1. Introduction to OLSr
Martin Morgan
Roswell Park Comprehensive Cancer Center, Buffalo, NY, USSource:
vignettes/introduction.Rmd
introduction.RmdThe Ontology Lookup Service (‘OLS’) is a repository of biomedical ontologies provided by the European Bioinformatics Institute (EMBL-EBI). OLSr provides an interface to the OLS service, allowing discovery of ontologies, terms, and relations. Results are presented in a ‘tidy’ framework, so that manipulation tasks are easily accomplished with verbs such as ‘filter()’, ‘select()’, and ‘mutate()’. An effort is made to integrate with other packages useful for more comprehensive biological and ontological analysis.
Written: 20 November, 2023
Installation
Install OLSr from GitHub using the remotes package.
if (!"remotes" in rownames(installed.packages()))
install.packages("remotes", repos = "https://cran.r-project.org")
remotes::install_github("mtmorgan/OLSr")Attach the library to the current session. Most functions in OLSr return results as a
tibble, so the dplyr package is
also attached.
Ontologies
Start by discovering ontologies available in the OLS.
onto <- get_ontologies()
onto
#> # A tibble: 249 × 31
#> id title description version numberOfTerms numberOfProperties
#> <chr> <chr> <chr> <chr> <int> <int>
#> 1 ado Alzheimer's D… Alzheimer'… 2.0.1 1963 186
#> 2 agro Agronomy Onto… AgrO is an… NA 4162 293
#> 3 aism Ontology for … The ontolo… 2023-0… 7443 547
#> 4 amphx Amphioxus Dev… An ontolog… NA 403 10
#> 5 apo Ascomycete Ph… A structur… 2023-1… 619 27
#> 6 apollo_sv Apollo Struct… An OWL2 on… 2023-0… 1691 366
#> 7 aro Antibiotic Re… Antibiotic… NA 7105 25
#> 8 bco Biological Co… An ontolog… 2021-1… 253 472
#> 9 bfo Basic Formal … The upper … NA 35 22
#> 10 bspo Biological Sp… An ontolog… 2023-0… 169 236
#> # ℹ 239 more rows
#> # ℹ 25 more variables: numberOfIndividuals <int>, languages <list>,
#> # loaded <chr>, updated <chr>, versionIri <chr>, namespace <chr>,
#> # preferredPrefix <chr>, homepage <chr>, mailingList <chr>, tracker <chr>,
#> # logo <lgl>, creators <lgl>, annotations <lgl>, fileLocation <chr>,
#> # oboSlims <lgl>, labelProperty <chr>, definitionProperties <list>,
#> # synonymProperties <list>, hierarchicalProperties <list>, baseUris <list>, …This vignette uses for examples the ontology with title ‘Cell
Ontology’, used by the CELLxGENE project to
classify cell types. Find the record corresponding to this ontology,
using glimpse() to provide a convenient view of the
ontology description.
onto |>
filter(title == "Cell Ontology" ) |>
glimpse()
#> Rows: 1
#> Columns: 31
#> $ id <chr> "cl"
#> $ title <chr> "Cell Ontology"
#> $ description <chr> "An ontology of cell types."
#> $ version <chr> "2023-10-19"
#> $ numberOfTerms <int> 16147
#> $ numberOfProperties <int> 531
#> $ numberOfIndividuals <int> 18
#> $ languages <list> ["en"]
#> $ loaded <chr> "2023-11-27T11:39:31.284869834"
#> $ updated <chr> "2023-11-27T11:39:31.284869834"
#> $ versionIri <chr> "http://purl.obolibrary.org/obo/cl/releases/202…
#> $ namespace <chr> "cl"
#> $ preferredPrefix <chr> "CL"
#> $ homepage <chr> "https://obophenotype.github.io/cell-ontology/"
#> $ mailingList <chr> "https://groups.google.com/g/cl_edit"
#> $ tracker <chr> "https://github.com/obophenotype/cell-ontology/…
#> $ logo <lgl> NA
#> $ creators <lgl> NA
#> $ annotations <lgl> NA
#> $ fileLocation <chr> "http://purl.obolibrary.org/obo/cl.owl"
#> $ oboSlims <lgl> FALSE
#> $ labelProperty <chr> "http://www.w3.org/2000/01/rdf-schema#label"
#> $ definitionProperties <list> <NULL>
#> $ synonymProperties <list> <NULL>
#> $ hierarchicalProperties <list> <NULL>
#> $ baseUris <list> <NULL>
#> $ hiddenProperties <list> <NULL>
#> $ preferredRootTerms <lgl> NA
#> $ isSkos <lgl> FALSE
#> $ allowDownload <lgl> FALSE
#> $ `_links` <list> [["https://www.ebi.ac.uk/ols4/api/ontologies/cl…Important information includes the ‘id’ of the ontology (used in many subsequent steps), the ontology home page, and the number of terms in the ontology.
onto |>
filter(title == "Cell Ontology") |>
select(id, homepage, numberOfTerms)
#> # A tibble: 1 × 3
#> id homepage numberOfTerms
#> <chr> <chr> <int>
#> 1 cl https://obophenotype.github.io/cell-ontology/ 16147Information on individual ontologies is also available with
get_ontology(); this record includes a link to the ontology
page in the OLS. The home page is a good starting place for
understanding the ontology in a more visual and interactive
environment.
cl <- get_ontology("cl")
glimpse(cl)
#> Rows: 1
#> Columns: 16
#> $ languages <list> "en"
#> $ lang <chr> "en"
#> $ ontologyId <chr> "cl"
#> $ loaded <chr> "2023-11-27T11:39:31.284869834"
#> $ updated <chr> "2023-11-27T11:39:31.284869834"
#> $ status <chr> "LOADED"
#> $ message <chr> ""
#> $ version <chr> "2023-10-19"
#> $ fileHash <lgl> NA
#> $ loadAttempts <int> 0
#> $ numberOfTerms <int> 16147
#> $ numberOfProperties <int> 531
#> $ numberOfIndividuals <int> 18
#> $ config <list> "cl"
#> $ baseUris <list> <NULL>
#> $ `_links` <list> ["https://www.ebi.ac.uk/ols4/api/ontologies/cl?la…
cl |> pull("_links")
#> [[1]]
#> [[1]]$href
#> [1] "https://www.ebi.ac.uk/ols4/api/ontologies/cl?lang=en"Terms
Terms are central to using an ontology. Retrieve all terms from the
‘cl’ ontology. By default, only terms defined in the ontology per
se are used; add the argument all_ontologies = TRUE to
this and subsequent functions to also include terms referenced in other
ontologies. This query can take several minutes to complete when first
executed, but the result is cached locally (as discussed in greater
detail below) so subsequent use is quick.
terms <- get_terms("cl")
terms
#> # A tibble: 2,734 × 13
#> label obo_id description iri synonyms annotation has_children short_form
#> <chr> <chr> <chr> <chr> <list> <list> <lgl> <chr>
#> 1 nonke… CL:00… A nonkerat… http… <NULL> <named list> TRUE CL_0002635
#> 2 nonke… CL:00… A nonkerat… http… <NULL> <named list> FALSE CL_0002636
#> 3 kerat… CL:00… An epithel… http… <NULL> <named list> FALSE CL_0002637
#> 4 bronc… CL:00… A respirat… http… <list> <named list> FALSE CL_0002638
#> 5 amnio… CL:00… An amnioti… http… <NULL> <named list> FALSE CL_0002639
#> 6 amnio… CL:00… An epithel… http… <NULL> <named list> FALSE CL_0002640
#> 7 epith… CL:00… An epithel… http… <NULL> <named list> FALSE CL_0002641
#> 8 epith… CL:00… An epithel… http… <NULL> <named list> FALSE CL_0002642
#> 9 nonke… CL:00… An epithel… http… <NULL> <named list> FALSE CL_0002643
#> 10 endo-… CL:00… An endothe… http… <NULL> <named list> FALSE CL_0002644
#> # ℹ 2,724 more rows
#> # ℹ 5 more variables: in_subset <list>, obo_definition_citation <list>,
#> # obo_xref <list>, obo_synonym <list>, `_links` <list>Glimpse the second term to get a sense of available information.
terms |>
slice(1) |>
glimpse()
#> Rows: 1
#> Columns: 13
#> $ label <chr> "nonkeratinized epithelial cell of anal column"
#> $ obo_id <chr> "CL:0002635"
#> $ description <chr> "A nonkeratinized epithelial cell of the anal …
#> $ iri <chr> "http://purl.obolibrary.org/obo/CL_0002635"
#> $ synonyms <list> <NULL>
#> $ annotation <list> [["https://orcid.org/0000-0003-1980-3228"], ["…
#> $ has_children <lgl> TRUE
#> $ short_form <chr> "CL_0002635"
#> $ in_subset <list> <NULL>
#> $ obo_definition_citation <list> [["A nonkeratinized epithelial cell of the ana…
#> $ obo_xref <list> [["FMA", "263146", <NULL>, "http://purl.org/si…
#> $ obo_synonym <list> <NULL>
#> $ `_links` <list> [["https://www.ebi.ac.uk/ols4/api/ontologies/…Hierarchical data
Some columns in the terms table represent hieararchical
(relational, 1:many mapping) data, e.g., the in_subset
column
terms |>
select(label, obo_id, in_subset)
#> # A tibble: 2,734 × 3
#> label obo_id in_subset
#> <chr> <chr> <list>
#> 1 nonkeratinized epithelial cell of anal column CL:00… <NULL>
#> 2 nonkeratinized epithelial cell of inferior part of anal can… CL:00… <NULL>
#> 3 keratinized epithelial cell of the anal canal CL:00… <NULL>
#> 4 bronchioalveolar stem cell CL:00… <NULL>
#> 5 amniotic stem cell CL:00… <NULL>
#> 6 amniotic epithelial stem cell CL:00… <NULL>
#> 7 epithelial cell of esophageal gland proper CL:00… <NULL>
#> 8 epithelial cell of esophageal cardiac gland CL:00… <NULL>
#> 9 nonkeratinized cell of stratum corneum of esophageal epithe… CL:00… <NULL>
#> 10 endo-epithelial cell of tympanic part of viscerocranial muc… CL:00… <NULL>
#> # ℹ 2,724 more rowsIndividual terms apparently belong to 0 or more subsets.
terms |>
count(lengths(in_subset))
#> # A tibble: 4 × 2
#> `lengths(in_subset)` n
#> <int> <int>
#> 1 0 2091
#> 2 1 579
#> 3 2 61
#> 4 3 3The R for Data Science chapter on hierarchical data suggests
using tidyr
functions unnest_wider() and unnest_longer()
to expand such data.
terms |>
select(in_subset, obo_id, label) |>
tidyr::unnest_longer(in_subset)
#> # A tibble: 710 × 3
#> in_subset obo_id label
#> <chr> <chr> <chr>
#> 1 human_reference_atlas CL:0002652 endothelial cell of high endothelial …
#> 2 human_reference_atlas CL:0002677 naive regulatory T cell
#> 3 blood_and_immune_upper_slim CL:0002679 natural helper lymphocyte
#> 4 human_reference_atlas CL:0004219 A2 amacrine cell
#> 5 human_reference_atlas CL:0004232 starburst amacrine cell
#> 6 human_reference_atlas CL:0004252 medium field retinal amacrine cell
#> 7 human_reference_atlas CL:0004253 wide field retinal amacrine cell
#> 8 human_reference_atlas CL:0005006 ionocyte
#> 9 human_reference_atlas CL:0005019 pancreatic epsilon cell
#> 10 human_reference_atlas CL:0008019 mesenchymal cell
#> # ℹ 700 more rowsDefined subsets and the number of terms in each can be computed as
terms |>
tidyr::unnest_longer(in_subset) |>
count(in_subset, sort = TRUE)
#> # A tibble: 8 × 2
#> in_subset n
#> <chr> <int>
#> 1 human_reference_atlas 486
#> 2 location_grouping 65
#> 3 BDS_subset 64
#> 4 blood_and_immune_upper_slim 36
#> 5 eye_upper_slim 24
#> 6 general_cell_types_upper_slim 24
#> 7 added_for_HCA 8
#> 8 _upper_level 3Use filter() with a helper function that summarizes
subsets of individual terms to identify the
human_reference_atlas subset. Using vapply()
(rather than, e.g., sapply()) can be considered a best
practice, so that the filter function is guaranteed to return a scalar
logical value regardless of input length.
is_human_reference_atlas <- function(x)
vapply(x, \(x) any(x == "human_reference_atlas"), logical(1))
human_reference_atlas <-
terms |>
filter(is_human_reference_atlas(in_subset))
human_reference_atlas
#> # A tibble: 486 × 13
#> label obo_id description iri synonyms annotation has_children short_form
#> <chr> <chr> <chr> <chr> <list> <list> <lgl> <chr>
#> 1 endot… CL:00… A venule e… http… <list> <named list> FALSE CL_0002652
#> 2 naive… CL:00… A regulato… http… <list> <named list> TRUE CL_0002677
#> 3 A2 am… CL:00… A bistrati… http… <NULL> <named list> FALSE CL_0004219
#> 4 starb… CL:00… An amacrin… http… <NULL> <named list> FALSE CL_0004232
#> 5 mediu… CL:00… An amicrin… http… <NULL> <named list> TRUE CL_0004252
#> 6 wide … CL:00… An amicrin… http… <NULL> <named list> TRUE CL_0004253
#> 7 ionoc… CL:00… Specialize… http… <NULL> <named list> TRUE CL_0005006
#> 8 pancr… CL:00… Ghrelin se… http… <list> <named list> FALSE CL_0005019
#> 9 mesen… CL:00… A non-pola… http… <list> <named list> TRUE CL_0008019
#> 10 pancr… CL:00… An endocri… http… <NULL> <named list> TRUE CL_0008024
#> # ℹ 476 more rows
#> # ℹ 5 more variables: in_subset <list>, obo_definition_citation <list>,
#> # obo_xref <list>, obo_synonym <list>, `_links` <list>There are 486 terms in the human_reference_atlas
subset.
Individual terms
Suppose we are interested in B cells. Discover term labels starting
with "B cell", selecting useful fields for display. For
subsequent work, create a variable capturing the obo_id of
the "B cell" term.
terms |>
filter(startsWith(label, "B cell")) |>
select(label, obo_id, description)
#> # A tibble: 6 × 3
#> label obo_id description
#> <chr> <chr> <chr>
#> 1 B cell of appendix CL:0009032 A B cell that is located i…
#> 2 B cell of medullary sinus of lymph node CL:0009045 A B cell found in the lymp…
#> 3 B cell of anorectum CL:0009050 A B cell that is located i…
#> 4 B cell zone reticular cell CL:0009104 A fibroblastic reticular c…
#> 5 B cell CL:0000236 A lymphocyte of B lineage …
#> 6 B cell, CD19-positive CL:0001201 A B cell that is CD19-posi…
b_cell <- # "CL:0000236"
terms |>
filter(label == "B cell") |>
pull(obo_id)Slightly richer information on individual terms is available with
get_term(), using the ontology id and term
obo_id as keys.
get_term("cl", b_cell) |>
glimpse()
#> Rows: 1
#> Columns: 22
#> $ obo_id <chr> "CL:0000236"
#> $ label <chr> "B cell"
#> $ description <chr> "A lymphocyte of B lineage that is capable of …
#> $ iri <chr> "http://purl.obolibrary.org/obo/CL_0000236"
#> $ lang <chr> "en"
#> $ synonyms <list> ["B lymphocyte", "B-cell", "B-lymphocyte"]
#> $ annotation <list> [["BTO:0000776", "CALOHA:TS-0068", "FMA:62869"…
#> $ ontology_name <chr> "cl"
#> $ ontology_prefix <chr> "CL"
#> $ ontology_iri <chr> "http://purl.obolibrary.org/obo/cl.owl"
#> $ is_obsolete <lgl> FALSE
#> $ term_replaced_by <lgl> NA
#> $ is_defining_ontology <lgl> TRUE
#> $ has_children <lgl> TRUE
#> $ is_root <lgl> FALSE
#> $ short_form <chr> "CL_0000236"
#> $ in_subset <list> ["blood_and_immune_upper_slim", "general_cell_…
#> $ obo_definition_citation <list> [["A lymphocyte of B lineage that is capable o…
#> $ obo_xref <list> [["BTO", "0000776", <NULL>, "http://purl.oboli…
#> $ obo_synonym <lgl> NA
#> $ is_preferred_root <lgl> FALSE
#> $ `_links` <list> [["https://www.ebi.ac.uk/ols4/api/ontologies/…Relatives
The functions get_parents() and
get_children() retrieve parent and child terms of
individual terms; get_ancestors() and
get_descendants() return all ancestors (to the root of the
ontology) or descendants (where the final term repreents a leaf). For B
cells the ancestors are
b_cell_ancestors <-
get_ancestors("cl", b_cell)Ancestors and descendants also contain the in_subset
column, so these groups are easily filtered to a particlur subset,
e.g.,
b_cell_ancestors |>
filter(is_human_reference_atlas(in_subset))
#> # A tibble: 3 × 14
#> obo_id label description iri synonyms annotation has_children is_root
#> <chr> <chr> <chr> <chr> <list> <list> <lgl> <lgl>
#> 1 CL:0000945 lymph… A lymphocy… http… <NULL> <named list> TRUE FALSE
#> 2 CL:0000542 lymph… A lymphocy… http… <NULL> <named list> TRUE FALSE
#> 3 CL:0000738 leuko… An achroma… http… <list> <named list> TRUE FALSE
#> # ℹ 6 more variables: short_form <chr>, in_subset <list>,
#> # obo_definition_citation <list>, obo_xref <list>, obo_synonym <list>,
#> # `_links` <list>Terms without children or descendants return a tibble without any rows.
get_children("cl", "CL:0002350")
#> # A tibble: 0 × 0
get_descendants("cl", "CL:0002350")
#> # A tibble: 0 × 0Graphs
See the Case Study: CELLxGENE Ontologies vignette for illustration of ancestors and descendants graphs.
Cache management
OLSr uses an on-disk cache to improve performance and reliability. Each query to the OLS server is cached on first use, so that subsequent responses can be read from the local disk. This greatly increases performance, and allows use of previously retrieved resources when off-line.
OLSr uses the memoise and cachem packages to
implement the cache. The disk cache is implemented using cachem’s
cache_disk() function using the default cache size (1 Gb)
and "lsu" (‘least recently used’) eviction policy. Objects
remain in the cache for 30 days.
A risk of using a cache is that the cache contains an ‘old’ version of the response. Circumvent this risk by manually manipulating the cache, e.g., removing objects that a known to be too old.
Use cache_directory() to discover the location of the
cache.
cache_directory()
#> [1] "/home/runner/.cache/R/OLSr"This location follows conventions of
tools::R_user_dir(). All files in this directory have been
created by OLSr.
cache_info() is a convenience function to summarize
files in the cache.
cache_info()
#> # A tibble: 1,465 × 5
#> file size mtime ctime atime
#> <chr> <dbl> <dttm> <dttm> <dttm>
#> 1 00504af55f… 6242 2023-11-28 23:19:57 2023-11-28 23:19:57 2023-11-28 23:19:57
#> 2 00645082a7… 154 2023-11-28 23:20:01 2023-11-28 23:20:01 2023-11-28 23:20:01
#> 3 006b7a52f7… 154 2023-11-28 23:19:39 2023-11-28 23:19:39 2023-11-28 23:19:39
#> 4 0082f3196e… 154 2023-11-28 23:21:47 2023-11-28 23:21:47 2023-11-28 23:21:47
#> 5 008b469362… 154 2023-11-28 23:20:47 2023-11-28 23:20:47 2023-11-28 23:20:47
#> 6 00988f2114… 2827 2023-11-28 23:20:43 2023-11-28 23:20:43 2023-11-28 23:20:43
#> 7 00d13d1836… 154 2023-11-28 23:20:12 2023-11-28 23:20:12 2023-11-28 23:20:12
#> 8 0123b83386… 154 2023-11-28 23:12:15 2023-11-28 23:12:15 2023-11-28 23:12:15
#> 9 0127f18a69… 2856 2023-11-28 23:19:47 2023-11-28 23:19:47 2023-11-28 23:19:47
#> 10 014dd2384c… 7430 2023-11-28 23:20:08 2023-11-28 23:20:08 2023-11-28 23:20:08
#> # ℹ 1,455 more rowsEach file represents the response to a single memoised function call. The file name is a hash of the function definition and arguments; it is not possible to associate individual files with specific function calls. Nonetheless, it can be informative to identify files modified more than, say 1 day ago, or the most recently modified file, or perhaps the total size of files in the cache.
## cache objects modified in the last 24 hours
cache_info() |>
filter(mtime > Sys.time() - 24 * 60 * 60)
#> # A tibble: 1,465 × 5
#> file size mtime ctime atime
#> <chr> <dbl> <dttm> <dttm> <dttm>
#> 1 00504af55f… 6242 2023-11-28 23:19:57 2023-11-28 23:19:57 2023-11-28 23:19:57
#> 2 00645082a7… 154 2023-11-28 23:20:01 2023-11-28 23:20:01 2023-11-28 23:20:01
#> 3 006b7a52f7… 154 2023-11-28 23:19:39 2023-11-28 23:19:39 2023-11-28 23:19:39
#> 4 0082f3196e… 154 2023-11-28 23:21:47 2023-11-28 23:21:47 2023-11-28 23:21:47
#> 5 008b469362… 154 2023-11-28 23:20:47 2023-11-28 23:20:47 2023-11-28 23:20:47
#> 6 00988f2114… 2827 2023-11-28 23:20:43 2023-11-28 23:20:43 2023-11-28 23:20:43
#> 7 00d13d1836… 154 2023-11-28 23:20:12 2023-11-28 23:20:12 2023-11-28 23:20:12
#> 8 0123b83386… 154 2023-11-28 23:12:15 2023-11-28 23:12:15 2023-11-28 23:12:15
#> 9 0127f18a69… 2856 2023-11-28 23:19:47 2023-11-28 23:19:47 2023-11-28 23:19:47
#> 10 014dd2384c… 7430 2023-11-28 23:20:08 2023-11-28 23:20:08 2023-11-28 23:20:08
#> # ℹ 1,455 more rows
## most recently modified file
cache_info() |>
arrange(desc(mtime)) |>
slice(1)
#> # A tibble: 1 × 5
#> file size mtime ctime atime
#> <chr> <dbl> <dttm> <dttm> <dttm>
#> 1 374a15c0c73… 154 2023-11-28 23:22:05 2023-11-28 23:22:05 2023-11-28 23:22:05
## total cache size
cache_info() |>
summarize(size = sum(size)) |>
pull(size) |>
structure(class = "object_size") |>
format(units = "auto")
#> [1] "17.5 Mb"In special cases it may be useful to invalidate the cache using
unlink() on the cache_directory(), or on
invalidate particular records using unlink() on file paths
created using file.path(), cache_directory()
and individual file names from
cache_info().
Session information
sessionInfo()
#> R version 4.3.2 (2023-10-31)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 22.04.3 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-r0.3.20.so; LAPACK version 3.10.0
#>
#> locale:
#> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
#> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
#> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] OLSr_0.0.2 dplyr_1.1.4 BiocStyle_2.28.1
#>
#> loaded via a namespace (and not attached):
#> [1] rappdirs_0.3.3 sass_0.4.7 utf8_1.2.4
#> [4] generics_0.1.3 tidyr_1.3.0 stringi_1.8.1
#> [7] hms_1.1.3 digest_0.6.33 magrittr_2.0.3
#> [10] evaluate_0.23 bookdown_0.36 fastmap_1.1.1
#> [13] rprojroot_2.0.4 jsonlite_1.8.7 progress_1.2.2
#> [16] BiocManager_1.30.22 purrr_1.0.2 fansi_1.0.5
#> [19] httr2_1.0.0 textshaping_0.3.7 jquerylib_0.1.4
#> [22] cli_3.6.1 rlang_1.1.2 crayon_1.5.2
#> [25] withr_2.5.2 cachem_1.0.8 yaml_2.3.7
#> [28] BiocBaseUtils_1.2.0 tools_4.3.2 memoise_2.0.1
#> [31] curl_5.1.0 vctrs_0.6.4 rjsoncons_1.0.0.9200
#> [34] R6_2.5.1 lifecycle_1.0.4 stringr_1.5.1
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