  useScore = TRUE,
  useEfficacyFromInputSeq = FALSE,
  outputUniqueREs = TRUE,
  foldgRNAs = FALSE,
  gRNA.backbone = gRNA.backbone_default,
  temperature = 37,
  overwrite = FALSE,
  scoring.method = c("Hsu-Zhang", "CFDscore"),
  subPAM.activity = subPAM.activity_default,
  subPAM.position = c(22, 23),
  PAM.location = "3prime",
  rule.set = c("Root_RuleSet1_2014", "Root_RuleSet2_2016", "CRISPRscan", "DeepCpf1"),
  chrom_acc = NULL,
  calculategRNAefficacyForOfftargets = TRUE,
  mismatch.activity.file = mismatch.activity.file_default(),
  predIndelFreq = FALSE,
  predictIndelFreq.onTargetOnly = TRUE,
  method.indelFreq = "Lindel",
  baseBeforegRNA.indelFreq = 13,
  baseAfterPAM.indelFreq = 24,
  findOffTargetsWithBulge = FALSE,
  method.findOffTargetsWithBulge = c("CasOFFinder_v3.0.0b3"),
  DNA_bulge = 2,
  RNA_bulge = 2
)

Arguments

inputFilePath	Path to an input sequence file or a 'DNAStringSet' object containing sequences to be searched for potential gRNAs.
format	Defaults to "fasta". Format of the input file, "fasta", "fastq", and "bed" are supported.
header	Defaults to FALSE. Indicates whether the input file contains header. Only relevant when 'format' is set to "bed".
gRNAoutputName	Defaults to "test". Specifies the name of the gRNA output file when 'inputFilePath' is a 'DNAStringSet' object instead of a file path.
findgRNAs	Defaults to TRUE. Specifies whether to find gRNAs from the sequences in 'inputFilePath'. Set to FALSE if the input file already contains user-selected gRNAs plus PAM.
exportAllgRNAs	Defaults to "both". Indicates whether to output all potential gRNAs to a file in fasta format, genbank format, or both.
findgRNAsWithREcutOnly	Defaults to TRUE. Specifies whether to search for gRNAs that overlap with restriction enzyme recognition sites only.
REpatternFile	Path to a file containing restriction enzyme cut patterns.
minREpatternSize	Defaults to 4. Minimum restriction enzyme recognition pattern length required for the enzyme pattern to be searched for.
overlap.gRNA.positions	Defaults to 'c(17, 18)'. Specifies the required overlapping positions of the gRNA and restriction enzyme cut site. For Cpf1, you can set it to 'c(19, 23)'.
findPairedgRNAOnly	Defaults to FALSE. Specifies whether to search only for paired gRNAs in such an orientation that the first one is on the minus strand (reverse gRNA) and the second one is on plus strand (forward gRNA).
annotatePaired	Defaults to TRUE. Specifies whether to output paired gRNA information.
paired.orientation	The "PAMin" orientation refers to the scenario where the two adjacent PAMs on the sense and antisense strands face inward toward each other, such as in "N21GG" and "CCN21". In contrast, the "PAMout" orientation occurs when the PAMs face away from each other, as seen in "CCN21" and "N21GG".
enable.multicore	Defaults to FALSE. Indicates whether to enable parallel. For super long sequences with lots of gRNAs, set it to TRUE.
n.cores.max	Defaults to 6. Specifies the maximum number of cores to use in multicore mode. Set it to 1 to disable multicore processing for small dataset.
min.gap	Defaults to 0. Minimum distance between two oppositely oriented gRNAs to be considered as valid paired gRNAs.
max.gap	Defaults to 20. Specifies the maximum distance between two oppositely oriented gRNAs to be considered as valid paired gRNAs.
gRNA.name.prefix	Defaults to "gRNA". Specifies the prefix used when assigning names to detected gRNAs.
gRNA.size	Defaults to 20. The size of the gRNA.
PAM	Defaults to "NGG". Defines the protospacer adjacent motif sequence.
PAM.size	Defaults to 'width(PAM)'. Specifies the PAM length.
PAM.pattern	Defaults to "NNG$|NGN$" (for spCas9). Specifies the regular expression of PAM. For cpf1, set to "^TTTN" since its PAM is at the 5 prime end.
BSgenomeName	A 'BSgenome' object containing the target genome sequence, used for off-target search. Please refer to available genomes in the "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
genomeSeqFile	Alternative to 'BSgenomeName'. Specifies the path to a custom target genome file in FASTA format, used for off-target search. It is applicable when 'BSgenomeName' is NOT set. When 'genomeSeqFile' is set, the 'annotateExon', 'txdb', and 'orgAnn' parameters will be ignored.
chromToSearch	Defaults to "all", meaning all chromosomes in the target genome are searched for off-targets. Set to a specific chromosome (e.g., "chrX") to restrict the search to that chromsome only.
chromToExclude	If set to "", means to search off-targets in chromosomes specified in 'chromToSearch'. By default, to exclude haplotype blocks from off-target search assuming using 'hg19' genome, 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	Defaults to 3. Maximum number of mismatches allowed in off-target search. Warning: search will be considerably slower if set to a value greater than 3.
allowed.mismatch.PAM	Defaults to 1. Maximum number of mismatches allowed in the PAM sequence for off-target search. The default value 1 allows "NGN" and "NNG" PAM patterns for off-target identification.
gRNA.pattern	Defaults to NULL (meaning no restriction). Specifies regular expression or IUPAC Extended Genetic Alphabet to represent gRNA pattern. E.g. to specify that the gRNA must start with "GG", set it to "^GG". Type '?translatePattern' for a list of IUPAC Extended Genetic Alphabet.
baseEditing	Defaults to FALSE. Specifies whether to design gRNAs for base editing. If set to TRUE, please set 'targetBase' and 'editingWidow'.
targetBase	Defaults to "C" (for converting C to T in the CBE system). Applicable only when 'baseEditing = TRUE'. Specifies the target base for base editing systems. Please change it to "A" if you intend to use the ABE system.
editingWindow	Defaults to '4:8' (for the CBE system). Applicable only when 'baseEditing = TRUE', and specifies the effective editing window. Please change it accordingly if the system you use have a different editing window.
editingWindow.offtargets	Defaults to '4:8' (for the original CBE system, 1 means the most distal site from the 3' PAM, the most proximal site from the 5' PAM). Applicable only when 'baseEditing = TRUE'. Indicates the effective editing window to consider for the off-targets search only. Please change it accordingly if the system you use have a different editing window, or if you would like to include off-targets with the target base in a larger editing window.
primeEditing	Defaults to FALSE. Specifies whether to design gRNAs for prime editing. If set to TRUE, please set 'PBS.length', 'RT.template.length', 'RT.template.pattern', 'targeted.seq.length.change', 'bp.after.target.end', 'target.start', 'target.end', and 'corrected.seq' accordingly.
PBS.length	Applicable only when 'primeEditing = TRUE'. Specifies the number of bases to output for primer binding site.
RT.template.length	Defaults to '8:18'. Applicable only when 'primeEditing = TRUE'. Specifies the number of bases required for RT template. Increase the length if the edit involves a large insertion. Only gRNAs with a calculated 'RT.template.length' within the specified range will be included 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	Defaults to not end with C (per https://doi.org/10.1038/s41586-019-1711-4). Applicable only when 'primeEditing = TRUE'. Specifies the RT template sequence pattern.
corrected.seq	Applicable only when 'primeEditing = TRUE'. Specifies the mutated or inserted sequences after successful editing.
targeted.seq.length.change	Applicable only when 'primeEditing = TRUE'. Specifies the change in the targeted sequence length. Set it to 0 for base changes, positive numbers for insertions, and negative number for deletions. For example, 10 indicates that the corrected sequence will have a 10-bp insertion, -10 means that the corrected sequence will have a 10-bp deletion, and 0 means that only base changes with no change in sequence length.
bp.after.target.end	Defaults to 15. Applicable only when 'primeEditing = TRUE'. Specifies the number of bases to add after the target change end site as part of the RT template. Refer to 'RT.template.length' for how this parameter affects the calculation of 'RT.template.length', which is used as a filtering criterion during pregRNA selection.
target.start	Defaults to 20. Applicable only when 'primeEditing = TRUE'. Specifies the start location in the input sequence to make changes, which will be used to obtain the RT template sequence. Refer to 'RT.template.length' for how this parameter affects the 'RT.template.length' calculation, which is used as a filtering criteria in pregRNA selection.
target.end	Defaults to 20. Applicable only when 'primeEditing = TRUE'. Specifies the end location in the input sequence to make changes, which will be used to obtain the RT template sequence. Refer to 'RT.template.length' for how this parameter affects the 'RT.template.length' calculation, which is used as a filtering criteria in pregRNA selection.
primeEditingPaired.output	Defaults to "pairedgRNAsForPE.xls". Applicable only when 'primeEditing = TRUE'. Specifies the file path where the pegRNA, second gRNA wit PBS, RT.template, and gRNA sequences will be saved.
min.score	Defaults to 0. Specifies the minimum score of an off-target to be included in the final output.
topN	Defaults to 1000. Specifies the top N off-targets to be included in the final output
topN.OfftargetTotalScore	Defaults to 10. Specifies the top N off-targets used to calculate the total off-target score.
annotateExon	Defaults to TRUE. Specifies whether to indicate if the off-target is located within an exon.
txdb	A 'TxDb' object containing organism-specific annotation data, required for 'annotateExon'. For creating and using a 'TxDb' object, refer to the 'GenomicFeatures' package. For a list of existing 'TxDb' objects, search for annotation packages 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	An 'OrgDb' object containing organism-specific annotation mapping information, required for 'annotateExon'.
ignore.strand	Defaults to TRUE. Specifies if strandness should be ignored when annotating off-targets to genes.
outputDir	Defaults to the current working directory. Specifies the path to the directory where the analysis results will be saved.
fetchSequence	Defaults to TRUE. Specifies whether to fetch flanking sequences for off-targets.
upstream	Defaults to 200. Specifies the upstream offset from the off-target start.
downstream	Defaults to 200. Specifies the downstream offset from the off-target end.
weights	Defaults to '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)' (used in Hsu et al., 2013 cited in the reference section). Applicable only when 'scoring.method = Hus-Zhang'. Specifies a numeric vector with a length equal to the size of the gRNA, containing the corresponding weight values.
baseBeforegRNA	Defaults to 4. Specifies the number of bases preceding the gRNA. It is used to calculate gRNA efficiency. Note that for PAMs located at the 5 prime end, the number of bases should include both the bases before the PAM sequence and the PAM size.
baseAfterPAM	Defaults to 3 (for spCas9). Specifies the number of bases after PAM. It is used to calculate gRNA efficiency. Note that for PAMs located on the 5 prime end, the number should include the length of the gRNA plus the extended sequence on the 3 prime end.
featureWeightMatrixFile	By default, the DoenchNBT2014 weight matrix is used. Specifies the feature weight matrix file used for calculating gRNA efficiency. To use an alternative matrix, provide a CSV where the first column contains the significant features and the second column contains the corresponding weights. For details, refer to Doench et al., 2014.
useScore	Defaults to TRUE. Displays in grayscale, with darkness indicating gRNA efficacy. The taller bar represents the Cas9 cutting site. If set to False, efficacy will not be shown. Instead, gRNAs on the plus strand will be colored red, and gRNAs on the minus strand will be colored green.
useEfficacyFromInputSeq	Defaults to FALSE. If TRUE, the summary file will contain gRNA efficacy calculated from the input sequences instead of from off-target analysis. Set it to TRUE if the input sequence is from a species different from the one used for off-target analysis.
outputUniqueREs	Defaults to 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	Defaults to FALSE. If set to TRUE, summary file will contain minimum free energy of the secondary structure of gRNA with gRNA backbone from 'GeneRfold' package given that 'GeneRfold' package has been installed.
gRNA.backbone	Defaults to the sequence in Sp gRNA backbone. Applicable only when 'foldgRNAs = TRUE'. Specifies the gRNA backbone constant region sequence.
temperature	Defaults to 30. Applicable only when 'foldgRNAs = TRUE'. Specifies the temperature in Celsius.
overwrite	Defaults to FALSE. Specifies whether to overwrite the existing files in the output directory.
scoring.method	Defaults to "Hsu-Zhang". Specifies the method to use for off-target cleavage rate estimation. Choose from "Hsu-Zhang" and "CFDscore"
subPAM.activity	Defaults to "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)". Applicable only when 'scoring.method = CFDscore'. Specifies a hash that represents the cleavage rate for each alternative sub PAM sequence relative to preferred PAM sequence.
subPAM.position	Defaults to 'c(22, 23)' (For spCas9 with 20-bp gRNA and NGG as preferred PAM). Applicable only when 'scoring.method = CFDscore'. Specifies the start and end positions of the sub PAM. For Cpf1, it should be 'c(1,2)'.
PAM.location	Defaults to "3prime" (for spCas9). Specifies the PAM location relative to the protospacer sequence. Set to "5prime" for cpf1 because its PAM is located at the 5 prime end of the protospacer.
rule.set	Defaults to "Root_RuleSet1_2014". Specifies a rule set scoring system for calculating gRNA efficacy. Note that "Root_RuleSet2_2016" requires the following packages with specified version: python 2.7, scikit-learn 0.16.1, pickle, pandas, numpy, and scipy.
chrom_acc	Specifies an optional binary variable indicating chromatin accessibility information with 1 representing accessible and 0 representing inaccessible.
calculategRNAefficacyForOfftargets	Defaults to TRUE. Specifies whether to output gRNA efficacy for both off-targets and on-targets. Set to FALSE if only on-target gRNA efficacy is needed to speed up the analysis. For potential use cases of off-target efficacies, refer to https://support.bioconductor.org/p/133538/#133661.
mismatch.activity.file	Defaults to use the supplementary Table 19 from Doench et al., Nature Biotechnology 2016. Applicable only when 'scoring.method = CFDscore'. Specifies a CSV file containing the cleavage rates for all possible types of single nucleotide mismatches at each position of the gRNA.
predIndelFreq	Defaults to FALSE. Specifies whether to output the predicted INDELs and their frequencies.
predictIndelFreq.onTargetOnly	Defaults to TRUE. Specifies whether to predict INDELs and their frequencies for on-targets only. Typically, researchers are only interested in predicting editing outcome for on-targets, as editing in off-targets is undesirable. Set to FALSE if you want to predict INDELs and their frequencies for off-targets as well. Note that this will increase the run time.
method.indelFreq	Defaults to "Lindel". Applicable only when 'predIndelFreq = TRUE'. Specifies the method to be used for predicting INDELs. Currently, only "Lindel" is supported, though additional methods can be added upon request. Type '?predictRelativeFreqIndels' to learn more.
baseBeforegRNA.indelFreq	Defaults to 13. Applicable only when 'predIndelFreq = TRUE'.
baseAfterPAM.indelFreq	Defaults to 24. Applicable only when 'predIndelFreq = TRUE'.
findOffTargetsWithBulge	Defaults to FALSE. Specifies whether to search for off-targets with bulges.
method.findOffTargetsWithBulge	Only applicable if 'findOffTargetsWithBulge = TRUE'. Choose from 'c("CasOFFinder_v3.0.0b3")'.
DNA_bulge	Defaults to 2. Maximum number of DNA bulges allowed in off-target search.
RNA_bulge	Defaults to 2. Maximum number of RNA bulges allowed in off-target search.

Value

Four Excel files are generated in the output directory:

Summary.xlsx	- Summary of the gRNAs
OfftargetAnalysis.xlsx	- Detailed information on off-targets
REcutDetails.xlsx	- Restriction enzyme cut sites for each gRNA
pairedgRNAs.xlsx	- Potential paired gRNAs

Author(s)

Lihua Julie Zhu, Kai Hu

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

# Load required libraries
library(CRISPRseek)
library(BSgenome.Hsapiens.UCSC.hg19)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(org.Hs.eg.db)

# Example 1: given FASTA input, search gRNAs and off-targets
outputDir <- tempdir()
inputFilePath <- system.file("extdata/inputseq.fa", package = "CRISPRseek")
REpatternFile <- system.file("extdata/NEBenzymes.fa", package = "CRISPRseek")

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

# Example 2: also predict indels and frequecies at target sites
results <- offTargetAnalysis(inputFilePath,
                             predIndelFreq = TRUE, 
                             predictIndelFreq.onTargetOnly = TRUE,
                             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)
names(results$indelFreq)
head(results$indelFreq[[1]])
  # Save the indel frequences to tab delimited files, 
  # one file for each target or offtarget site.
mapply(openxlsx::write.xlsx, results$indelFreq, 
       file = paste0(names(results$indelFreq), '.xlsx'))

# Example 3: predict gRNA efficacy using CRISPRscan
featureWeightMatrixFile <- system.file("extdata/Morenos-Mateo.csv", 
                                       package = "CRISPRseek")

results <- offTargetAnalysis(inputFilePath, 
                             rule.set = "CRISPRscan",
                             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,
                             baseBeforegRNA = 6, 
                             baseAfterPAM = 6,
                             featureWeightMatrixFile = featureWeightMatrixFile,
                             outputDir = outputDir, 
                             overwrite = TRUE)

# Example 4: when PAM is on the 5 prime side, e.g., Cpf1
if (interactive()) {
  results <- offTargetAnalysis(inputFilePath = 
                               system.file("extdata/cpf1-2.fa", 
                                           package = "CRISPRseek"), 
                               PAM.location = "5prime",
                               rule.set = "DeepCpf1",
                               PAM.size = 4,
                               PAM = "TTTN", 
                               PAM.pattern = "^TNNN", 
                               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,
                               overlap.gRNA.positions = c(19, 23),
                               useEfficacyFromInputSeq = FALSE,
                               outputDir = outputDir,
                               overwrite = TRUE, 
                               allowed.mismatch.PAM = 2,
                               subPAM.position = c(1, 2))
}

# Example 5: when PAM is on the 5 prime side, and using Root_RuleSet1_2014
results <- offTargetAnalysis(inputFilePath, 
                             PAM.location = "5prime",
                             PAM = "TGT", 
                             PAM.pattern = "^T[A|G]N", 
                             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, 
                             allowed.mismatch.PAM = 2, 
                             subPAM.position = c(1, 2), 
                             baseEditing = TRUE, 
                             editingWindow = 20, 
                             targetBase = "G")

# Example 6: base editor
results <- offTargetAnalysis(inputFilePath, 
                             baseEditing = TRUE,
                             editingWindow = 10:20, 
                             targetBase = "A",
                             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,
                             PAM.location = "5prime",
                             PAM = "TGT", 
                             PAM.pattern = "^T[A|G]N", 
                             allowed.mismatch.PAM = 2,
                             subPAM.position = c(1, 2),
                             outputDir = outputDir, 
                             overwrite = TRUE)

# Example 7: prime editor
inputFilePath <- DNAStringSet(paste0("CCAGTTTGTGGATCCTGCTCTGGTGTCCTCCACACC",
                                     "AGAATCAGGGATCGAAAACTCATCAGTCGATGCGAG", 
                                     "TCATCTAAATTCCGATCAATTTCACACTTTAAACG"))
results <- offTargetAnalysis(inputFilePath,
                             primeEditing = TRUE, 
                             overlap.gRNA.positions = c(17, 18),
                             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",
                             findPairedgRNAOnly = TRUE,
                             BSgenomeName = Hsapiens, 
                             chromToSearch = "chrX",
                             txdb = TxDb.Hsapiens.UCSC.hg19.knownGene,
                             orgAnn = org.Hs.egSYMBOL, 
                             max.mismatch = 1,
                             outputDir = outputDir, 
                             overwrite = TRUE,
                             PAM.size = 3,
                             gRNA.size = 20,
                             min.gap = 20, 
                             max.gap = 90)

---

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")

---

REpatternFile_default

Description

Default value for REpatternFile, use REpatternFile_default() to show its value.

Usage

REpatternFile_default()

---

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.xlsx",
       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.xlsx",
          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 = NULL,
  BSgenomeName = NULL,
  chromToSearch = "all",
  chromToExclude = NULL,
  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.xlsx",
	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.xlsx",
             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)

---

subPAM.activity_default

Description

subPAM.activity_default

Usage

subPAM.activity_default

Format

An object of class hash of length 16.

---

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)

---

weights_default

Description

weights_default

Usage

weights_default

Format

An object of class numeric of length 20.

---

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 = NULL,
  seqname = NULL,
  matches = NULL,
  strand = NULL,
  file = NULL,
  gRNA.size = 20L,
  PAM = "NGG",
  PAM.pattern = "N[A|G]G$",
  max.mismatch = 4L,
  chrom.len = NULL,
  append = FALSE,
  PAM.location = "3prime",
  PAM.size = 3L,
  allowed.mismatch.PAM = 1L,
  seqs = NULL,
  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 = NULL,
  seqname = NULL,
  matches = NULL,
  strand = NULL,
  file = tempfile(),
  gRNA.size = 20L,
  PAM = "NGG",
  PAM.pattern = "N[A|G]G$",
  max.mismatch = 4L,
  chrom.len = NULL,
  append = FALSE,
  PAM.location = "3prime",
  PAM.size = 3L,
  allowed.mismatch.PAM = 1L,
  BSgenomeName = NULL,
  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)

---