single_molecule_sorting_by_TF
Introduction
This vignette exemplifies how to perform single molecule sorting based on prior TF binding site annotation as per Sönmezer et al., 2021 and Kleinendorst & Barzaghi et al., 2021.
Loading libraries
Sorting by single TF
Sorting by single TF requires designing three bins with coordinates
[-35;-25], [-15;+15], [+25,+35] relative to the center of the TF
motif.
Here we exemplify the process manually for clarity.
RegionOfInterest = GRanges("chr12", IRanges(20464551, 20465050))
Methylation = qs::qread(system.file("extdata", "Methylation_3.qs", package="SingleMoleculeFootprinting"))
TFBSs = qs::qread(system.file("extdata", "TFBSs_3.qs", package="SingleMoleculeFootprinting"))
motif_center = start(IRanges::resize(TFBSs, 1, "center"))
SortingBins = c(
GRanges("chr1", IRanges(motif_center-35, motif_center-25)),
GRanges("chr1", IRanges(motif_center-15, motif_center+15)),
GRanges("chr1", IRanges(motif_center+25, motif_center+35))
)
PlotAvgSMF(
MethGR = Methylation[[1]], RegionOfInterest = RegionOfInterest,
TFBSs = TFBSs, SortingBins = SortingBins
)
## No sorted reads passed...plotting counts from all reads
Sorting molecules involves averaging, for each molecule, the binary
methylation values for the cytosines overlapping those bins.
Reducing those averages to integers summarises each molecule to three
binary digits.
We sort and plot molecules based on these digits.
The whole process is wrapped in the SortReadsBySingleTF
function.
SortedReads = SortReadsBySingleTF(MethSM = Methylation[[2]], TFBS = TFBSs)
## Designing sorting bins
## Collecting summarized methylation for bins
## Subsetting those reads that cover all bins
## Summarizing reads into patterns
## Splitting reads by patternN.b.: custom bins can be used by through the argument
bins.
The function returns a list with one item per sample.
Each of these is itself a list of up to eight items, one per possible
combination of three binary digits, i.e. 000, 001, 010, etc.
Each of these items contains the IDs of the molecules sorted.
N.b.: patterns with 0 molecules will not be reported.
lapply(SortedReads$SMF_MM_TKO_DE_, head, 2)
## $`000`
## [1] "D00404:273:H5757BCXY:1:1102:2767:29201"
## [2] "D00404:273:H5757BCXY:1:1109:17973:76532"
##
## $`001`
## [1] "D00404:273:H5757BCXY:1:2107:14462:35292"
## [2] "D00404:273:H5757BCXY:1:2116:11877:22847"
##
## $`010`
## [1] "D00404:283:HCFT7BCXY:2:2206:12683:36087"
## [2] "NB501735:9:HLG5VBGX2:2:13108:18702:4995"
##
## $`011`
## [1] "D00404:273:H5757BCXY:1:2103:13490:80813"
## [2] "D00404:273:H5757BCXY:1:2210:11790:19673"
##
## $`100`
## [1] "D00404:273:H5757BCXY:1:1102:2692:28832"
## [2] "D00404:273:H5757BCXY:1:1108:7836:31800"
##
## $`101`
## [1] "D00404:273:H5757BCXY:1:1101:16782:14912"
## [2] "D00404:273:H5757BCXY:1:1103:15983:34280"
##
## $`110`
## [1] "D00404:273:H5757BCXY:1:1203:5101:74304"
## [2] "D00404:273:H5757BCXY:2:2111:8077:43320"
##
## $`111`
## [1] "D00404:273:H5757BCXY:1:1106:20721:33897"
## [2] "D00404:273:H5757BCXY:1:1107:16458:47084"The number of molecules per pattern can be checked using
lenghts.
lengths(SortedReads$SMF_MM_TKO_DE_)
## 000 001 010 011 100 101 110 111
## 216 72 6 19 301 2372 51 932Here most molecules show the 101 pattern.
These patterns are not immediately human readable. For convenience we
hard-coded a biological interpretation in the function
SingleTFStates.
SingleTFStates()
## $bound
## [1] "101"
##
## $accessible
## [1] "111"
##
## $closed
## [1] "000" "100" "001"
##
## $unassigned
## [1] "010" "110" "011"This function can be used together with the function
StateQuantification, to compute the frequencies of the
biological states associated with single TF binding.
The function StateQuantificationBySingleTF hard-codes the
states argument for convenience.
StateQuantification(SortedReads = SortedReads, states = SingleTFStates())
## # A tibble: 4 × 4
## Sample State Counts Freqs
## <chr> <chr> <int> <dbl>
## 1 SMF_MM_TKO_DE_ bound 2372 59.8
## 2 SMF_MM_TKO_DE_ accessible 932 23.5
## 3 SMF_MM_TKO_DE_ closed 589 14.8
## 4 SMF_MM_TKO_DE_ unassigned 76 1.91Sorted molecules can be visualized with the PlotSM
function.
PlotSM(MethSM = Methylation[[2]], RegionOfInterest = RegionOfInterest, sorting.strategy = "classical", SortedReads = SortedReads)
## Arranging reads according to classical sorting.strategy
## Inferring sorting was performed by single TF
## Warning: Removed 80 rows containing missing values or values outside the scale range
## (`geom_tile()`).
A corresponding barplot can be obtained through the
StateQuantificationPlot or the
SingleTFStateQuantificationPlot which hard-codes the
states parameter.
Sorting by TF clusters
Similarly to single TFs, sorting by TF motif clusters requires
designing bins. Here we draw a [-7;+7] bin around each TF motif, a
[-35;-25] bin relative to the most upstream TF motif and a [+25,+35] bin
relative to the center of the most downstream TFBS, for a total of n + 2 bins, with n being the number of TF
motifs.
Here we exemplify the process manually for clarity.
RegionOfInterest = GRanges("chr6", IRanges(88106000, 88106500))
Methylation = qs::qread(system.file("extdata", "Methylation_4.qs", package="SingleMoleculeFootprinting"))
TFBSs = qs::qread(system.file("extdata", "TFBSs_1.qs", package="SingleMoleculeFootprinting"))
motif_center_1 = start(IRanges::resize(TFBSs[1], 1, "center"))
motif_center_2 = start(IRanges::resize(TFBSs[2], 1, "center"))
SortingBins = c(
GRanges("chr6", IRanges(motif_center_1-35, motif_center_1-25)),
GRanges("chr6", IRanges(motif_center_1-7, motif_center_1+7)),
GRanges("chr6", IRanges(motif_center_2-7, motif_center_2+7)),
GRanges("chr6", IRanges(motif_center_2+25, motif_center_2+35))
)
PlotAvgSMF(
MethGR = Methylation[[1]], RegionOfInterest = RegionOfInterest, TFBSs = TFBSs, SortingBins = SortingBins
)
## No sorted reads passed...plotting counts from all reads
Sorting molecules involves averaging, for each molecule, the binary
methylation values for the cytosines overlapping those bins.
Reducing those averages to integers summarises each molecule to three
binary digits.
We sort and plot molecules based on these digits.
The whole process is wrapped in the SortReadsByTFCluster
function.
SortedReads = SortReadsByTFCluster(MethSM = Methylation[[2]], TFBS_cluster = TFBSs)
## Sorting TFBSs by genomic coordinates
## Designing sorting bins
## Collecting summarized methylation for bins
## Subsetting those reads that cover all bins
## Summarizing reads into patterns
## Splitting reads by patternN.b.: custom bins can be used by through the argument
bins.
The function returns a list with one item per sample.
Each of these is itself a list of up to 16 items, one per possible
combination of three binary digits, i.e. 0000, 0001, 0010, etc.
Each of these items contains the IDs of the molecules sorted.
N.b.: patterns with 0 molecules will not be reported.
lapply(SortedReads$SMF_MM_TKO_DE_, head, 2)
## $`0000`
## [1] "D00404:273:H5757BCXY:2:1208:11811:79680"
## [2] "D00404:283:HCFT7BCXY:1:2215:2273:66163"
##
## $`0001`
## [1] "D00404:273:H5757BCXY:2:2112:17179:78420"
## [2] "D00404:283:HCFT7BCXY:1:1110:19551:78460"
##
## $`0101`
## [1] "NB501735:34:H32KCBGX3:3:12504:23493:4814"
##
## $`0111`
## [1] "D00404:273:H5757BCXY:2:2106:19859:63697"
## [2] "NB501735:34:H32KCBGX3:4:11405:8733:10957"
##
## $`1000`
## [1] "NB501735:34:H32KCBGX3:1:21111:15939:10193"
## [2] "NB501735:34:H32KCBGX3:1:21207:15670:17600"
##
## $`1001`
## [1] "D00404:273:H5757BCXY:1:1102:14949:3420"
## [2] "D00404:273:H5757BCXY:1:1205:4416:100764"
##
## $`1010`
## [1] "D00404:283:HCFT7BCXY:1:2106:8461:79305"
## [2] "D00404:283:HCFT7BCXY:1:2210:13980:37090"
##
## $`1011`
## [1] "D00404:273:H5757BCXY:1:1102:20457:73436"
## [2] "D00404:273:H5757BCXY:1:1110:7624:25325"
##
## $`1100`
## [1] "NB501735:9:HLG5VBGX2:1:11204:8511:6241"
## [2] "NB501735:9:HLG5VBGX2:2:13209:15843:20165"
##
## $`1101`
## [1] "D00404:273:H5757BCXY:1:1211:14401:50622"
## [2] "D00404:273:H5757BCXY:2:1101:1697:87040"
##
## $`1110`
## [1] "D00404:273:H5757BCXY:1:1215:8843:22685"
## [2] "D00404:273:H5757BCXY:2:1103:16878:7787"
##
## $`1111`
## [1] "D00404:273:H5757BCXY:2:1102:9323:16615"
## [2] "D00404:273:H5757BCXY:2:1107:2792:41093"The number of molecules per pattern can be checked using
lenghts.
lengths(SortedReads$SMF_MM_TKO_DE_)
## 0000 0001 0101 0111 1000 1001 1010 1011 1100 1101 1110 1111
## 7 9 1 5 5 71 4 40 3 19 9 41Here most molecules show the 1001 pattern.
These patterns are not immediately human readable. For convenience we
hard-coded a biological interpretation in the function
SingleTFStates.
TFPairStates()
## $bound_bound
## bound_bound
## "1001"
##
## $misc_bound
## unassigned_bound accessible_bound
## "0101" "1101"
##
## $bound_misc
## bound_unassigned bound_accessible
## "1010" "1011"
##
## $misc
## nucleosome_nucleosome bound_nucleosome unassigned_nucleosome
## "0000" "1000" "0100"
## accessible_nucleosome nucleosome_unassigned unassigned_unassigned
## "1100" "0010" "0110"
## accessible_unassigned nucleosome_bound nucleosome_accessible
## "1110" "0001" "0011"
## unassigned_accessible accessible_accessible
## "0111" "1111"This function can be used together with the function
StateQuantification, to compute the frequencies of the
biological states associated with single TF binding.
The function StateQuantificationByTFPair hard-codes the
states argument for convenience.
StateQuantification(SortedReads = SortedReads, states = TFPairStates())
## # A tibble: 4 × 4
## Sample State Counts Freqs
## <chr> <chr> <int> <dbl>
## 1 SMF_MM_TKO_DE_ bound_bound 71 33.2
## 2 SMF_MM_TKO_DE_ misc_bound 20 9.35
## 3 SMF_MM_TKO_DE_ bound_misc 44 20.6
## 4 SMF_MM_TKO_DE_ misc 79 36.9Sorted molecules can be visualized with the PlotSM
function.
PlotSM(MethSM = Methylation[[2]], RegionOfInterest = RegionOfInterest, sorting.strategy = "classical", SortedReads = SortedReads)
## Arranging reads according to classical sorting.strategy
## Inferring sorting was performed by TF pair
A corresponding barplot can be obtained through the
StateQuantificationPlot or the
TFPairStateQuantificationPlot which hard-codes the
states parameter.
Genome-wide wrappers
Performing genome-wide single molecule sorting involves calling
methylation at the single molecule level for multiple loci of
interest.
Let’s say we wanted to sort single molecules around the first 500 bound
KLF4 motifs in chr19.
KLF4s = qs::qread(system.file("extdata", "KLF4_chr19.qs", package="SingleMoleculeFootprinting"))
KLF4s
## GRanges object with 500 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## TFBS_10755077 chr19 3283483-3283492 +
## TFBS_10755111 chr19 3297310-3297319 +
## TFBS_10755159 chr19 3322114-3322123 +
## TFBS_10755163 chr19 3324079-3324088 +
## TFBS_10755168 chr19 3325484-3325493 +
## ... ... ... ...
## TFBS_10761853 chr19 5990580-5990589 +
## TFBS_10761855 chr19 5991634-5991643 +
## TFBS_10761874 chr19 6001105-6001114 +
## TFBS_10761879 chr19 6003358-6003367 +
## TFBS_10761890 chr19 6010914-6010923 +
## -------
## seqinfo: 66 sequences (1 circular) from mm10 genomeIterating linearly (or in parallel) across several hundreds of
thousands of loci can take an impractically long time. This is why we
prefer to call methylation at the single molecule level for up to Mb
large genomic windows, for which memory requirements are still
compatible with most computational infrastructures.
The function Create_MethylationCallingWindows outputs a
GRanges of large methylation calling windows encompassing multiple of
the loci of interest passed as input.
Create_MethylationCallingWindows(RegionsOfInterest = KLF4s)
## Group TFBS_clusters that fall within 1e+05bp from each other in broader searching windows
## Trimming searching windows
## GRanges object with 2 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr19 3283188-5166083 *
## [2] chr19 5272307-6011218 *
## -------
## seqinfo: 66 sequences (1 circular) from mm10 genomeUsed with default parameters, the function minimizes the number of
methylation calling windows in output. The parameters
max_intercluster_distance, max_window_width
and min_cluster_width can be tuned to customise this
behavior.
However, sometimes this leads to highly disproportionate windows,
very few of which will overlap with most of the
RegionsOfInterest. This in turn might cause
disproportionate resource allocations during subsequent
computations.
This is why the function can forced to output methylation calling
windows encompassing an even number of RegionsOfInterest. The parameters
fix.window.size and max.window.size can be
used to tune this behavior.
Create_MethylationCallingWindows(
RegionsOfInterest = KLF4s,
fix.window.size = TRUE, max.window.size = 50
)
## GRanges object with 10 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr19 3283482-3859342 *
## [2] chr19 3864777-4099977 *
## [3] chr19 4099997-4228150 *
## [4] chr19 4229736-4732743 *
## [5] chr19 4748703-5059079 *
## [6] chr19 5060902-5375826 *
## [7] chr19 5378076-5559346 *
## [8] chr19 5560673-5729717 *
## [9] chr19 5731830-5924274 *
## [10] chr19 5924647-6010924 *
## -------
## seqinfo: 66 sequences from an unspecified genome; no seqlengthsIn case of single molecule sorting by TFBS clusters, it is useful to
define the motif clusters ahead of the generation of methylation calling
windows.
The function Arrange_TFBSs_clusters groups TF motifs by
proximity and returns two objects:
ClusterCoordinates, which can be input directly to
Create_MethylationCallingWindows
ClusterComposition which can be fed to one of the single
molecule sorting functions.
KLF4_clusters = Arrange_TFBSs_clusters(TFBSs = KLF4s)
## Removing self-overlaps and redundant pairs
## Removing pairs containing overlapping factors
## Constructing GRanges object of clusters coordinates
## Computing number of sites per cluster
## Discaring clusters with more than 6sites
## Creating TFBS_cluster ID on the fly
## Constructing GRangesList of sites per cluster
KLF4_clusters$ClusterCoordinates
## GRanges object with 35 ranges and 1 metadata column:
## seqnames ranges strand | number_of_TF
## <Rle> <IRanges> <Rle> | <integer>
## TFBS_cluster_1 chr19 3325484-3325561 * | 2
## TFBS_cluster_2 chr19 3388255-3388309 * | 3
## TFBS_cluster_3 chr19 3849423-3849460 * | 2
## TFBS_cluster_4 chr19 3972615-3972680 * | 2
## TFBS_cluster_5 chr19 3986414-3986443 * | 2
## ... ... ... ... . ...
## TFBS_cluster_31 chr19 5803543-5803591 * | 2
## TFBS_cluster_32 chr19 5806798-5806826 * | 3
## TFBS_cluster_33 chr19 5850440-5850490 * | 2
## TFBS_cluster_34 chr19 5875053-5875079 * | 2
## TFBS_cluster_35 chr19 5990562-5990589 * | 2
## -------
## seqinfo: 66 sequences from an unspecified genome; no seqlengths
KLF4_clusters$ClusterComposition
## GRangesList object of length 35:
## $TFBS_cluster_1
## GRanges object with 2 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## TFBS_10755168 chr19 3325484-3325493 +
## TFBS_10755169 chr19 3325552-3325561 +
## -------
## seqinfo: 66 sequences (1 circular) from mm10 genome
##
## $TFBS_cluster_2
## GRanges object with 3 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## TFBS_10755331 chr19 3388255-3388264 +
## TFBS_10755332 chr19 3388280-3388289 +
## TFBS_10755334 chr19 3388300-3388309 +
## -------
## seqinfo: 66 sequences (1 circular) from mm10 genome
##
## $TFBS_cluster_3
## GRanges object with 2 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## TFBS_10756428 chr19 3849423-3849432 +
## TFBS_10756431 chr19 3849451-3849460 +
## -------
## seqinfo: 66 sequences (1 circular) from mm10 genome
##
## ...
## <32 more elements>The function can be tuned using the parameters
max_intersite_distance,
min_intersite_distance, max_cluster_size and
max_cluster_width.
Finally, we provide two convenience wrappers to conduct genome-wide
single molecule sorting:
SortReadsBySingleTF_MultiSiteWrapper and
SortReadsByTFCluster_MultiSiteWrapper.
Both functions automate the tasks of arranging TFBSs into clusters (if
necessary), creating methylation calling windows, calling methylation,
sorting molecules and calculating state frequencies.
The relevant arguments of all the inner functions are available for
tuning.
SortReadsBySingleTF_MultiSiteWrapper(
sampleFile = "/g/krebs/barzaghi/HTS/SMF/MM/QuasR_input_files/QuasR_input_AllCanWGpooled_dprm_DE_only.txt",
samples = "SMF_MM_TKO_DE_",
genome = BSgenome.Mmusculus.UCSC.mm10,
coverage = 20, ConvRate.thr = NULL,
CytosinesToMask = NULL,
TFBSs = KLF4s,
max_interTF_distance = NULL, max_window_width = NULL, min_cluster_width = NULL,
fix.window.size = TRUE, max.window.size = 50,
cores = 4
) -> sorting_resultsThe function outputs a list of three objects: the TFBSs (or TFBS clusters) used to sort, sorted molecules and a data.frame with sorting results.
sorting_results = qs::qread(system.file("extdata", "gw_sorting.qs", package="SingleMoleculeFootprinting"))
sorting_results[[1]]
## GRanges object with 500 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## TFBS_1 chr19 3283483-3283492 +
## TFBS_2 chr19 3297310-3297319 +
## TFBS_3 chr19 3322114-3322123 +
## TFBS_4 chr19 3324079-3324088 +
## TFBS_5 chr19 3325484-3325493 +
## ... ... ... ...
## TFBS_496 chr19 5990580-5990589 +
## TFBS_497 chr19 5991634-5991643 +
## TFBS_498 chr19 6001105-6001114 +
## TFBS_499 chr19 6003358-6003367 +
## TFBS_500 chr19 6010914-6010923 +
## -------
## seqinfo: 66 sequences (1 circular) from mm10 genome
# sorting_results[[2]]
sorting_results[[3]]
## # A tibble: 1,165 × 5
## Sample State Counts Freqs TFBS_cluster
## <chr> <chr> <int> <dbl> <chr>
## 1 <NA> <NA> NA NA TFBS_1
## 2 <NA> <NA> NA NA TFBS_2
## 3 SMF_MM_TKO_DE_ 000 112 44.3 TFBS_3
## 4 SMF_MM_TKO_DE_ 001 12 4.74 TFBS_3
## 5 SMF_MM_TKO_DE_ 010 6 2.37 TFBS_3
## 6 SMF_MM_TKO_DE_ 011 2 0.791 TFBS_3
## 7 SMF_MM_TKO_DE_ 100 62 24.5 TFBS_3
## 8 SMF_MM_TKO_DE_ 101 4 1.58 TFBS_3
## 9 SMF_MM_TKO_DE_ 110 41 16.2 TFBS_3
## 10 SMF_MM_TKO_DE_ 111 14 5.53 TFBS_3
## # ℹ 1,155 more rowsThe function SortReadsByTFCluster_MultiSiteWrapper works
analogously, with the addition of arranging TFBS clusters according to
parameters
sessionInfo
## R version 4.4.2 (2024-10-31)
## 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] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] ggplot2_3.5.1 SingleMoleculeFootprintingData_1.14.0
## [3] BSgenome.Mmusculus.UCSC.mm10_1.4.3 BSgenome_1.75.0
## [5] rtracklayer_1.67.0 BiocIO_1.17.1
## [7] Biostrings_2.75.1 XVector_0.47.0
## [9] GenomicRanges_1.59.1 GenomeInfoDb_1.43.2
## [11] IRanges_2.41.1 S4Vectors_0.45.2
## [13] BiocGenerics_0.53.3 generics_0.1.3
## [15] SingleMoleculeFootprinting_2.1.0
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 sys_3.4.3
## [3] jsonlite_1.8.9 magrittr_2.0.3
## [5] GenomicFeatures_1.59.1 farver_2.1.2
## [7] rmarkdown_2.29 zlibbioc_1.52.0
## [9] vctrs_0.6.5 memoise_2.0.1
## [11] Rsamtools_2.23.1 RCurl_1.98-1.16
## [13] QuasR_1.47.0 ggpointdensity_0.1.0
## [15] htmltools_0.5.8.1 S4Arrays_1.7.1
## [17] progress_1.2.3 AnnotationHub_3.15.0
## [19] curl_6.0.1 SparseArray_1.7.2
## [21] sass_0.4.9 bslib_0.8.0
## [23] plyr_1.8.9 httr2_1.0.7
## [25] cachem_1.1.0 buildtools_1.0.0
## [27] GenomicAlignments_1.43.0 mime_0.12
## [29] lifecycle_1.0.4 pkgconfig_2.0.3
## [31] Matrix_1.7-1 R6_2.5.1
## [33] fastmap_1.2.0 GenomeInfoDbData_1.2.13
## [35] MatrixGenerics_1.19.0 digest_0.6.37
## [37] colorspace_2.1-1 ShortRead_1.65.0
## [39] patchwork_1.3.0 AnnotationDbi_1.69.0
## [41] ExperimentHub_2.15.0 RSQLite_2.3.8
## [43] hwriter_1.3.2.1 labeling_0.4.3
## [45] filelock_1.0.3 fansi_1.0.6
## [47] httr_1.4.7 abind_1.4-8
## [49] compiler_4.4.2 Rbowtie_1.47.0
## [51] withr_3.0.2 bit64_4.5.2
## [53] BiocParallel_1.41.0 viridis_0.6.5
## [55] DBI_1.2.3 qs_0.27.2
## [57] biomaRt_2.63.0 rappdirs_0.3.3
## [59] DelayedArray_0.33.2 rjson_0.2.23
## [61] tools_4.4.2 glue_1.8.0
## [63] restfulr_0.0.15 grid_4.4.2
## [65] gtable_0.3.6 tzdb_0.4.0
## [67] tidyr_1.3.1 RApiSerialize_0.1.4
## [69] hms_1.1.3 stringfish_0.16.0
## [71] xml2_1.3.6 utf8_1.2.4
## [73] BiocVersion_3.21.1 ggrepel_0.9.6
## [75] pillar_1.9.0 stringr_1.5.1
## [77] vroom_1.6.5 dplyr_1.1.4
## [79] BiocFileCache_2.15.0 lattice_0.22-6
## [81] bit_4.5.0 deldir_2.0-4
## [83] tidyselect_1.2.1 maketools_1.3.1
## [85] knitr_1.49 gridExtra_2.3
## [87] SummarizedExperiment_1.37.0 xfun_0.49
## [89] Biobase_2.67.0 matrixStats_1.4.1
## [91] stringi_1.8.4 UCSC.utils_1.3.0
## [93] yaml_2.3.10 evaluate_1.0.1
## [95] codetools_0.2-20 interp_1.1-6
## [97] GenomicFiles_1.43.0 tibble_3.2.1
## [99] BiocManager_1.30.25 cli_3.6.3
## [101] RcppParallel_5.1.9 munsell_0.5.1
## [103] jquerylib_0.1.4 Rcpp_1.0.13-1
## [105] dbplyr_2.5.0 tidyverse_2.0.0
## [107] png_0.1-8 XML_3.99-0.17
## [109] readr_2.1.5 blob_1.2.4
## [111] prettyunits_1.2.0 latticeExtra_0.6-30
## [113] jpeg_0.1-10 plyranges_1.27.0
## [115] bitops_1.0-9 pwalign_1.3.0
## [117] txdbmaker_1.3.1 viridisLite_0.4.2
## [119] VariantAnnotation_1.53.0 scales_1.3.0
## [121] purrr_1.0.2 crayon_1.5.3
## [123] rlang_1.1.4 KEGGREST_1.47.0