Original version: 26 September, 2023
The AlphaMissense publication outlines how a variant of AlphaFold / DeepMind was used to predict missense variant pathogenicity. Supporting data on Zenodo include, for instance 70+M variants across hg19 and hg38 genome builds. The AlphaMissense package allows ready access to the data, downloading individual files to DuckDB databases for ready exploration and integration into R and Bioconductor workflows.
Install the package from Bioconductor or GitHub, ensuring correct Bioconductor dependencies.
if (!"BiocManager" %in% rownames(installed.packages()))
install.packages("BiocManager", repos = "https://cran.r-project.org")When the package is available on Bioconductor, use
if (BiocManager::version() >= "3.19") {
BiocManager::install("AlphaMissenseR")
} else {
stop(
"'AlphaMissenseR' requires Bioconductor version 3.19 or later, ",
"install from GitHub?"
)
}Use the pre-release or devel version with
Load the library.
Learn about available data by visiting the Zenodo record where data will be retrieved from.
Use am_available() to discover data resources available
for representation in DuckDB databases. The cached column
is initially FALSE for all data sets; TRUE
indicates that a data set has been downloaded by am_data(),
as described below.
am_available()
#> # A tibble: 7 × 6
#> record key size cached filename link
#> <chr> <chr> <dbl> <lgl> <chr> <chr>
#> 1 10813168 gene_hg38 253636 FALSE AlphaMissense_gene… http…
#> 2 10813168 isoforms_hg38 1177361934 FALSE AlphaMissense_isof… http…
#> 3 10813168 isoforms_aa_substitutions 2461351945 FALSE AlphaMissense_isof… http…
#> 4 10813168 hg38 642961469 TRUE AlphaMissense_hg38… http…
#> 5 10813168 hg19 622293310 FALSE AlphaMissense_hg19… http…
#> 6 10813168 gene_hg19 243943 FALSE AlphaMissense_gene… http…
#> 7 10813168 aa_substitutions 1207278510 TRUE AlphaMissense_aa_s… http…The available data sets use the most recent record as of 25
September, 2023; this can be changed by specifying an alternative as the
record= argument or changed globally by setting an
environment variable ALPHAMISSENSE_RECORD before
loading the package.
Use am_data() to download a data resource and store it
in a DuckDB database. The key= argument is from the column
returned by am_available(). Files are cached locally (using
BiocFileCache)
so this operation is expensive only the first time. Each
record= is stored in a different database.
tbl <- am_data("hg38")
tbl
#> # Source: table<hg38> [?? x 10]
#> # Database: DuckDB v1.1.1 [unknown@Linux 6.5.0-1025-azure:R 4.4.1//github/home/.cache/R/BiocFileCache/1e4e4a1eb813_1e4e4a1eb813]
#> CHROM POS REF ALT genome uniprot_id transcript_id protein_variant
#> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 chr1 69094 G T hg38 Q8NH21 ENST00000335137.4 V2L
#> 2 chr1 69094 G C hg38 Q8NH21 ENST00000335137.4 V2L
#> 3 chr1 69094 G A hg38 Q8NH21 ENST00000335137.4 V2M
#> 4 chr1 69095 T C hg38 Q8NH21 ENST00000335137.4 V2A
#> 5 chr1 69095 T A hg38 Q8NH21 ENST00000335137.4 V2E
#> 6 chr1 69095 T G hg38 Q8NH21 ENST00000335137.4 V2G
#> 7 chr1 69097 A G hg38 Q8NH21 ENST00000335137.4 T3A
#> 8 chr1 69097 A C hg38 Q8NH21 ENST00000335137.4 T3P
#> 9 chr1 69097 A T hg38 Q8NH21 ENST00000335137.4 T3S
#> 10 chr1 69098 C A hg38 Q8NH21 ENST00000335137.4 T3N
#> # ℹ more rows
#> # ℹ 2 more variables: am_pathogenicity <dbl>, am_class <chr>The return value tbl is a table from a DuckDB database.
A (read-only) connection to the database itself is available with
This connection remains open throughout the session; call
db_disconnect(db) or db_disconnect_all() to
close it at the end of the session.
The database contains tables for each key downloaded. As an
alternative to am_available() / am_data(),
view available tables and create a dplyr / dbplyr tibble of
the table of interest.
db_tables(db)
#> [1] "aa_substitutions" "clinvar" "hg38"
tbl <- tbl(db, "hg38")
tbl
#> # Source: table<hg38> [?? x 10]
#> # Database: DuckDB v1.1.1 [unknown@Linux 6.5.0-1025-azure:R 4.4.1//github/home/.cache/R/BiocFileCache/1e4e4a1eb813_1e4e4a1eb813]
#> CHROM POS REF ALT genome uniprot_id transcript_id protein_variant
#> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 chr1 69094 G T hg38 Q8NH21 ENST00000335137.4 V2L
#> 2 chr1 69094 G C hg38 Q8NH21 ENST00000335137.4 V2L
#> 3 chr1 69094 G A hg38 Q8NH21 ENST00000335137.4 V2M
#> 4 chr1 69095 T C hg38 Q8NH21 ENST00000335137.4 V2A
#> 5 chr1 69095 T A hg38 Q8NH21 ENST00000335137.4 V2E
#> 6 chr1 69095 T G hg38 Q8NH21 ENST00000335137.4 V2G
#> 7 chr1 69097 A G hg38 Q8NH21 ENST00000335137.4 T3A
#> 8 chr1 69097 A C hg38 Q8NH21 ENST00000335137.4 T3P
#> 9 chr1 69097 A T hg38 Q8NH21 ENST00000335137.4 T3S
#> 10 chr1 69098 C A hg38 Q8NH21 ENST00000335137.4 T3N
#> # ℹ more rows
#> # ℹ 2 more variables: am_pathogenicity <dbl>, am_class <chr>It is fast and straight-forward to summarize the data, e.g., the number of variants assigned to each pathogenicity class.
tbl |>
count(am_class)
#> # Source: SQL [3 x 2]
#> # Database: DuckDB v1.1.1 [unknown@Linux 6.5.0-1025-azure:R 4.4.1//github/home/.cache/R/BiocFileCache/1e4e4a1eb813_1e4e4a1eb813]
#> am_class n
#> <chr> <dbl>
#> 1 likely_pathogenic 22770557
#> 2 ambiguous 8009648
#> 3 likely_benign 40917351Or the average pathogenicity score in each class…
tbl |>
group_by(am_class) |>
summarize(n = n(), pathogenecity = mean(am_pathogenicity, na.rm = TRUE))
#> # Source: SQL [3 x 3]
#> # Database: DuckDB v1.1.1 [unknown@Linux 6.5.0-1025-azure:R 4.4.1//github/home/.cache/R/BiocFileCache/1e4e4a1eb813_1e4e4a1eb813]
#> am_class n pathogenecity
#> <chr> <dbl> <dbl>
#> 1 likely_pathogenic 22770557 0.863
#> 2 ambiguous 8009648 0.444
#> 3 likely_benign 40917351 0.147Or the number of transitions between REF and
ALT nucleotides across all variants.
tbl |>
count(REF, ALT) |>
tidyr::pivot_wider(names_from = "ALT", values_from = "n") |>
select("REF", "A", "C", "G", "T") |>
arrange(REF)
#> # Source: SQL [4 x 5]
#> # Database: DuckDB v1.1.1 [unknown@Linux 6.5.0-1025-azure:R 4.4.1//github/home/.cache/R/BiocFileCache/1e4e4a1eb813_1e4e4a1eb813]
#> # Ordered by: REF
#> REF A C G T
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 A NA 6420083 5449617 5953590
#> 2 C 6183248 NA 6963298 4909869
#> 3 G 4894493 6937258 NA 6161594
#> 4 T 5955039 5445540 6423927 NAIt is straight-forward to select variants in individual regions of interest, e.g., the first 200000 nucleoties of chromosome 4.
tbl |>
filter(CHROM == "chr4", POS > 0, POS <= 200000)
#> # Source: SQL [?? x 10]
#> # Database: DuckDB v1.1.1 [unknown@Linux 6.5.0-1025-azure:R 4.4.1//github/home/.cache/R/BiocFileCache/1e4e4a1eb813_1e4e4a1eb813]
#> CHROM POS REF ALT genome uniprot_id transcript_id protein_variant
#> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 chr4 59430 G C hg38 Q8IYB9 ENST00000610261.6 E2Q
#> 2 chr4 59430 G A hg38 Q8IYB9 ENST00000610261.6 E2K
#> 3 chr4 59431 A C hg38 Q8IYB9 ENST00000610261.6 E2A
#> 4 chr4 59431 A G hg38 Q8IYB9 ENST00000610261.6 E2G
#> 5 chr4 59431 A T hg38 Q8IYB9 ENST00000610261.6 E2V
#> 6 chr4 59432 A C hg38 Q8IYB9 ENST00000610261.6 E2D
#> 7 chr4 59432 A T hg38 Q8IYB9 ENST00000610261.6 E2D
#> 8 chr4 59433 C A hg38 Q8IYB9 ENST00000610261.6 L3I
#> 9 chr4 59433 C T hg38 Q8IYB9 ENST00000610261.6 L3F
#> 10 chr4 59433 C G hg38 Q8IYB9 ENST00000610261.6 L3V
#> # ℹ more rows
#> # ℹ 2 more variables: am_pathogenicity <dbl>, am_class <chr>This section illustrates how AlphaMissense data can be integrated with other Bioconductor workflows, including the GenomicRanges infrastructure and the ensembldb package / AnnotationHub resources.
The GenomicRanges
infrastructure provides standard data structures that allow range-based
operations across Bioconductor packages. The GPos data
structure provides a convenient and memory-efficient representation of
single-nucleotide variants like those in the hg19 and
hg38 AlphaMissense resources. Start by installing (if
necessary) the GenomicRanges
package.
Select the hg38 data resource, and filter to a subset of
variants; GPos is an in-memory data structure and can
easily manage 10’s of millions of variants.
Use to_GPos() to coerce to a GPos
object.
gpos <-
tbl |>
to_GPos()
gpos
#> UnstitchedGPos object with 26751 positions and 7 metadata columns:
#> seqnames pos strand | REF ALT uniprot_id
#> <Rle> <integer> <Rle> | <character> <character> <character>
#> [1] chr2 41613 * | G C Q1W6H9
#> [2] chr2 41615 * | G C Q1W6H9
#> [3] chr2 41615 * | G A Q1W6H9
#> [4] chr2 41615 * | G T Q1W6H9
#> [5] chr2 41618 * | G C Q1W6H9
#> ... ... ... ... . ... ... ...
#> [26747] chr2 9999028 * | G A Q9NZI5
#> [26748] chr2 9999028 * | G T Q9NZI5
#> [26749] chr2 9999029 * | G C Q9NZI5
#> [26750] chr2 9999029 * | G A Q9NZI5
#> [26751] chr2 9999029 * | G T Q9NZI5
#> transcript_id protein_variant am_pathogenicity am_class
#> <character> <character> <numeric> <character>
#> [1] ENST00000327669.5 R321G 0.0848 likely_benign
#> [2] ENST00000327669.5 S320C 0.0946 likely_benign
#> [3] ENST00000327669.5 S320F 0.1593 likely_benign
#> [4] ENST00000327669.5 S320Y 0.1429 likely_benign
#> [5] ENST00000327669.5 P319R 0.0765 likely_benign
#> ... ... ... ... ...
#> [26747] ENST00000324907.14 G581R 0.9997 likely_pathogenic
#> [26748] ENST00000324907.14 G581W 0.9996 likely_pathogenic
#> [26749] ENST00000324907.14 G581A 0.9970 likely_pathogenic
#> [26750] ENST00000324907.14 G581E 0.9995 likely_pathogenic
#> [26751] ENST00000324907.14 G581V 0.9994 likely_pathogenic
#> -------
#> seqinfo: 25 sequences (1 circular) from hg38 genomeVignettes in the GenomicRanges package illustrate use of these objects.
Start by identifying the most recent EnsDb resource for Homo Sapiens.
hub <- AnnotationHub::AnnotationHub()
AnnotationHub::query(hub, c("EnsDb", "Homo sapiens"))
#> AnnotationHub with 27 records
#> # snapshotDate(): 2024-10-24
#> # $dataprovider: Ensembl
#> # $species: Homo sapiens
#> # $rdataclass: EnsDb
#> # additional mcols(): taxonomyid, genome, description,
#> # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
#> # rdatapath, sourceurl, sourcetype
#> # retrieve records with, e.g., 'object[["AH53211"]]'
#>
#> title
#> AH53211 | Ensembl 87 EnsDb for Homo Sapiens
#> AH53715 | Ensembl 88 EnsDb for Homo Sapiens
#> AH56681 | Ensembl 89 EnsDb for Homo Sapiens
#> AH57757 | Ensembl 90 EnsDb for Homo Sapiens
#> AH60773 | Ensembl 91 EnsDb for Homo Sapiens
#> ... ...
#> AH109336 | Ensembl 108 EnsDb for Homo sapiens
#> AH109606 | Ensembl 109 EnsDb for Homo sapiens
#> AH113665 | Ensembl 110 EnsDb for Homo sapiens
#> AH116291 | Ensembl 111 EnsDb for Homo sapiens
#> AH116860 | Ensembl 112 EnsDb for Homo sapiens
AnnotationHub::AnnotationHub()["AH113665"]
#> AnnotationHub with 1 record
#> # snapshotDate(): 2024-10-24
#> # names(): AH113665
#> # $dataprovider: Ensembl
#> # $species: Homo sapiens
#> # $rdataclass: EnsDb
#> # $rdatadateadded: 2023-04-25
#> # $title: Ensembl 110 EnsDb for Homo sapiens
#> # $description: Gene and protein annotations for Homo sapiens based on Ensem...
#> # $taxonomyid: 9606
#> # $genome: GRCh38
#> # $sourcetype: ensembl
#> # $sourceurl: http://www.ensembl.org
#> # $sourcesize: NA
#> # $tags: c("110", "Annotation", "AnnotationHubSoftware", "Coverage",
#> # "DataImport", "EnsDb", "Ensembl", "Gene", "Protein", "Sequencing",
#> # "Transcript")
#> # retrieve record with 'object[["AH113665"]]'Load the ensembldb library and retrieve the record. Unfortunately, there are many conflicts between function names in different packages, so it becomes necessary to fully resolve functions to the package where they are defined.
library(ensembldb)
edb <- AnnotationHub::AnnotationHub()[["AH113665"]]
edb
#> EnsDb for Ensembl:
#> |Backend: SQLite
#> |Db type: EnsDb
#> |Type of Gene ID: Ensembl Gene ID
#> |Supporting package: ensembldb
#> |Db created by: ensembldb package from Bioconductor
#> |script_version: 0.3.10
#> |Creation time: Mon Aug 7 09:02:07 2023
#> |ensembl_version: 110
#> |ensembl_host: 127.0.0.1
#> |Organism: Homo sapiens
#> |taxonomy_id: 9606
#> |genome_build: GRCh38
#> |DBSCHEMAVERSION: 2.2
#> |common_name: human
#> |species: homo_sapiens
#> | No. of genes: 71440.
#> | No. of transcripts: 278545.
#> |Protein data available.In this section we will work more directly with the database, including writing temporary tables. For illustration purposes, we use a connection that can read and write; in practice, read-only permissions are sufficient for creating temporary tables.
As an illustration, use ensembldb to identify the exons of the canonical transcript of a particular gene.
bcl2l11 <-
edb |>
ensembldb::filter(
~ symbol == "BCL2L11" &
tx_biotype == "protein_coding" &
tx_is_canonical == TRUE
) |>
exonsBy("tx")
bcl2l11
#> GRangesList object of length 1:
#> $ENST00000393256
#> GRanges object with 4 ranges and 2 metadata columns:
#> seqnames ranges strand | exon_id exon_rank
#> <Rle> <IRanges> <Rle> | <character> <integer>
#> [1] 2 111120914-111121188 + | ENSE00004011574 1
#> [2] 2 111123733-111124139 + | ENSE00001008808 2
#> [3] 2 111150044-111150147 + | ENSE00003483971 3
#> [4] 2 111164133-111168444 + | ENSE00001924953 4
#> -------
#> seqinfo: 1 sequence from GRCh38 genomeMunge the data to a tibble, updating the seqnames to a
column CHROM with identifiers such as "chr1"
(as in the AlphaMissense data). Write the tibble to a temporary table
(it will be deleted when db is disconnected from the
database) so that it can be used in ‘lazy’ SQL queries with the
AlphaMissense data.
bcl2l11_tbl <-
bcl2l11 |>
dplyr::as_tibble() |>
dplyr::mutate(CHROM = paste0("chr", seqnames)) |>
dplyr::select(CHROM, everything(), -seqnames)
db_temporary_table(db_rw, bcl2l11_tbl, "bcl2l11")
#> # Source: table<bcl2l11> [4 x 9]
#> # Database: DuckDB v1.1.1 [unknown@Linux 6.5.0-1025-azure:R 4.4.1//github/home/.cache/R/BiocFileCache/1e4e4a1eb813_1e4e4a1eb813]
#> CHROM group group_name start end width strand exon_id exon_rank
#> <chr> <int> <chr> <int> <int> <int> <fct> <chr> <int>
#> 1 chr2 1 ENST00000393256 111120914 111121188 275 + ENSE00… 1
#> 2 chr2 1 ENST00000393256 111123733 111124139 407 + ENSE00… 2
#> 3 chr2 1 ENST00000393256 111150044 111150147 104 + ENSE00… 3
#> 4 chr2 1 ENST00000393256 111164133 111168444 4312 + ENSE00… 4The temporary table is now available on the db_rw
connection; the tables will be removed on disconnect,
db_disconnect(db_rw).
Use db_range_join() to join the AlphaMissense data with
the ranges defining the exons in our gene of interest. The arguments are
the database connection, the AlphaMissense table of interest (this table
must have columns CHROM and POS), the table
containing ranges of interest (with columns CHROM,
start, end), and the temporary table to
contain the results. A range join is like a standard database join,
expect that the constraints can be relations, in our case that
POS >= start and POS <= end for each
range of interest; implementation details are in this DuckDB blog. The
range join uses closed intervals (the start and end positions are
included in the query), following Bioconductor convention.
Writing to a temporary table avoids bringing potentially large datasets
into R memory, and makes the table available for subsequent manipulation
in the current session.
rng <- db_range_join(db_rw, "hg38", "bcl2l11", "bcl2l11_overlaps")
rng
#> # Source: table<bcl2l11_overlaps> [?? x 18]
#> # Database: DuckDB v1.1.1 [unknown@Linux 6.5.0-1025-azure:R 4.4.1//github/home/.cache/R/BiocFileCache/1e4e4a1eb813_1e4e4a1eb813]
#> CHROM POS REF ALT genome uniprot_id transcript_id protein_variant
#> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 chr2 111164133 G A hg38 O43521 ENST0000039325… V167I
#> 2 chr2 111164133 G T hg38 O43521 ENST0000039325… V167L
#> 3 chr2 111164133 G C hg38 O43521 ENST0000039325… V167L
#> 4 chr2 111164134 T C hg38 O43521 ENST0000039325… V167A
#> 5 chr2 111164134 T A hg38 O43521 ENST0000039325… V167E
#> 6 chr2 111164134 T G hg38 O43521 ENST0000039325… V167G
#> 7 chr2 111164136 T A hg38 O43521 ENST0000039325… F168I
#> 8 chr2 111164136 T C hg38 O43521 ENST0000039325… F168L
#> 9 chr2 111164136 T G hg38 O43521 ENST0000039325… F168V
#> 10 chr2 111164137 T G hg38 O43521 ENST0000039325… F168C
#> # ℹ more rows
#> # ℹ 10 more variables: am_pathogenicity <dbl>, am_class <chr>, group <int>,
#> # group_name <chr>, start <int>, end <int>, width <int>, strand <fct>,
#> # exon_id <chr>, exon_rank <int>This query takes place almost instantly. A larger query of 71M variants against 1000 ranges took about 20 seconds.
The usual database and dplyr verbs can be used to summarize the results, e.g., the number of variants in each pathogenecity class in each exon.
rng |>
dplyr::count(exon_id, am_class) |>
tidyr::pivot_wider(names_from = "am_class", values_from = "n")
#> # Source: SQL [3 x 4]
#> # Database: DuckDB v1.1.1 [unknown@Linux 6.5.0-1025-azure:R 4.4.1//github/home/.cache/R/BiocFileCache/1e4e4a1eb813_1e4e4a1eb813]
#> exon_id likely_benign likely_pathogenic ambiguous
#> <chr> <dbl> <dbl> <dbl>
#> 1 ENSE00001008808 494 257 108
#> 2 ENSE00003483971 128 71 33
#> 3 ENSE00001924953 172 16 19It is perhaps instructive to review the range join as a source of inspiration for other computations that might be of interest.
-- range join of 'hg38' with 'bcl2l11'; overwrite any existing table
-- 'bcl2l11_overlaps'
DROP TABLE IF EXISTS bcl2l11_overlaps;
CREATE TEMP TABLE bcl2l11_overlaps AS
SELECT
hg38.*,
bcl2l11.* EXCLUDE (CHROM)
FROM hg38
JOIN bcl2l11
ON bcl2l11.CHROM = hg38.CHROM
AND bcl2l11.start <= hg38.POS
AND bcl2l11.end >= hg38.POS;As best practice, disconnect from the writable database connection when work is complete.
There are likely more straight-forward ways of performing the query
in the previous section, e.g., by filtering hg38 on the
relevant transcript id(s), retrieving to R, and working with
edb to classify variants by exon. The transcript we are
interested in is "ENST00000393256".
Select the relevant variants and, because there are not too many, load into R.
Coerce the AlphaMissense data to a GRanges::GPos
object.
gpos <-
variants_of_interest |>
to_GPos()
## make gpos 'genome' and 'seqlevels' like bcl2l11
GenomeInfoDb::genome(gpos) <- "GRCh38"
GenomeInfoDb::seqlevelsStyle(gpos) <- "Ensembl"
gpos
#> UnstitchedGPos object with 1298 positions and 7 metadata columns:
#> seqnames pos strand | REF ALT uniprot_id
#> <Rle> <integer> <Rle> | <character> <character> <character>
#> [1] 2 111123749 * | G C O43521
#> [2] 2 111123749 * | G T O43521
#> [3] 2 111123749 * | G A O43521
#> [4] 2 111123750 * | C A O43521
#> [5] 2 111123750 * | C G O43521
#> ... ... ... ... . ... ... ...
#> [1294] 2 111164227 * | A G O43521
#> [1295] 2 111164227 * | A T O43521
#> [1296] 2 111164227 * | A C O43521
#> [1297] 2 111164228 * | T A O43521
#> [1298] 2 111164228 * | T G O43521
#> transcript_id protein_variant am_pathogenicity am_class
#> <character> <character> <numeric> <character>
#> [1] ENST00000393256.8 A2P 0.6610 likely_pathogenic
#> [2] ENST00000393256.8 A2S 0.3398 likely_benign
#> [3] ENST00000393256.8 A2T 0.7219 likely_pathogenic
#> [4] ENST00000393256.8 A2E 0.7283 likely_pathogenic
#> [5] ENST00000393256.8 A2G 0.3883 ambiguous
#> ... ... ... ... ...
#> [1294] ENST00000393256.8 H198R 0.1226 likely_benign
#> [1295] ENST00000393256.8 H198L 0.2526 likely_benign
#> [1296] ENST00000393256.8 H198P 0.1323 likely_benign
#> [1297] ENST00000393256.8 H198Q 0.1781 likely_benign
#> [1298] ENST00000393256.8 H198Q 0.1781 likely_benign
#> -------
#> seqinfo: 25 sequences (1 circular) from GRCh38 genomeOne can then use GenomicRanges functionality, e.g., to count the number of variants in each exon.
Remember to disconnect and shutdown all managed DuckDB connections.
Database connections that are not closed correctly trigger warning messages.
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: /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] ensembldb_2.29.1 AnnotationFilter_1.29.0 GenomicFeatures_1.57.1
#> [4] AnnotationDbi_1.67.0 Biobase_2.65.1 GenomicRanges_1.57.2
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