splicelogic: differential transcripts to splice events
Beatriz Campillo
Michael I. Love
03/20/2026
Source:vignettes/splicelogic.Rmd
splicelogic.RmdIntroduction
splicelogic is an R/Bioconductor package for detecting alternative splicing events from exon-level data stored as GRanges objects. Given a set of exons annotated with a coefficient column indicating differential transcript usage (DTU), splicelogic identifies the following types of splicing events:
- Skipped exons (SE) – exons skipped in up-regulated transcripts
- Included exons (IE) – exons included in up-regulated transcripts
- Mutually exclusive exons (MXE) – pairs of exons where one is included and another is excluded in up-regulated transcripts compared to down-regulated transcripts
- Retained introns (RI) – intronic regions that are retained as part of an exon in up-regulated transcripts
- Alternative 5’ (A5SS) – exons in up-regulated transcripts that share the same 3’ splice site but differ at the 5’ splice site from exons in down-regulated transcripts
- Alternative 3’ (A3SS) – as above, but differing in the 3’ splice site
These are detected using a series of functions,
e.g. find_se(), find_ie(), etc. described
below.
Quick start
With DTU results attached to a GRanges of the exons from significant transcripts, one can use the following code to identify splice events:
Input data
splicelogic assumes the user has run a differential transcript usage (DTU) or differential splicing analysis providing an error bound (e.g. adjusted p-value / FDR) and and effect estimate with direction (e.g. GLM estimated coefficient or deltaPSI) (see upstream methods).
It also assumes the user has obtained genomic ranges representing the exon structure of each transcript being analyzed (see obtaining exon ranges).
The exons should be provided in a flat GRanges
object (one range per exon), containing exon-level metadata in
mcols(exons) such as the gene ID, transcript ID, rank in
the transcript, and the estimated change or at least direction of change
from the differential analysis.
Required columns for exons:
| Column | Description |
|---|---|
gene_id |
Gene identifier |
tx_id |
Transcript identifier |
exon_rank |
Exon rank within the transcript |
coef_col = "<user supplied>" |
differential effect estimate for the transcript |
The differential effect estimate column is specified by the user in
preprocess(). This should be the differential effect
estimate associated with the specific transcript containing the exon,
and minimally could indicate the direction of effect with
+1/-1. Positive values indicate up-regulated exons and
negative values indicate down-regulated exons. All exons from the same
transcript will share the same value for this column.
Jones et al mouse long read dataset
For demonstration, we will use a published mouse long read dataset and its reported splicing changes.
The citation is:
Emma F. Jones, Timothy C. Howton, Victoria L. Flanary, Amanda D. Clark, Brittany N. Lasseigne Long-read RNA sequencing identifies region- and sex-specific C57BL/6J mouse brain mRNA isoform expression and usage Mol Brain 17, 40 (2024). doi: 10.1186/s13041-024-01112-7
Information about the paper, including code and publicly available data can be found at this URL:
https://github.com/lasseignelab/230227_EJ_MouseBrainIsoDiv
In the abstract, Jones et al. describe the experiment:
To assess differences in AS across the cerebellum, cortex, hippocampus, and striatum by sex, we generated and analyzed Oxford Nanopore Technologies (ONT) long-read RNA sequencing (lrRNA-Seq) C57BL/6J mouse brain cDNA libraries. From > 85 million reads that passed quality control metrics, we calculated differential gene expression (DGE), differential transcript expression (DTE), and differential transcript usage (DTU) across brain regions and by sex.
Here we will focus on the cortex-specific sex comparison, comparing female to male mice.
Loading example data
In this section we load the small example dataset that has been prepared for this vignette. Note that in a typical splicelogic workflow, the exons GRanges would be loaded from a TxDb. See the obtaining exon ranges section below for further details.
We load the differential transcript usage analysis from the Jones et al. paper. Specifically, we load the transcripts found to exhibit DTU in the F vs M comparison in cortex.
The DTU table for this example, and the exons for the differential
transcripts have been saved in the extdata directory of
splicelogic.
library(readr)
# load DTU results
dir <- system.file("extdata", package="splicelogic")
dtu_table <- readr::read_delim(file.path(dir, "dtu_table.tsv"))We load from BED file the 601 exons from GENCODE M31 annotation for the 49 differential transcripts, and attach the metadata columns.
library(plyranges)
exons_file <- "exons_M31.bed.gz"
exons_mcols_file <- "exons_mcols.tsv.gz"
exons <- plyranges::read_bed(file.path(dir, exons_file))
mcols(exons) <- DataFrame(readr::read_delim(file.path(dir, exons_mcols_file)))Finally, we add Seqinfo for mm49, the reference mouse
genome for GENCODE M31.
si <- Seqinfo::Seqinfo(genome="mm39")
seqlevels(exons) <- seqlevels(si)
seqinfo(exons) <- siNext, we join the DTU results onto the exons GRanges.
This is necessary for the splicelogic functions to know which
transcripts are up- or down-regulated, and to which gene the exons and
transcripts belong.
The code below matches the transcript IDs in the exons
metadata with the DTU results table, and then binds the two tables
before assigning back to exons.
txp_idx <- match(exons$tx_id, dtu_table$tx_id)
cols_to_add <- dtu_table[txp_idx, ] |> dplyr::select(-tx_id)
merged_DF <- S4Vectors::cbind(mcols(exons), cols_to_add)
mcols(exons) <- merged_DFAs of Bioc 3.23 and plyranges version 1.32, the above code to add metadata columns from an additional table can be replaced with the following:
exons <- exons |>
plyranges::join_mcols_left(dtu_table, by = "tx_id")splicelogic is designed to take as input the exons from significantly changed transcripts, so we first filter out transcripts that were not signficant at FDR 10%.
sig_exons <- exons |> filter(padj < .1)Finding splicing events
Preprocessing input data
The first step is to run preprocess(), which prepares
the exons for event detection. This function checks the
input data, ensures that the necessary columns are present:
library(splicelogic)
sig_exons <- sig_exons |>
preprocess(coef_col = "estimate")Finding individual events
Next we can run the various functions for calculating different types of splicing events.
Skipped exons (SE)
For example, we can calculate exons that are skipped in up-regulated transcripts relative to down-regulated transcripts, across all genes. As these are skipped in the up-regulated transcripts, it is expected that the exons returned belong to down-regulated transcripts:
skipped <- sig_exons |> find_se()
skipped## GRanges object with 1 range and 10 metadata columns:
## seqnames ranges strand | exon_id exon_name
## <Rle> <IRanges> <Rle> | <numeric> <character>
## [1] chr17 66647479-66647535 - | 480827 ENSMUSE00000443570.7
## exon_rank tx_id gene_id gene_name
## <numeric> <character> <character> <character>
## [1] 14 ENSMUST00000097291.10 ENSMUSG00000052105.18 Mtcl1
## estimate padj event tx_event
## <numeric> <numeric> <character> <character>
## [1] -2.9732 0.00970121 se ENSMUST00000086693.12
## -------
## seqinfo: 61 sequences (1 circular) from mm39 genomeIncluded exons (IE)
included <- sig_exons |> find_ie()
included## GRanges object with 3 ranges and 10 metadata columns:
## seqnames ranges strand | exon_id exon_name
## <Rle> <IRanges> <Rle> | <numeric> <character>
## [1] chr14 20517526-20517591 - | 408079 ENSMUSE00001423050.2
## [2] chr1 163739641-163739706 + | 12966 ENSMUSE00000368805.4
## [3] chr8 120884207-120884236 + | 250989 ENSMUSE00001243257.2
## exon_rank tx_id gene_id gene_name
## <numeric> <character> <character> <character>
## [1] 6 ENSMUST00000100844.6 ENSMUSG00000021814.18 Anxa7
## [2] 20 ENSMUST00000077642.12 ENSMUSG00000026585.14 Kifap3
## [3] 10 ENSMUST00000034281.13 ENSMUSG00000031824.16 6430548M08Rik
## estimate padj event tx_event
## <numeric> <numeric> <character> <character>
## [1] 3.02406 0.018472554 ie ENSMUST00000065504.17
## [2] 1.04389 0.010395185 ie ENSMUST00000027877.7
## [3] 4.14230 0.000995041 ie ENSMUST00000108951.8
## -------
## seqinfo: 61 sequences (1 circular) from mm39 genomeMutually exclusive exons (MXE)
mx <- sig_exons |> find_mxe()
mx## GRanges object with 2 ranges and 10 metadata columns:
## seqnames ranges strand | exon_id exon_name
## <Rle> <IRanges> <Rle> | <numeric> <character>
## [1] chr12 91799829-91799996 - | 374021 ENSMUSE00001304078.2
## [2] chr12 91798541-91798558 - | 374018 ENSMUSE00001473756.2
## exon_rank tx_id gene_id gene_name
## <numeric> <character> <character> <character>
## [1] 4 ENSMUST00000021347.12 ENSMUSG00000020964.15 Sel1l
## [2] 4 ENSMUST00000178462.8 ENSMUSG00000020964.15 Sel1l
## estimate padj event tx_event
## <numeric> <numeric> <character> <character>
## [1] -3.28535 0.00171934 mxe ENSMUST00000178462.8
## [2] 2.99384 0.00171934 mxe ENSMUST00000021347.12
## -------
## seqinfo: 61 sequences (1 circular) from mm39 genomeRetained introns (RI)
Here we do not find retained introns, and the function returns an empty vector.
ri <- sig_exons |> find_ri()
ri## GRanges object with 0 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## -------
## seqinfo: no sequencesAlternative 5’ and 3’ splice sites (A5/3SS)
a5ss <- sig_exons |> find_a5ss()
a5ss## GRanges object with 5 ranges and 10 metadata columns:
## seqnames ranges strand | exon_id exon_name
## <Rle> <IRanges> <Rle> | <numeric> <character>
## [1] chr10 88081618-88081868 + | 304334 ENSMUSE00001310024.2
## [2] chr14 20529963-20530189 - | 408097 ENSMUSE00000901772.3
## [3] chr8 120887954-120892045 + | 250998 ENSMUSE00000446870.6
## [4] chr9 21858242-21858348 + | 266133 ENSMUSE00001322549.2
## [5] chr9 21858900-21860203 + | 266139 ENSMUSE00001327764.2
## exon_rank tx_id gene_id gene_name
## <numeric> <character> <character> <character>
## [1] 7 ENSMUST00000182183.8 ENSMUSG00000020056.17 Washc3
## [2] 1 ENSMUST00000100844.6 ENSMUSG00000021814.18 Anxa7
## [3] 13 ENSMUST00000034281.13 ENSMUSG00000031824.16 6430548M08Rik
## [4] 7 ENSMUST00000190387.7 ENSMUSG00000040563.14 Plppr2
## [5] 9 ENSMUST00000190387.7 ENSMUSG00000040563.14 Plppr2
## estimate padj event tx_event
## <numeric> <numeric> <character> <character>
## [1] 9.19059 0.086737576 a5ss ENSMUST00000020248.16
## [2] 3.02406 0.018472554 a5ss ENSMUST00000065504.17
## [3] 4.14230 0.000995041 a5ss ENSMUST00000108951.8
## [4] 6.33671 0.006078234 a5ss ENSMUST00000046371.13
## [5] 6.33671 0.006078234 a5ss ENSMUST00000046371.13
## -------
## seqinfo: 61 sequences (1 circular) from mm39 genome
a3ss <- sig_exons |> find_a3ss()
a3ss## GRanges object with 9 ranges and 10 metadata columns:
## seqnames ranges strand | exon_id exon_name
## <Rle> <IRanges> <Rle> | <numeric> <character>
## [1] chr10 88037014-88037154 + | 304312 ENSMUSE00001309977.2
## [2] chr10 88055115-88055222 + | 304327 ENSMUSE00001223513.2
## [3] chr14 20505329-20506669 - | 408059 ENSMUSE00000564348.6
## [4] chr8 120840891-120841056 + | 250964 ENSMUSE00000678589.2
## [5] chr8 112437109-112438026 - | 262490 ENSMUSE00001391518.2
## [6] chr4 101504990-101505022 + | 107521 ENSMUSE00000631777.3
## [7] chr4 101513375-101513492 + | 107524 ENSMUSE00000671573.2
## [8] chr9 21849570-21849860 + | 266124 ENSMUSE00001334761.2
## [9] chr17 66643977-66645149 - | 480823 ENSMUSE00000791759.2
## exon_rank tx_id gene_id gene_name
## <numeric> <character> <character> <character>
## [1] 1 ENSMUST00000182183.8 ENSMUSG00000020056.17 Washc3
## [2] 5 ENSMUST00000182183.8 ENSMUSG00000020056.17 Washc3
## [3] 14 ENSMUST00000100844.6 ENSMUSG00000021814.18 Anxa7
## [4] 1 ENSMUST00000034281.13 ENSMUSG00000031824.16 6430548M08Rik
## [5] 7 ENSMUST00000212349.2 ENSMUSG00000031955.11 Bcar1
## [6] 1 ENSMUST00000106927.2 ENSMUSG00000035212.15 Leprot
## [7] 3 ENSMUST00000106927.2 ENSMUSG00000035212.15 Leprot
## [8] 1 ENSMUST00000190387.7 ENSMUSG00000040563.14 Plppr2
## [9] 14 ENSMUST00000086693.12 ENSMUSG00000052105.18 Mtcl1
## estimate padj event tx_event
## <numeric> <numeric> <character> <character>
## [1] 9.19059 0.086737576 a3ss ENSMUST00000020248.16
## [2] 9.19059 0.086737576 a3ss ENSMUST00000020248.16
## [3] 3.02406 0.018472554 a3ss ENSMUST00000065504.17
## [4] 4.14230 0.000995041 a3ss ENSMUST00000108951.8
## [5] 3.85659 0.027009405 a3ss ENSMUST00000166232.4
## [6] 9.13055 0.018472554 a3ss ENSMUST00000030254.15
## [7] 9.13055 0.018472554 a3ss ENSMUST00000030254.15
## [8] 6.33671 0.006078234 a3ss ENSMUST00000046371.13
## [9] 2.88524 0.013967132 a3ss ENSMUST00000097291.10
## -------
## seqinfo: 61 sequences (1 circular) from mm39 genomeFinding all events
The following function wraps the above ones to find all events.
all_events <- sig_exons |> find_all_events()## Calculating skipped exon events...## Calculating included exon events...## Calculating mutually exclusive exon events...## Calculating retained intron events...## Calculating alternative 5' splice site events...## Calculating alternative 3' splice site events...## Done! 20 events detected.
all_events## GRanges object with 20 ranges and 10 metadata columns:
## seqnames ranges strand | exon_id exon_name
## <Rle> <IRanges> <Rle> | <numeric> <character>
## [1] chr17 66647479-66647535 - | 480827 ENSMUSE00000443570.7
## [2] chr14 20517526-20517591 - | 408079 ENSMUSE00001423050.2
## [3] chr1 163739641-163739706 + | 12966 ENSMUSE00000368805.4
## [4] chr8 120884207-120884236 + | 250989 ENSMUSE00001243257.2
## [5] chr12 91799829-91799996 - | 374021 ENSMUSE00001304078.2
## ... ... ... ... . ... ...
## [16] chr8 112437109-112438026 - | 262490 ENSMUSE00001391518.2
## [17] chr4 101504990-101505022 + | 107521 ENSMUSE00000631777.3
## [18] chr4 101513375-101513492 + | 107524 ENSMUSE00000671573.2
## [19] chr9 21849570-21849860 + | 266124 ENSMUSE00001334761.2
## [20] chr17 66643977-66645149 - | 480823 ENSMUSE00000791759.2
## exon_rank tx_id gene_id gene_name
## <numeric> <character> <character> <character>
## [1] 14 ENSMUST00000097291.10 ENSMUSG00000052105.18 Mtcl1
## [2] 6 ENSMUST00000100844.6 ENSMUSG00000021814.18 Anxa7
## [3] 20 ENSMUST00000077642.12 ENSMUSG00000026585.14 Kifap3
## [4] 10 ENSMUST00000034281.13 ENSMUSG00000031824.16 6430548M08Rik
## [5] 4 ENSMUST00000021347.12 ENSMUSG00000020964.15 Sel1l
## ... ... ... ... ...
## [16] 7 ENSMUST00000212349.2 ENSMUSG00000031955.11 Bcar1
## [17] 1 ENSMUST00000106927.2 ENSMUSG00000035212.15 Leprot
## [18] 3 ENSMUST00000106927.2 ENSMUSG00000035212.15 Leprot
## [19] 1 ENSMUST00000190387.7 ENSMUSG00000040563.14 Plppr2
## [20] 14 ENSMUST00000086693.12 ENSMUSG00000052105.18 Mtcl1
## estimate padj event tx_event
## <numeric> <numeric> <character> <character>
## [1] -2.97320 0.009701213 se ENSMUST00000086693.12
## [2] 3.02406 0.018472554 ie ENSMUST00000065504.17
## [3] 1.04389 0.010395185 ie ENSMUST00000027877.7
## [4] 4.14230 0.000995041 ie ENSMUST00000108951.8
## [5] -3.28535 0.001719345 mxe ENSMUST00000178462.8
## ... ... ... ... ...
## [16] 3.85659 0.02700941 a3ss ENSMUST00000166232.4
## [17] 9.13055 0.01847255 a3ss ENSMUST00000030254.15
## [18] 9.13055 0.01847255 a3ss ENSMUST00000030254.15
## [19] 6.33671 0.00607823 a3ss ENSMUST00000046371.13
## [20] 2.88524 0.01396713 a3ss ENSMUST00000097291.10
## -------
## seqinfo: 61 sequences (1 circular) from mm39 genome
Upstream methods
splicelogic is designed to operate downstream of
differential transcript usage (DTU). DTU methods test
whether the relative proportions of transcripts within a
gene differ between experimental conditions.
In general, any upstream method that produces transcript-resolved
differential usage statistics can be used with splicelogic,
provided that results include:
- a per-transcript directional effect estimate
(e.g. a model coefficient, change in isoform fraction, deltaPSI, etc.),
and
- an adjusted p-value (or equivalent significance metric by thresholding).
Common upstream methods include:
satuRn — fits quasi-binomial generalized linear models to transcript usage proportions and performs scalable transcript-level DTU testing. Particularly well suited to larger datasets.
DRIMSeq — models transcript proportions within genes using a Dirichlet-multinomial framework, with both gene-level and transcript-level testing.
BANDITS — a Bayesian hierarchical DTU method that models transcript usage with a Dirichlet-multinomial and explicitly accounts for mapping uncertainty using equivalence classes, with both gene-level and transcript-level testing.
DEXSeq — primarily a differential exon usage (DEU) method based on negative binomial GLMs, but commonly used in transcript-level DTU workflows. The log2FC coefficients from DEXSeq can be used directly with splicelogic.
limma and edgeR (
diffSplice/diffSpliceDGE) — can be used for DTU analyses (as well as exon-level), with both gene-level and transcript-level testing.
Regardless of which method is used, the per-transcript DTU statistics
(effect estimates and adjusted p-values) have to be mapped onto the
individual exons of each transcript to produce an exon-level
GRanges (see Obtaining exon
ranges). This annotated GRanges is the starting point for
splicelogic, beginning with preprocess() and then
followed by event-specific functions (find_se(),
find_ie(), etc.).
Obtaining exon ranges
Using prepare_exons()
prepare_exons() extracts exon ranges from a
TxDb object and merges them with your DTU results table. It
returns a flat GRanges ready for preprocess() and
the find_* functions. functions. We demonstrate using
GENCODE v32 (human genes).
The first step is to load a TxDb object. Typically, a user
would supply their own GTF to txdbmaker::makeTxDbFromGFF()
to generate this. For this demonstration we will load a pre-constructed
TxDb from Bioconductor’s AnnotationHub.
suppressPackageStartupMessages({
library(AnnotationHub)
library(AnnotationDbi)
library(GenomicFeatures)
})
ah <- AnnotationHub()
txdb <- ah[["AH75191"]] # GENCODE v32 (human)## loading from cache
suppressPackageStartupMessages({
library(tibble)
})
txps <- txdb |>
AnnotationDbi::select(keys(txdb, "TXID"), c("TXNAME","GENEID"), "TXID") |>
tibble::as_tibble() |>
dplyr::select(tx_num = TXID, tx_id = TXNAME, gene_id = GENEID)## 'select()' returned 1:1 mapping between keys and columns
txps## # A tibble: 227,462 × 3
## tx_num tx_id gene_id
## <int> <chr> <chr>
## 1 1 ENST00000456328.2 ENSG00000223972.5
## 2 2 ENST00000450305.2 ENSG00000223972.5
## 3 3 ENST00000473358.1 ENSG00000243485.5
## 4 4 ENST00000469289.1 ENSG00000243485.5
## 5 5 ENST00000607096.1 ENSG00000284332.1
## 6 6 ENST00000606857.1 ENSG00000268020.3
## 7 7 ENST00000642116.1 ENSG00000240361.2
## 8 8 ENST00000492842.2 ENSG00000240361.2
## 9 9 ENST00000641515.2 ENSG00000186092.6
## 10 10 ENST00000335137.4 ENSG00000186092.6
## # ℹ 227,452 more rows
# simulate DTU results
sim_dtu_table <- txps |>
dplyr::mutate(
padj = runif(dplyr::n()),
effect_est = rnorm(dplyr::n())
)
sim_dtu_table## # A tibble: 227,462 × 5
## tx_num tx_id gene_id padj effect_est
## <int> <chr> <chr> <dbl> <dbl>
## 1 1 ENST00000456328.2 ENSG00000223972.5 0.0808 -0.497
## 2 2 ENST00000450305.2 ENSG00000223972.5 0.834 0.938
## 3 3 ENST00000473358.1 ENSG00000243485.5 0.601 -1.84
## 4 4 ENST00000469289.1 ENSG00000243485.5 0.157 1.53
## 5 5 ENST00000607096.1 ENSG00000284332.1 0.00740 0.882
## 6 6 ENST00000606857.1 ENSG00000268020.3 0.466 -2.10
## 7 7 ENST00000642116.1 ENSG00000240361.2 0.498 -0.443
## 8 8 ENST00000492842.2 ENSG00000240361.2 0.290 -1.60
## 9 9 ENST00000641515.2 ENSG00000186092.6 0.733 -0.124
## 10 10 ENST00000335137.4 ENSG00000186092.6 0.773 -0.576
## # ℹ 227,452 more rowsHere we name this output human_exons to not collide with
the example above for the mouse dataset.
human_exons <- prepare_exons(txdb, sim_dtu_table, coef_col = "effect_est", verbose = TRUE)## Extracting exons from TxDb...## Mapping transcript IDs...## Merging DTU results onto exons...## Done. Returned 1372308 exon ranges from 227462 unique transcripts.
human_exons <- human_exons |>
filter(padj < .01) |>
preprocess(coef_col = "effect_est")Manual construction
This section walks through the steps that
prepare_exons() performs internally. This is useful if you
need more control over the process or want to understand how exon ranges
are built from a TxDb.
The following extracts the exons grouped by transcript from the TxDb:
# extract exons as a GRangesList
exons_list <- GenomicFeatures::exonsBy(
txdb,
by="tx"
)
# Our DTU table aligns with txps, which aligns with the names of the GRangesList.
# prepare_exons() handles this with alignment checks
names(exons_list) <- sim_dtu_table$tx_idNext flattening the exons (here using a new name
flat_exons to not collide with the exons mouse
dataset above):
flat_exons <- unlist(exons_list)
# swap tx_id with exon_name as the names of the GRanges
flat_exons$tx_id <- names(flat_exons) # store transcript ids
names(flat_exons) <- flat_exons$exon_nameAdding DTU results and gene ID:
Session info
## R version 4.5.2 (2025-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 LTS
##
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## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
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## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
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## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: UTC
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##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
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## other attached packages:
## [1] tibble_3.3.1 GenomicFeatures_1.62.0 AnnotationDbi_1.72.0
## [4] Biobase_2.70.0 AnnotationHub_4.0.0 BiocFileCache_3.0.0
## [7] dbplyr_2.5.2 splicelogic_0.99.0 plyranges_1.30.1
## [10] dplyr_1.2.0 GenomicRanges_1.62.1 Seqinfo_1.0.0
## [13] IRanges_2.44.0 S4Vectors_0.48.0 BiocGenerics_0.56.0
## [16] generics_0.1.4 readr_2.2.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 blob_1.3.0
## [3] filelock_1.0.3 Biostrings_2.78.0
## [5] bitops_1.0-9 fastmap_1.2.0
## [7] RCurl_1.98-1.17 GenomicAlignments_1.46.0
## [9] XML_3.99-0.22 digest_0.6.39
## [11] lifecycle_1.0.5 KEGGREST_1.50.0
## [13] RSQLite_2.4.6 magrittr_2.0.4
## [15] compiler_4.5.2 rlang_1.1.7
## [17] sass_0.4.10 tools_4.5.2
## [19] utf8_1.2.6 yaml_2.3.12
## [21] rtracklayer_1.70.1 knitr_1.51
## [23] S4Arrays_1.10.1 htmlwidgets_1.6.4
## [25] bit_4.6.0 curl_7.0.0
## [27] DelayedArray_0.36.0 abind_1.4-8
## [29] BiocParallel_1.44.0 purrr_1.2.1
## [31] withr_3.0.2 desc_1.4.3
## [33] grid_4.5.2 SummarizedExperiment_1.40.0
## [35] cli_3.6.5 rmarkdown_2.30
## [37] crayon_1.5.3 ragg_1.5.1
## [39] otel_0.2.0 httr_1.4.8
## [41] tzdb_0.5.0 rjson_0.2.23
## [43] DBI_1.3.0 cachem_1.1.0
## [45] parallel_4.5.2 BiocManager_1.30.27
## [47] XVector_0.50.0 restfulr_0.0.16
## [49] matrixStats_1.5.0 vctrs_0.7.1
## [51] Matrix_1.7-4 jsonlite_2.0.0
## [53] hms_1.1.4 bit64_4.6.0-1
## [55] systemfonts_1.3.2 jquerylib_0.1.4
## [57] glue_1.8.0 pkgdown_2.2.0
## [59] codetools_0.2-20 BiocVersion_3.22.0
## [61] GenomeInfoDb_1.46.2 BiocIO_1.20.0
## [63] UCSC.utils_1.6.1 pillar_1.11.1
## [65] rappdirs_0.3.4 htmltools_0.5.9
## [67] R6_2.6.1 httr2_1.2.2
## [69] textshaping_1.0.5 vroom_1.7.0
## [71] evaluate_1.0.5 lattice_0.22-9
## [73] png_0.1-9 Rsamtools_2.26.0
## [75] cigarillo_1.0.0 memoise_2.0.1
## [77] bslib_0.10.0 SparseArray_1.10.9
## [79] xfun_0.56 fs_1.6.7
## [81] MatrixGenerics_1.22.0 pkgconfig_2.0.3