CNVfilteR: Remove false positives of CNV calling tools by using SNV calls
Introduction
Many tools for germline copy number variant (CNV) detection from NGS data have been developed. Usually, these tools were designed for different input data like WGS, WES or panel data, and their performance may depend on the CNV size. Available benchmarks show that all these tools produce false positives, sometimes reaching a very high number of them.
With the aim of reducing the number of false positives, CNVfilteR identifies those germline CNVs that can be discarded. This task is performed by using the germline single nucleotide variant (SNV) calls that are usually obtained in common NGS pipelines. As VCF field interpretation is key when working with these files, CNVfilteR specifically supports VCFs produced by VarScan2, Strelka/Strelka2, freeBayes, HaplotypeCaller (GATK), UnifiedGenotyper (GATK) and Torrent Variant Caller. Additionally, results can be plotted using the functions provided by the R/Bioconductor packages karyoploteR and CopyNumberPlots.
Installation
CNVfilteR is a Bioconductor package and to install it we have to use BiocManager.
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("CNVfilteR")We can also install the package from github to get the latest devel version.
Quick Start
Below we show a full example that covers the usual steps: CNVs data loading, SNVs loading, identifying false postives and plotting the results.
First, we can load some CNV tool results:
library(CNVfilteR)
cnvs.file <- system.file("extdata", "DECoN.CNVcalls.csv", package = "CNVfilteR", mustWork = TRUE)
cnvs.gr <- loadCNVcalls(cnvs.file = cnvs.file, chr.column = "Chromosome", start.column = "Start", end.column = "End", cnv.column = "CNV.type", sample.column = "Sample", genome = "hg19")Then, we load the SNVs stored in a couple of VCF files.
vcf.files <- c(system.file("extdata", "variants.sample1.vcf.gz", package = "CNVfilteR", mustWork = TRUE),
system.file("extdata", "variants.sample2.vcf.gz", package = "CNVfilteR", mustWork = TRUE))
vcfs <- loadVCFs(vcf.files, cnvs.gr = cnvs.gr, genome = "hg19")## Scanning file /tmp/RtmpHPKl8x/Rinst1ba757df7bf9/CNVfilteR/extdata/variants.sample1.vcf.gz...## VarScan2 was found as source in the VCF metadata, RD will be used as ref allele depth field, AD will be used as alt allele depth field.## Scanning file /tmp/RtmpaznNs9/variants.sample2.vcf.gz...## VarScan2 was found as source in the VCF metadata, RD will be used as ref allele depth field, AD will be used as alt allele depth field.We observe that the function recognized VarScan2 as the source, so
fields were selected and allele frequency consequently. Now we can call
filterCNVs() to identify those CNVs that can be
discarded.
## [1] "cnvs" "variantsForEachCNV" "filterParameters"And we can check those CNVs that can be filtered out:
## GRanges object with 3 ranges and 10 metadata columns:
## seqnames ranges strand | cnv sample cnv.id
## <Rle> <IRanges> <Rle> | <character> <character> <character>
## 3 chr2 48025751-48028294 * | duplication sample1 3
## 16 chr17 41243453-41247939 * | duplication sample1 16
## 19 chr13 32900637-32929425 * | deletion sample2 19
## filter n.total.variants n.hm.variants n.ht.discard.CNV
## <character> <character> <character> <character>
## 3 TRUE 5 0 3
## 16 TRUE 2 0 2
## 19 TRUE 10 4 6
## n.ht.confirm.CNV ht.pct score
## <character> <character> <character>
## 3 2 2.539241834223
## 16 0 1.99927691434002
## 19 60
## -------
## seqinfo: 7 sequences from an unspecified genome; no seqlengthsAs an example, we can observe that the CNV with cnv.id=3
contains 4 variants: 2 in favor of discarding it, two against discarding
it. If we want to know more about the variants falling in a certain CNV
we can do:
## seqnames start end width strand ref alt ref.support alt.support
## 1 chr2 48026019 48026019 1 * G C 516 521
## 2 chr2 48027019 48027019 1 * G C 1528 964
## 3 chr2 48027182 48027182 1 * G A 1506 971
## 4 chr2 48027434 48027434 1 * A T 1593 1462
## 5 chr2 48027763 48027763 1 * G A 900 863
## alt.freq total.depth indel type score
## 1 50.2411 1037 FALSE ht 0.9999596
## 2 38.6838 2492 FALSE ht -0.3169985
## 3 39.2006 2477 FALSE ht -0.1417051
## 4 47.8560 3055 FALSE ht 0.9981889
## 5 48.9507 1763 FALSE ht 0.9997969Two variants are close to the default expected heterozygous
frequency, 0.5, so they obtain a positive score. The other two variants
are not so clearly close to the default expected duplication value,
0.33, so they obtain a low negative score. All these default values and
others can be modified in the filterCNVs() function.
Finally, we may be interested in plotting the results. For example,
we can plot easily the duplication CNV with cnv.id=3 and
all the variants falling in it.
We can do the same to plot the deletion CNV with
cnv.id=19, where all variants discard the CNV except one
homozygous variant that does not give us any information for supporting
or discarding the CNV:
On the opposite side, we can observe those CNVs that cannot be discarded:
## GRanges object with 17 ranges and 10 metadata columns:
## seqnames ranges strand | cnv sample cnv.id
## <Rle> <IRanges> <Rle> | <character> <character> <character>
## 1 chr2 47641409-47641557 * | duplication sample1 1
## 2 chr2 47698105-47698201 * | duplication sample1 2
## 4 chr3 10091059-10091189 * | duplication sample1 4
## 5 chr3 14219967-14220068 * | duplication sample1 5
## 6 chr3 37042447-37042544 * | duplication sample1 6
## .. ... ... ... . ... ... ...
## 14 chr13 32953455-32969070 * | duplication sample1 14
## 15 chr17 41209070-41215390 * | duplication sample1 15
## 17 chr17 41251793-41256973 * | duplication sample1 17
## 18 chr17 41267744-41276113 * | duplication sample1 18
## 20 chr17 59870959-59938900 * | deletion sample2 20
## filter n.total.variants n.hm.variants n.ht.discard.CNV
## <character> <character> <character> <character>
## 1 0 0
## 2 0 0
## 4 0 0
## 5 0 0
## 6 1 0 1
## .. ... ... ... ...
## 14 0 0
## 15 0 0
## 17 0 0
## 18 0 0
## 20 0 0
## n.ht.confirm.CNV ht.pct score
## <character> <character> <character>
## 1
## 2
## 4
## 5
## 6 0 0.372384685846936
## .. ... ... ...
## 14
## 15
## 17
## 18
## 20
## -------
## seqinfo: 7 sequences from an unspecified genome; no seqlengthsFor example, the CNV with cnv.id=14 contains one
variant. If we get the variant info, we see that the variant has an
allele frequency very close to the default expected duplication value,
0.66.
## NULLSo, no evidence was found for discarding the CNV. We can also plot the CNV and the variant:
Loading Copy-Number Data
CNVfilteR
functions expect germline CNVs calls to be a GRanges object
with a few specificic metadata columns:
- cnv for storing the CNV type: duplication or deletion.
- sample for storing the sample name of the CNV.
You can create this object yourself, but CNVfilter
provides the proper function to do this, loadCNVcalls().
This function can interpret any tsv o csv file by indicating which
columns store the information. For example, in the following code, the
chr.column column is stored in the “Chromosome” column of
the cnvs.file.
cnvs.file <- system.file("extdata", "DECoN.CNVcalls.csv", package = "CNVfilteR", mustWork = TRUE)
cnvs.gr <- loadCNVcalls(cnvs.file = cnvs.file, chr.column = "Chromosome", start.column = "Start", end.column = "End", cnv.column = "CNV.type", sample.column = "Sample", genome = "hg19")loadCNVcalls() can interpret different types of CNVs.
Among other options, separator can be selected using the
sep parameter (defaults to \t), and first lines
can be skipped using the skip parameter (defaults to 0).
Also, the value used in cnv.column to store the CNV type
can be modified using the deletion and
duplication parameters (defaults to “deletion” and
“duplication”, respectively). If, for example, our
cnv.column uses “CN1” and “CN3” for deletion and
duplication (respectively), we should indicate the function to use these
values:
cnvs.gr.2 <- loadCNVcalls(cnvs.file = cnvs.file.2, deletion = "CN1", duplication = "CN3", chr.column = "Chromosome", start.column = "Start", end.column = "End", cnv.column = "CNV.type", sample.column = "Sample")Some CNV tools generate results where the CNV location is stored in a
single column with the format chr:start-end
(i.e. 1:538001-540000). In this case, we can call
loadCNVcalls() using the coord.column instead
of the chr.column, start.column and
end.column columns.
Loading Variants Data
Common NGS pipelines produce germline variant calling (SNVs or
INDELs) in a VCF file. However, VCF interpretation is challenging due to
the flexibility provided by the VCF format definition. It is not
straightforward to interpret correctly the fields in the VCF file and
compute the allele frequency. loadVCFs() interprets
automatically VCFs produced by VarScan2, Strelka/Strelka2, freeBayes,
HaplotypeCaller (GATK), UnifiedGenotyper (GATK) and Torrent Variant
Caller.
In the following example the function recognizes VarScan2 as the source.
vcf.files <- c(system.file("extdata", "variants.sample1.vcf.gz", package = "CNVfilteR", mustWork = TRUE),
system.file("extdata", "variants.sample2.vcf.gz", package = "CNVfilteR", mustWork = TRUE))
vcfs <- loadVCFs(vcf.files, cnvs.gr = cnvs.gr)## Scanning file /tmp/RtmpHPKl8x/Rinst1ba757df7bf9/CNVfilteR/extdata/variants.sample1.vcf.gz...## VarScan2 was found as source in the VCF metadata, RD will be used as ref allele depth field, AD will be used as alt allele depth field.## Scanning file /tmp/RtmpaznNs9/variants.sample2.vcf.gz...## VarScan2 was found as source in the VCF metadata, RD will be used as ref allele depth field, AD will be used as alt allele depth field.We can also load the VCF file spicifying how to interpret it, which can be useful if the VCF was generated by a caller not supported by CNVfilteR. For example we can specify the ref/alt fields:
vcfs <- loadVCFs(vcf.files, cnvs.gr = cnvs.gr, vcf.source = "MyCaller", ref.support.field = "RD", alt.support.field = "AD")## Scanning file /tmp/RtmpHPKl8x/Rinst1ba757df7bf9/CNVfilteR/extdata/variants.sample1.vcf.gz...## VCF source MyCaller is not supported, but ref.support.field/alt.support.field were provided.## Scanning file /tmp/RtmpaznNs9/variants.sample2.vcf.gz...## VCF source MyCaller is not supported, but ref.support.field/alt.support.field were provided.Alternatively, we can set the list.support.field
parameter so that field will be loaded assuming that it is a list in
this order: reference allele, alternative allele. As an example:
vcf.file3 <- c(system.file("extdata", "variants.sample3.vcf", package = "CNVfilteR", mustWork = TRUE))
vcfs3 <- loadVCFs(vcf.file3, cnvs.gr = cnvs.gr, vcf.source = "MyCaller", list.support.field = "AD")## Scanning file /tmp/RtmpaznNs9/variants.sample3.vcf.gz...## VCF source MyCaller is not supported, but list.support.field was provided.VCF free of artifacts
CNVfilteR uses SNVs to identify false-positive CNV calls. Therefore, its performance depends on the SNV calls quality. We recommend using VCF files free of false-positive SNVs (as possible) to improve CNVfilteR accuracy. Some considerations can be followed in order to provide reliable SNVs to CNVfilteR.
Minimun total depth
Use the min.total.depth parameter to discard SNVs with
low depth coverage in the loadVCFs function. The default
value is 10, which may be appropriate in many WGS samples, but
this value should be adapted to your experiment
conditions. For example, we used a min.total.depth
of 30 when using CNVfilteR on panel (targeted-enrinched) samples with
high coverage and VarScan2 as SNV caller.
Regions to exclude
Low complexity and repetitive regions are genome areas where SNV
callers (also CNV callers) perform poorly. If possible, ignore these
regions when using CNVfilteR. We can exclude those complex regions that
have already known alignement artifacts with the parameter
regions.to.exclude. In this example, we exclude PMS2,
PRSS1, and FANCD2 genes because they are pseudogenes with alignments
artifacts:
regions.to.exclude <- GRanges(seqnames = c("chr3","chr7", "chr7"), ranges = IRanges(c(10068098, 6012870, 142457319), c(10143614, 6048756, 142460923)))
vcfs4 <- loadVCFs(vcf.files, cnvs.gr = cnvs.gr, regions.to.exclude = regions.to.exclude)## Scanning file /tmp/RtmpHPKl8x/Rinst1ba757df7bf9/CNVfilteR/extdata/variants.sample1.vcf.gz...## VarScan2 was found as source in the VCF metadata, RD will be used as ref allele depth field, AD will be used as alt allele depth field.## Scanning file /tmp/RtmpaznNs9/variants.sample2.vcf.gz...## VarScan2 was found as source in the VCF metadata, RD will be used as ref allele depth field, AD will be used as alt allele depth field.INDELs excluded by default
Also, the parameter exclude.indels indicates whether to
exclude INDELs when loading the variants. TRUE is the default and
recommended value given that INDELs allele frequency
varies differently than SNVs. Including INDELs may allow the algorithm
to identify more CNVs to discard with a greater risk of identifying them
wrongly. Additionally, any SNV overlapping an INDEL will be ignored
because the SNV allele frequency may be affected in that region.
Other settings
The function loadVCFs() also adapts to different needs.
If sample.names parameter is not provided, the sample names
included in the VCF itself will be used. Both single-sample and
multi-sample VCFs are accepted, but when multi-sample VCFs are used,
sample.names parameter must be NULL.
If VCF is not compressed with bgzip, the function compresses it and generates the .gz file. If .tbi file does not exist for a given VCF file, the function also generates it. All files are generated in a temporary folder.
See loadVCFs() documentation to see other parameters
info.
Limitations
Currlently CNVfilteR does not support mutiallelic sites in VCF files,
such as chr3 193372598 .;. TTA T,TTT. As an easy work
around, mutiallelic sites can be split by using bcftools:
bcftools norm -N -m -both yourSample.vcf > splitSample.vcf
Identifying false positives
The task of identifying false positives is performed by the
filterCNVs() function. It checks all the variants (SNVs and
optionally INDELs) falling in each CNV present in cnvs.gr
to identify those CNVs that can be filtered out. It returns an S3 object
with 3 elements: cnvs, variantsForEachCNV and
filterParameters:
## 3 out of 20 (15%) CNVs can be filtered## 3 out of 5 (60%) CNVs with overlapping SNVs can be filtered## GRanges object with 6 ranges and 10 metadata columns:
## seqnames ranges strand | cnv sample cnv.id
## <Rle> <IRanges> <Rle> | <character> <character> <character>
## 15 chr17 41209070-41215390 * | duplication sample1 15
## 16 chr17 41243453-41247939 * | duplication sample1 16
## 17 chr17 41251793-41256973 * | duplication sample1 17
## 18 chr17 41267744-41276113 * | duplication sample1 18
## 19 chr13 32900637-32929425 * | deletion sample2 19
## 20 chr17 59870959-59938900 * | deletion sample2 20
## filter n.total.variants n.hm.variants n.ht.discard.CNV
## <character> <character> <character> <character>
## 15 0 0
## 16 TRUE 2 0 2
## 17 0 0
## 18 0 0
## 19 TRUE 10 4 6
## 20 0 0
## n.ht.confirm.CNV ht.pct score
## <character> <character> <character>
## 15
## 16 0 1.99927691434002
## 17
## 18
## 19 60
## 20
## -------
## seqinfo: 7 sequences from an unspecified genome; no seqlengthsObserve that those CNVs that can be filtered out have the
value TRUE in the column filter. CNVfilteR
employs two different strategies for identifying those CNVs:
- A deletion CNV can be filtered out if there is at
least
ht.deletions.threshold% of heterozygous variants in the CNV. Defaultht.deletions.thresholdvalue is 30, so 30% is required. - A duplication CNV can be filtered out if the total
scoreis >=dup.threshold.scoreafter computing all heterozygous variants falling in that CNV. Defaultdup.threshold.scorevalue is 0.5. How the score is computed for each variant is explained in the next section.
Scoring model for duplication CNVs
The scoring model for determining whether a certain duplication CNV can be discarded is based on the allele frequency for each heterozygous variant falling in that CNV:
- In common conditions with no presence of a duplication CNV, the
allele frequency of a heterozygous variant is expected to be close to
50% (
expected.ht.mean). So, a variant with an allele frequency close to 50% gives us evidence of the non-existence of a duplication CNV, so the CNV could be discarded. - On the opposite side, if the variant occurs in the same region of a
certain duplication CNV, the allele frequency is expected to be close to
33.3% (
expected.dup.ht.mean1) when the variant is not in the same allele than the duplication CNV, and 66.6% (expected.dup.ht.mean2) when the variant is in the same allele than the duplication CNV call. So, a variant with an allele frequency close to 33.3% or 66.6% gives us evidence of the existence of duplication CNV.
Although we can expect that most of the variants are close to the
expected means (33.3%, 50%, and 66.6%), many of them can be far from any
expected mean. The scoring model implemented in the
filterCNVs() function measures the evidence - for
discarding a certain CNV - using a scoring model. The scoring model is
based on the fuzzy logic, where elements can have any value between 1
(True) and 0 (False). Following this idea, each variant will be
scored with a value between 0 and 1 depending on how close is the allele
frequency to the nearest expected mean.
- For those variants with an allele frequency close to the expected mean when no duplication CNV occurs (defaults 50%), the score will be positive in the interval [0, 1].
- For those variants with an allele frequency close to the expected mean when a duplication CNV occurs (defaults 33.3%, 66.6%), the score will be negative in the interval [-1, 0].
The total score is computed among all the variants
falling in the CNV. If the score is greater than the
dup.threshold.score, the CNV can be discarded.
A common way of applying the fuzzy logic is using the sigmoid function. CNVfilteR uses the sigmoid function implemented in the pracma package, which is defined as $$ \begin{aligned} y = 1/(1 + e^{-c1(x−c2)}) \end{aligned} $$
The scoring model is built on 6 sigmoids defined on 6 different
intervals. The c1 parameter is 2 by default (sigmoid.c1),
and the c2 parameter is defined for the 6 sigmoids
(sigmoid.c2.vector).
- First sigmoid: interval [20,
expected.dup.ht.mean1], c2=28 - Second sigmoid: interval [
expected.dup.ht.mean1,sigmoid.int1], c2=38.3 - Third sigmoid: interval [
sigmoid.int1,expected.ht.mean], c2=44.7 - Fourth sigmoid: interval [
expected.ht.mean,sigmoid.int2], c2=55.3 - Fifth sigmoid: interval [
sigmoid.int2,expected.dup.ht.mean2], c`=61.3 - Sixth sigmoid: interval [
expected.dup.ht.mean2, 80], c2=71.3
Where sigmoid.int1 is the mean between
expected.dup.ht.mean1 and expected.ht.mean,
and sigmoid.int2 is the mean between
expected.dup.ht.mean2 and
expected.ht.mean.
The scoring model can be plotted using the
plotScoringModel() function.
p <- results$filterParameters
plotScoringModel(expected.ht.mean = p$expected.ht.mean,
expected.dup.ht.mean1 = p$expected.dup.ht.mean1,
expected.dup.ht.mean2 = p$expected.dup.ht.mean2,
sigmoid.c1 = p$sigmoid.c1,
sigmoid.c2.vector = p$sigmoid.c2.vector)
And the scoring model can be modified when calling the
filterCNVs() function. Let’s see how the model changes when
we modify the parameter sigmoid.c1 (1 instead of 2):
plotScoringModel(expected.ht.mean = p$expected.ht.mean,
expected.dup.ht.mean1 = p$expected.dup.ht.mean1,
expected.dup.ht.mean2 = p$expected.dup.ht.mean2,
sigmoid.c1 = 1,
sigmoid.c2.vector = p$sigmoid.c2.vector)
We can also modify the sigmoid.c2.vector parameter for
each sigmoid function. For example, to make the central sigmoids
narrower:
plotScoringModel(expected.ht.mean = p$expected.ht.mean,
expected.dup.ht.mean1 = p$expected.dup.ht.mean1,
expected.dup.ht.mean2 = p$expected.dup.ht.mean2,
sigmoid.c1 = p$sigmoid.c1,
sigmoid.c2.vector = c(28, 38.3, 46.7, 53.3, 61.3, 71.3))
The margin.pct parameter
Many CNV callers produce inaccurate CNV calls. These inaccurate CNV
calls are more likely to be true (to overlap the real CNV) in the middle
of the CNV than in the extremes. So, the margin.pct
parameter defines the percentage of CNV (from each CNV limit) where SNVs
will be ignored. By default, only 10% of SNVs from each CNV extreme will
be ignored. This margin.pct parameter can be modified to
better adapt it to your CNV caller. For example, we observed that DECoN
produced very accurate CNV calls in our genes panel dataset, so
margin.pct value was updated to 0 in this context.
Summarizing, variants in the CNV call but close to the ends of the
CNV call will be ignored. margin.pct defines the percentage
of CNV length, located at each CNV limit, where variants will be
ignored. For example, for a CNV chr1:1000-2000 and a
margin.pct value of 10, variants within chr1:1000-1100 and
chr1:1900-2000 will be ignored.
Plotting results
We can plot easily a certain CNV and the variants in it. For example,
the duplication CNV with cnv.id=17 can be plotted as
follows:
Some parameters can be customized, like points.cex and
points.pch. It is also possible to plot all CNVs in a
global schema where all the chromosomes are plotted:
cnvs.file <- system.file("extdata", "DECoN.CNVcalls.2.csv",
package = "CNVfilteR", mustWork = TRUE)
cnvs.gr.2 <- loadCNVcalls(cnvs.file = cnvs.file, chr.column = "Chromosome",
start.column = "Start", end.column = "End",
cnv.column = "CNV.type", sample.column = "Sample",
genome = "hg19")
plotAllCNVs(cnvs.gr.2)
Note that if a CNV is too small, it will not be visible when calling
plotAllCNVs().
Session Info
## 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: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 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: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] BSgenome.Hsapiens.UCSC.hg19.masked_1.3.993
## [2] BSgenome.Hsapiens.UCSC.hg19_1.4.3
## [3] BSgenome_1.73.1
## [4] rtracklayer_1.65.0
## [5] BiocIO_1.17.0
## [6] Biostrings_2.75.0
## [7] XVector_0.45.0
## [8] GenomicRanges_1.57.2
## [9] GenomeInfoDb_1.41.2
## [10] IRanges_2.39.2
## [11] S4Vectors_0.43.2
## [12] BiocGenerics_0.53.0
## [13] CNVfilteR_1.21.0
## [14] knitr_1.48
## [15] BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] DBI_1.2.3 bitops_1.0-9
## [3] gridExtra_2.3 rlang_1.1.4
## [5] magrittr_2.0.3 biovizBase_1.55.0
## [7] matrixStats_1.4.1 compiler_4.4.1
## [9] RSQLite_2.3.7 GenomicFeatures_1.57.1
## [11] png_0.1-8 vctrs_0.6.5
## [13] ProtGenerics_1.37.1 stringr_1.5.1
## [15] pkgconfig_2.0.3 crayon_1.5.3
## [17] fastmap_1.2.0 CopyNumberPlots_1.21.0
## [19] backports_1.5.0 utf8_1.2.4
## [21] Rsamtools_2.21.2 rmarkdown_2.28
## [23] pracma_2.4.4 UCSC.utils_1.1.0
## [25] bit_4.5.0 xfun_0.48
## [27] cn.mops_1.51.0 zlibbioc_1.51.2
## [29] cachem_1.1.0 jsonlite_1.8.9
## [31] blob_1.2.4 highr_0.11
## [33] rhdf5filters_1.17.0 DelayedArray_0.31.14
## [35] Rhdf5lib_1.27.0 BiocParallel_1.39.0
## [37] parallel_4.4.1 cluster_2.1.6
## [39] R6_2.5.1 VariantAnnotation_1.51.2
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## [55] abind_1.4-8 yaml_2.3.10
## [57] codetools_0.2-20 curl_5.2.3
## [59] lattice_0.22-6 tibble_3.2.1
## [61] regioneR_1.37.0 Biobase_2.67.0
## [63] KEGGREST_1.45.1 evaluate_1.0.1
## [65] foreign_0.8-87 karyoploteR_1.31.0
## [67] pillar_1.9.0 BiocManager_1.30.25
## [69] MatrixGenerics_1.17.1 checkmate_2.3.2
## [71] RCurl_1.98-1.16 ensembldb_2.29.1
## [73] ggplot2_3.5.1 munsell_0.5.1
## [75] scales_1.3.0 glue_1.8.0
## [77] lazyeval_0.2.2 Hmisc_5.2-0
## [79] maketools_1.3.1 tools_4.4.1
## [81] data.table_1.16.2 sys_3.4.3
## [83] GenomicAlignments_1.41.0 buildtools_1.0.0
## [85] XML_3.99-0.17 rhdf5_2.49.0
## [87] grid_4.4.1 AnnotationDbi_1.69.0
## [89] colorspace_2.1-1 GenomeInfoDbData_1.2.13
## [91] htmlTable_2.4.3 restfulr_0.0.15
## [93] Formula_1.2-5 cli_3.6.3
## [95] fansi_1.0.6 S4Arrays_1.5.11
## [97] AnnotationFilter_1.31.0 gtable_0.3.6
## [99] sass_0.4.9 digest_0.6.37
## [101] SparseArray_1.5.45 rjson_0.2.23
## [103] htmlwidgets_1.6.4 memoise_2.0.1
## [105] htmltools_0.5.8.1 lifecycle_1.0.4
## [107] httr_1.4.7 bit64_4.5.2
## [109] bamsignals_1.39.0