Package 'CrispRVariants'

Description

Breaks cigar strings into individual operations

Usage

.explodeCigarOpCombs(cigar, ops = GenomicAlignments::CIGAR_OPS)

Arguments

Value

The operations, as a CharacterList

Exploded cigar operations with operation widths

Author(s)

Helen Lindsay

Description

Internal CrispRVariants function for creating allele labels given variants and positions

Assume that the reference may be on the negative strand and regions are given with respect to the reference sequence.

Usage

.formatVarLabels(
  grl,
  labels,
  position = c("start", "end"),
  genome.to.pos = NULL,
  pos.to.lab = ":",
  as.string = TRUE
)

.findMismatches(
  alns,
  ref.seq,
  ref.start,
  regions = NULL,
  strand = "+",
  min.pct = 0
)

Arguments

Value

A data frame of sequence indices, genomic position of mismatch and mismatch base

Description

Manually specify x-tick locations and labels, as sometimes ggplot defaults are too dense. Used internally by CrispRVariants for creating alignment plot with plotAlignments.

Usage

.getAxisCoords(
  locations,
  labels = NULL,
  loc.boundaries = NULL,
  lab.boundaries = c(-1, 1),
  label.at = 5,
  min.tick.sep = 1
)

Arguments

Value

A list containing vectors named tick_locs and tick_labs

Author(s)

Helen Lindsay

Description

create a vector of elements in outer interspersed with elements in inner. Similar to python zip. No element checking.

Usage

.intersperse(outer, inner)

Arguments

Value

A vector interspersing elements of outer and inner. If outer is c(a,b,c) and inner is c(d,e), returns c(a,d,b,e,c)

Author(s)

Helen Lindsay

Examples

CrispRVariants:::.intersperse(c(1:10), c(1:9)*10)

Description

(.invertKeepRanges) Internal CrispRVariants function used by seqsToPartialAlns for checking arguments and getting region to delete. Returns FALSE if no region to delete found, or region to be deleted is entire target.

(.checkRelativeLocs) Shift keep to start at 1 if it is within the target

(.adjustRelativeInsLocs) Internal CrispRVariants function for shifting insertion locations relative to the target region when removing a segment of the alignments. Note that this function does not do input checking but assumes this has been done upstream. Insertions at the left border of a gap region are removed.

(.offsetIndices) Get indices of a vector grouped by "x", cumulatively adding offsets to each group according to "offset"

Usage

.invertKeepRanges(target, keep)

.checkRelativeLocs(target, keep)

.adjustRelativeInsLocs(target, keep, starts, gap_nchars)

.offsetIndices(x, offset)

Arguments

Value

Gaps between keep (IRanges), or FALSE if no gap ranges found

insertion_sites (data.frame) with modified start column

Author(s)

Helen Lindsay

Examples

CrispRVariants:::.offsetIndices(rep(2,5), c(0:4)*10)

Description

This is an R implementation of Wibowo Arindrarto's abifpy.py trimming module, which itself implement's Richard Mott's trimming algorithm See https://github.com/bow/abifpy for more details.

Usage

abifToFastq(
  seqname,
  fname,
  outfname,
  trim = TRUE,
  cutoff = 0.05,
  min_seq_len = 20,
  offset = 33,
  recall = FALSE
)

Arguments

Details

Requires Bioconductor package SangerseqR

Value

None. Sequences are appended to the outfname.

Author(s)

Helen Lindsay

Examples

# Running this code will write the fastq file to "IM2033.fastq"
ab1_fname <- system.file("extdata", "IM2033.ab1", package = "CrispRVariants")
abifToFastq("IM2033", ab1_fname, "IM2033.fastq")

Description

Extrapolates the mapping location of a read by assuming that the clipped regions should map adjacent to the mapped locations. This is not always a good assumption, particularly in the case of chimeric reads!

Usage

addClipped(bam, ...)

## S4 method for signature 'GAlignments'
addClipped(bam, ...)

Arguments

Value

A GRanges representation of the extended mapping locations

Author(s)

Helen Lindsay

Description

Adds vertical dotted lines in intervals of three nucleotides. Codon frame is supplied, alignments are assumed not to span an intron-exon junction.

Usage

addCodonFrame(p, width, codon.frame)

Arguments

Value

A ggplot object with added vertical lines indicating the frame

Author(s)

Helen Lindsay

Description

Function to access allele names

Usage

alleles(obj, ...)

## S4 method for signature 'CrisprSet'
alleles(obj, ...)

Arguments

Value

A data frame relating CIGAR strings to variant labels

Author(s)

Helen Lindsay

Examples

data("gol_clutch1")
alleles <- alleles(gol)

Description

Return alignments from an object that contains them. For a CrisprSet object, these are truncated, non-chimeric alignments

Usage

alns(obj, ...)

## S4 method for signature 'CrisprSet'
alns(obj, ...)

Arguments

Value

A GAlignmentsList of consensus sequences on the positive strand.

Author(s)

Helen Lindsay

Examples

data("gol_clutch1")
alns <- alns(gol)

Description

Plots the gene structure, annotates this with the target location

Usage

annotateGenePlot(
  txdb,
  target,
  target.colour = "red",
  target.size = 1,
  gene.text.size = 10,
  panel.spacing = grid::unit(c(0.1, 0.1, 0.1, 0.1), "lines"),
  plot.title = NULL,
  all.transcripts = TRUE
)

Arguments

Value

A ggplot2 plot of the transcript structures

Description

Arranges 3 plots in two rows. The vertical margins of the left.plot and right.plot constrained to be equal

Usage

arrangePlots(
  top.plot,
  left.plot,
  right.plot,
  fig.height = NULL,
  col.wdth.ratio = c(2, 1),
  row.ht.ratio = c(1, 6),
  left.plot.margin = grid::unit(c(0.1, 0.2, 3, 0.2), "lines")
)

Arguments

Value

The arranged plots

Description

For signature "matrix", this function optionally does a very naive classification of variants by size. Frameshift variant combinations are those whose sum is not divisible by three. Intron boundaries are *NOT* considered, use with caution! For signature "CrisprSet", the function uses the VariantAnnotation package to localize variant alleles with respect to annotated transcripts. Variants are annotated as "coding" when they are coding in any transcript.

(signature("CrisprSet")) Groups variants by size and type and produces a barplot showing the variant spectrum for each sample. Accepts all arguments accepted by barplotAlleleFreqs for signature("matrix"). Requires package "VariantAnnotation"

signature("matrix") Accepts a matrix of allele counts, with rownames being alleles and column names samples.

Usage

barplotAlleleFreqs(obj, ...)

## S4 method for signature 'CrisprSet'
barplotAlleleFreqs(
  obj,
  ...,
  txdb,
  min.freq = 0,
  include.chimeras = TRUE,
  group = NULL,
  palette = c("rainbow", "bluered")
)

## S4 method for signature 'matrix'
barplotAlleleFreqs(
  obj,
  category.labels = NULL,
  group = NULL,
  bar.colours = NULL,
  group.colours = NULL,
  legend.text.size = 10,
  axis.text.size = 10,
  legend.symbol.size = 1,
  snv.label = "SNV",
  novar.label = "no variant",
  chimera.label = "Other",
  include.table = TRUE,
  classify = TRUE
)

Arguments

Value

A ggplot2 barplot of the allele distribution and optionally a table of allele counts

Author(s)

Helen Lindsay

Examples

data("gol_clutch1")
barplotAlleleFreqs(variantCounts(gol))

# Just show the barplot without the counts table:
barplotAlleleFreqs(variantCounts(gol), include.table = FALSE)

Description

Given a set of alignments to a target region, finds read pairs. Compares insertion/deletion locations within pairs using the cigar string. Pairs with non-identical indels are excluded. Pairs with identical indels are collapsed to a single read, taking the consensus sequence of the pairs.

Usage

collapsePairs(
  alns,
  use.consensus = TRUE,
  keep.unpaired = TRUE,
  verbose = TRUE,
  ...
)

Arguments

Value

The alignments, with non-concordant pairs removed and concordant pairs represented by a single read.

Author(s)

Helen Lindsay

Description

Return consensus sequences of variant alleles. At present, chimeric alignments are not included.

Usage

consensusSeqs(obj, ...)

## S4 method for signature 'CrisprSet'
consensusSeqs(obj, ..., top.n = NULL, min.freq = 0, min.count = 1)

Arguments

Value

A DNAStringSet of consensus sequences on the positive strand.

Author(s)

Helen Lindsay

Examples

data("gol_clutch1")
seqs <- consensusSeqs(gol, sample = 2)

Description

Counts the number of reads containing a deletion or insertion (indel) of any size in a set of aligned reads. For countDeletions and countInsertions Reads may be filtered according to whether they contain more than one indel of the same or different types.

Usage

countDeletions(alns, ...)

## S4 method for signature 'GAlignments'
countDeletions(
  alns,
  ...,
  multi.del = FALSE,
  del.and.ins = FALSE,
  del.ops = c("D")
)

countInsertions(alns, ...)

## S4 method for signature 'GAlignments'
countInsertions(
  alns,
  ...,
  ins.and.del = FALSE,
  multi.ins = FALSE,
  del.ops = c("D")
)

countIndels(alns)

## S4 method for signature 'GAlignments'
countIndels(alns)

indelPercent(alns)

## S4 method for signature 'GAlignments'
indelPercent(alns)

Arguments

Value

countDeletions: The number of reads containing a deletion (integer)

countInsertions: The number of reads containing an insertion (integer)

countIndels: The number of reads containing at least one insertion

indelPercent: The percentage of reads containing an insertion or deletion (numeric)

Author(s)

Helen Lindsay

Examples

bam_fname <- system.file("extdata", "gol_F1_clutch_2_embryo_4_s.bam",
                         package = "CrispRVariants")
bam <- GenomicAlignments::readGAlignments(bam_fname, use.names = TRUE)
countDeletions(bam)
countInsertions(bam)
countIndels(bam)
indelPercent(bam)

Description

A ReferenceClass container for a single sample of alignments narrowed to a target region. Typically CrisprRun objects will not be accessed directly, but if necessary via a CrisprSet class which contains a list of CrisprRun objects. Note that the CrispRVariants plotting functions don't work on CrisprRun objects.

Arguments

Fields

alns

A GAlignments object containing the narrowed reads. Note that if the alignments are represented with respect to the reverse strand, the "start" remains with repect to the forward strand, whilst the cigar and the sequence are reverse complemented.

name

The name of the sample

cigar_labels

A vector of labels for the reads, based on the cigar strings, optionally renumbered with respect to a new zero point (e.g. the cut site) and shortened to only insertion and deletion locations. Set at initialisation of a CrisprSet object, but not at initialisation of a CrisprRun object.

chimeras

Chimeric, off-target alignments corresponding to alignments in alns

Methods

getCigarLabels( target, target.loc, genome_to_target, ref, separate.snv, rc, match.label, mismatch.label, keep.ops = c("I", "D", "N"), upstream = min(target.loc, 8), downstream = min(6, width(ref) - target.loc + 1), regions = NULL, snv.regions = NULL )

Description: Sets the "cig_labels" field, returns the cigar labels.

Input parameters: target: (GRanges) the counting region. target.loc: The location of the cut site with respect to the target genome_to_target: A vector with names being genomic locations and values being locations with respect to the cut site separate.snv: Should single nucleotide variants be called? (Default: TRUE) match.label: Label for non-variant reads (Default: no variant) mismatch.label: Label for single nucleotide variants (Default: SNV) rc: Should the variants be displayed with respect to the negative strand? (Default: FALSE) keep.ops: CIGAR operations to remain in the variant label (usually indels) upstream: distance upstream of the cut site to call SNVs downstream: distance downstream of the cut site to call SNVs regions: IRanges(k) Regions for counting insertions and deletions. Insertions on the right border are not counted. snv.regions Regions for calling SNVS

getInsertionSeqs(target)

Description: Return a table relating insertion sequences to alignment indices Input parameters:

Author(s)

Helen Lindsay

See Also

CrisprSet

Examples

# readsToTarget with signature("GAlignments", "GRanges") returns a 
# CrisprRun object

bam_fname <- system.file("extdata", "gol_F1_clutch_1_embryo_1_s.bam",
                        package = "CrispRVariants")
param <- Rsamtools::ScanBamParam(what = c("seq", "flag"))
alns <- GenomicAlignments::readGAlignments(bam_fname, param = param, 
         use.names = TRUE)

reference <- Biostrings::DNAString("GGTCTCTCGCAGGATGTTGCTGG")
gd <- GenomicRanges::GRanges("18", IRanges::IRanges(4647377, 4647399), strand = "+")

crispr_run <- readsToTarget(alns, target = gd, reference = reference,
                           name = "Sample name", target.loc = 17)

# Alternatively, CrisprRun objects can be accessed from a CrisprSet object
# e.g. crispr_set$crispr_runs[[1]]

Description

A ReferenceClass container for holding a set of narrowed alignments, each corresponding to the same target region. Individual samples are represented as CrisprRun objects. CrisprRun objects with no on-target reads are excluded. CrisprSet objects are constructed with readsToTarget or readsToTargets. For most use cases, a CrisprSet object should not be initialized directly.

Arguments

Fields

crispr_runs

A list of CrisprRun objects, typically corresponding to samples of an experiment.

ref

The reference sequence for the target region, as a Biostrings::DNAString object

cigar_freqs

A matrix of counts for each variant

target

The target location, as a GRanges object

Methods

classifyCodingBySize(var_type, cutoff = 10)

Description: This is a naive classification of variants as frameshift or in-frame Coding indels are summed, and indels with sum divisible by 3 are considered frameshift. Note that this may not be correct for variants that span an intron-exon boundary Input paramters: var_type: A vector of var_type. Only variants with var_type == "coding" are considered. Intended to work with classifyVariantsByLoc cutoff: Variants are divided into those less than and greater than "cutoff" (Default: 10) Result: A character vector with a classification for each variant allele

classifyVariantsByLoc(txdb, add_chr = TRUE, verbose = TRUE, ...)

Description: Uses the VariantAnnotation package to look up the location of the variants. VariantAnnotation allows multiple classification tags per variant, this function returns a single tag. The following preference order is used: spliceSite > coding > intron > fiveUTR > threeUTR > promoter > intergenic

Input parameters: txdb: A BSgenome transcription database add_chr: Add "chr" to chromosome names to make compatible with UCSC (default: TRUE) verbose: Print progress (default: TRUE) ...: Filtering arguments for variantCounts

Return value: A vector of classification tags, matching the rownames of .self$cigar_freqs (the variant count table)

classifyVariantsByType(...)

Description: Classifies variants as insertions, deletions, or complex (combinations). In development Input parameters: ... Optional arguments to "variantCounts" for filtering variants before classification Return value: A named vector classifying variant alleles as insertions, deletions, etc

consensusAlleles( cig_freqs = .self$cigar_freqs, return_nms = TRUE, match.ops = c("M", "X", "=") )

Description: Get variants by their cigar string, make the pairwise alignments for the consensus sequence for each variant allele

Input parameters: cig_freqs: A table of variant allele frequencies (by default: .self$cigar_freqs, but could also be filtered) return_nms: If true, return a list of sequences and labels (Default:FALSE) match.ops: CIGAR operations for 1-1 alignments

Return: A DNAStringSet of the consensus sequences for the specified alleles, or a list containing the consensus sequences and names for the labels if return_nms = TRUE

filterUniqueLowQual(min_count = 2, max_n = 0, verbose = TRUE)

Description: Deletes reads containing rare variant combinations and more than a minimum number of ambiguity characters within the target region. These are assumed to be alignment errors.

Input parameters: min_count: the number of times a variant combination must occur across all samples to keep (default: 2, i.e. a variant must occur at least twice in one or more samples to keep) max_n: maximum number of ambiguity ("N") bases a read with a rare variant combination may contain. (default: 0) verbose: If TRUE, print the number of sequences removed (default: TRUE)

filterVariants( cig_freqs = NULL, names = NULL, columns = NULL, include.chimeras = TRUE )

Description: Relabels specified variants in a table of variant allele counts as non-variant, e.g. variants known to exist in control samples. Accepts either a size, e.g. "1D", or a specific mutation, e.g. "-4:3D". For alleles that include one variant to be filtered and one other variant, the other variant will be retained. If SNVs are included, these will be removed entirely, but note that SNVs are only called in reads that do not contain an insertion/deletion variant

Input parameters: cig_freqs: A table of variant allele counts (Default: NULL, i.e. .self$cigar_freqs) names: Labels of variants alleles to remove (Default: NULL) columns: Indices or names of control samples. Remove all variants that occur in these columns. (Default: NULL) include.chimeras: Should chimeric reads be included? (Default: TRUE)

heatmapCigarFreqs( as.percent = TRUE, x.size = 8, y.size = 8, x.axis.title = NULL, x.angle = 90, min.freq = 0, min.count = 0, top.n = nrow(.self$cigar_freqs), type = c("counts", "proportions"), header = c("default", "counts", "efficiency"), order = NULL, alleles = NULL, create.plot = TRUE, ... )

Description: Internal method for CrispRVariants:plotFreqHeatmap, optionally filters the table of variants, then a table of variant counts, coloured by counts or proportions.

Input parameters: as.percent: Should colours represent the percentage of reads per sample (TRUE) or the actual counts (FALSE)? (Default: TRUE) x.size: Font size for x axis labels (Default: 8) y.size: Font size for y axis labels (Default: 8) x.axis.title: Title for x axis min.freq: Include only variants with frequency at least min.freq in at least one sample min.count: Include only variants with count at least min.count in at least one sample top.n: Include only the n most common variants type: Should labels show counts or proportions? (Default: counts) header: What should be displayed in the header of the heatmap. Default: total count for type = "counts" or proportion of reads shown in the matrix for type = "proportions". If "counts" is selected, total counts will be shown for both types. "efficiency" shows the mutation efficiency (calculated with default settings) order: Reorder the columns according to this order (Default: NULL) alleles: Names of alleles to include. Selection of alleles takes place after filtering (Default: NULL). exclude: Names of alleles to exclude (Default: NULL) create.plot: Should the plot be created (TRUE, default), or the data used in the plot returned. ...: Extra filtering or plotting options

Return value: A ggplot2 plot object. Call "print(obj)" to display

See also: CrispRVariants::plotFreqHeatmap

makePairwiseAlns(cig_freqs = .self$cigar_freqs, ...)

Description: Get variants by their cigar string, make the pairwise alignments for the consensus sequence for each variant allele

Input parameters: cig_freqs: A table of variant allele frequencies (by default: .self$cigar_freqs, but could also be filtered) ...: Extra arguments for CrispRVariants::seqsToAln, e.g. which symbol should be used for representing deleted bases

mutationEfficiency( snv = c("non_variant", "include", "exclude"), include.chimeras = TRUE, exclude.cols = NULL, group = NULL, filter.vars = NULL, filter.cols = NULL, count.alleles = FALSE, per.sample = TRUE, min.freq = 0 )

Description: Calculates summary statistics for the mutation efficiency, i.e. the percentage of reads that contain a variant. Reads that do not contain and insertion or deletion, but do contain a single nucleotide variant (snv) can be considered as mutated, non-mutated, or not included in efficiency calculations as they are ambiguous. Note: mutationEfficiency does not treat partial alignments differently

Input parameters: snv: One of "include" (consider reads with mismatches to be mutated), "exclude" (do not include reads with snvs in efficiency calculations), and "non_variant" (consider reads with mismatches to be non-mutated). include.chimeras: Should chimeras be counted as variants? (Default: TRUE) exclude.cols: A list of column names to exclude from calculation, e.g. if one sample is a control (default: NULL, i.e. include all columns) group: A grouping variable. Efficiency will be calculated per group, instead of for individual. Cannot be used with exclude.cols. filter.vars: Variants that should not be counted as mutations. filter.cols: Column names to be considered controls. Variants occuring in a control sample will not be counted as mutations. count.alleles: If TRUE, also report statistics about the number of alleles per sample/per group. (Default: FALSE) per.sample: Return efficiencies for each sample (Default: TRUE) min.freq: Minimum frequency for counting alleles. Does not apply to calculating efficiency. To filter when calculating efficiency, first use "variantCounts". (Default: 0, i.e. no filtering) Return value: A vector of efficiency statistics per sample and overall, or a matrix if a group is supplied.

plotVariants( min.freq = 0, min.count = 0, top.n = nrow(.self$cigar_freqs), alleles = NULL, renumbered = .self$pars["renumbered"], add.other = TRUE, create.plot = TRUE, plot.regions = NULL, allow.partial = TRUE, ... )

Description: Internal method for CrispRVariants:plotAlignments, optionally filters the table of variants, then plots variants with respect to the reference sequence, collapsing insertions and displaying insertion sequences below the plot.

Input parameters: min.freq: i( in at least one sample min.count i (integer) include variants that occur at leas i times in at least one sample top.n: n (integer) Plot only the n most frequent variants (default: plot all) Note that if there are ties in variant ranks, top.n only includes ties with all members ranking <= top.n alleles: Alleles to include after filtering. Default NULL means use all alleles that pass filtering. renumbered: If TRUE, the x-axis is numbered with respect to the target (cut) site. If FALSE, x-axis shows genomic locations. (default: TRUE) add.other Add a blank row named "Other" for chimeric alignments, if there are any (Default: TRUE) create.plot Data is plotted if TRUE and returned without if FALSE. (Default: TRUE) plot.regions Subregion of the target to plot (Default: NULL) allow.partial Should partial alignments be allowed? (Default: TRUE) ... additional arguments for plotAlignments

Return value: A ggplot2 plot object. Call "print(obj)" to display

Author(s)

Helen Lindsay

See Also

readsToTarget and readsToTargets for initialising a CrisprSet, CrisprRun container for sample data.

Examples

# Load the metadata table
md_fname <- system.file("extdata", "gol_F1_metadata_small.txt", package = "CrispRVariants")
md <- read.table(md_fname, sep = "\t", stringsAsFactors = FALSE)

# Get bam filenames and their full paths
bam_fnames <- sapply(md$bam.filename, function(fn){
 system.file("extdata", fn, package = "CrispRVariants")})

reference <- Biostrings::DNAString("GGTCTCTCGCAGGATGTTGCTGG")
gd <- GenomicRanges::GRanges("18", IRanges::IRanges(4647377, 4647399), strand = "+")

crispr_set <- readsToTarget(bam_fnames, target = gd, reference = reference,
                           names = md$experiment.name, target.loc = 17)

Description

Update default values for func with values from dot args

Usage

dispatchDots(func, ..., call = FALSE)

Arguments

Value

A list of arguments to pass to func, or if call is TRUE, the result of calling func with these arguments.

Author(s)

Helen Lindsay

Examples

# Set up a function to dispatch dot arguments to:
f <- function(a=1, b=2, c=3){
 print(c(a,b,c))
}
# Set up a function for passing dots:
g <- function(...){
 CrispRVariants:::dispatchDots(f, ...)
}

g(a = 5)
g(a = 5, call = TRUE)
# Unrelated arguments will not be passed on
g(a = 5, d = 6)

Description

Returns a GAlignments excluding reads based on either name and/or location

Usage

excludeFromBam(bam, exclude.ranges = GRanges(), exclude.names = NA)

Arguments

Value

The bam minus the excluded regions

Author(s)

Helen Lindsay

Description

Find chimeric reads, assuming that the GAlignments object does not contain multimapping reads. That is, read names that appear more than ones in the file are considered chimeras. Chimeric reads are reads that cannot be mapped as a single, linear alignment. Reads from structual rearrangements such as inversions can be mapped as chimeras. Note that the indices of all chimeric reads are returned, these are not separated into individual chimeric sets.

Usage

findChimeras(bam, by.flag = FALSE)

Arguments

Value

A vector of indices of chimeric sequences within the original bam

Author(s)

Helen Lindsay

See Also

plotChimeras for plotting chimeric alignment sets.

Examples

bam_fname <- system.file("extdata", "gol_F1_clutch_2_embryo_4_s.bam",
                         package = "CrispRVariants")
bam <- GenomicAlignments::readGAlignments(bam_fname, use.names = TRUE)
chimera_indices <- findChimeras(bam)
chimeras <- bam[chimera_indices]

Description

Find single nucleotide variants (SNVs) above a specified frequency in a table of variants.

Usage

findSNVs(obj, ...)

## S4 method for signature 'CrisprSet'
findSNVs(obj, ..., freq = 0.25, include.chimeras = TRUE)

Arguments

Value

A vector of SNVs and their frequencies

Author(s)

Helen Lindsay

Description

Return chimeric alignments from a collection of aligned sequences

Usage

getChimeras(obj, ...)

## S4 method for signature 'CrisprSet'
getChimeras(obj, ..., sample)

Arguments

Value

A GAlignment object containing the chimeric read groups

Author(s)

Helen Lindsay

Examples

data("gol_clutch1")
chimeras <- getChimeras(gol, sample = 2)

Description

Returns a table of insertion sequences present in a GAlignments object. This table is aggregated and used by plotAlignments.

Usage

getInsertionsTable(alns, pos = 1L)

Arguments

Value

A data frame of insertion sequences, genomic and relative locations

Author(s)

Helen Lindsay

Description

This dataset is a subset of the crispant data for the golden gene used by Burger et al (submitted).

Usage

data(gol_clutch1)

Format

A CrisprSet object countaining 8 samples

Details

• gol The variants as a CrisprSet object

Value

A CrisprSet object named "gol"

Description

Makes allele labels for insertion / deletion variants

Usage

indelLabels(
  alns,
  rc = FALSE,
  genome.to.pos = NULL,
  keep.ops = c("I", "D", "N"),
  regions = NULL,
  as.string = TRUE,
  ...
)

Arguments

Value

A vector of labels for alns

Author(s)

Helen Lindsay

Description

Takes a matrix of characters, x and y locations and colours, creates a ggplot geom_tile plot with tiles labelled by the characters.

Usage

makeAlignmentTilePlot(
  m,
  ref,
  xlab,
  plot.text.size,
  axis.text.size,
  xtick.labs,
  xtick.breaks,
  tile.height
)

Arguments

Value

A ggplot object

Author(s)

Helen Lindsay

Description

Merges chimeric alignments where the individual segments border an unmapped region (a long deletion). If bases of the read are mapped to both ends of the gap, the multimapped reads are only included in the leftmost genomic segment. If there are more than max_unmapped unmapped bases between the mapped bases, the read is not considered mergeable. Currently experimental and only tested with reads mapped by bwa mem.

Usage

mergeChimeras(
  bam,
  chimera_idxs = NULL,
  verbose = TRUE,
  max_read_overlap = 10,
  max_unmapped = 4,
  name = NULL
)

Arguments

Value

A list of the merged and unmerged chimeric alignments

Author(s)

Helen Lindsay

Description

Merge two CrisprSet objects sharing a reference and target location

Usage

mergeCrisprSets(x, y, ...)

## S4 method for signature 'CrisprSet,CrisprSet'
mergeCrisprSets(
  x,
  y,
  ...,
  x.samples = NULL,
  y.samples = NULL,
  names = NULL,
  order = NULL
)

Arguments

Value

A merged CrisprSet object

Author(s)

Helen Lindsay

Examples

# Load the metadata table
md_fname <- system.file("extdata", "gol_F1_metadata_small.txt", package = "CrispRVariants")
md <- read.table(md_fname, sep = "\t", stringsAsFactors = FALSE)

# Get bam filenames and their full paths
bam_fnames <- sapply(md$bam.filename, function(fn){
 system.file("extdata", fn, package = "CrispRVariants")})

reference <- Biostrings::DNAString("GGTCTCTCGCAGGATGTTGCTGG")
gd <- GenomicRanges::GRanges("18", IRanges::IRanges(4647377, 4647399),
      strand = "+")

crispr_set1 <- readsToTarget(bam_fnames[c(1:4)], target = gd,
      reference = reference, names = md$experiment.name[1:4], target.loc = 17)
crispr_set2 <- readsToTarget(bam_fnames[c(5:8)], target = gd,
      reference = reference, names = md$experiment.name[5:8], target.loc = 17)
mergeCrisprSets(crispr_set1,crispr_set2)

Description

Make variant labels for variants without an insertion or deletion

Usage

mismatchLabels(
  alns,
  target,
  ref.seq,
  regions = NULL,
  min.pct = 0,
  mismatch.label = "SNV",
  genome.to.pos = NULL,
  as.string = TRUE
)

Arguments

Value

A data frame of sequence indices, genomic position of mismatch and mismatch base

Description

Returns the percentage of sequences that contain at least one mutation.

Usage

mutationEfficiency(obj, ...)

## S4 method for signature 'CrisprSet'
mutationEfficiency(
  obj,
  ...,
  snv = c("non_variant", "include", "exclude"),
  include.chimeras = TRUE,
  exclude.cols = NULL,
  filter.vars = NULL,
  filter.cols = NULL,
  group = NULL
)

Arguments

Value

A vector of efficiency statistics per sample and overall, or a matrix of efficiency statistics per group if a group is provided

Author(s)

Helen Lindsay

Examples

data("gol_clutch1")
mutationEfficiency(gol)

Description

Aligned reads are narrowed to the target region. In the case of reads with deletions spanning the boundaries of the target, reads are narrowed to the start of the deletion,

Usage

narrowAlignments(alns, target, ...)

## S4 method for signature 'GAlignments,GRanges'
narrowAlignments(
  alns,
  target,
  ...,
  reverse.complement,
  minoverlap = NULL,
  verbose = FALSE,
  clipping.ops = c("S", "H"),
  match.ops = c("M", "X", "=")
)

Arguments

Value

The narrowed alignments (GAlignments)

Author(s)

Helen Lindsay

Examples

bam_fname <- system.file("extdata", "gol_F1_clutch_2_embryo_4_s.bam",
                         package = "CrispRVariants")
bam <- GenomicAlignments::readGAlignments(bam_fname, use.names = TRUE)
target <- GenomicRanges::GRanges("18", IRanges::IRanges(4647377, 4647399),
          strand = "+")
narrowAlignments(bam, target, reverse.complement = FALSE)

Description

(signature("CrisprSet")) Wrapper for CrisprSet$plotVariants. Optionally filters a CrisprSet frequency table, then plots variants. More information in CrisprSet

(signature("DNAString")) Plots a set of pairwise alignments to a reference sequence. Alignments should all be the same length as the reference sequences. This is achieved by removing insertions with respect to the reference, see seqsToAln. Insertions are indicated by symbols in the plot and a table showing the inserted sequences below the plot. The default options are intended for a figure 6-8 inches wide, with figure height best chosen according to the number of different variants and insertions to be displayed.

Usage

plotAlignments(obj, ...)

## S4 method for signature 'CrisprSet'
plotAlignments(
  obj,
  ...,
  min.freq = 0,
  min.count = 1,
  top.n = 50,
  renumbered = obj$pars[["renumbered"]],
  add.other = TRUE,
  create.plot = TRUE
)

## S4 method for signature 'character'
plotAlignments(
  obj,
  ...,
  alns,
  ins.sites,
  highlight.pam = TRUE,
  show.plot = FALSE,
  target.loc = 17,
  pam.start = NA,
  pam.end = NA,
  ins.size = 2,
  legend.cols = 3,
  xlab = NULL,
  xtick.labs = NULL,
  xtick.breaks = NULL,
  plot.text.size = 2,
  axis.text.size = 8,
  legend.text.size = 6,
  highlight.guide = TRUE,
  guide.loc = NULL,
  tile.height = 0.55,
  max.insertion.size = 20,
  min.insertion.freq = 5,
  line.weight = 1,
  legend.symbol.size = ins.size,
  add.other = FALSE,
  codon.frame = NULL,
  style = c("all", "mismatches")
)

## S4 method for signature 'DNAString'
plotAlignments(
  obj,
  ...,
  alns,
  ins.sites,
  highlight.pam = TRUE,
  show.plot = FALSE,
  target.loc = 17,
  pam.start = NA,
  pam.end = NA,
  ins.size = 2,
  legend.cols = 3,
  xlab = NULL,
  xtick.labs = NULL,
  xtick.breaks = NULL,
  plot.text.size = 2,
  axis.text.size = 8,
  legend.text.size = 6,
  highlight.guide = TRUE,
  guide.loc = NULL,
  tile.height = 0.55,
  max.insertion.size = 20,
  min.insertion.freq = 5,
  line.weight = 1,
  legend.symbol.size = ins.size,
  add.other = FALSE,
  codon.frame = NULL
)

Arguments

Value

A ggplot2 figure

Author(s)

Helen Lindsay

See Also

seqsToAln, ggplot

Examples

#Load a CrisprSet object and plot
data("gol_clutch1")
plotAlignments(gol)

Description

Produces a dot plot of a set of chimeric alignments. For chimeric alignments, a single read is split into several, possibly overlapping alignmed blocks. Aligned sections of chimeric reads can be separated by large genomic distances, or on separate chromosomes. plotChimeras produces a dot plot, each aligned block highlighted, and chromosomes shown in different colours. Large gaps between aligned segments are collapsed and indicated on the plot with horizontal lines. The X-axis shows each base of the entire read. Note that the mapping to the fwd strand is shown if all strands agree. The chimeric alignments must be sorted!

Usage

plotChimeras(
  chimeric.alns,
  max.gap = 10,
  tick.sep = 20,
  text.size = 10,
  title.size = 16,
  gap.pad = 20,
  legend.title = "Chromosome",
  xangle = 90,
  wrt.forward = FALSE,
  annotate.within = 20,
  annotations = GenomicRanges::GRanges()
)

Arguments

Value

A ggplot2 dotplot of the chimeric alignments versus the reference sequence

Author(s)

Helen Lindsay

See Also

findChimeras for finding chimeric alignment sets.

Examples

bam_fname <- system.file("extdata", "gol_F1_clutch_2_embryo_4_s.bam",
                         package = "CrispRVariants")
bam <- GenomicAlignments::readGAlignments(bam_fname, use.names = TRUE)
# Choose a single chimeric read set to plot:
chimeras <- bam[names(bam) == "AB3092"]

# This read aligns in 3 pieces, all on chromosome 18.
# The plot shows the alignment annot_alns a small duplication and
# a long gap.
plotChimeras(chimeras)

Description

Creates a heatmap from a matrix of counts or proportions, where tiles are coloured by the proportion and labeled with the value.

Usage

plotFreqHeatmap(obj, ...)

## S4 method for signature 'matrix'
plotFreqHeatmap(
  obj,
  ...,
  col.sums = NULL,
  header = NA,
  header.name = "Total",
  group = NULL,
  group.colours = NULL,
  as.percent = TRUE,
  x.axis.title = NULL,
  x.size = 6,
  y.size = 8,
  x.angle = 90,
  legend.text.size = 6,
  plot.text.size = 3,
  line.width = 1,
  x.hjust = 1,
  legend.position = "right",
  x.labels = NULL,
  legend.key.height = grid::unit(1, "lines")
)

## S4 method for signature 'CrisprSet'
plotFreqHeatmap(
  obj,
  ...,
  top.n = 50,
  min.freq = 0,
  min.count = 1,
  type = c("counts", "proportions"),
  order = NULL,
  alleles = NULL
)

Arguments

Value

The ggplot2 plot of the variant frequencies

Examples

#Load a CrisprSet object for plotting
data("gol_clutch1")

# Plot the frequency heatmap
plotFreqHeatmap(gol)

Description

Combines a plot of transcript structure, alleles aligned with respect to a reference genome and a heatmap of counts or proportions of each allele in a set of data.

Usage

plotVariants(obj, ...)

## S4 method for signature 'CrisprSet'
plotVariants(
  obj,
  ...,
  txdb = NULL,
  add.chr = TRUE,
  plotAlignments.args = list(),
  plotFreqHeatmap.args = list()
)

Arguments

Value

A ggplot2 plot of the variants

See Also

arrangePlots for general layout options and annotateGenePlot for options relating to the transcript plot.

Examples

#Load a CrisprSet object for plotting
data("gol_clutch1")

#Load the transcript db.  This is a subset of the Ensembl Danio Rerio v73 gtf
# for the region 18:4640000-4650000 which includes the targeted gol gene

library(GenomicFeatures)
fn <- system.file("extdata", "Danio_rerio.Zv9.73.gol.sqlite",
                 package = "CrispRVariants")
txdb <- loadDb(fn)

# Plot the variants
p <- plotVariants(gol, txdb = txdb)

#In the above plot, the bottom margin is too large, the legend is
#cut off, and the text within the plots should be larger.
#These issues can be fixed with some adjustments:
p <- plotVariants(gol, txdb = txdb,
                 plotAlignments.args = list(plot.text.size = 4, legend.cols = 2),
                 plotFreqHeatmap.args = list(plot.text.size = 4),
                 left.plot.margin = grid::unit(c(0.1,0.2,0.5,1), "lines"))

Description

Function for determining whether reads should be oriented to the target strand, always displayed on the positive strand, or oriented to

Usage

rcAlns(target.strand, orientation)

Arguments

Value

A logical value indicating whether reads should be reverse complemented

Author(s)

Helen Lindsay

Description

Short reads amplified with PCR primers should start and end at defined positions. However, the ends of an aligned read may be clipped as sequencing technologies are prone to making errors at the start and end. readsByPCRPrimer extrapolates the genomic location of entire reads from their aligned sections by adding clipped sections, then finds near exact matches to a set of PCR primers. Note that this is not always a good assumption, and is misleading in the case of chimeric reads where sections clipped in one part of a chimera are aligned in another.

Usage

readsByPCRPrimer(bam, primers, ...)

## S4 method for signature 'GAlignments,GRanges'
readsByPCRPrimer(
  bam,
  primers,
  ...,
  tolerance = 0,
  verbose = TRUE,
  ignore.strand = TRUE,
  allow.partial = TRUE,
  chimera.idxs = NULL
)

## S4 method for signature 'GRanges,GRanges'
readsByPCRPrimer(
  bam,
  primers,
  ...,
  tolerance = 0,
  verbose = TRUE,
  ignore.strand = TRUE,
  allow.partial = TRUE,
  chimera.idxs = NULL
)

Arguments

Value

A Hits object where "query" is the index with respect to bam and "subject" is the index with respect to the primers.

Author(s)

Helen Lindsay

See Also

GRanges, GAlignments

Description

Trims aligned reads to one or several target regions, optionally reverse complementing the alignments.

Usage

readsToTarget(reads, target, ...)

## S4 method for signature 'GAlignments,GRanges'
readsToTarget(
  reads,
  target,
  ...,
  reverse.complement = TRUE,
  chimeras = NULL,
  collapse.pairs = FALSE,
  use.consensus = FALSE,
  store.chimeras = FALSE,
  verbose = TRUE,
  name = NULL,
  minoverlap = NULL,
  orientation = c("target", "opposite", "positive")
)

## S4 method for signature 'GAlignmentsList,GRanges'
readsToTarget(
  reads,
  target,
  ...,
  reference = reference,
  names = NULL,
  reverse.complement = TRUE,
  target.loc = 17,
  chimeras = NULL,
  collapse.pairs = FALSE,
  use.consensus = FALSE,
  orientation = c("target", "opposite", "positive"),
  minoverlap = NULL,
  verbose = TRUE
)

## S4 method for signature 'character,GRanges'
readsToTarget(
  reads,
  target,
  ...,
  reference,
  reverse.complement = TRUE,
  target.loc = 17,
  exclude.ranges = GRanges(),
  exclude.names = NA,
  chimeras = c("count", "exclude", "ignore", "merge"),
  collapse.pairs = FALSE,
  use.consensus = FALSE,
  orientation = c("target", "opposite", "positive"),
  names = NULL,
  minoverlap = NULL,
  verbose = TRUE
)

readsToTargets(reads, targets, ...)

## S4 method for signature 'character,GRanges'
readsToTargets(
  reads,
  targets,
  ...,
  references,
  primer.ranges = NULL,
  target.loc = 17,
  reverse.complement = TRUE,
  collapse.pairs = FALSE,
  use.consensus = FALSE,
  ignore.strand = TRUE,
  names = NULL,
  bpparam = BiocParallel::SerialParam(),
  orientation = c("target", "opposite", "positive"),
  chimera.to.target = 5,
  verbose = TRUE
)

## S4 method for signature 'GAlignmentsList,GRanges'
readsToTargets(
  reads,
  targets,
  ...,
  references,
  primer.ranges = NULL,
  target.loc = 17,
  reverse.complement = TRUE,
  collapse.pairs = FALSE,
  use.consensus = FALSE,
  ignore.strand = TRUE,
  names = NULL,
  bpparam = BiocParallel::SerialParam(),
  chimera.to.target = 5,
  orientation = c("target", "opposite", "positive"),
  verbose = TRUE
)

Arguments

Value

(signature("GAlignments", "GRanges")) A CrisprRun object

(signature("character", "GRanges")) A CrisprSet object

Author(s)

Helen Lindsay

Examples

# Load the metadata table
md_fname <- system.file("extdata", "gol_F1_metadata_small.txt", package = "CrispRVariants")
md <- read.table(md_fname, sep = "\t", stringsAsFactors = FALSE)

# Get bam filenames and their full paths
bam_fnames <- sapply(md$bam.filename, function(fn){
 system.file("extdata", fn, package = "CrispRVariants")})

reference <- Biostrings::DNAString("GGTCTCTCGCAGGATGTTGCTGG")
gd <- GenomicRanges::GRanges("18", IRanges::IRanges(4647377, 4647399),
       strand = "+")

crispr_set <- readsToTarget(bam_fnames, target = gd, reference = reference,
                           names = md$experiment.name, target.loc = 17)

Description

Includes options for excluding reads either by name or range. The latter is useful if chimeras are excluded. Reads are excluded before chimeras are detected, thus a chimeric read consisting of two sections, one of which overlaps an excluded region, will not be considered chimeric. Chimeric reads can be ignored, excluded, which means that all sections of a chimeric read will be removed, or merged, which means that chimeras will be collapsed into a single read where possible. (Not implemented yet) If chimeras = "merge", chimeric reads are merged if all segments

Usage

readTargetBam(
  file,
  target,
  exclude.ranges = GRanges(),
  exclude.names = NA,
  chimera.to.target = 5,
  chimeras = c("count", "ignore", "exclude", "merge"),
  max.read.overlap = 10,
  max.unmapped = 4,
  by.flag = TRUE,
  verbose = TRUE
)

Arguments

Value

A GenomicAlignments::GAlignment obj

Description

Reconstruct the reference sequence from alignments reads using the CIGAR

Usage

refFromAlns(alns, location, ...)

## S4 method for signature 'GAlignments,ANY'
refFromAlns(alns, location, ..., keep.names = FALSE)

## S4 method for signature 'GAlignments,GRanges'
refFromAlns(alns, location, ...)

Arguments

Value

The reference sequences corresponding to the provided alignments

A DNAStringSet (signature = c("GAlignments", "ANY"))

A DNAString (signature = c("GAlignments", "GRanges"))

Author(s)

Helen Lindsay

Examples

bam <- system.file("extdata", "bam/ab1_ptena_wildtype_looking_embryo_1_s.bam",
                  package = "CrispRVariants")
alns <- GenomicAlignments::readGAlignments(bam,
          param = Rsamtools::ScanBamParam(tag = "MD", what = "seq"))
# To get the reference sequence from the a given location:
location <- GenomicRanges::GRanges("chr17", IRanges::IRanges(23648420,23648430))
refFromAlns(alns, location = location)

Description

For example, the string "20M5D15M" would become "15M5D20M"

Usage

reverseCigar(cigars)

Arguments

Value

The reversed cigar string

Description

Finds and removes sets of chimeric read alignments that overlap more than one guide, i.e. that cannot be unambiguously assigned to a single guide.

Usage

rmMultiPCRChimera(readnames, pcrhits, chimera_idxs, ...)

## S4 method for signature 'character,Hits,integer'
rmMultiPCRChimera(readnames, pcrhits, chimera_idxs, ..., verbose = TRUE)

Arguments

Value

pcrhits, with chimeric reads mapping to different primers omitted.

Author(s)

Helen Lindsay

Description

select CIGAR operations in a region of interest.

Usage

selectOps(cigar, ...)

## S4 method for signature 'character'
selectOps(
  cigar,
  ...,
  ops = GenomicAlignments::CIGAR_OPS,
  op.regions = NULL,
  pos = 1L
)

Arguments

Value

A GRanges list of opertion locations in reference space with a metadata column for the operation width in query space.

Author(s)

Helen Lindsay

Description

Creates a one-to-one text alignment of a set of cigar strings with respect to the reference sequence by collapsing insertions and introducing gaps across deletions.

When genomic coordinates for the alignment start and the target region are provided, aligned sequences are cropped to the target region

Given a character vector of pairwise alignments and a region to display, trims alignments to the display regions, joined by a separator "join". Alignments should be equal length, e.g. created by seqsToAln

Usage

seqsToAln(
  cigar,
  dnaseq,
  target,
  del_char = "-",
  aln_start = NULL,
  reverse_complement = FALSE,
  allow_partial = FALSE
)

selectAlnRegions(
  alns,
  reference,
  target,
  keep,
  join = "/ %s /",
  border.gaps = FALSE
)

Arguments

Value

The sequences with insertions collapsed and deletions padded

A list of the truncated alignments (alns) and reference (ref)

Author(s)

Helen Lindsay

Description

Sets tile colours for plotAlignments with a DNA alphabet

Usage

setDNATileColours(m)

Arguments

Value

A matrix with additional columns specifying tile and text colours

Author(s)

Helen Lindsay

Description

Sets tile colours for plotAlignments with a DNA alphabet.

Usage

setMismatchTileColours(m)

Arguments

Value

A matrix with additional columns specifying tile and text colours

Author(s)

Helen Lindsay

Description

Orders and transforms a reference sequence and a set of aligned sequences into long format, i.e. one observation (tile position) per row. Used internally by plotAlignments.

Usage

transformAlnsToLong(ref, alns, add.other = FALSE)

Arguments

Value

A matrix of characters and plotting locations

Author(s)

Helen Lindsay

Description

Returns a matrix of counts where rows are sequence variants and columns are samples

Usage

variantCounts(obj, ...)

## S4 method for signature 'CrisprSet'
variantCounts(
  obj,
  ...,
  top.n = NULL,
  min.freq = 0,
  min.count = 1,
  include.chimeras = TRUE,
  include.nonindel = TRUE,
  result = "counts",
  filter.vars = NULL
)

Arguments

Value

A matrix of counts where rows are variants and columns are samples

Author(s)

Helen Lindsay

Examples

data("gol_clutch1")

#Return a matrix of the 5 most frequent variants
variantCounts(gol, top.n = 5)

Description

Used by abifToFastq to write sanger sequences to fastq format As abifToFastq appends output to files, writeFastq checks that sequence names are unique. This function is faster with checking switched off.

Usage

writeFastq(outf, vals, allow_spaces = FALSE, check = TRUE)

Arguments

Value

None. The sequences in "vals" are written to outf

Author(s)

Helen Lindsay