Contents

1 Introduction

The CRISPR-Cas9 system is an efficient method of introducing mutations into genomic DNA. A guide RNA directs nuclease activity to an approximately 20 nucleotide target region, resulting in efficient mutagenesis. Repair of the cleaved DNA can introduce insertions and deletions centred around the cleavage site. Once the target sequence is mutated, the guide RNA will no longer bind and the DNA will not be cleaved again. SNPs within the target region, depending on their location, may also disrupt cleavage. The efficiency of a CRISPR-Cas9 experiment is typically measured by amplifying and sequencing the region surrounding the target sequence, then counting the number of sequenced reads that have insertions and deletions at the target site. The CrispRVariants package formalizes this process and takes care of various details of managing and manipulating data for such confirmatory and exploratory experiments.

This guide shows an example illustrating how raw data is preprocessed and mapped and how mutation information is extracted relative to the reference sequence. The package comprehensively summarizes and plots the spectrum of variants introduced by CRISPR-Cas9 or similar genome editing experiments.

2 Quickstart

This section is intended for people familiar with mapping reads and working with core Bioconductor classes. See the case study in the next section for a complete step-by-step analysis.

The CrisprSet class stores aligned reads which have been trimmed to a target region along with annotations of where insertion and deletions are located with respect to a specified location. CrisprSet objects are created using the functions readsToTarget (for a single target region) and readsToTargets (for multiple target locations). The following objects are needed to create a CrisprSet for a single guide sequence. For multiple guides, the equivalent parameters to target and reference are named targets and references respectively.

Other important readsToTarget parameters:

Assuming the above parameters are defined, the following code will set up a CrisprSet object and plot the variants:

crispr_set <- readsToTarget(reads, target = target, reference = reference,
                            target.loc = target.loc)
plotVariants(crispr_set)
# or use plotVariants(crispr_set, txdb) to additionally show the target
# location with respect to the transcripts if a Transcript Database 
# txdb is available

3 Case study: Analysis of ptena mutant spectrum in zebrafish

The data used in this case study is from the Mosimann laboratory, UZH.

3.1 Convert AB1-format Sanger sequences to FASTQ

This data set is from 5 separate clutches of fish (1 control - uninjected, 2 injected with strong phenotype, 2 injected with mild phenotype), with injections from a guide against the ptena gene. For this exercise, the raw data comes as AB1 (Sanger) format. To convert AB1 files to FASTQ, we use ab1ToFastq(), which is a wrapper for functions in the “sangerseqR” package with additional quality score trimming.

Although there are many ways to organize such a project, we organize the data (raw and processed) data into a set of directories, with a directory for each type of data (e.g., ‘ab1’ for AB1 files, ‘fastq’ for FASTQ files, ‘bam’ for BAM files, etc.); this can continue with directories for scripts, for figures, and so on. With this structure in place, the following code sets up various directories.

library(CrispRVariants)
library(sangerseqR)

# List AB1 filenames, get sequence names,  make names for the fastq files
# Note that we only include one ab1 file with CrispRVariants because
# of space constraints.  All bam files are included

data_dir <- system.file(package="CrispRVariants", "extdata/ab1/ptena")
fq_dir <- tempdir()
ab1_fnames <- dir(data_dir, "ab1$", recursive=TRUE, full=TRUE)
sq_nms <- gsub(".ab1","",basename(ab1_fnames))

# Replace spaces and slashes in filename with underscores
fq_fnames  <- paste0(gsub("[\ |\\/]", "_", dirname(ab1_fnames)), ".fastq")

# abifToFastq to read AB1 files and write to FASTQ
dummy <- mapply( function(u,v,w) {
        abifToFastq(u,v,file.path(fq_dir,w))
}, sq_nms, ab1_fnames, fq_fnames)

We will collect sequences from each embryo into the same FASTQ file. Note that abifToFastq appends output to existing files where possible. In this example, there is only 1 sequence, which will be output to 1 file:

length(unique(ab1_fnames))
## [1] 1
length(unique(fq_fnames))
## [1] 1

Some of the AB1 files may not have a sufficient number of bases after quality score trimming (default is 20 bases). In these cases, abifToFastq() issues a warning (suppressed here).

3.2 Map the FASTQ reads

We use FASTQ format because it is the major format used by most genome alignment algorithms. At this stage, the alignment could be done outside of R (e.g., using command line tools), but below we use R and a call to system() to keep the whole workflow within R. Note that this also requires various software tools (e.g., bwa, samtools) to already be installed.

The code below iterates through all the FASTQ files generated above and aligns them to a pre-indexed genome.

library("Rsamtools")

# BWA indices were generated using bwa version 0.7.10
bwa_index <- "GRCHz10.fa.gz"
bam_dir <- system.file(package="CrispRVariants", "extdata/bam")
fq_fnames <- file.path(fq_dir,unique(fq_fnames))
bm_fnames <- gsub(".fastq$",".bam",basename(fq_fnames))
srt_bm_fnames <- file.path(bam_dir, gsub(".bam","_s",bm_fnames))

# Map, sort and index the bam files, remove the unsorted bams
for(i in 1:length(fq_fnames)) {
  cmd <- paste0("bwa mem ", bwa_index, " ", fq_fnames[i],
                " | samtools view -Sb - > ", bm_fnames[i])
  message(cmd, "\n"); system(cmd)
  indexBam(sortBam(bm_fnames[i],srt_bm_fnames[i]))
  unlink(bm_fnames[i])
}

See the help for bwa index at the bwa man page and for general details on mapping sequences to a genome reference.

3.3 List the BAM files

To allow easy matching to experimental condition (e.g., useful for colour labeling) and for subsetting to experiments of interest, we often organize the list of BAM files together with accompanying metadata in a machine-readable table beforehand. Here we read the bam filenames from a metadata table which also contains sample names and experimental grouping information. Note that we could also have used the bam filenames listed above.

# The metadata and bam files for this experiment are included with CrispRVariants
library("readxl")
md_fname <- system.file(package="CrispRVariants", "extdata/metadata/metadata.xls")
md <- readxl::read_xls(md_fname, 1)
md
## # A tibble: 5 × 7
##   bamfile                directory Short.name Targeting.type sgRNA1 sgRNA2 Group
##   <chr>                  <chr>     <chr>      <chr>          <chr>  <lgl>  <chr>
## 1 ab1_ptena_phenotype_e… ptena/ph… ptena 1    single         ptena… NA     stro…
## 2 ab1_ptena_phenotype_e… ptena/ph… ptena 2    single         ptena… NA     stro…
## 3 ab1_ptena_wildtype_lo… ptena/wi… ptena 3    single         ptena… NA     mild 
## 4 ab1_ptena_wildtype_lo… ptena/wi… ptena 4    single         ptena… NA     mild 
## 5 ab1_ptena_uninjected_… ptena/un… control    single         ptena… NA     cont…
# Get the bam filenames from the metadata table
bam_dir <- system.file(package="CrispRVariants", "extdata/bam")
bam_fnames <- file.path(bam_dir, md$bamfile)

# check that all files exist
all( file.exists(bam_fnames) )
## [1] TRUE

3.4 Create the target location and reference sequence

Given a set of BAM files with the amplicon sequences of interest mapped to the reference genome, we need to collect a few additional pieces of information about the guide sequence and define the area around the guide that we want to summarize the mutation spectrum over.

The coordinates of the region of interest can be obtained by running BLAST or BLAT on the guide sequence or by mapping the guide sequence to the reference sequence. The coordinates, or “target” should be represented as a GenomicRanges::GRanges object. This can be created directly, but here we will import the coordinates of the guide sequence from a BED file using the rtracklayer package. The import() commmand below returns a GRanges object.

library(rtracklayer)
# Represent the guide as a GenomicRanges::GRanges object
gd_fname <- system.file(package="CrispRVariants", "extdata/bed/guide.bed")
gd <- rtracklayer::import(gd_fname)
gd
## GRanges object with 1 range and 2 metadata columns:
##       seqnames            ranges strand |        name     score
##          <Rle>         <IRanges>  <Rle> | <character> <numeric>
##   [1]    chr17 23648474-23648496      - |   ptena_ccA         0
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

The 23bp (including PAM) ptena guide sequence used in this experiment is located on chromosome chr17 from 23648474-23648496. We prefer to analyse a slightly larger region. Below, we’ll extend the guide region by 5 bases on each side when counting variants. Note that the expected cut site (used later for labeling variants), after extension, isat base 22 with respect to the start of the new target sequence.

gdl <- GenomicRanges::resize(gd, width(gd) + 10, fix = "center")

With the Bioconductor BSgenome packages, the reference sequence itself can be retrieved directly into a DNAStringSet object. For other genomes, the reference sequence can be retrieved from a genome by first indexing the genome with samtools faidx and then fetching the required region (for an alternative method, see the note for Windows and Galaxy users below). Here we are using the GRCHz10 zebrafish genome. The reference sequence was fetched and saved as follows:

system("samtools faidx GRCHz10.fa.gz")

reference=system(sprintf("samtools faidx GRCHz10.fa.gz %s:%s-%s",
                         seqnames(gdl)[1], start(gdl)[1], end(gdl)[1]),
                 intern = TRUE)[[2]]

# The guide is on the negative strand, so the reference needs to be reverse complemented
reference=Biostrings::reverseComplement(Biostrings::DNAString(reference))
save(reference, file = "ptena_GRCHz10_ref.rda")

We’ll load the previously saved reference sequence.

ref_fname <- system.file(package="CrispRVariants", "extdata/ptena_GRCHz10_ref.rda")
load(ref_fname)
reference
## 33-letter DNAString object
## seq: GCCATGGGCTTTCCAGCCGAACGATTGGAAGGT

Note the NGG sequence (here, TGG) is present with the 5 extra bases on the end.

3.4.1 Note for Windows and Galaxy Users

If you do not have a copy of the genome you used for mapping on the computer you are using to analyse your data, or you cannot install samtools because you are working on Windows, CrispRVariants provides an alternative, albeit slower, method for fetching the reference sequence:

# First read the alignments into R.  The alignments must include
# the read sequences and the MD tag
alns <- GenomicAlignments::readGAlignments(bam_fnames[[1]], 
          param = Rsamtools::ScanBamParam(tag = "MD", what = c("seq", "flag")),
          use.names = TRUE)

# Then reconstruct the reference for the target region.
# If no target region is given, this function will reconstruct
# the complete reference sequence for all reads.
rfa <- refFromAlns(alns, gdl)

# The reconstructed reference sequence is identical to the sequence
# extracted from the reference above
print(rfa == reference)
## [1] TRUE

Note that the object alns created above can be directly passed to the function readsToTarget (see below) instead of the bam filenames. If there is more than one bam file, readsToTarget can also accept a GAlignmentsList object (see the GenomicAlignments package) for more details).

3.5 Creating a CrisprSet

The next step is to create a CrisprSet object, which is the container that stores the relevant sequence information, alignments, observed variants and their frequencies.

# Note that the zero point (target.loc parameter) is 22
crispr_set <- readsToTarget(bam_fnames, target = gdl, reference = reference,
                            names = md$Short.name, target.loc = 22)
crispr_set
## CrisprSet object containing 5 CrisprRun samples
## Target location:
## GRanges object with 1 range and 2 metadata columns:
##       seqnames            ranges strand |        name     score
##          <Rle>         <IRanges>  <Rle> | <character> <numeric>
##   [1]    chr17 23648469-23648501      - |   ptena_ccA         0
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths
## [1] "Most frequent variants:"
##            ptena 1 ptena 2 ptena 3 ptena 4 control
## no variant       3       4       4       0       7
## -1:4D            0       0       0       2       0
## 6:1D             0       0       0       1       1
## 1:7I             1       0       0       0       0
## 2:1D,4:5I        0       0       0       1       0
## Other            0       0       1       1       0
# The counts table can be accessed with the "variantCounts" function
vc <- variantCounts(crispr_set)
print(class(vc))
## [1] "matrix" "array"

You can see that in the table of variant counts, variants are summarised by the location of their insertions and deletions with respect to the target site. Non-variant sequences and sequences with a single nucleotide variant (SNV) but no insertion or deletion (indel) are displayed first, followed by the indel variants from most to least frequent For example, the most frequent non-wild-type variant, “-1:4D” is a 4 base pair deletion starting 1 base upstream of the zero point.

3.6 Creating summary plots of variants

We want to plot the variant frequencies along with the location of the guide sequence relative to the known transcripts. If you do this repeatedly for the same organism, it is worthwhile to save the database in a local file and read in with loadDb(), since this is quicker than retrieving it from UCSC (or Ensembl) each time.

We start by creating a transcript database of Ensembl genes. The gtf was downloaded from Ensembl version 81. We first took a subset of just the genes on chromosome 17 and then generated a transcript database.

# Extract genes on chromosome 17 (command line)
# Note that the Ensembl gtf does not include the "chr" prefix, so we add it here 
gtf=Danio_rerio.GRCz10.81.gtf.gz
zcat ${gtf} | awk '($1 == 17){print "chr"$0}' > Danio_rerio.GRCz10.81_chr17.gtf
# In R
library(GenomicFeatures)
gtf_fname <- "Danio_rerio.GRCz10.81_chr17.gtf"
txdb <- GenomicFeatures::makeTxDbFromGFF(gtf_fname, format = "gtf")
saveDb(txdb, file= "GRCz10_81_chr17_txdb.sqlite")

We now load the the previously saved database

plotVariants() is a wrapper function that groups together a plot of the transcripts of the gene/s overlapping the guide (optional), CrispRVariants::plotAlignments(), which displays the alignments of the consensus variant sequences to the reference, and CrispRVariants::plotFreqHeatmap(), which produces a table of the variant counts per sample, coloured by either their counts or percentage contribution to the variants observed for a given sample. If a transcript database is supplied, the transcript plot is annotated with the guide location. Arguments for plotAlignments() and plotFreqHeatmap() can be passed to plotVariants() as lists named plotAlignments.args and plotFreqHeatmap.args, respectively.

# The gridExtra package is required to specify the legend.key.height 
# as a "unit" object.  It is not needed to call plotVariants() with defaults
library(gridExtra)

# Match the clutch id to the column names of the variants
group <- md$Group
p <- plotVariants(crispr_set, txdb = txdb, gene.text.size = 8, 
    row.ht.ratio = c(1,8), col.wdth.ratio = c(4,2),
    plotAlignments.args = list(line.weight = 0.5, ins.size = 2, 
                               legend.symbol.size = 4),
    plotFreqHeatmap.args = list(plot.text.size = 3, x.size = 8, group = group, 
                                legend.text.size = 8, 
                                legend.key.height = grid::unit(0.5, "lines"))) 
## Warning in min(xranges): no non-missing arguments to min; returning Inf
## Warning in max(xranges): no non-missing arguments to max; returning -Inf
## Warning in max(yranges): no non-missing arguments to max; returning -Inf
## Warning: Vectorized input to `element_text()` is not officially supported.
## ℹ Results may be unexpected or may change in future versions of ggplot2.
(Top) schematic of gene structure showing guide location (left) consensus sequences for variants (right) variant counts in each embryo.

Figure 1: (Top) schematic of gene structure showing guide location (left) consensus sequences for variants (right) variant counts in each embryo

The plotVariants() options set the text size of the transcript plot annotation (gene.text.size) and the relative heights (row.ht.ratio) and widths (col.wdth.ratio) of the plots.

The plotAlignments arguments set the symbol size in the figure (ins.size) and in the legend (legend.symbol), the line thickness for the (optional) annotation of the guide region and cleavage site (line.weight).

For plotFreqHeatmap we define an grouping variable for colouring the x-axis labels (group), the size of the text within the plot (plot.text.size) and on the x-axis (x.size) and set the size of the legend text (legend.text.size).

3.7 Calculating the mutation efficiency

The mutation efficiency is the number of reads that include an insertion or deletion. Chimeric reads and reads containing single nucleotide variants near the cut site may be counted as variant reads, non-variant reads, or excluded entirely. See the help page for the function mutationEfficiency for more details.

We can see in the plot above that the control sample includes a variant sequence 6:1D, also present in sample ptena 4. We will exclude all sequences with this variant from the efficiency calculation. We also demonstrate below how to exclude particular variants.

# Calculate the mutation efficiency, excluding indels that occur in the "control" sample
# and further excluding the "control" sample from the efficiency calculation
eff <- mutationEfficiency(crispr_set, filter.cols = "control", exclude.cols = "control")
eff
##   ptena 1   ptena 2   ptena 3   ptena 4   Average    Median   Overall     StDev 
##     25.00     42.86     20.00     80.00     41.96     33.93     42.86      1.50 
## ReadCount 
##     21.00
# Suppose we just wanted to filter particular variants, not an entire sample.
# This can be done using the "filter.vars" argument
eff2 <- mutationEfficiency(crispr_set, filter.vars = "6:1D", exclude.cols = "control")

# The results are the same in this case as only one variant was filtered from the control
identical(eff,eff2)
## [1] TRUE

We see above that sample ptena 4 has an efficiency of 80%, i.e. 4 variant sequences, plus one sequence “6:1D” which is counted as a non-variant sequence as it also occurs in the control sample.

3.8 Get consensus alleles

The consensus sequences for variant alleles can be accessed using consensusSeqs. This function allows filtering by variant frequency or read count, as for plotAlignments and plotFreqHeatmap. Consensus alleles are returned with respect to the positive strand.

sqs <- consensusSeqs(crispr_set)
sqs
## DNAStringSet object of length 8:
##     width seq                                               names               
## [1]    33 ACCTTCCAATCGTTCGGCTGGAAAGCCCATGGC                 no variant
## [2]    29 NCCTTCCANTCGGCTGGAAAGCCCATGGC                     -1:4D
## [3]    32 ACCTTCNNTCGTTCGGCTGGAAAGCCCATGGC                  6:1D
## [4]    40 NCCTTCCAATCAGTTCGAATTCGGCTGGAGAGCCCATAGC          1:7I
## [5]    34 NCCTTCCAATCGGTTCGGCTGGAAAGCCCATGGC                1:1I
## [6]    40 NCCTTCCANCNCCTTCCTTTTCGGCTGGAAAGCCCATGGC          1:2I,3:5I
## [7]    29 ANNTTCNNATCGGCTGGNAAGCCNNTGGC                     -2:4D
## [8]    37 NCCTTCCACAAACACNTTCGGCTGGAAAGCCCATGGC             2:1D,4:5I
# The ptena guide is on the negative strand.
# Confirm that the reverse complement of the "no variant" allele
# matches the reference sequence:
Biostrings::reverseComplement(sqs[["no variant"]]) == reference
## [1] TRUE

3.9 Plot chimeric alignments

When deciding whether chimeric alignments should be considered as variant sequences, it can be useful to plot the frequent chimeras.

ch <- getChimeras(crispr_set, sample = "ptena 4")

# Confirm that all chimeric alignments are part of the same read
length(unique(names(ch))) == 1
## [1] TRUE
# Set up points to annotate on the plot
annotations <- c(resize(gd, 1, fix = "start"), resize(gd, 1, fix = "end"))
annotations$name <- c("ptena_start", "ptena_end")

plotChimeras(ch, annotations = annotations)

Here we see the read aligns as two tandem copies of the region chr17:23648420-23648656. The endpoint of each copy is not near the guide sequence. We do not consider this a genuine mutation, so we’ll recalculate the mutation efficiency excluding the chimeric reads and the control variant as before.

mutationEfficiency(crispr_set, filter.cols = "control", exclude.cols = "control",
                   include.chimeras = FALSE)
##   ptena 1   ptena 2   ptena 3   ptena 4   Average    Median   Overall     StDev 
##     25.00     42.86      0.00     75.00     35.71     33.93     36.84      1.50 
## ReadCount 
##     19.00

We see that the mutation effiency for “ptena 4” is now 75%, i.e. 3 genuine variant sequences, 1 sequence counted as “non-variant” because it occurs in the control, and the chimeric read excluded completely

4 Choosing the strand for display

CrispRVariants is capable of tabulating variants with respect to either strand. By default, variant alleles are displayed with respect to the target strand, i.e. sequences for a guide on the negative strand are reverse complemented for display. For some applications it may be preferable to display the variants on the opposite strand, for example if a guide on the negative strand is used to target a gene on the positive strand. The display strand is controlled using the orientation parameter in readsToTarget(s) during initialization.

To illustrate, we will plot the variants for ptena on the positive strand. Note that the only changes to the initialization code is the orientation parameter. In particular, the target.loc is still specified with respect to the guide sequence and the reference is still the guide sequence, not its reverse complement.

crispr_set_rev <- readsToTarget(bam_fnames, target = gdl, reference = reference,
                                names = md$Short.name, target.loc = 22, 
                                orientation = "opposite")
plotVariants(crispr_set_rev)

## TableGrob (2 x 1) "arrange": 2 grobs
##   z     cells    name                grob
## 1 1 (1-1,1-1) arrange rect[GRID.rect.216]
## 2 2 (2-2,1-1) arrange     gtable[arrange]

Note that variants are labelled with respect to their leftmost coordinate, so the labelled variant location changes when plotting on the opposite strand.

5 Multiple guides

CrispRVariants accepts an arbitrarily long reference sequence and target region. By default, reads must span the target region to be counted. Since v1.3.6, a new argument minoverlap to readsToTarget is available, which allows reads which do not span the target region to be counted, provided they have at least minoverlap aligned bases overlapping the target. This is particularly important when the target region is close to or greater than the sequencing read length.

When using CrispRVariants with multiple guides, initialisation of a CrisprSet object is done as for a single guide, but with the reference and target parameters corresponding to a region spanning the guides of interest. One important parameter is the target location, or target.loc which determines how the variant alleles are numbered. For a single guide, position zero would typically be the cut site. With multiple guides, possible zero points might include the cut site of the leftmost guide or the first base of the amplified sequence. Multiple guide sequences are first indicated at the stage of plotting.

In the example below, we reuse the ptena data used in the case study. In this experiment, a single guide was injected. However, for illustrative purposes we will treat the data as if it was a paired injection of two nearby guides.

# We create a longer region to use as the "target"
# and the corresponding reference sequence
gdl <- GenomicRanges::resize(gd, width(gd) + 20, fix = "center")
reference <- Biostrings::DNAString("TCATTGCCATGGGCTTTCCAGCCGAACGATTGGAAGGTGTTTA")

# At this stage, target should be the entire region to display and target.loc should
# be the zero point with respect to this region
crispr_set <- readsToTarget(bam_fnames, target = gdl, reference = reference,
                            names = md$Short.name, target.loc = 10,
                            verbose = FALSE)

# Multiple guides are added at the stage of plotting
# The boundaries of the guide regions must be specified with respect to the
# given target region
p <- plotVariants(crispr_set, 
       plotAlignments.args = list(pam.start = c(6,35),
                              target.loc = c(10, 32),
                              guide.loc = IRanges::IRanges(c(6, 25),c(20, 37))))

p
## TableGrob (2 x 1) "arrange": 2 grobs
##   z     cells    name                grob
## 1 1 (1-1,1-1) arrange rect[GRID.rect.334]
## 2 2 (2-2,1-1) arrange     gtable[arrange]

In the above call to plotAlignments, pam.start and pam.end control where the box around the PAM sequence is drawn, target.loc controls where vertical lines are drawn (note this does not have to match the target.loc passed to readsToTarget), and guide.loc controls where the box around the guide is drawn.

6 Changing the appearance of plots

Note that arguments for CrispRVariants::plotAlignments described below can be passed to CrispRVariants::plotVariants as a list, e.g. plotAlignments.args = list(axis.text.size = 14). Similarly, arguments for CrispRVariants::plotFreqHeatmap are passed through plotVariants via plotFreqHeatmap.args.

6.1 Filtering data in plotVariants

For the following examples, we will use the ptena data set. We must first load the data and create a CrispRVariants::CrisprSet object.

# Setup for ptena data set
library("CrispRVariants")
library("rtracklayer")
library("GenomicFeatures")
library("readxl")

# Load the guide location
gd_fname <- system.file(package="CrispRVariants", "extdata/bed/guide.bed")
gd <- rtracklayer::import(gd_fname)
gdl <- resize(gd, width(gd) + 10, fix = "center")

# The saved reference sequence corresponds to the guide 
# plus 5 bases on either side, i.e. gdl
ref_fname <- system.file(package="CrispRVariants", 
                         "extdata/ptena_GRCHz10_ref.rda")
load(ref_fname)

# Load the metadata table, which gives the sample names
md_fname <- system.file(package="CrispRVariants",
                        "extdata/metadata/metadata.xls")
md <- readxl::read_xls(md_fname, 1)

# Get the list of bam files
bam_dir <- system.file(package="CrispRVariants", "extdata/bam")
bam_fnames <- file.path(bam_dir, md$bamfile)

# Check that all files were found
all(file.exists(bam_fnames))
## [1] TRUE
crispr_set <- readsToTarget(bam_fnames, target = gdl, reference = reference,
                            names = md$Short.name, target.loc = 22,
                            verbose = FALSE)

# Load the transcript database
txdb_fname <- system.file("extdata/GRCz10_81_ptena_txdb.sqlite", 
                          package="CrispRVariants")
txdb <- AnnotationDbi::loadDb(txdb_fname)

Here is the ptena data set plotted with default options:

p <- plotVariants(crispr_set, txdb = txdb)
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns

The layout of this plot is controlled mainly by two parameters: row.ht.ratio and col.wdth.ratio. row.ht.ratio (default c(1,6)) controls the relative sizes of the transcript plot and the other plots. Below we show how to change the ratio so that the transcript plot is relatively larger:

p <- plotVariants(crispr_set, txdb = txdb, row.ht.ratio = c(1,3))
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns

Similarly, col.wdth.ratio controls the width ratio of the alignment plot and the heatmap (default c(2,1), i.e. the alignment plot is twice as wide as the heatmap). Below we alter this to make the alignment plot comparatively wider:

p <- plotVariants(crispr_set, txdb = txdb, col.wdth.ratio = c(4,1))

The remaining examples in this section use the gol data set.

# Load gol data set
library("CrispRVariants")
data("gol_clutch1")

The data used in plotAlignments and plotFreqHeatmap can be filtered by either frequency via min.freq, count via min.count, or to show a set number of alleles sorted by frequency, via top.n. Within plotVariants, these filtering options need to be set for both plotAlignments and plotFreqHeatmap. We also add space to the bottom of the plot to prevent clipping of the labels.

library(GenomicFeatures)
p <- plotVariants(gol, plotAlignments.args = list(top.n = 3),
             plotFreqHeatmap.args = list(top.n = 3),
             left.plot.margin = ggplot2::unit(c(0.1,0,5,0.2), "lines"))

At present, filtering by sample (column) is possible for plotFreqHeatmap via the order parameter (which can also be used to reorder columns), but not plotAlignments.

plotVariants(gol, plotAlignments.args = list(top.n = 3),
             plotFreqHeatmap.args = list(top.n = 3, order = c(1,5,3)),
             left.plot.margin = ggplot2::unit(c(0.1,0,5,0.2), "lines"))

## TableGrob (2 x 1) "arrange": 2 grobs
##   z     cells    name                grob
## 1 1 (1-1,1-1) arrange rect[GRID.rect.993]
## 2 2 (2-2,1-1) arrange     gtable[arrange]

6.2 plotAlignments

6.2.1 Insertion symbols

The symbols indicating insertions are controlled by four parameters. ins.size (default 3) controls the size of the symbols within the plot area.

plotAlignments(gol, top.n = 3, ins.size = 6)

By default the symbols in the legend are the same size as those in the plot, but this can be controlled separately with legend.symbol.size.

plotAlignments(gol, top.n = 3, legend.symbol.size = 6)

As long sequences can make the plot difficult to read, by default only the length of insertions greater then 20bp is shown. This can be changed with the max.insertion.size parameter. If there is more than one allele, the number of (frequent) alleles is indicated.

plotAlignments(gol, top.n = 5, max.insertion.size = 25)

Finally, the parameter min.insertion.freq (default 5%) controls how many alleles are displayed at each insertion locus. In large data sets, there will be a substantial proportion of reads with sequencing errors, and we may only wish to display the most common sequences.

# Here we set a fairly high value of 50% for min.insertion.freq
# As ambiguous nucleotides occur frequently in this data set,
# there are no alleles passing this cutoff.
plotAlignments(gol, top.n = 5, min.insertion.freq = 50)

max.insertion.size and min.insertion.freq can be combined. In this case, alleles longer than max.insertion.size but less frequent than min.insertion.freq will be collapsed.

plotAlignments(gol, top.n = 5, max.insertion.size = 25, min.insertion.freq = 50)

6.2.2 Whitespace between rows

The space between rows is controlled with the tile.height parameter (default 0.55). Values closer to 0 increase the space between rows, whilst values closer to 1 decrease the space between rows.

# No white space between rows
plotAlignments(gol, top.n = 3, tile.height = 1)

# More white space between rows
plotAlignments(gol, top.n = 3, tile.height = 0.3)

6.2.3 Box around guide

The black box around the guide sequence can be removed by setting highlight.guide = FALSE.

plotAlignments(gol, top.n = 3, highlight.guide = FALSE)

By default, the box around the guide is drawn from 17 bases upstream of the target.loc to 6 bases downstream. For experiments with a truncated guide, or other non-standard guide location, the box must be manually specified. The guide location can be altered by setting the guide.loc parameter. This can be either an IRanges::IRanges or GenomicRanges::GRanges object.

library(IRanges)
guide <- IRanges::IRanges(15,28)
plotAlignments(gol, top.n = 3, guide.loc = guide)

6.2.4 Text sizes

The text showing bases within the alignment plot is controlled by plot.text.size (default 2), and can be removed completely by setting plot.text.size = 0. The axis labels and legend labels are controlled with axis.text.size (default 8) and legend.text.size (default 6) respectively. The number of columns in the legend is controlled by legend.cols (default 3).

# Here we increase the size of the axis labels and make
# two columns for the legend
plotAlignments(gol, top.n = 5, axis.text.size = 12, 
               legend.text.size = 12, legend.cols = 2)

6.2.5 Box around PAM

The argument highlight.pam determines whether a box around the PAM should be drawn.

# Don't highlight the PAM sequence
plotAlignments(gol, top.n = 3, highlight.pam = FALSE)

By default this box is drawn 3 nucleotides downstream of the target.loc. Other applications might require a different region highlighted. This can be achieved by explicitly setting the start and end positions of the box, with respect to the reference sequence.

# Highlight 3 bases upstream to 3 bases downstream of the target.loc
plotAlignments(gol, top.n = 3, pam.start = 19, pam.end = 25)

The boxes around the guide and the PAM can both be changed to arbitrary locations, however note that the guide box is specified by a ranges object whilst the PAM box is specified by start and end coordinates. Both coordinates are with respect to the start of the reference sequence. The box around the guide is slightly wider than the box around the PAM.

plotAlignments(gol, top.n = 3, guide.loc = IRanges(5,10),
               pam.start = 8, pam.end = 13)

The thickness of the lines showing the cut site, the guide and the PAM are controlled with line.weight (default 1).

plotAlignments(gol, top.n = 3, line.weight = 3)

6.2.6 Add a codon frame

If the codon frame with respect to the first base of the target region is known, it can be added to plot.alignments using the argument codon.frame

plotAlignments(gol, top.n = 3, codon.frame = 1)

6.2.7 Other modifications

To retreive the information used in plotAlignments when starting from a CrisprSet object, use the argument create.plot = FALSE.

plot_data <- plotAlignments(gol, top.n = 3, create.plot = FALSE)
names(plot_data)
# This data can be modified as required, then replotted using:
do.call(plotAlignments, plot_data)

6.3 plotFreqHeatmap

plotFreqHeatmap produces a heatmap of the counts or proportions of the variant alleles. Typically, plotFreqHeatmap is passed a CrisprSet object, but it can also accept a matrix if greater flexibility is required (see below). As plotFreqHeatmap returns a ggplot object, it can also be modified using standard ggplot2 syntax. For example, we add a title to the plot below.

By default, when given an object of class CrisprSet, plotFreqHeatmap shows the allele counts and the header shows the total number of on-target reads in each sample. For example, the following code shows the three most common variant alleles in the gol dataset. The header here does not equal the sum of the columns as not all variants are shown in the plot.

# Save the plot to a variable then add a title using ggplot2 syntax.
# If the plot is not saved to a variable the unmodified plot is displayed.
p <- plotFreqHeatmap(gol, top.n = 3)
## Warning in min(xranges): no non-missing arguments to min; returning Inf
## Warning in max(xranges): no non-missing arguments to max; returning -Inf
## Warning in max(yranges): no non-missing arguments to max; returning -Inf
p + labs(title = "A. plotFreqHeatmap with default options")
plotFreqHeatmap with default options

Figure 2: plotFreqHeatmap with default options

6.3.1 Plotting allele proportions

When calling plotFreqHeatmap with a CrisprSet object the type argument controls the information shown in text in the heatmap. Setting type = "counts" (the default) shows allele counts, setting type = "proportions" shows allele proportions. This also affects the default header values. When type = "proportions" the header shows the column sums, i.e. the percentage of the total number of reads shown in the plot.

plotFreqHeatmap(gol, top.n = 3, type = "proportions")
## Warning in min(xranges): no non-missing arguments to min; returning Inf
## Warning in max(xranges): no non-missing arguments to max; returning -Inf
## Warning in max(yranges): no non-missing arguments to max; returning -Inf
plotFreqHeatmap showing allele proportions

Figure 3: plotFreqHeatmap showing allele proportions

6.3.2 Changing the header

There are three standard options for the header when calling plotFreqHeatmap with an object of class CrisprSet:

  • header = "default" shows total read counts when type = "counts" or column sums when type = "proportions". See the examples above.
  • header = "counts" shows total read counts. If variants are excluded, the values in the header do necessarily equal the column totals. For example, see plot “C. Modified plotFreqHeatmap” below.
  • header = "efficiency" shows the mutation efficiency, i.e. the percentage of reads that have an insertion or deletion variant. The mutation efficiency is calculated using the default options of the function mutationEfficiency.

6.3.3 Heatmap colours

The tiles may be coloured by either the percentage of the column totals (default), or by the counts, by setting as.percent = FALSE. For example, see plot “B. coloured X labels with tiles coloured by count” below and contrast with plot “A. plotFreqHeatmap with default options” above.

6.3.4 Changing colours of x-labels

The x-labels can be coloured by experimental group. To do this, a grouping vector must be supplied by setting parameter group. Columns are ordered according to the levels of the group. There should be one group value per column in the data.

ncolumns <- ncol(variantCounts(gol))
ncolumns
## [1] 8
grp <- rep(c(1,2), each = ncolumns/2)
p <- plotFreqHeatmap(gol, top.n = 3, group = grp, as.percent = FALSE)
## Warning in min(xranges): no non-missing arguments to min; returning Inf
## Warning in max(xranges): no non-missing arguments to max; returning -Inf
## Warning in max(yranges): no non-missing arguments to max; returning -Inf
## Warning: Vectorized input to `element_text()` is not officially supported.
## ℹ Results may be unexpected or may change in future versions of ggplot2.
p + labs(title = "B. coloured X labels with tiles coloured by count")
plotFreqHeatmap with X-axis labels coloured by experimental group and tiles coloured by count instead of proportion

Figure 4: plotFreqHeatmap with X-axis labels coloured by experimental group and tiles coloured by count instead of proportion

The default colours are designed to be readable on a white background and colour-blind safe. These can be changed by supplying a vector of colours for each level of the group. Colours must be supplied if there are more than 7 experimental groups.

grp_clrs <- c("red", "purple")
p <- plotFreqHeatmap(gol, top.n = 3, group = grp, group.colours = grp_clrs,
                type = "proportions", header = "counts",
                legend.position = "bottom")
## Warning in min(xranges): no non-missing arguments to min; returning Inf
## Warning in max(xranges): no non-missing arguments to max; returning -Inf
## Warning in max(yranges): no non-missing arguments to max; returning -Inf
## Warning: Vectorized input to `element_text()` is not officially supported.
## ℹ Results may be unexpected or may change in future versions of ggplot2.
p <- p + labs(title = "C. Modified plotFreqHeatmap")
p
plotFreqHeatmap with labels showing allele proportions, header showing counts per sample and modified legend position.

Figure 5: plotFreqHeatmap with labels showing allele proportions, header showing counts per sample and modified legend position

6.3.5 Controlling the appearance of the legend

The legend position is controlled via the plotFreqHeatmap argument legend.position, which is passed to ggplot2::theme. Similarly legend.key.height controls the height of the legend. See the ggplot docs for more information.

plotFreqHeatmap(gol, top.n = 3, 
                legend.key.height = ggplot2::unit(1.5, "lines"))
## Warning in min(xranges): no non-missing arguments to min; returning Inf
## Warning in max(xranges): no non-missing arguments to max; returning -Inf
## Warning in max(yranges): no non-missing arguments to max; returning -Inf

An additional example where the legend is placed at the bottom is shown above in plot C named “Modified plotFreqHeatmap” above.

6.3.6 Further customisation

The function variantCounts returns a matrix of allele counts or proportions which can be passed to plotFreqHeatmap. variantCounts allows filtering by number of alleles or allele frequency. When passing plotFreqHeatmap a matrix instead of a CrisprSet, a header vector can also be supplied. If no header is supplied, the header is the column sums.

var_counts <- variantCounts(gol, top.n = 3)
# (additional modifications to var_counts can be added here)
plotFreqHeatmap(var_counts)

6.4 barplotAlleleFreqs

barplotAlleleFreqs includes two different colour schemes - a default rainbow scheme and a blue-red gradient. Note that the transcript database txdb must be passed by name as this function accepts ellipsis arguments.

Here barplotAlleleFreqs is run with the default parameters:

barplotAlleleFreqs(crispr_set, txdb = txdb)
## Looking up variant locations
## Loading required namespace: VariantAnnotation
## 'select()' returned many:1 mapping between keys and columns
## 'select()' returned many:1 mapping between keys and columns
## Classifying variants
## Warning in dispatchDots(.self$.getFilteredCigarTable, ...): dispatchDots may
## not work as expected with S4 functions

In this case barplotAlleleFreqs is run with the alternative palette.

barplotAlleleFreqs(crispr_set, txdb = txdb, palette = "bluered")
## Warning in dispatchDots(.self$.getFilteredCigarTable, ...): dispatchDots may
## not work as expected with S4 functions

By default, a table of the number of sequences and alleles is plotted next to the barplot. This can be switched off. In this case, barplotAlleleFreqs will return an ggplot object, allowing further alteration of the appearance through the usual ggplot2::theme settings.

barplotAlleleFreqs(crispr_set, txdb = txdb, include.table = FALSE)
## Warning in dispatchDots(.self$.getFilteredCigarTable, ...): dispatchDots may
## not work as expected with S4 functions

barplotAlleleFreqs.CrisprSet uses VariantAnnotation::locateVariants to look up the variant locations with respect to a transcript database. The default behaviour of barplotAlleleFreqs.matrix is to perform a naive classification of the variants as frameshift or non-frameshift by size. This approach ignores transcript scructure, but can be useful to give a faster overview, or in cases where the transcript structure is unknown.

var_counts <- variantCounts(crispr_set)
barplotAlleleFreqs(var_counts)

If the parameter classify is set to FALSE, the variants are plotted with no further aggregation. If there are more than seven variants, colours must be provided.

rainbowPal10 <- c("#781C81","#3F479B",
                  "#4277BD","#529DB7",
                  "#62AC9B","#86BB6A",
                  "#C7B944","#E39C37",
                  "#E76D2E","#D92120")

barplotAlleleFreqs(var_counts, classify = FALSE, bar.colours = rainbowPal10)

An arbitrary classification can also be used. CrispRVariants provides some utility functions to assist in classifying variants. Note that methods of the CrisprSet class are accessed with crisprSet$function() rather than function(crisprSet).

Here are some examples of variant classification:

# Classify variants as insertion/deletion/mixed
byType <- crispr_set$classifyVariantsByType()
## Warning in dispatchDots(.self$.getFilteredCigarTable, ...): dispatchDots may
## not work as expected with S4 functions
byType
##            no variant                 -1:4D                  6:1D 
##          "no variant"            "deletion"            "deletion" 
##                  1:7I                  1:1I             1:2I,3:5I 
##           "insertion"           "insertion" "multiple insertions" 
##                 -2:4D             2:1D,4:5I                 Other 
##            "deletion"  "insertion/deletion"               "Other"
# Classify variants by their location, without considering size
byLoc <- crispr_set$classifyVariantsByLoc(txdb=txdb)
## Looking up variant locations
## 'select()' returned many:1 mapping between keys and columns
## 'select()' returned many:1 mapping between keys and columns
## Classifying variants
## Warning in dispatchDots(.self$.getFilteredCigarTable, ...): dispatchDots may
## not work as expected with S4 functions
byLoc
##   no variant        -1:4D         6:1D         1:7I         1:1I    1:2I,3:5I 
## "no variant"     "coding"     "coding"     "coding"     "coding"     "coding" 
##        -2:4D    2:1D,4:5I        Other 
##     "coding"     "coding"      "Other"
# Coding variants can then be classified by setting a size cutoff
byLoc <- crispr_set$classifyCodingBySize(byLoc, cutoff = 6)
byLoc
##             no variant                  -1:4D                   6:1D 
##           "no variant" "frameshift indel < 6" "frameshift indel < 6" 
##                   1:7I                   1:1I              1:2I,3:5I 
## "frameshift indel > 6" "frameshift indel < 6" "frameshift indel > 6" 
##                  -2:4D              2:1D,4:5I                  Other 
## "frameshift indel < 6"    "inframe indel > 6"                "Other"
# Combine filtering and variant classification, using barplotAlleleFreqs.matrix
vc <- variantCounts(crispr_set)

# Select variants that occur in at least two samples
keep <- names(which(rowSums(vc > 0) > 1))
keep
## [1] "no variant" "6:1D"       "Other"
# Use this classification and the selected variants
barplotAlleleFreqs(vc[keep,], category.labels = byLoc[keep])

6.4.1 Other modifications

plotAlignments and plotFreqHeatmap both return ggplot objects, which can be adjusted via theme(). For example, to decrease the space between the legend and the plot:

p <- plotAlignments(gol, top.n = 3)
p + theme(legend.margin = ggplot2::unit(0, "cm"))
## Warning: `legend.margin` must be specified using `margin()`
## ℹ For the old behavior use `legend.spacing`

7 Using CrispRVariants plotting functions independently

The CrispRVariants plotting functions are intended to be used within a typical CrispRVariants pipeline, where the correct arguments are extracted from a CrisprSet object. However, with some data formatting, it is also possible to use these functions with standard R objects.

An example adapting CrispRVariants::plotVariants to display pairwise alignments can be found in the code accompanying the CrispRVariants paper: https://github.com/markrobinsonuzh/CrispRvariants_manuscript

7.1 Plot the reference sequence

Processing large data with CrispRVariants requires some time. It can be useful to first plot the reference sequence to check that the intended target location is specified. Here we use the reference sequence from the gol data set included in CrispRVariants. Any Biostrings::DNAString can be used. Note that CrispRVariants::plotAlignments accepts elliptical arguments in its signature, so non-signature arguments must be supplied by name. The code below shows the minimum arguments required for running CrispRVariants::plotAlignments.

# Get a reference sequence
library("CrispRVariants")
data("gol_clutch1")
ref <- gol$ref

#Then to make the plot:
plotAlignments(ref, alns = NULL, target.loc = 22, ins.sites = data.frame())

8 Note about handling of large deletions

BWA reports deletions above a threshold length as “chimeric” reads, with separate entries in the bam file for each mapped segment. By default, CrispRVariants only counts chimeric reads where one mapped endpoint is near the cut site. This setting was chosen as we observed long chimeric deletions in both on- and off-target CRISPR amplicon sequencing experiments in several independent data sets. The mapped endpoints were more likely to be in the vicinity of the cut site in on-target experiments. The off-target experiments did not have the other mutant alleles we expect to see if the long deletions are genuine CRISPR-induced variants. Some of the chimeric reads we observed appeared to be primer dimers. See the supplementary material of the CrispRVariants paper for more details:

Lindsay H, Burger A, Biyong B, Felker A, Hess C, Zaugg J, Chiavacci E, Anders C, Jinek M, 
Mosimann C and Robinson MD (2016). “CrispRVariants charts the mutation spectrum of genome
engineering experiments.” Nature Biotechnology, 34, pp. 701–702. doi: 10.1038/nbt.3628.

The default chimera setting prioritises avoiding false positives such as primer dimers at the expense of potentially missing some genuine variants. This can be changed during initialisation by setting the chimera.to.target parameter to a large value.

library(Biostrings)
library(CrispRVariants)
library(rtracklayer)
# This is a small, manually generated data set with a variety of different mutations 
bam_fname <- system.file("extdata", "cntnap2b_test_data_s.bam", 
                         package = "CrispRVariants")
guide_fname <- system.file("extdata", "cntnap2b_test_data_guide.bed",
                           package = "CrispRVariants")
guide <- rtracklayer::import(guide_fname)
guide <- guide + 5
reference <- Biostrings::DNAString("TAGGCGAATGAAGTCGGGGTTGCCCAGGTTCTC")

cset <- readsToTarget(bam_fname, guide, reference = reference, verbose = FALSE, 
                      name = "Default")
cset2 <- readsToTarget(bam_fname, guide, reference = reference, verbose = FALSE, 
                       chimera.to.target = 100, name = "Including long dels")

default_var_counts <- variantCounts(cset)
print(default_var_counts)
##            cntnap2b_test_data_s.bam
## no variant                        6
## SNV:6G                            1
## 3:10I                             2
## 6:3D                              1
## 4:3D                              1
## -9:2D                             1
## -25:58D                           1
## 4:26D                             1
## 6:7D                              1
## 6:3I                              1
## 4:3I                              1
## 5:20I                             1
## 4:2D                              1
print(c("Total number of reads: ", colSums(default_var_counts)))
##                            cntnap2b_test_data_s.bam 
## "Total number of reads: "                      "19"
# With chimera.to.target = 100, an additional read representing a large deletion is 
# reported in the "Other" category.
var_counts_inc_long_dels <- variantCounts(cset2)
print(var_counts_inc_long_dels)
##            cntnap2b_test_data_s.bam
## no variant                        6
## SNV:6G                            1
## 3:10I                             2
## 6:3D                              1
## 4:3D                              1
## -9:2D                             1
## -25:58D                           1
## 4:26D                             1
## 6:7D                              1
## 6:3I                              1
## 4:3I                              1
## 5:20I                             1
## 4:2D                              1
## Other                             1
print(c("Total number of reads: ", colSums(var_counts_inc_long_dels)))
##                            cntnap2b_test_data_s.bam 
## "Total number of reads: "                      "20"
# This alignment can be viewed using `plotChimeras` 
ch <- getChimeras(cset2, sample = 1)
plotChimeras(ch, annotations = cset2$target)

9 Session Info

sessionInfo()
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: America/New_York
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] gridExtra_2.3          GenomicFeatures_1.58.0 AnnotationDbi_1.68.0  
##  [4] Biobase_2.66.0         rtracklayer_1.66.0     GenomicRanges_1.58.0  
##  [7] readxl_1.4.3           sangerseqR_1.42.0      stringr_1.5.1         
## [10] pwalign_1.2.0          Biostrings_2.74.0      GenomeInfoDb_1.42.0   
## [13] XVector_0.46.0         IRanges_2.40.0         S4Vectors_0.44.0      
## [16] BiocGenerics_0.52.0    CrispRVariants_1.34.0  ggplot2_3.5.1         
## [19] BiocStyle_2.34.0      
## 
## loaded via a namespace (and not attached):
##  [1] DBI_1.2.3                   bitops_1.0-9               
##  [3] rlang_1.1.4                 magrittr_2.0.3             
##  [5] matrixStats_1.4.1           compiler_4.4.1             
##  [7] RSQLite_2.3.7               png_0.1-8                  
##  [9] vctrs_0.6.5                 reshape2_1.4.4             
## [11] pkgconfig_2.0.3             crayon_1.5.3               
## [13] fastmap_1.2.0               magick_2.8.5               
## [15] labeling_0.4.3              utf8_1.2.4                 
## [17] Rsamtools_2.22.0            promises_1.3.0             
## [19] rmarkdown_2.28              UCSC.utils_1.2.0           
## [21] tinytex_0.53                bit_4.5.0                  
## [23] xfun_0.48                   zlibbioc_1.52.0            
## [25] cachem_1.1.0                jsonlite_1.8.9             
## [27] blob_1.2.4                  highr_0.11                 
## [29] later_1.3.2                 DelayedArray_0.32.0        
## [31] BiocParallel_1.40.0         parallel_4.4.1             
## [33] VariantAnnotation_1.52.0    R6_2.5.1                   
## [35] bslib_0.8.0                 stringi_1.8.4              
## [37] jquerylib_0.1.4             cellranger_1.1.0           
## [39] Rcpp_1.0.13                 bookdown_0.41              
## [41] SummarizedExperiment_1.36.0 knitr_1.48                 
## [43] httpuv_1.6.15               Matrix_1.7-1               
## [45] tidyselect_1.2.1            abind_1.4-8                
## [47] yaml_2.3.10                 codetools_0.2-20           
## [49] curl_5.2.3                  lattice_0.22-6             
## [51] tibble_3.2.1                plyr_1.8.9                 
## [53] shiny_1.9.1                 withr_3.0.2                
## [55] KEGGREST_1.46.0             evaluate_1.0.1             
## [57] pillar_1.9.0                BiocManager_1.30.25        
## [59] MatrixGenerics_1.18.0       generics_0.1.3             
## [61] RCurl_1.98-1.16             munsell_0.5.1              
## [63] scales_1.3.0                xtable_1.8-4               
## [65] glue_1.8.0                  tools_4.4.1                
## [67] BiocIO_1.16.0               BSgenome_1.74.0            
## [69] GenomicAlignments_1.42.0    XML_3.99-0.17              
## [71] grid_4.4.1                  colorspace_2.1-1           
## [73] GenomeInfoDbData_1.2.13     restfulr_0.0.15            
## [75] cli_3.6.3                   fansi_1.0.6                
## [77] S4Arrays_1.6.0              dplyr_1.1.4                
## [79] gtable_0.3.6                sass_0.4.9                 
## [81] digest_0.6.37               SparseArray_1.6.0          
## [83] farver_2.1.2                rjson_0.2.23               
## [85] memoise_2.0.1               htmltools_0.5.8.1          
## [87] lifecycle_1.0.4             httr_1.4.7                 
## [89] mime_0.12                   bit64_4.5.2