Package 'crisprDesign'

Add on-target composite score to a GuideSet object.

Description

Add on-target composite score to a GuideSet object.

Usage

addCompositeScores(object, ...)

## S4 method for signature 'GuideSet'
addCompositeScores(
  object,
  methods = c("azimuth", "ruleset1", "ruleset3", "lindel", "deepcpf1", "deephf",
    "deepspcas9", "enpamgb", "casrxrf", "crisprater", "crisprscan", "crispra", "crispri"),
  scoreName = "score_composite"
)

## S4 method for signature 'PairedGuideSet'
addCompositeScores(
  object,
  methods = c("azimuth", "ruleset1", "ruleset3", "lindel", "deepcpf1", "deephf",
    "deepspcas9", "enpamgb", "crisprater", "crisprscan", "casrxrf", "crispra", "crispri"),
  scoreName = "score_composite"
)

## S4 method for signature 'NULL'
addCompositeScores(object)

Arguments

Details

The function creates a composite score across a specified list of on-target scores by first transforming each individual score into a rank, and then taking the average rank across all specified methods. This can improve on-target activity prediction robustness. A higher score indicates higher on-target activity.

Value

guideSet with column specified by scoreName appended in mcols(guideSet).

Author(s)

Jean-Philippe Fortin

See Also

addOnTargetScores to add on-target scores.

Examples

gs <- findSpacers("CCAACATAGTGAAACCACGTCTCTATAAAGAATAAAAAATTAGCCGGGTTA")
gs <- addOnTargetScores(gs, methods=c("ruleset1", "crisprater"))
gs <- addCompositeScores(gs, methods=c("ruleset1", "crisprater"))

Add on-target composite score to a GuideSet object.

Description

Add on-target composite score to a GuideSet object.

Usage

addConservationScores(object, ...)

## S4 method for signature 'GuideSet'
addConservationScores(
  object,
  conservationFile,
  nucExtension = 9,
  fun = c("mean", "max"),
  scoreName = "score_conservation"
)

## S4 method for signature 'PairedGuideSet'
addConservationScores(
  object,
  conservationFile,
  nucExtension = 9,
  fun = c("mean", "max"),
  scoreName = "score_conservation"
)

## S4 method for signature 'NULL'
addConservationScores(object)

Arguments

Details

The function creates a conservation score for each gRNA by using the max, or average, conservation score in the genomic region where the cut occurs. A BigWig file storing conservation stores must be provided. Such files can be downloaded from the UCSC genome browser. See vignette for more information.

Value

guideSet with column specified by scoreName appended in mcols(guideSet).

Author(s)

Jean-Philippe Fortin

Add CRISPRa/CRISPRi on-target scores to a GuideSet object.

Description

Add CRISPRa/CRISPRi on-target scores to a GuideSet object. Only available for SpCas9, and for hg38 genome. Requires crisprScore package to be installed.

Usage

addCrispraiScores(object, ...)

## S4 method for signature 'GuideSet'
addCrispraiScores(
  object,
  gr,
  tssObject,
  geneCol = "gene_id",
  modality = c("CRISPRi", "CRISPRa"),
  chromatinFiles = NULL,
  fastaFile = NULL
)

## S4 method for signature 'PairedGuideSet'
addCrispraiScores(
  object,
  gr,
  tssObject,
  geneCol = "gene_id",
  modality = c("CRISPRi", "CRISPRa"),
  chromatinFiles = NULL,
  fastaFile = NULL
)

## S4 method for signature 'NULL'
addCrispraiScores(object)

Arguments

Value

guideSet with an added column for the CRISPRai score.

Author(s)

Jean-Philippe Fortin

See Also

addOnTargetScores to add other on-target scores.

Description

Add distance to TSS for a specificed TSS id.

Usage

addDistanceToTss(object, ...)

## S4 method for signature 'GuideSet'
addDistanceToTss(object, tss_id)

## S4 method for signature 'PairedGuideSet'
addDistanceToTss(object, tss_id)

Arguments

Value

A A GuideSet object or a PairedGuideSet object with an additional metadata column called distance_to_tss reporting the distance (in nucleotides) between the TSS position of the TSS specified by tss_id and the protospacer position. The pam_site coordinate is used as the representative position of protospacer sequences.

Note that a TSS annotation must be available in the object. A TSS annotation can be added using addTssAnnotation.

Author(s)

Jean-Philippe Fortin

See Also

addTssAnnotation to add TSS annotation.

Examples

data(guideSetExampleFullAnnotation)
tss_id <- "ENSG00000120645_P1"
gs <- guideSetExampleFullAnnotation
gs <- addDistanceToTss(gs, tss_id)

Description

To add edited alleles for a CRISPR base editing GuideSet.

Usage

addEditedAlleles(
  guideSet,
  baseEditor,
  editingWindow = NULL,
  nMaxAlleles = 100,
  addFunctionalConsequence = TRUE,
  addSummary = TRUE,
  txTable = NULL,
  verbose = TRUE
)

Arguments

Value

The original guideSet object with an additional metadata column (editedAlleles) storing the annotated edited alelles. The edited alleles are always reported from 5' to 3' direction on the strand corresponding to the gRNA strand.

Author(s)

Jean-Philippe Fortin

Examples

data(BE4max, package="crisprBase")
data(grListExample, package="crisprDesign")
library(BSgenome.Hsapiens.UCSC.hg38)
bsgenome <- BSgenome.Hsapiens.UCSC.hg38
gr <- queryTxObject(grListExample,
                    featureType="cds",
                    queryColumn="gene_symbol",
                    queryValue="IQSEC3")
gs <- findSpacers(gr[1],
                  crisprNuclease=BE4max,
                  bsgenome=bsgenome)
gs <- unique(gs)
gs <- gs[1:2] # For the sake of time

# Getting transcript info:
txid="ENST00000538872"
txTable <- getTxInfoDataFrame(tx_id=txid,
    txObject=grListExample,
    bsgenome=bsgenome)

#Adding alelles:
editingWindow <- c(-20,-8)
gs <- addEditedAlleles(gs,
                       baseEditor=BE4max,
                       txTable=txTable,
                       editingWindow=editingWindow)

Description

Add optimal editing site for base editing gRNAs.

Usage

addEditingSites(object, ...)

## S4 method for signature 'GuideSet'
addEditingSites(object, substitution)

## S4 method for signature 'PairedGuideSet'
addEditingSites(object, substitution)

## S4 method for signature 'NULL'
addEditingSites(object)

Arguments

Value

An updated object with a colum editing_site added to mcols(object).

Author(s)

Jean-Philippe Fortin

Add a gene-specific exon table to a GuideSet object.

Description

Add a gene-specific exon table to a GuideSet object.

Add a gene-specific exon table to a GuideSet object.

Usage

addExonTable(
  guideSet,
  gene_id,
  txObject,
  valueColumn = "percentCDS",
  useConsensusIsoform = FALSE
)

Arguments

Value

A GuideSet object with a "exonTable" DataFrame stored in mcols(guideSet). The entries in the DataFrame correspond to the values specified by valueColumn. Rows correspond to gRNAs in the GuideSet, columns correspond to all exons found in txObject for gene specified by gene_id.

Author(s)

Jean-Philippe Fortin

See Also

addGeneAnnotation to add gene annotation and addTxTable to add a transcript table.

Examples

if (interactive()){
    data(guideSetExample, package="crisprDesign")
    data(grListExample, package="crisprDesign")
    guideSet <- addGeneAnnotation(guideSetExample,
                                  txObject=grListExample)
    guideSet <- addExonTable(guideSet,
                             gene_id="ENSG00000120645",
                             txObject=grListExample)

    guideSet$exonTable
}

Add gene context annotation to a GuideSet object

Description

Add gene context annotation to spacer sequence stored in a GuideSet object

Usage

addGeneAnnotation(object, ...)

## S4 method for signature 'GuideSet'
addGeneAnnotation(
  object,
  txObject,
  anchor = c("cut_site", "pam_site", "editing_site"),
  ignore_introns = TRUE,
  ignore.strand = TRUE,
  addPfam = FALSE,
  mart_dataset = NULL
)

## S4 method for signature 'PairedGuideSet'
addGeneAnnotation(
  object,
  txObject,
  anchor = c("cut_site", "pam_site", "editing_site"),
  ignore_introns = TRUE,
  ignore.strand = TRUE,
  addPfam = FALSE,
  mart_dataset = NULL
)

Arguments

Details

For DNA-targeting nucleases, the different columns stored in mcols(guideSet)[["geneAnnotation"]] are:

• tx_id Transcript ID.

• gene_symbol Gene symbol.

• gene_id Gene ID.

• protein_id Protein ID.

• ID gRNA ID.

• pam_site gRNA PAM site coordinate.

• cut_site gRNA cut site coordinate.

• chr gRNA chromosome name.

• strand gRNA strand.

• cut_cds Is the gRNA cut site located within the coding sequence (CDS) of the targeted isoform?

• cut_fiveUTRs Is the gRNA cut site located within the 5'UTR of the targeted isoform?

• cut_threeUTRs Is the gRNA cut site located within the 3'UTR of the targeted isoform?

• cut_introns Is the gRNA cut site located within an intron of the targeted isoform?

• percentCDS Numeric value to indicate the relative position of the cut site with respect to the start of the CDS sequence when cut_cds is TRUE. The relative position is expressed as a percentage from the total length of the CDS.

• percentTx Numeric value to indicate the relative position of the cut site with respect to the start of the mRNA sequence (therefore including 5' UTR). The relative position is expressed as a percentage from the total length of the mRNA sequence.

• aminoAcidIndex If cut_cds is TRUE, integer value indicating the amino acid index with respect to the start of the protein.

• downstreamATG Number of potential reinitiation sites (ATG codons) downstream of the gRNA cut site, within 85 amino acids.

• nIsoforms Numeric value indicating the number of isoforms targeted by the gRNA.

• totalIsoforms Numeric value indicating the total number of isoforms existing for the gene targeted by the gRNA and specified in gene_id.

• percentIsoforms Numeric value indicating the percentage of isoforms for the gene specified in gene_id targeted by the gRNA. Equivalent to nIsoforms/totalIsoforms*100.

• isCommonExon Logical value to indicate whether or not the gRNA is targeing an exon common to all isoforms.

• nCodingIsoforms Numeric value indicating the number of coding isoforms targeted by the gRNA. 5' UTRs and 3' UTRs are excluded.

• totalCodingIsoforms Numeric value indicating the total number of coding isoforms existing for the gene targeted by the gRNA and specified in gene_id.

• percentCodingIsoforms Numeric value indicating the percentage of coding isoforms for the gene specified in gene_id targeted by the gRNA. Equivalent to nCodingIsoforms/totalCodingIsoforms*100. 5' UTRs and 3' UTRs are excluded.

• isCommonCodingExon Logical value to indicate whether or not the gRNA is targeing an exon common to all coding isoforms.

Value

A GuideSet object with a "geneAnnotation" list column stored in mcols(guideSet). See details section for a description of the different gene annotation columns.

Author(s)

Jean-Philippe Fortin, Luke Hoberecht

See Also

addTssAnnotation to add TSS annotation, and geneAnnotation to retrieve an existing gene annotation.

Examples

data(guideSetExample, package="crisprDesign")
data(grListExample, package="crisprDesign")
guideSet <- addGeneAnnotation(guideSetExample[1:6],
                              txObject=grListExample)

# To access a gene annotation already added:
ann <- geneAnnotation(guideSet)

Add isoform-specific annotation to a GuideSet object

Description

Add isoform-specific annotation to a GuideSet object.

Usage

addIsoformAnnotation(object, ...)

## S4 method for signature 'NULL'
addDistanceToTss(object)

## S4 method for signature 'GuideSet'
addIsoformAnnotation(object, tx_id)

## S4 method for signature 'PairedGuideSet'
addIsoformAnnotation(object, tx_id)

## S4 method for signature 'NULL'
addIsoformAnnotation(object)

Arguments

Value

A A GuideSet object or a PairedGuideSet object.with the following added columns: percentCDS, percentCodingIsoforms, and isCommonCodingExon. The column values are specific to the transcript specified by tx_id. The percentCDS column indicates at what percentage of the coding sequence the gRNA is cutting. The column percentCodingIsoforms indicates the percentage of coding isoforms that are targeted by the gRNA. The column isCommonCodingExon indicates whether or not the exon targetd by the gRNA is common to all isoforms for the gene.

Author(s)

Jean-Philippe Fortin

Examples

data(guideSetExampleFullAnnotation)
tx_id <- "ENST00000538872"
gs <- guideSetExampleFullAnnotation
gs <- addIsoformAnnotation(gs, tx_id)

Add non-targeting control (NTC) sequences to GuideSet

Description

Add non-targeting control (NTC) sequences to a GuideSet object.

Usage

addNtcs(object, ...)

## S4 method for signature 'GuideSet'
addNtcs(object, ntcs)

## S4 method for signature 'PairedGuideSet'
addNtcs(object, ntcs)

## S4 method for signature 'NULL'
addNtcs(object, ...)

Arguments

Details

NTC sequences are appended as spacers to the GuideSet object. Each NTC sequence is assigned to its own "chromosome" in the ntc genome, as reflected in the Seqinfo of the resulting GuideSet object. As placeholder values, NTC ranges are set to 0 and strands set to *.

All annotation for NTC spacers appended to object are set to NA or empty list elements. To annotate NTC spacers, you must call the appropriate function after adding NTCs to the GuideSet object.

Value

The original object with appended ntcs spacers. Pre-existing annotation in object will be set to NA or empty list elements for appended NTC spacers.

Examples

set.seed(1000)
data(guideSetExample, package="crisprDesign")
ntcs <- vapply(1:4, function(x){
    seq <- sample(c("A", "C", "G", "T"), 20, replace=TRUE)
    paste0(seq, collapse="")
}, FUN.VALUE=character(1))
names(ntcs) <- paste0("ntc_", 1:4)
gs <- addNtcs(guideSetExample, ntcs)
gs

Add CFD and MIT scores to a GuideSet object.

Description

Add CFD and MIT off-target scores to a GuideSet object. Both the CFD and MIT methods are available for the SpCas9 nuclease. The CFD method is also available for the CasRx nuclease. Other nucleases are currently not supported.

Usage

addOffTargetScores(object, ...)

## S4 method for signature 'GuideSet'
addOffTargetScores(object, max_mm = 2, includeDistance = TRUE, offset = 0)

## S4 method for signature 'PairedGuideSet'
addOffTargetScores(object, max_mm = 2, includeDistance = TRUE, offset = 0)

## S4 method for signature 'NULL'
addOffTargetScores(object)

Arguments

Details

See the crisprScore package for a description of the different off-target scoring methods.

Value

A GuideSet or a PairedGuideSet object with added scores. The alignments annotation returned by alignments(object) will have additional column storing off-target scores. Those scores representing the off-target score for each gRNA and off-target pair. For SpCas9, a column containing an aggregated specificity off-target score for each scoring method is added to the metadata columns obtained by mcols(object).

Author(s)

Jean-Philippe Fortin, Luke Hoberecht

See Also

link{addOnTargetScores} to add on-target scores.

Examples

data(guideSetExampleWithAlignments, package="crisprDesign")
gs <- guideSetExampleWithAlignments
gs <- addOffTargetScores(gs)

Add on-target scores to a GuideSet object.

Description

Add on-target scores to a GuideSet object for all methods available in the crisprScore package for a given CRISPR nuclease. Requires crisprScore package to be installed.

Usage

addOnTargetScores(object, ...)

## S4 method for signature 'GuideSet'
addOnTargetScores(
  object,
  enzyme = c("WT", "ESP", "HF"),
  promoter = c("U6", "T7"),
  tracrRNA = c("Hsu2013", "Chen2013"),
  directRepeat = "aacccctaccaactggtcggggtttgaaac",
  binaries = NULL,
  methods = c("azimuth", "ruleset1", "ruleset3", "lindel", "deepcpf1", "deephf",
    "deepspcas9", "enpamgb", "casrxrf", "crisprater", "crisprscan")
)

## S4 method for signature 'PairedGuideSet'
addOnTargetScores(
  object,
  enzyme = c("WT", "ESP", "HF"),
  promoter = c("U6", "T7"),
  tracrRNA = c("Hsu2013", "Chen2013"),
  directRepeat = "aacccctaccaactggtcggggtttgaaac",
  binaries = NULL,
  methods = c("azimuth", "ruleset1", "ruleset3", "lindel", "deepcpf1", "deephf",
    "deepspcas9", "enpamgb", "crisprater", "crisprscan", "casrxrf")
)

## S4 method for signature 'NULL'
addOnTargetScores(object)

Arguments

Details

See crisprScore package for a description of each score.

Value

guideSet with columns of on-target scores appended in mcols(guideSet).

Author(s)

Jean-Philippe Fortin, Luke Hoberecht

See Also

addOffTargetScores to add off-target scores.

Examples

if (interactive()){
    gs <- findSpacers("CCAACATAGTGAAACCACGTCTCTATAAAGAATAAAAAATTAGCCGGGTTA")
    gs <- addOnTargetScores(gs)
}

Description

Add optical pooled screening (OPS) barcodes.

Usage

addOpsBarcodes(guideSet, n_cycles = 9, rt_direction = c("5prime", "3prime"))

Arguments

Value

The original guideSet object with an additional column opsBarcode stored in mcols(guideSet). The column is a DNAStringSet storing the OPS barcode.

Author(s)

Jean-Philippe Fortin

Examples

data(guideSetExample, package="crisprDesign")
guideSetExample <- addOpsBarcodes(guideSetExample)

Add PAM scores to a GuideSet object.

Description

Add PAM scores to a GuideSet object based on the CrisprNuclease object stored in the GuideSet object. PAM scores indicate nuclease affinity (recognition) to different PAM sequences. A score of 1 indicates a PAM sequence that is fully recognized by the nuclease.

Usage

addPamScores(object, ...)

## S4 method for signature 'GuideSet'
addPamScores(object)

## S4 method for signature 'PairedGuideSet'
addPamScores(object)

## S4 method for signature 'NULL'
addPamScores(object)

Arguments

Value

guideSet with an appended score_pam column in mcols(guideSet).

Author(s)

Jean-Philippe Fortin

Examples

# Using character vector as input:
data(enAsCas12a, package="crisprBase")
gs <- findSpacers("CCAACATAGTGAAACCACGTCTCTATAAAGAATACAAAAAATTAGCCGGGTGTTA",
                  canonical=FALSE,
                  crisprNuclease=enAsCas12a)
gs <- addPamScores(gs)

Add Pfam domains annotation to GuideSet object

Description

Add Pfam domains annotation to GuideSet object.

Usage

addPfamDomains(object, ...)

## S4 method for signature 'GuideSet'
addPfamDomains(object, pfamTable)

## S4 method for signature 'PairedGuideSet'
addPfamDomains(object, pfamTable)

## S4 method for signature 'NULL'
addPfamDomains(object)

Arguments

Details

In order to call this function, the object must contain a gene annotation by calling first addGeneAnnotation.

Value

An updated object with a colum pfam added to geneAnnotation(object).

Author(s)

Jean-Philippe Fortin

See Also

See preparePfamTable to prepare the Pfam domain DataFrame object, and see addGeneAnnotation to add a gene annotation to the object.

Description

Add a logical flag for gRNAs leading to potential reinitiation.

Usage

addReinitiationFlag(
  guideSet,
  tx_id,
  grnaLocationUpperLimit = 150,
  cdsCutoff = 0.2
)

Arguments

Value

The original object with an appended column reinitiationFlag with logical values. A TRUE value indicates a gRNA in proximity of a potential reinitiation site, and therefore should be avoided.

Author(s)

Jean-Philippe Fortin

Annotate a GuideSet object with repeat elements

Description

Add an annotation column to a GuideSet object that identifies spacer sequences overlapping repeat elements.

Usage

addRepeats(object, ...)

## S4 method for signature 'GuideSet'
addRepeats(object, gr.repeats = NULL, ignore.strand = TRUE)

## S4 method for signature 'PairedGuideSet'
addRepeats(object, gr.repeats = NULL, ignore.strand = TRUE)

## S4 method for signature 'NULL'
addRepeats(object)

Arguments

Value

guideSet with an inRepeats column appended in mcols(guideSet) that signifies whether the spacer sequence overlaps a repeat element.

Author(s)

Jean-Philippe Fortin, Luke Hoberecht

See Also

link{removeRepeats}.

Examples

data(guideSetExample, package="crisprDesign")
data(grRepeatsExample, package="crisprDesign")
guideSet <- addRepeats(guideSetExample,
                       gr.repeats=grRepeatsExample)

Description

Add restriction site enzymes annotation.

Usage

addRestrictionEnzymes(object, ...)

## S4 method for signature 'GuideSet'
addRestrictionEnzymes(
  object,
  enzymeNames = NULL,
  patterns = NULL,
  includeDefault = TRUE,
  flanking5 = "ACCG",
  flanking3 = "GTTT"
)

## S4 method for signature 'PairedGuideSet'
addRestrictionEnzymes(
  object,
  enzymeNames = NULL,
  patterns = NULL,
  includeDefault = TRUE,
  flanking5 = "ACCG",
  flanking3 = "GTTT"
)

## S4 method for signature 'NULL'
addRestrictionEnzymes(object)

Arguments

Details

Restriction enzymes are often used for cloning purpose during the oligonucleotide synthesis of gRNA lentiviral constructs. Consequently, it is often necessary to avoid restriction sites of the used restriction enzymes in and around the spacer sequences. addRestrictionEnzymes allows for flagging problematic spacer sequences by searching for restriction sites in the [flanking5][spacer][flanking3] sequence.

The following enzymes are included when includeDefault=TRUE: EcoRI, KpnI, BsmBI, BsaI, BbsI, PacI, and MluI.

Custom recognition sequences in patterns may use the IUPAC nucleotide code, excluding symbols indicating gaps. Avoid providing enzyme names in patterns that are already included by default (if includeDefault=TRUE) or given by enzymeNames. Patterns with duplicated enzyme names will be silently ignored, even if the recognition sequence differs. See example.

Value

Adds a DataFrame indicating whether cutting sites for the specified enzymes are found in the gRNA cassette (flanking sequences + spacer sequences).

Author(s)

Jean-Philippe Fortin, Luke Hoberecht

See Also

enzymeAnnotation to retrieve existing enzyme annotation from a GuideSet object.

Examples

data(SpCas9, package="crisprBase")
seq <- c("ATTTCCGGAGGCGAATTCGGCGGGAGGAGGAAGACCGG")
guideSet <- findSpacers(seq, crisprNuclease=SpCas9)

# Using default enzymes:
guideSet <- addRestrictionEnzymes(guideSet)

# Using custom enzymes:
guideSet <- addRestrictionEnzymes(guideSet,
                                  patterns=c(enz1="GGTCCAA",
                                             enz2="GGTCG"))

# Avoid duplicate enzyme names
guideSet <- addRestrictionEnzymes(guideSet,
                                  patterns=c(EcoRI="GANNTC")) # ignored

Add spacer sequence feature annotation columns to a GuideSet object

Description

Add spacer sequence feature annotation columns, such as GC content, homopolymers, and hairpin predictions, to a GuideSet object.

Usage

addSequenceFeatures(object, ...)

## S4 method for signature 'GuideSet'
addSequenceFeatures(
  object,
  addHairpin = FALSE,
  backbone = "AGGCTAGTCCGT",
  tp53 = TRUE,
  ...
)

## S4 method for signature 'PairedGuideSet'
addSequenceFeatures(
  object,
  addHairpin = FALSE,
  backbone = "AGGCTAGTCCGT",
  tp53 = TRUE,
  ...
)

## S4 method for signature 'NULL'
addSequenceFeatures(object, ...)

Arguments

Details

The addHairpin argument set to TRUE will indicates which spacers are predicted to form internal hairpins. Such hairpins can happen when there is a palindromic sequence within the spacer having arms of >=4nt and >=50% GC content, and are separated by a loop of >=4nt. Backbone hairpin formation is predicted when the spacer and backbone share a complementary sequence of >=5nt and >=50% GC content. The argument backbone allows users to specify the vector backbone sequence directly downstream of the spacer sequence.

The tp53 argument set to TRUE will add sequence-based features that have been reported to make SpCas9 gRNAs toxic for cells with wildtype TP53 (see https://doi.org/10.1038/s41467-022-32285-1). Currently, only one feature is reported and consists of the extended NNGG PAM sequence (1 nucleotide + PAM sequence) for SpCas9. gRNAs with extended CNGG PAM sequences, and in particular CCGG, should be avoided.

Value

The original object with the following columns appended to mcols(object):

• percentGC — percent GC content

• polyA, polyC, polyG, polyT — presence of homopolymers of 4nt or longer

• selfHairpin — prediction of hairpin formation within the spacer sequence via self-complementarity if addHairpin is TRUE.

• backboneHairpin — prediction of hairpin formation with the backbone sequence via complementarity if addHairpin is TRUE.

• NNGG – extended PAM sequence for SpCas9 if tp53 is TRUE corresponding to one nucleotide upstream of the PAM sequence followed by the PAM sequence itself.

Examples

custom_seq <- c("ATTTCCGGAGGCGGAGAGGCGGGAGGAGCG")
data(SpCas9, package="crisprBase")
guideSet <- findSpacers(custom_seq, crisprNuclease=SpCas9)
guideSet <- addSequenceFeatures(guideSet)

Add SNP annotation to a GuideSet object

Description

Add SNP annotation to a GuideSet object. Only available for sgRNAs designed for human genome.

Usage

addSNPAnnotation(object, ...)

## S4 method for signature 'NULL'
addGeneAnnotation(object)

## S4 method for signature 'GuideSet'
addSNPAnnotation(object, vcf, maf = 0.01)

## S4 method for signature 'PairedGuideSet'
addSNPAnnotation(object, vcf, maf = 0.01)

## S4 method for signature 'NULL'
addSNPAnnotation(object)

Arguments

Details

The different columns stored in mcols(guideSet)[["snps"]] are:

• ID sgRNA ID.

• rs Reference SNP cluster ID (e.g. rs17852242)

• rs_site Genomic coordinate of the SNP.

• rs_site_rel Position of SNP relative to the PAM site.

• allele_ref DNAString specifying the SNP reference allele.

• allele_minor DNAString specifying the SNP minor allele.

• MAF_1000G Minor allele frequency in the 1000 Genomes project.

• MAF_TOPMED Minor allele frequency in the TOPMed project.

• type Type of SNP ("ins": insertion, "del": deletion).

• length Length of SNP in nucleotides.

Value

guideSet appended with hasSNP column and snps list-column, both stored in mcols{guideSet}.

See Also

link{snpAnnotation} to retrieve an existing SNP annotation stored in a GuideSet object. See details section for a description of the different columns.

Examples

vcf <- system.file("extdata",
                   file="common_snps_dbsnp151_example.vcf.gz",
                   package="crisprDesign")
data(guideSetExample, package="crisprDesign")
guideSet <- addSNPAnnotation(guideSetExample, vcf=vcf)

Description

Functions for finding and characterizing on- and off-targets of spacer sequences.

Usage

addSpacerAlignments(object, ...)

addSpacerAlignmentsIterative(object, ...)

## S4 method for signature 'GuideSet'
addSpacerAlignmentsIterative(
  object,
  aligner = c("bowtie", "bwa", "biostrings"),
  colname = "alignments",
  addSummary = TRUE,
  txObject = NULL,
  tssObject = NULL,
  custom_seq = NULL,
  aligner_index = NULL,
  bsgenome = NULL,
  n_mismatches = 0,
  all_alignments = FALSE,
  canonical = TRUE,
  standard_chr_only = FALSE,
  both_strands = TRUE,
  anchor = c("cut_site", "pam_site"),
  annotationType = c("gene_symbol", "gene_id"),
  tss_window = NULL,
  alignmentThresholds = c(n0 = 5, n1 = 100, n2 = 100, n3 = 1000, n4 = 1000)
)

## S4 method for signature 'PairedGuideSet'
addSpacerAlignmentsIterative(
  object,
  aligner = c("bowtie", "bwa", "biostrings"),
  colname = "alignments",
  addSummary = TRUE,
  txObject = NULL,
  tssObject = NULL,
  custom_seq = NULL,
  aligner_index = NULL,
  bsgenome = NULL,
  n_mismatches = 0,
  all_alignments = FALSE,
  canonical = TRUE,
  standard_chr_only = FALSE,
  both_strands = TRUE,
  anchor = c("cut_site", "pam_site"),
  annotationType = c("gene_symbol", "gene_id"),
  tss_window = NULL,
  alignmentThresholds = c(n0 = 5, n1 = 100, n2 = 100, n3 = 1000, n4 = 1000)
)

## S4 method for signature 'NULL'
addSpacerAlignmentsIterative(object)

## S4 method for signature 'GuideSet'
addSpacerAlignments(
  object,
  aligner = c("bowtie", "bwa", "biostrings"),
  colname = "alignments",
  addSummary = TRUE,
  txObject = NULL,
  tssObject = NULL,
  custom_seq = NULL,
  aligner_index = NULL,
  bsgenome = NULL,
  n_mismatches = 0,
  n_max_alignments = 1000,
  all_alignments = TRUE,
  canonical = TRUE,
  standard_chr_only = FALSE,
  both_strands = TRUE,
  anchor = c("cut_site", "pam_site"),
  annotationType = c("gene_symbol", "gene_id"),
  tss_window = NULL
)

## S4 method for signature 'PairedGuideSet'
addSpacerAlignments(
  object,
  aligner = c("bowtie", "bwa", "biostrings"),
  colname = "alignments",
  addSummary = TRUE,
  txObject = NULL,
  tssObject = NULL,
  custom_seq = NULL,
  aligner_index = NULL,
  bsgenome = NULL,
  n_mismatches = 0,
  n_max_alignments = 1000,
  all_alignments = FALSE,
  canonical = TRUE,
  standard_chr_only = FALSE,
  both_strands = TRUE,
  anchor = c("cut_site", "pam_site"),
  annotationType = c("gene_symbol", "gene_id"),
  tss_window = NULL
)

## S4 method for signature 'NULL'
addSpacerAlignments(object)

getSpacerAlignments(
  spacers,
  aligner = c("bowtie", "bwa", "biostrings"),
  custom_seq = NULL,
  aligner_index = NULL,
  bsgenome = NULL,
  n_mismatches = 0,
  n_max_alignments = 1000,
  all_alignments = TRUE,
  crisprNuclease = NULL,
  canonical = TRUE,
  standard_chr_only = FALSE,
  both_strands = TRUE
)

Arguments

Details

The columns stored in mcols(guideSet)[["alignments"]] are:

• spacer Spacer sequence of the query gRNA.

• protospacer Protospacer sequence in the target DNA.

• pam PAM sequence.

• pam_site PAM site of the found protospacer.

• n_mismatches Integer value specifying the number of nucleotide mismatches between the gRNA spacer sequence and the protospacer sequence found in the genome or custom sequence.

• canonical Whether the PAM sequence of the found protospacer sequence is canonical.

• cute_site Cut site of the found protospacer.

The following columns are also stored when a txObject is provided:

• cds Character vector specifying gene names of CDS overlapping the found protospacer sequence.

• fiveUTRs Character vector specifying gene names of 5'UTRs overlapping the found protospacer sequence.

• threeUTRs Character vector specifying gene names of 3'UTRs overlapping the found protospacer sequence.

• exons Character vector specifying gene names of exons overlapping the found protospacer sequence.

• introns Character vector specifying gene names of introns overlapping the found protospacer sequence.

• intergenic Character vector specifying the nearest gene when the found protospacer sequence is not located in a gene.

• intergenic_distance Distance in base pairs from the nearest gene when the found protospacer sequence is not located in a gene.

The following columns are also stored when a tssObject is provided:

• promoters Character vector specifying gene names of promoters, as defined by tss_window relative to the gene TSS, overlapping the found protospacer sequence.

Value

getSpacerAlignments returns a GRanges object storing spacer alignment data, including genomic coordinates, spacer and PAM sequences, and position of mismatches relative to pam_site.

addSpacerAlignments is similar to getSpacerAlignments, with the addition of adding the alignment data to a list-column in mcols(guideSet) specified by colname.

addSpacerAlignmentsIterative is similar to addSpacerAlignments, except that it avoids finding alignments for spacer sequences that have a large number of on-targets and/or off-targets to speed up the off-target search. The parameters n0_max, n1_max and n2_max specify the maximum number of on-targets (n0) and off-targets (n1 for 1-mismatch off-targets, and n2 for 2-mismatch off-targets) tolerated before the algorithm stops finding additional off-targets for spacer sequences that exceed those quotas.

Author(s)

Jean-Philippe Fortin, Luke Hoberecht

Examples

if (interactive()){
# Creating a bowtie index:
library(Rbowtie)
library(BSgenome.Hsapiens.UCSC.hg38)
fasta <- system.file(package="crisprDesign", "fasta/chr12.fa")
outdir <- tempdir()
Rbowtie::bowtie_build(fasta,
                      outdir=outdir,
                      force=TRUE,
                      prefix="chr12")
bowtieIndex <- file.path(outdir, "chr12")

# Adding spacer alignments with bowtie:
data(guideSetExample, package="crisprDesign")
data(grListExample, package="crisprDesign")
guideSet <- addSpacerAlignments(guideSetExample,
                                aligner="bowtie",
                                aligner_index=bowtieIndex,
                                bsgenome=BSgenome.Hsapiens.UCSC.hg38,
                                n_mismatches=2,
                                txObject=grListExample)
}

Add TSS context annotation to a GuideSet object

Description

Add transcription start site (TSS) context annotation to spacer sequences stored in a GuideSet object.

Usage

addTssAnnotation(object, ...)

## S4 method for signature 'GuideSet'
addTssAnnotation(
  object,
  tssObject,
  anchor = c("cut_site", "pam_site"),
  tss_window = NULL,
  ignore.strand = TRUE
)

## S4 method for signature 'PairedGuideSet'
addTssAnnotation(
  object,
  tssObject,
  anchor = c("cut_site", "pam_site"),
  tss_window = NULL,
  ignore.strand = TRUE
)

## S4 method for signature 'NULL'
addTssAnnotation(object)

Arguments

Details

mcols(guideSet)[["tssAnnotation"]] includes all columns from mcols(tssObject) in addition to the columns described below.

• chr — gRNA chromosome name.

• anchor_site — Genomic coordinate used to search for overlapping TSS regions.

• strand — Strand the gRNA is located on.

• tss_id — The ID for the TSS in tssObject, if present.

• tss_strand — Strand the TSS is located on, as provided in tssObject

• tss_pos — Genomic coordinate of the TSS, as provided in tssObject.

• dist_to_tss — Distance (in nucleotides) between the gRNA anchor_site and tss_pos. Negative values indicate gRNA targets upstream of the TSS.

Value

A GuideSet object with a tssAnnotation list column stored in mcols(guideSet). See details section for descriptions of TSS annotation columns.

Author(s)

Jean-Philippe Fortin, Luke Hoberecht

See Also

addGeneAnnotation to add gene annotation, and tssAnnotation to retrieve an existing TSS annotation.

Examples

data(guideSetExample, package="crisprDesign")
data(tssObjectExample, package="crisprDesign")
guideSet <- addTssAnnotation(guideSetExample,
                             tssObject=tssObjectExample)

# To access TSS annotation:
ann <- tssAnnotation(guideSet)

Add a gene-specific transcript table to a GuideSet object.

Description

Add a gene-specific transcript table to a GuideSet object.

Add a gene-specific transcript table to a GuideSet object.

Usage

addTxTable(guideSet, gene_id, txObject, valueColumn = "percentCDS")

Arguments

Value

A GuideSet object with a "txTable" DataFrame stored in mcols(guideSet). The entries in the DataFrame correspond to the values specified by valueColumn. Rows correspond to gRNAs in the GuideSet, columns correspond to all transcripts found in txObject for gene specified by gene_id.

Author(s)

Jean-Philippe Fortin

See Also

addGeneAnnotation to add gene annotation.

Examples

if (interactive()){
    data(guideSetExample, package="crisprDesign")
    data(grListExample, package="crisprDesign")
    guideSet <- addGeneAnnotation(guideSetExample,
                                  txObject=grListExample)
    guideSet <- addTxTable(guideSet,
                           gene_id="ENSG00000120645",
                           txObject=grListExample)

    guideSet$txTable
}

Description

These functions serve to "fill-in-the-blank" for spacers lacking information.

Usage

getPAMSequence(chr, pam_site, strand, crisprNuclease = NULL, bsgenome = NULL)

getSpacerSequence(
  chr,
  pam_site,
  strand,
  crisprNuclease = NULL,
  bsgenome = NULL,
  spacerLen = NULL
)

getPAMSiteFromStartAndEnd(
  start = NULL,
  end = NULL,
  strand,
  crisprNuclease = NULL,
  spacerLen = NULL
)

Arguments

Details

Functions that return coordinates (getPAMSite, getCutSite, getSpacerRanges) do not check whether coordinates exceed chromosomal lengths.

The start and end coordinates of a genomic range is strand-independent, and always obeys start <= end.

Value

A numeric or character vector, depending on the function.

getPAMSequence returns a character vector of PAM sequences.

getSpacerSequence returns a character vector of spacer sequences.

Examples

if (requireNamespace("BSgenome.Hsapiens.UCSC.hg38")){
library(BSgenome.Hsapiens.UCSC.hg38)
bsgenome <- BSgenome.Hsapiens.UCSC.hg38
dat <- data.frame(chr='chr4', start=1642343, strand='+')
dat$pam_site <- getPAMSiteFromStartAndEnd(start=dat$start,
                                          strand=dat$strand)
dat$pam <- getPAMSequence(chr=dat$chr,
                          pam_site=dat$pam_site,
                          strand=dat$strand,
                          bsgenome=bsgenome)
dat$spacer <- getSpacerSequence(chr=dat$chr,
                                pam_site=dat$pam_site,
                                strand=dat$strand,
                                bsgenome=bsgenome)
}

Description

Convert a GuideSet object into a GRanges object containing the minimum and maximum coordinates for all targeting gRNAs.

Usage

convertToMinMaxGRanges(guideSet, anchor = c("cut_site", "pam_site"))

Arguments

Value

A GRanges object with start and end coordinates corresponding to the minimum and maximum coordinates of the GuideSet object sites defined by anchor.

Author(s)

Jean-Philippe Fortin, Luke Hoberecht

Examples

data(guideSetExample, package="crisprDesign")
gr <- convertToMinMaxGRanges(guideSetExample)

Description

Convert PAM site coordinates to protospacer start and end coordinates.

Usage

convertToProtospacerGRanges(guideSet)

Arguments

Value

A GuideSet object with start and end coordinates corresponding to the start and end coordinates of the protospacer sequences.

Author(s)

Jean-Philippe Fortin

Examples

data(guideSetExample, package="crisprDesign")
gr <- convertToProtospacerGRanges(guideSetExample)

Description

An S4 class to store CRISPR gRNA sequences with modular annotations.

Usage

crisprNuclease(object, ...)

targetOrigin(object, ...)

customSequences(object, ...)

bsgenome(object, ...)

spacers(object, ...)

protospacers(object, ...)

pamSites(object, ...)

snps(object, ...)

alignments(object, ...)

onTargets(object, ...)

offTargets(object, ...)

geneAnnotation(object, ...)

tssAnnotation(object, ...)

enzymeAnnotation(object, ...)

editedAlleles(object, ...)

txTable(object, ...)

exonTable(object, ...)

tssAnnotation(object) <- value

geneAnnotation(object) <- value

enzymeAnnotation(object) <- value

snps(object) <- value

alignments(object) <- value

addCutSites(object, ...)

GuideSet(
  ids = NA_character_,
  protospacers = NA_character_,
  pams = NULL,
  seqnames = NA_character_,
  pam_site = 0L,
  strand = "*",
  CrisprNuclease = NULL,
  targetOrigin = c("bsgenome", "customSequences"),
  bsgenome = NULL,
  customSequences = NULL,
  ...,
  seqinfo = NULL,
  seqlengths = NULL
)

## S4 method for signature 'GuideSet'
targetOrigin(object)

## S4 method for signature 'GuideSet'
customSequences(object)

## S4 method for signature 'GuideSet'
bsgenome(object)

## S4 method for signature 'GuideSet'
crisprNuclease(object)

## S4 method for signature 'GuideSet'
spacers(object, as.character = FALSE, returnAsRna = FALSE)

## S4 method for signature 'GuideSet'
pams(object, as.character = FALSE, returnAsRna = FALSE)

## S4 method for signature 'GuideSet'
pamSites(object)

## S4 method for signature 'GuideSet'
cutSites(object)

## S4 method for signature 'GuideSet'
addCutSites(object)

## S4 method for signature 'GuideSet'
protospacers(
  object,
  as.character = FALSE,
  include.pam = FALSE,
  returnAsRna = FALSE
)

## S4 method for signature 'GuideSet'
spacerLength(object)

## S4 method for signature 'GuideSet'
prototypeSequence(object)

## S4 method for signature 'GuideSet'
pamLength(object)

## S4 method for signature 'GuideSet'
pamSide(object)

## S4 method for signature 'GuideSet'
snps(object, unlist = TRUE, use.names = TRUE)

## S4 method for signature 'GuideSet'
alignments(object, columnName = "alignments", unlist = TRUE, use.names = TRUE)

## S4 method for signature 'GuideSet'
onTargets(object, columnName = "alignments", unlist = TRUE, use.names = TRUE)

## S4 method for signature 'GuideSet'
offTargets(
  object,
  columnName = "alignments",
  max_mismatches = Inf,
  unlist = TRUE,
  use.names = TRUE
)

## S4 replacement method for signature 'GuideSet'
alignments(object) <- value

## S4 replacement method for signature 'GuideSet'
geneAnnotation(object) <- value

## S4 replacement method for signature 'GuideSet'
tssAnnotation(object) <- value

## S4 replacement method for signature 'GuideSet'
enzymeAnnotation(object) <- value

## S4 replacement method for signature 'GuideSet'
snps(object) <- value

## S4 method for signature 'GuideSet'
geneAnnotation(
  object,
  unlist = TRUE,
  gene_id = NULL,
  tx_id = NULL,
  gene_symbol = NULL,
  use.names = TRUE
)

## S4 method for signature 'GuideSet'
editedAlleles(object, unlist = TRUE, use.names = TRUE)

## S4 method for signature 'GuideSet'
tssAnnotation(
  object,
  unlist = TRUE,
  gene_id = NULL,
  gene_symbol = NULL,
  use.names = TRUE
)

## S4 method for signature 'GuideSet'
enzymeAnnotation(object, unlist = TRUE, use.names = TRUE)

## S4 method for signature 'GuideSet'
txTable(object, unlist = TRUE, use.names = TRUE)

## S4 method for signature 'GuideSet'
exonTable(object, unlist = TRUE, use.names = TRUE)

Arguments

Value

A GuideSet object.

Functions

• GuideSet(): Create a GuideSet object

Constructors

Use the constructor link{GuideSet} to create a GuideSet object.

Accessors

crisprNuclease:

To get CrisprNuclease object used to design gRNAs.

spacers:

To get spacer sequences.

protospacers:

To get protospacer sequences.

spacerLength:

To get spacer length.

pams:

To get PAM sequences.

pamSites:

To get PAM site coordinates.

pamLength:

To get PAM length.

pamSide:

To return the side of the PAM sequence with respect to the protospacer sequence.

prototypeSequence:

To get a prototype protospacer sequence.

cutSites:

To get cut sites.

alignments:

To get genomic alignments annotation.

onTargets:

To get on-target alignments annotation

offTargets:

To get off-target alignments annotation

snps:

Tp get SNP annotation.

geneAnnotation:

To get gene annotation.

tssAnnotation:

To get TSS annotation.

enzymeAnnotation:

To get restriction enzymes annotation.

editedAlleles:

To get edited alleles annotation.

Examples

protospacers <- c("AGGTCGTGTGTGGGGGGGGG",
                  "AGGTCGTGTGTGGGGGGGGG")
pams <- c("AGG", "CGG")
pam_site=c(10,11)
seqnames="chr7"
data(SpCas9, package="crisprBase")
CrisprNuclease <- SpCas9
strand=c("+", "-")
ids <- paste0("grna_", seq_along(protospacers))
gr <- GuideSet(ids=ids,
               protospacers=protospacers,
               pams=pams,
               seqnames=seqnames,
               CrisprNuclease=CrisprNuclease,
               pam_site=pam_site,
               strand=strand,
               targetOrigin="customSequences",
               customSequences=protospacers)

Description

One-step gRNA design and annotation function to faciliate the design and generation of genome-wide gRNA databases for a combination of parameters such as nuclease, organism, and CRISPR modality.

Usage

designCompleteAnnotation(
  queryValue = NULL,
  queryColumn = "gene_id",
  featureType = "cds",
  modality = c("CRISPRko", "CRISPRa", "CRISPRi", "CRISPRkd"),
  bsgenome = NULL,
  bowtie_index = NULL,
  vcf = NULL,
  crisprNuclease = NULL,
  tssObject = NULL,
  txObject = NULL,
  grRepeats = NULL,
  scoring_methods = NULL,
  tss_window = NULL,
  n_mismatches = 3,
  max_mm = 2,
  canonical_ontarget = TRUE,
  canonical_offtarget = FALSE,
  all_alignments = TRUE,
  fastaFile = NULL,
  chromatinFiles = NULL,
  geneCol = "gene_symbol",
  conservationFile = NULL,
  nucExtension = 9,
  binaries = NULL,
  canonicalIsoforms = NULL,
  pfamTable = NULL,
  verbose = TRUE
)

Arguments

Value

A GuideSet object.

Author(s)

Jean-Philippe Fortin

Description

Design gRNA library for optical pooled screening

Usage

designOpsLibrary(
  df,
  n_guides = 4,
  gene_field = "gene",
  min_dist_edit = 2,
  dist_method = c("hamming", "levenshtein"),
  splitByChunks = FALSE
)

Arguments

Value

A subset of the df containing the gRNAs selected for the OPS library.

Author(s)

Jean-Philippe Fortin

Examples

data(guideSetExample, package="crisprDesign")
guideSet <- unique(guideSetExample)
guideSet <- addOpsBarcodes(guideSet)
guideSet <- guideSet[1:200]

df <- data.frame(ID=names(guideSet),
                 spacer=spacers(guideSet, as.character=TRUE),
                 opsBarcode=as.character(guideSet$opsBarcode))

# Creating mock gene:
df$gene <- rep(paste0("gene",1:10),each=20)
df$rank <- rep(1:20,10)
opsLib <- designOpsLibrary(df)

Description

Returns all possible, valid gRNA sequences for a given CRISPR nuclease from either a GRanges object or a set of sequence(s) contained in either a DNAStringSet, DNAString or character vector of genomic sequences.

Usage

findSpacerPairs(
  x1,
  x2,
  sortWithinPair = TRUE,
  pamOrientation = c("all", "out", "in"),
  minCutLength = NULL,
  maxCutLength = NULL,
  crisprNuclease = NULL,
  bsgenome = NULL,
  canonical = TRUE,
  both_strands = TRUE,
  spacer_len = NULL,
  strict_overlap = TRUE,
  remove_ambiguities = TRUE
)

Arguments

Details

This function returns a PairedGuideSet object that stores gRNA pairs targeting the two genomic regions provided as input. The gRNAs in position 1 target the first genomic region, and the gRNAs in position 2 target the second genomic region.

This function can be used for the following scenarios:

1. Designing pairs of gRNAs targeting different genes, for instance for dual-promoter Cas9 systems, or polycystronic Cas12a constructs. This can also be used to target a given gene with multiple gRNAs for improved efficacy (for instance CRISPRa and CRISPRi)

2. Designing pairs of gRNAs for double nicking systems such as Cas9 D10A.

See vignette for more examples.

Value

A PairedGuideSet object.

Author(s)

Jean-Philippe Fortin

See Also

findSpacers to find unpaired spacer sequences, and the PairedGuideSet object documentation to understand the output of findSpacerPairs.

Examples

library(GenomicRanges)
library(BSgenome.Hsapiens.UCSC.hg38)
library(crisprBase)
bsgenome <- BSgenome.Hsapiens.UCSC.hg38

# Region 1:
gr1 <- GRanges(c("chr12"),
               IRanges(start=22224014, end=22225007))

# Region 2:
gr2 <- GRanges(c("chr13"),
               IRanges(start=23224014, end=23225007))

# Pairs targeting the same region:
pairs <- findSpacerPairs(gr1, gr1, bsgenome=bsgenome)

# Pairs targeting two regions:
# The gRNA in position targets gr1
# and the gRNA in position 2 targets gr2
pairs <- findSpacerPairs(gr1, gr2, bsgenome=bsgenome)

Description

Returns all possible, valid gRNA sequences for a given CRISPR nuclease from either a GRanges object or a set of sequence(s) contained in either a DNAStringSet, DNAString or character vector of genomic sequences.

Usage

findSpacers(
  x,
  crisprNuclease = NULL,
  bsgenome = NULL,
  canonical = TRUE,
  both_strands = TRUE,
  spacer_len = NULL,
  strict_overlap = TRUE,
  remove_ambiguities = TRUE,
  remove_duplicates = TRUE
)

Arguments

Details

If x is a GRanges object then a BSgenome must be supplied to bsgenome, from which the genomic sequence is obtained, unless the bsgenome can be inferred from genome(x), for example, "hg38". Otherwise, all supplied sequences are treated as the "+" strands of chromosomes in a "custom" genome.

Ranges or sequences in x may contain names where permitted. These names are stored in region in the mcols of the output, and as seqnames of the output if x is not a GRanges object. If not NULL, names(x) must be unique, otherwise ranges or sequences are enumerated with the "region_" prefix.

When x is a GRanges, the * strand is interpreted as both strands. Consequently, the both_strands argument has no effect on such ranges.

Value

A GuideSet object.

Author(s)

Jean-Philippe Fortin, Luke Hoberecht

Examples

# Using custom sequence as input:
my_seq <- c(my_seq="CCAANAGTGAAACCACGTCTCTATAAAGAATACAAAAAATTAGCCGGGTGTTA")
guides <- findSpacers(my_seq)

# Exon-intro region of human KRAS specified 
# using a GRanges object:
library(GenomicRanges)
library(BSgenome.Hsapiens.UCSC.hg38)
bsgenome <- BSgenome.Hsapiens.UCSC.hg38

gr_input <- GRanges(c("chr12"),
                    IRanges(start=25224014, end=25227007))
guideSet <- findSpacers(gr_input, bsgenome=bsgenome)

# Designing guides for enAsCas12a nuclease:
data(enAsCas12a, package="crisprBase")
guideSet <- findSpacers(gr_input, 
                        canonical=FALSE,
                        bsgenome=bsgenome,
                        crisprNuclease=enAsCas12a)

Description

Create a list of annotation tables from a GuideSet object

Usage

flattenGuideSet(guideSet, useSpacerCoordinates = TRUE, primaryOnly = FALSE)

Arguments

Value

A simple list of tables containing annotations derived from a GuideSet object. The first table ("primary") is always available, while the other tables will be only available when the annotations were added to the GuideSet object.

• primary Primary table containing genomic coordinates and sequence information of the gRNA sequences. Also contains on-target and off-target scores when available.

• alignments Table of on- and off-target alignments.

• geneAnnotation Gene context annotation table.

• tssAnnotation TSS context annotation table.

• enzymeAnnotation Boolean table indicating whether or not recognition motifs of restriction enzymes are found.

• snps SNP annotation table (human only).

Author(s)

Jean-Philippe Fortin

Description

Get distance between query and target sets of barcodes

Usage

getBarcodeDistanceMatrix(
  queryBarcodes,
  targetBarcodes = NULL,
  binnarize = TRUE,
  min_dist_edit = NULL,
  dist_method = c("hamming", "levenshtein"),
  ignore_diagonal = TRUE,
  splitByChunks = FALSE,
  n_chunks = NULL
)

Arguments

Value

A sparse matrix of class dgCMatrix or dsCMatrix in which rows correspond to queryBarcodes and columns correspond to targetBarcodes. If binnarize=TRUE, a value of 0 indicates that two barcodes have a distance greater of equal to min_dist_edit, otherwise the value is 1. If If binnarize=FALSE, values represent the actual calculated distances between barcodes.

Author(s)

Jean-Philippe Fortin

Examples

data(guideSetExample, package="crisprDesign")
guideSetExample <- addOpsBarcodes(guideSetExample)
barcodes <- as.character(guideSetExample$opsBarcode)
dist <- getBarcodeDistanceMatrix(barcodes, min_dist_edit=2)

Description

Get the genomic ranges of a consensus isoform. The consensus isoform is taken as the union of exons across all isoforms where overlapping exons are merged to produce a simplified set through the reduce method of the GenomicRanges package.

Usage

getConsensusIsoform(gene_id, txObject)

Arguments

Value

A GRanges object.

Author(s)

Jean-Philippe Fortin

Examples

data(grListExample)
gene_id <- "ENSG00000120645"
gr <- getConsensusIsoform(gene_id, grListExample)

Description

A function for retrieving mRNA sequences of select transcripts.

Usage

getMrnaSequences(txids, txObject, bsgenome)

Arguments

Value

A DNAStringSet object of mRNA sequences. Note that sequences are returned as DNA rather than RNA.

Author(s)

Jean-Philippe Fortin

Examples

library(BSgenome.Hsapiens.UCSC.hg38)
data(grListExample)
bsgenome <- BSgenome.Hsapiens.UCSC.hg38
txids <- c("ENST00000538872", "ENST00000382841")
out <- getMrnaSequences(txids, grListExample, bsgenome)

Description

A function for retrieving pre-mRNA sequences of select transcripts.

Usage

getPreMrnaSequences(txids, txObject, bsgenome)

Arguments

Value

A DNAStringSet object of mRNA sequences. Note that sequences are returned as DNA rather than RNA.

Author(s)

Jean-Philippe Fortin

Examples

library(BSgenome.Hsapiens.UCSC.hg38)
data(grListExample)
bsgenome <- BSgenome.Hsapiens.UCSC.hg38
txids <- c("ENST00000538872", "ENST00000382841")
out <- getPreMrnaSequences(txids, grListExample, bsgenome)

Description

Extract TSS coordinates from a gene model object.

Usage

getTssObjectFromTxObject(txObject)

Arguments

Value

A GRanges object containing TSS coordinates

Author(s)

Jean-Philippe Fortin

Examples

data(grListExample, package="crisprDesign")
tss <- getTssObjectFromTxObject(grListExample)

Description

Convenience function for constructing a TxDb object.

Usage

getTxDb(file = NA, organism, release = NA, tx_attrib = "gencode_basic", ...)

Arguments

Value

A TxDb object.

Author(s)

Jean-Philippe Fortin, Luke Hoberecht

Examples

if (interactive()){
    # To obtain a TxDb for Homo sapiens from Ensembl:
    txdb <- getTxDb()

    # To obtain a TxDb from a GFF file:
    file='https://www.mirbase.org/ftp/CURRENT/genomes/hsa.gff3'
    txdb <- getTxDb(file=file)
}

Description

To obtain a DataFrame of transcript-specific CDS and mRNA coordinates.

Usage

getTxInfoDataFrame(
  tx_id,
  txObject,
  bsgenome,
  extend = 30,
  checkCdsLength = TRUE
)

Arguments

Value

A DataFrame containing nucleotide and amino acid information. The columns are:

• chr Character specifying chromosome.

• pos Integer value specifying coordinate in reference genome.

• strand Character specifying strand of transcript.

• nuc Character specifying nucleotide on the strand specified by strand.

• aa Character specifying amino acid.

• aa_number Integer specifying amino acid number from 5' end.

• exon Integer specifying exon number.

• pos_plot Integer specifying plot coordinate. Useful for plotting.

• pos_mrna Integer specifying relative mRNA coordinate from the start of the mRNA.

• pos_cds Integer specifying relative CDS coordinate from the start of the CDS.

• region Character specifying gene region: 3UTR, 5UTR, CDS, Intron, Upstream (promoter) or downstream.

Author(s)

Jean-Philippe Fortin

Examples

library(BSgenome.Hsapiens.UCSC.hg38)
bsgenome <- BSgenome.Hsapiens.UCSC.hg38
data("grListExample")
tx_id <- "ENST00000538872"
df <- getTxInfoDataFrame(tx_id=tx_id,
    txObject=grListExample,
    bsgenome=bsgenome)

Example of a TxDb object converted to a GRangesList

Description

Example of a TxDb object converted to a GRangesList object for human gene IQSEC3 (ENSG00000120645).

Usage

data(grListExample, package="crisprDesign")

Format

Named GRangesList with 7 elements: transcripts, exons, cds, fiveUTRs, threeUTRs, introns and tss.

Details

The full human transcriptome TxDb object was obtained from the Ensembl 104 release using the getTxDb function and converted to a GRangesList object using the TxDb2GRangesList function and subsequently subsetted to only contain the IQSEC3 gene (ENSG00000120645) located at the start of chr12 in the human genome (hg38 build).

Example of a GRanges object containing repeat elements

Description

Example of a GRanges object containing genomic coordinates of repeat elements found in the neighborhood of human gene IQSEC3 (ENSG00000120645).

Usage

data(grRepeatsExample, package="crisprDesign")

Format

A GRanges object.

Description

Create a list of annotation tables from a GuideSet object

Usage

GuideSet2DataFrames(guideSet, useSpacerCoordinates = TRUE, primaryOnly = FALSE)

Arguments

Value

A simple list of tables containing annotations derived from a GuideSet object. The first table ("primary") is always available, while the other tables will be only available when the annotations were added to the GuideSet object.

• primary Primary table containing genomic coordinates and sequence information of the gRNA sequences. Also contains on-target and off-target scores when available.

• alignments Table of on- and off-target alignments.

• geneAnnotation Gene context annotation table.

• tssAnnotation TSS context annotation table.

• enzymeAnnotation Boolean table indicating whether or not recognition motifs of restriction enzymes are found.

• snps SNP annotation table (human only).

Author(s)

Jean-Philippe Fortin, Luke Hoberecht

Examples

data(guideSetExampleFullAnnotation)
tables <- GuideSet2DataFrames(guideSetExampleFullAnnotation)

Example of a GuideSet object storing gRNA sequences targeting the CDS of IQSEC3

Description

Example of a GuideSet object storing gRNA sequences targeting the coding sequence of human gene IQSEC3 (ENSG00000120645) for SpCas9 nuclease.

Usage

data(guideSetExample, package="crisprDesign")

Format

A GuideSet object.

Details

The object was obtained by calling findSpacers on the CDS region of human gene IQSEC3. See code in inst/scripts/generateGuideSet.R.

Example of a fully-annotated GuideSet object storing gRNA sequences targeting the CDS of IQSEC3

Description

Example of a fully-annotated GuideSet object storing gRNA sequences targeting the coding sequence of human gene IQSEC3 (ENSG00000120645) for SpCas9 nuclease.

Usage

data(guideSetExampleFullAnnotation, package="crisprDesign")

Format

A GuideSet object.

Details

The object was obtained by applying all available add* annotation functions in crisprDesign (e.g. addSequenceFeatures) to a randomly selected 20-guide subset of guideSetExample. See code in inst/scripts/generateGuideSetFullAnnotation.R.

Example of a GuideSet object storing gRNA sequences targeting the CDS of IQSEC3 with off-target alignments.

Description

Example of a GuideSet object storing gRNA sequences targeting the coding sequence of human gene IQSEC3 (ENSG00000120645) for SpCas9 nuclease with off-target alignments.

Usage

data(guideSetExampleWithAlignments, package="crisprDesign")

Format

A GuideSet object.

Details

The object was obtained by adding off-target alignments to a randomly selected 20-guide subset of guideSetExample. See code in inst/scripts/generateGuideSetFullAnnotation.R.

Description

An S4 class to store pairs of CRISPR gRNA sequences.

Usage

pamOrientation(object, ...)

pamDistance(object, ...)

spacerDistance(object, ...)

cutLength(object, ...)

PairedGuideSet(GuideSet1 = NULL, GuideSet2 = NULL)

## S4 method for signature 'PairedGuideSet'
pamOrientation(object)

## S4 method for signature 'PairedGuideSet'
pamDistance(object)

## S4 method for signature 'PairedGuideSet'
spacerDistance(object)

## S4 method for signature 'PairedGuideSet'
cutLength(object)

## S4 method for signature 'PairedGuideSet'
crisprNuclease(object, index = NULL)

## S4 method for signature 'PairedGuideSet'
spacers(object, as.character = FALSE, returnAsRna = FALSE, index = NULL)

## S4 method for signature 'PairedGuideSet'
pams(object, as.character = FALSE, returnAsRna = FALSE, index = NULL)

## S4 method for signature 'PairedGuideSet'
pamSites(object, index = NULL)

## S4 method for signature 'PairedGuideSet'
cutSites(object, index = NULL)

## S4 method for signature 'PairedGuideSet'
protospacers(
  object,
  as.character = FALSE,
  include.pam = FALSE,
  returnAsRna = FALSE,
  index = NULL
)

## S4 method for signature 'PairedGuideSet'
spacerLength(object, index = NULL)

## S4 method for signature 'PairedGuideSet'
pamLength(object, index = NULL)

## S4 method for signature 'PairedGuideSet'
pamSide(object, index = NULL)

Arguments

Value

A PairedGuideSet object.

Functions

• PairedGuideSet(): Create a PairedGuideSet object

Constructors

Use the constructor link{PairedGuideSet} to create a PairedGuideSet object.

Examples

library(crisprDesign)
data(guideSetExample, package="crisprDesign")
gs <- guideSetExample
gs <- gs[order(BiocGenerics::start(gs))]
gs1 <- gs[1:10]
gs2 <- gs[1:10+10]
pgs <- PairedGuideSet(gs1, gs2)

Description

Obtain Pfam domains from biomaRt for all transcripts found in a gene model object.

Usage

preparePfamTable(txObject, mart_dataset)

Arguments

Value

A DataFrame object with the following columns:

• ensembl_transcript_id Ensembl transcript ID.

• pfam Pfam domain name.

• pfam_start Start amino acid coordinate of the Pfam domain.

• pfam_end End amino acid coordinate of the Pfam domain.

Author(s)

Jean-Philippe Fortin, Luke Hoberecht

Examples

data(grListExample, package="crisprDesign")

if (interactive()){
    pfamTable <- preparePfamTable(grListExample,
                                  mart_dataset="hsapiens_gene_ensembl")
}

Description

Convenience function to search for TSS coordinates.

Usage

queryTss(tssObject, queryColumn, queryValue, tss_window = NULL)

Arguments

Value

A GRanges object. Searches yielding no results will return an empty GRanges object.

Author(s)

Luke Hoberecht, Jean-Philippe Fortin

See Also

queryTxObject for querying gene annotations.

Examples

data(tssObjectExample, package="crisprDesign")
queryTss(tssObjectExample,
         queryColumn="gene_symbol",
         queryValue="IQSEC3")

Description

Convenience function to search for gene coordinates.

Usage

queryTxObject(
  txObject,
  featureType = c("transcripts", "exons", "cds", "fiveUTRs", "threeUTRs", "introns"),
  queryColumn,
  queryValue
)

Arguments

Value

A GRanges object. Searches yielding no results will return an empty GRanges object.

Author(s)

Luke Hoberecht, Jean-Philippe Fortin

See Also

queryTss for querying TSS annotations.

Examples

data(grListExample, package="crisprDesign")
queryTxObject(grListExample,
              featureType="cds",
              queryColumn="gene_symbol",
              queryValue="IQSEC3")

Description

Function for ranking spacers using recommended crisprDesign criteria. CRISPRko, CRISPRa and CRISPRi modalities are supported.

Usage

rankSpacers(
  guideSet,
  tx_id = NULL,
  commonExon = FALSE,
  modality = c("CRISPRko", "CRISPRa", "CRISPRi"),
  useDistanceToTss = TRUE
)

Arguments

Details

For each nuclease, we rank gRNAs based on several rounds of priority. For SpCas9, gRNAs with unique target sequences and without 1-or 2-mismatch off-targets located in coding regions are placed into the first round. Then, gRNAs with a small number of one- or two-mismatch off-targets (less than 5) are placed into the second round. Remaining gRNAs are placed into the third round. Finally, any gRNAs overlapping a common SNP (human only), containing a polyT stretch, or with extreme GC content (below 20 are placed into the fourth round.

If tx_id is specified, within each round of selection, gRNAs targeting the first 85 percent of the specific transcript are prioritized first. If tx_id is specified, and commonExon is set to TRUE, gRNAs targeting common exons across isoforms are also prioritized. If a conservation score is available, gRNAs targeting conserved regions (phyloP conservation score greater than 0), are also prioritized.

Within each bin, gRNAs are ranked by a composite on-target activity rank to prioritize active gRNAs. The composite on-target activity rank is calculated by taking the average rank across the DeepHF and DeepSpCas9 scores for CRISPRko. For CRISPRa or CRISPRi, the CRISPRai scores are used if available.

The process is identical for enAsCas12a, with the exception that the enPAMGb method is used as the composite score.

For CasRx, gRNAs targeting all isoforms of a given gene, with no 1- or 2-mismatch off-targets, are placed into the first round. gRNAs targeting at least 50 percent of the isoforms of a given gene, with no 1- or 2-mismatch off-targets, are placed into the second round. Remaining gRNAs are placed into the third round. Within each round of selection, gRNAs are further ranked by the CasRxRF on-target score.

Value

A GuideSet object ranked from best to worst gRNAs, with a column rank stored in mcols(guideSet) indicating gRNA rank.

Author(s)

Luke Hoberecht, Jean-Philippe Fortin

Examples

data(guideSetExampleFullAnnotation, package="crisprDesign")
gs <- rankSpacers(guideSetExampleFullAnnotation,
                  tx_id = "ENST00000538872")
gs

Remove GuideSet gRNAs that overlap repeat elements

Description

Remove GuideSet gRNAs that overlap repeat elements.

Usage

removeRepeats(object, ...)

## S4 method for signature 'GuideSet'
removeRepeats(object, gr.repeats = NULL, ignore.strand = TRUE)

## S4 method for signature 'PairedGuideSet'
removeRepeats(object, gr.repeats = NULL, ignore.strand = TRUE)

## S4 method for signature 'NULL'
removeRepeats(object)

Arguments

Value

object filtered for spacer sequences not overlapping any repeat elements. An inRepeats column is also appended in mcols(object).

Author(s)

Jean-Philippe Fortin, Luke Hoberecht

See Also

link{addRepeats}.

Examples

data(guideSetExample, package="crisprDesign")
data(grRepeatsExample, package="crisprDesign")
guideSet <- removeRepeats(guideSetExample,
                          gr.repeats=grRepeatsExample)

Description

Remove gRNAs targeting secondary targets

Usage

removeSpacersWithSecondaryTargets(
  guideSet,
  geneID,
  geneColumn = "gene_id",
  ignoreGenesWithoutSymbols = TRUE,
  ignoreReadthroughs = TRUE
)

Arguments

Details

The protospacer target sequence of gRNAs can be located in overlapping genes, and this function allows users to filter out such gRNAs. This ensures remaining gRNAs are targeting only one gene.

Value

A GuideSet object with gRNAs targeting multiple targets removed.

Author(s)

Jean-Philippe Fortin

Example of a GRanges object containing TSS coordinates

Description

Example of a GRanges containing transcription starting site (TSS) coordinates for human gene IQSEC3 (ENSG00000120645).

Usage

data(tssObjectExample, package="crisprDesign")

Format

GRanges object of length 2 corresponding to the 2 TSSs of gene IQSEC3.

Details

The TSS coordinates were obtained from the two transcript stored in the grListExample object for gene IQSEC3.

Convert a TxDb object into a GRangesList

Description

Convenience function to reformat a TxDb object into a GRangesList.

Usage

TxDb2GRangesList(
  txdb,
  standardChromOnly = TRUE,
  genome = NULL,
  seqlevelsStyle = c("UCSC", "NCBI")
)

Arguments

Value

A named GRangesList of length 7 with the following elements: transcripts, exons, introns, cds, fiveUTRs, threeUTRs and tss.

Author(s)

Jean-Philippe Fortin, Luke Hoberecht

See Also

getTxDb to obtain a TxDb object.

Examples

if (interactive()){
    # To obtain a TxDb for Homo sapiens from Ensembl:
    txdb <- getTxDb()
    
    # To convert to a GRanges list:
    txdb <- TxDb2GRangesList(txdb)
}

Description

Update OPS library with additional gRNAs

Usage

updateOpsLibrary(
  opsLibrary,
  df,
  n_guides = 4,
  gene_field = "gene",
  min_dist_edit = 2,
  dist_method = c("hamming", "levenshtein"),
  splitByChunks = FALSE
)

Arguments

Value

A data.frame containing the original gRNAs from the input opsLibrary data.frame as well as additional gRNAs selected from the input data.frame df.

Author(s)

Jean-Philippe Fortin

Examples

data(guideSetExample, package="crisprDesign")
guideSet <- unique(guideSetExample)
guideSet <- addOpsBarcodes(guideSet)
guideSet1 <- guideSet[1:200]
guideSet2 <- guideSet[201:400]

df1 <- data.frame(ID=names(guideSet1),
                  spacer=spacers(guideSet1, as.character=TRUE),
                  opsBarcode=as.character(guideSet1$opsBarcode))
df2 <- data.frame(ID=names(guideSet2),
                  spacer=spacers(guideSet2, as.character=TRUE),
                  opsBarcode=as.character(guideSet2$opsBarcode))

# Creating mock gene:
df1$gene <- rep(paste0("gene",1:10),each=20)
df2$gene <- rep(paste0("gene",1:10+10),each=20)
df1$rank <- rep(1:20,10)
df2$rank <- rep(1:20,10)
opsLib <- designOpsLibrary(df1)
opsLib <- updateOpsLibrary(opsLib, df2)

Description

Validate gRNA library for optical pooled screening

Usage

validateOpsLibrary(
  df,
  min_dist_edit = 2,
  dist_method = c("hamming", "levenshtein")
)

Arguments

Value

The original df is all checks pass. Otherwise, a stop error.

Author(s)

Jean-Philippe Fortin

Examples

data(guideSetExample, package="crisprDesign")
guideSet <- unique(guideSetExample)
guideSet <- addOpsBarcodes(guideSet)
df <- data.frame(ID=names(guideSet),
                 spacer=spacers(guideSet, as.character=TRUE),
                 opsBarcode=as.character(guideSet$opsBarcode))
df$gene <- rep(paste0("gene",1:40),each=20)
df$rank <- rep(1:20,40)
opsLib <- designOpsLibrary(df)
opsLib <- validateOpsLibrary(opsLib)