memes uses the
universalmotif package to simplify working with motif
metadata. universalmotif objects can be represented in an
alternative form, the unviversalmotif_df which allows users
to manipulate motif metadata just as they would a normal R data.frame
(in fact, they are just R data.frames).
These objects are useful for tidying motif metadata to prepare a motif database for use with memes, or performing any other data-driven tasks involving motifs. Here I describe one way these data structures can be used to construct a motif database for use with memes.
The MotifDb package makes it easy to query thousands of
motifs from public databases. Here, I will describe how to use one of
these queries as input to memes functions, and how to manipulate the
resulting motifs to prepare them for MEME suite tools.
I will use the motifs from the FlyFactorSurvey as an example.
They can be accessed from MotifDb using the following
query.
flyFactorDb <- MotifDb::MotifDb %>%
MotifDb::query("FlyFactorSurvey")
#> See system.file("LICENSE", package="MotifDb") for use restrictions.Use universalmotif::convert_motifs() to convert a
MotifDb query into motif objects. In many cases, the
resulting list can be used directly as input to memes functions, like
runTomTom, or runAme.
But there are some issues with this database. For example, the following motif name is the FlyBase gene number, and the alternate name is the actual informative name of the PWM. The MEME Suite relies more heavily on the primary name, so it would be nice if the database used interpretable names.
flyFactorMotifs %>%
head(1)
#> [[1]]
#>
#> Motif name: FBgn0259750
#> Alternate name: ab_SANGER_10_FBgn0259750
#> Organism: Dmelanogaster
#> Alphabet: DNA
#> Type: PPM
#> Strands: +-
#> Total IC: 15.01
#> Pseudocount: 1
#> Consensus: BWNRCCAGGWMCNNTNNNGNN
#> Target sites: 20
#> Extra info: [dataSource] FlyFactorSurvey
#>
#> B W N R C C A G G W M C N N T N N N
#> A 0.0 0.50 0.20 0.35 0 0 1 0 0 0.55 0.35 0.05 0.20 0.45 0.20 0.10 0.40 0.40
#> C 0.3 0.15 0.25 0.00 1 1 0 0 0 0.10 0.65 0.70 0.45 0.25 0.10 0.25 0.25 0.10
#> G 0.4 0.05 0.50 0.65 0 0 0 1 1 0.00 0.00 0.05 0.05 0.15 0.05 0.20 0.05 0.15
#> T 0.3 0.30 0.05 0.00 0 0 0 0 0 0.35 0.00 0.20 0.30 0.15 0.65 0.45 0.30 0.35
#> G N N
#> A 0.25 0.50 0.30
#> C 0.10 0.25 0.25
#> G 0.55 0.15 0.45
#> T 0.10 0.10 0.00The universalmotif function to_df() converts
universalmotif lists into universalmotif_df format which
can be used to update motif entries. This is particularly useful when
dealing with several motifs at once.
The columns of the universalmotif_df can be freely
changed to edit the properties of the motifs stored in the
motif column. Just like standard data.frames, additional
columns can be added to store additional metadata. For more details on
these objects see the help page:
?universalmotif::to_df.
# The following columns can be changed to update motif metadata
flyFactor_data %>%
names
#> [1] "motif" "name" "altname" "family" "organism"
#> [6] "consensus" "alphabet" "strand" "icscore" "nsites"
#> [11] "bkgsites" "pval" "qval" "eval" "type"
#> [16] "pseudocount" "bkg" "dataSource"using the universalmotif_df, we can quickly see that the
issue with FBgn numbers only applies to certain entries. And TFs which
are represented by multiple motifs in the database are assigned the same
name. The MEME Suite tools which use a motif database (like TomTom and
AME) require that the entries have unique primary identifiers, therefore
the default names will not be appropriate.
flyFactor_data %>%
head(5)
#> motif name altname organism
#> 1 <mot:FBgn..> FBgn0259750 ab_SANGER_10_FBgn0259750 Dmelanogaster
#> 2 <mot:FBgn..> FBgn0259750 ab_SOLEXA_5_FBgn0259750 Dmelanogaster
#> 3 <mot:abd-A> abd-A Abd-A_FlyReg_FBgn0000014 Dmelanogaster
#> 4 <mot:Abd-B> Abd-B Abd-B_FlyReg_FBgn0000015 Dmelanogaster
#> 5 <mot:abd-A> abd-A AbdA_Cell_FBgn0000014 Dmelanogaster
#> consensus alphabet strand icscore nsites type pseudocount
#> 1 BWNRCCAGGWMCNNTNNNGNN DNA +- 15.013759 20 PPM 1
#> 2 NNNNHNRCCAGGWMCYNNNNNNNNNN DNA +- 14.570384 446 PPM 1
#> 3 KNMATWAW DNA +- 7.371641 37 PPM 1
#> 4 YCATAAAA DNA +- 7.831193 7 PPM 1
#> 5 TTAATKA DNA +- 8.976569 18 PPM 1
#> bkg dataSource
#> 1 0.25, 0..... FlyFactorSurvey
#> 2 0.25, 0..... FlyFactorSurvey
#> 3 0.25, 0..... FlyFactorSurvey
#> 4 0.25, 0..... FlyFactorSurvey
#> 5 0.25, 0..... FlyFactorSurvey
#>
#> [Hidden empty columns: family, bkgsites, pval, qval, eval.]However, the altname slots from the motifDb
query are already unique, so we can make them the primary name.
An easy way is to use dplyr::rename to swap the
columns.
The name column now contains the full motif name.
flyFactor_data %>%
head(3)
#> motif altname name organism
#> 1 * <mot:FBgn..> FBgn0259750 ab_SANGER_10_FBgn0259750 Dmelanogaster
#> 2 * <mot:FBgn..> FBgn0259750 ab_SOLEXA_5_FBgn0259750 Dmelanogaster
#> 3 * <mot:abd-A> abd-A Abd-A_FlyReg_FBgn0000014 Dmelanogaster
#> consensus alphabet strand icscore nsites type pseudocount
#> 1 BWNRCCAGGWMCNNTNNNGNN DNA +- 15.013759 20 PPM 1
#> 2 NNNNHNRCCAGGWMCYNNNNNNNNNN DNA +- 14.570384 446 PPM 1
#> 3 KNMATWAW DNA +- 7.371641 37 PPM 1
#> bkg dataSource
#> 1 0.25, 0..... FlyFactorSurvey
#> 2 0.25, 0..... FlyFactorSurvey
#> 3 0.25, 0..... FlyFactorSurvey
#>
#> [Hidden empty columns: family, bkgsites, pval, qval, eval.]
#> [Rows marked with * are changed. Run update_motifs() or to_list() to
#> apply changes.]Next to solve the issue with the FBgn’s. FBgn numbers are unique identifiers for a gene within a given FlyBase reference assembly. However, FBgn numbers are not stable over time (i.e. the same gene may have a different FBgn number between reference assemblies), therefore they are unreliable values to determine the correct gene symbol. FlyBase.org has a nice conversion tool which can be used to update FBgn numbers.
As of this writing in March 2021, the FBgn entries provided by the Fly Factor Survey database are out of date. In order to demonstrate an example of methods for tidying motif metadata, I won’t use the FlyBase conversion tool, but will instead highlight some approaches which may be more generally useful when working with motif databases from disparate sources.
For this example, we will try to grab the correct gene name from the
motif name, which is stored in the first field of the name, formatted as
follows: “
We use tidyr::separate to split out the first entry to
the tifd column, then only use this value if the altname
contains an FBgn.
flyFactor_data %<>%
# Critical to set remove = FALSE to keep the `name` column
tidyr::separate(name, c("tfid"), remove = FALSE, extra = "drop") %>%
# Only use the tfid if the altname contains an FBgn
dplyr::mutate(altname = ifelse(grepl("^FBgn", altname), tfid, altname))Now, the first two entries are listed as “ab” instead of “FBgn0259750”.
flyFactor_data %>%
head(3)
#> motif altname name tfid family organism
#> 1 <mot:FBgn..> ab ab_SANGER_10_FBgn0259750 ab <NA> Dmelanogaster
#> 2 <mot:FBgn..> ab ab_SOLEXA_5_FBgn0259750 ab <NA> Dmelanogaster
#> 3 <mot:abd-A> abd-A Abd-A_FlyReg_FBgn0000014 Abd <NA> Dmelanogaster
#> consensus alphabet strand icscore nsites bkgsites pval
#> 1 BWNRCCAGGWMCNNTNNNGNN DNA +- 15.013759 20 NA NA
#> 2 NNNNHNRCCAGGWMCYNNNNNNNNNN DNA +- 14.570384 446 NA NA
#> 3 KNMATWAW DNA +- 7.371641 37 NA NA
#> qval eval type pseudocount bkg dataSource
#> 1 NA NA PPM 1 0.25, 0..... FlyFactorSurvey
#> 2 NA NA PPM 1 0.25, 0..... FlyFactorSurvey
#> 3 NA NA PPM 1 0.25, 0..... FlyFactorSurveyNext, because the FBgn’s are out of date, we will remove them from the “names” to shorten up the motif names. This also makes the motif name more comparable to the original motif names from the FlyFactor Survey.
It’s worth taking a look at the instances where the
altname and our parsed tfid do not match. This
is a good way to ensure we haven’t missed any important edge cases in
the data. As new edge cases are encountered, we can develop new rules
for tidying the data to ensure a high quality set of motifs.
Start by simply filtering for all instance where there is a mismatch
between altname and tfid.
Carefully compare the altname, name, and
tfid columns. Why might the values differ? Are there
instances that make you question the data?
flyFactor_data %>%
dplyr::filter(altname != tfid) %>%
# I'm only showing the first 5 rows for brevity, but take a look at the full
# data and see what patterns you notice
head(5)
#> motif altname name tfid family organism consensus alphabet
#> 1 <mot:abd-A> abd-A Abd-A_FlyReg Abd <NA> Dmelanogaster KNMATWAW DNA
#> 2 <mot:Abd-B> Abd-B Abd-B_FlyReg Abd <NA> Dmelanogaster YCATAAAA DNA
#> 3 <mot:abd-A> abd-A AbdA_Cell AbdA <NA> Dmelanogaster TTAATKA DNA
#> 4 <mot:abd-A> abd-A AbdA_SOLEXA AbdA <NA> Dmelanogaster NTTAATKR DNA
#> 5 <mot:Abd-B> Abd-B AbdB_Cell AbdB <NA> Dmelanogaster GWTTTATKA DNA
#> strand icscore nsites bkgsites pval qval eval type pseudocount bkg
#> 1 +- 7.371641 37 NA NA NA NA PPM 1 0.25, 0.....
#> 2 +- 7.831193 7 NA NA NA NA PPM 1 0.25, 0.....
#> 3 +- 8.976569 18 NA NA NA NA PPM 1 0.25, 0.....
#> 4 +- 8.330000 662 NA NA NA NA PPM 1 0.25, 0.....
#> 5 +- 9.147674 21 NA NA NA NA PPM 1 0.25, 0.....
#> dataSource
#> 1 FlyFactorSurvey
#> 2 FlyFactorSurvey
#> 3 FlyFactorSurvey
#> 4 FlyFactorSurvey
#> 5 FlyFactorSurveyOne thing that becomes obvious is that many motifs have mismatched
altname/tfid values because of capitalization
or hyphenation differences. You can use domain-specific knowledge to
assess which one is correct. For Drosophila, “abd-A” is correct
over “AbdA”, for example.
After manually inspecting these rows, I determined that instances of different capitalization, hyphenation, or names that contain “.” or “()” can be ignored. To further investigate the data, I will ignore capitalization and special character differences as follows:
flyFactor_data %>%
# calling tolower() on both columns removes capitalization as a difference
dplyr::filter(tolower(altname) != tolower(tfid),
# Select all altnames that do not contain "-", "." or "("
!grepl("-|\\.|\\(", altname),
) %>%
# I'll visalize only these columns for brevity
dplyr::select(altname, tfid, name, consensus) %>%
head(10)
#> altname tfid name consensus
#> 1 da ac ac_da_SANGER_5 RCACCTGC
#> 2 da amos amos_da_SANGER_10 RMCAYMTGBCV
#> 3 da ase ase_da_SANGER_10 CACCTGY
#> 4 da ato ato_da_SANGER_10 MCAYMTGNCRC
#> 5 da ato ato_da_SANGER_5_2 GNCAKRTGN
#> 6 da ato ato_da_SANGER_5_3 MCAYMTGNC
#> 7 da cato cato_da_SANGER_10 MRCANMTGWC
#> 8 Zif CG10267 CG10267_SANGER_5 WNYAACACTR
#> 9 Zif CG10267 CG10267_SOLEXA_5 CNNNNWAYAACACTASNMNN
#> 10 mamo CG11071 CG11071_SANGER_5 YMMGCCTAYNNMNext, what is obvious is that several altnames set to
“da” have a high number of mismatched tfids. For instance,
amos_da_SANGER_10. When checking the FlyFactorSurvey
page for da, it reveals only 1 motif corresponds to this factor.
Checking the page
for amos shows a match to amos_da_SANGER_10. Therefore,
we can conclude that factors assigned the name of da are
incorrectly assigned, and we should prefer our parsed
tfid.
flyFactor_data %<>%
# rename all "da" instances using their tfid value instead
dplyr::mutate(altname = ifelse(altname == "da", tfid, altname))Now we’ve handled the “da” mismatches, we filter them out to identify new special cases.
flyFactor_data %>%
dplyr::filter(tolower(altname) != tolower(tfid),
!grepl("-|\\.|\\(", altname)) %>%
dplyr::select(altname, tfid, name, consensus) %>%
head(10)
#> altname tfid name consensus
#> 1 Zif CG10267 CG10267_SANGER_5 WNYAACACTR
#> 2 Zif CG10267 CG10267_SOLEXA_5 CNNNNWAYAACACTASNMNN
#> 3 mamo CG11071 CG11071_SANGER_5 YMMGCCTAYNNM
#> 4 lms CG13424 CG13424_Cell NYTAATTR
#> 5 lms CG13424 CG13424_SOLEXA_2 NNYTAATTRN
#> 6 lms CG13424 CG13424_SOLEXA YTAATTR
#> 7 erm CG31670 CG31670_SANGER_5 AAAWGMGCAWC
#> 8 erm CG31670 CG31670_SOLEXA_5 NNAAAWGAGCAAYNV
#> 9 Spps CG5669 CG5669_SANGER_10 DRKGGGCGKGGCCAM
#> 10 Spps CG5669 CG5669_SOLEXA_5 NGKGGGCGKGGCNWNThe next thing to notice about these data is that entries with “CG”
prefixed tfids are often mismatched. This is because when the FlyFactor
survey was conducted, many genes were unnamed, and thus assigned a CG
from FlyBase. As time has gone on, some CG’s have been named. Checking
the FlyBase page for
CG10267 reveals that it has been renamed “Zif”. This matches with
the altname, so we conclude that rows with a “CG”
tfid can be safely skipped as their altname
contains the new gene symbol.
flyFactor_data %>%
dplyr::filter(tolower(altname) != tolower(tfid),
!grepl("-|\\.|\\(", altname),
# Remove CG genes from consideration
!grepl("CG\\d+", tfid)
) %>%
dplyr::select(altname, tfid, name, consensus)
#> altname tfid name consensus
#> 1 cyc Clk Clk_cyc_SANGER_5 MCACGTGA
#> 2 CG6272 Crc Crc_CG6272_SANGER_5 ATTACRTCABC
#> 3 Max dm dm_Max_SANGER_10 RNCACGTGGT
#> 4 Clk gce gce_Clk_SANGER_5 GCCACGTG
#> 5 Jra kay kay_Jra_SANGER_5 NATGASTCAYC
#> 6 schlank Lag1 Lag1_Cell CYACYAAAWT
#> 7 schlank Lag1 Lag1_SOLEXA CYACYAAWWT
#> 8 Lim1 lim lim_SOLEXA_2 NNTAATTRV
#> 9 Mnt Max Max_Mnt_SANGER_5 CCACGTG
#> 10 Clk Met Met_Clk_SANGER_5 GACACGTG
#> 11 bigmax Mio Mio_bigmax_SANGER_5 ATCACGTG
#> 12 Bgb run run_Bgb_NBT WAACCGCAR
#> 13 Clk tai tai_Clk_SANGER_5 RCACGTGTC
#> 14 cyc tgo tgo_cyc_SANGER_5 GTCACGTGM
#> 15 sim tgo tgo_sim_SANGER_5 GWACGTGACC
#> 16 sima tgo tgo_sima_SANGER_5 GTACGTGAC
#> 17 ss tgo tgo_ss_SANGER_5 TGCGTGAC
#> 18 tai tgo tgo_tai_SANGER_5 RCACGTGAC
#> 19 trh tgo tgo_trh_SANGER_5 RTACGTGACC
#> 20 CG6272 Xrp1 Xrp1_CG6272_SANGER_5 ATTRCRYMAYThe remaining rows (only 20 values) can be manually inspected for any discrepancies. I went through each entry by hand, looking up their motifs on FlyFactor, and their gene names on FlyBase to determine the best way to handle these motifs. Sometimes the best way to be sure your data are high quality is to carefully inspect it!
I determined from this that a few altnames need
swapping, and one motif I will remove because it is unusual (Bgb
has an identical motif to run,
but the motif is marked “run” on the FlyFactor website).
I’ll make those changes to the data:
swap_alt_id <- c("CG6272", "Clk", "Max", "Mnt", "Jra")
remove <- "Bgb"
flyFactor_data %<>%
dplyr::mutate(altname = ifelse(altname %in% swap_alt_id, tfid, altname)) %>%
dplyr::filter(!(altname %in% remove))Finally, the remaining motif metadata is also OK based on my manual inspection.
flyFactor_data %>%
dplyr::filter(tolower(altname) != tolower(tfid),
!grepl("-|\\.|\\(", altname),
# Remove CG genes from consideration
!grepl("CG\\d+", tfid)
) %>%
dplyr::select(altname, tfid, name, consensus)
#> altname tfid name consensus
#> 1 cyc Clk Clk_cyc_SANGER_5 MCACGTGA
#> 2 schlank Lag1 Lag1_Cell CYACYAAAWT
#> 3 schlank Lag1 Lag1_SOLEXA CYACYAAWWT
#> 4 Lim1 lim lim_SOLEXA_2 NNTAATTRV
#> 5 bigmax Mio Mio_bigmax_SANGER_5 ATCACGTG
#> 6 cyc tgo tgo_cyc_SANGER_5 GTCACGTGM
#> 7 sim tgo tgo_sim_SANGER_5 GWACGTGACC
#> 8 sima tgo tgo_sima_SANGER_5 GTACGTGAC
#> 9 ss tgo tgo_ss_SANGER_5 TGCGTGAC
#> 10 tai tgo tgo_tai_SANGER_5 RCACGTGAC
#> 11 trh tgo tgo_trh_SANGER_5 RTACGTGACCJust because the metadata for each entry is unique, this does not mean that the motif matrix for each entry is unique. There are many reasons why two different factors could have identical motifs: some biological, others technical. In the case of the FlyFactorSurvey, some entries are duplicated in MotifDb which should not be.
For instance, the following motif is a duplicate where the tidied metadata matches:
flyFactor_data %>%
dplyr::filter(consensus == "MMCACCTGYYV")
#> motif altname name tfid family organism
#> 1 <mot:da> HLH54F HLH54F_da_SANGER_5 HLH54F <NA> Dmelanogaster
#> 2 <mot:HLH54F> HLH54F HLH54F_da_SANGER_5 HLH54F <NA> Dmelanogaster
#> consensus alphabet strand icscore nsites bkgsites pval qval eval type
#> 1 MMCACCTGYYV DNA +- 12.24152 23 NA NA NA NA PPM
#> 2 MMCACCTGYYV DNA +- 12.24152 23 NA NA NA NA PPM
#> pseudocount bkg dataSource
#> 1 1 0.25, 0..... FlyFactorSurvey
#> 2 1 0.25, 0..... FlyFactorSurveyIt is difficult to determine in a high-throughput way whether any matrix entries are identical in a large database, and it is not always possible to rely on metadata to determine matrix duplication.
In order to identify and remove duplicate motif matrices, memes
provides remove_duplicate_motifs(), which can be used to
deduplicate a list of motifs based solely on their motif matrices
(i.e. it ignores motif name & other metadata). We will use this
strategy to deduplicate the flyFactor data.
(NOTE: When working with other motif databases, it is critical to understand the data source to determine appropriate measures for handling duplicated entries.)
# This operation takes a while to run on large motif lists
flyFactor_dedup <- remove_duplicate_motifs(flyFactor_data)
#> Warning in universalmotif::compare_motifs(x, method = "PCC"): Some comparisons
#> failed due to low motif ICDuplicate removal identifies and removes 57 identical matrices.
# Rows before cleanup
nrow(flyFactor_data)
#> [1] 613
# Rows after cleanup
nrow(flyFactor_dedup)
#> [1] 556Using the example from before now shows only 1 motif corresponding to this sequence.
flyFactor_dedup %>%
dplyr::filter(consensus == "MMCACCTGYYV")
#> motif name altname organism consensus alphabet
#> 1 <mot:HLH5..> HLH54F_da_SANGER_5 HLH54F Dmelanogaster MMCACCTGYYV DNA
#> strand icscore nsites type pseudocount bkg tfid dataSource
#> 1 +- 12.24152 23 PPM 1 0.25, 0..... HLH54F FlyFactorSurvey
#>
#> [Hidden empty columns: family, bkgsites, pval, qval, eval.]Finally, now that the database has been tidied and deduplicated, the
resulting data.frame can be converted back into a universalmotif list
using to_list(). To discard the additional columns we
created so they are not passed on to the universalmotif,
set extrainfo = FALSE.
# extrainfo = FALSE drops the extra columns we added during data cleaning which are now unneeded
flyFactorMotifs_final <- to_list(flyFactor_dedup, extrainfo = FALSE)
#> Discarding unknown slot(s) 'tfid', 'dataSource' (set `extrainfo=TRUE`
#> to preserve these).The resulting universalmotif list object now reflects the changes we
made to the data.frame and can now be exported as a .meme
format file using universalmotif::write_meme or can be used
directly as input to tools like runTomTom or
runAme.
flyFactorMotifs_final %>%
head(1)
#> [[1]]
#>
#> Motif name: ab_SANGER_10
#> Alternate name: ab
#> Organism: Dmelanogaster
#> Alphabet: DNA
#> Type: PPM
#> Strands: +-
#> Total IC: 15.01
#> Pseudocount: 1
#> Consensus: BWNRCCAGGWMCNNTNNNGNN
#> Target sites: 20
#>
#> B W N R C C A G G W M C N N T N N N
#> A 0.0 0.50 0.20 0.35 0 0 1 0 0 0.55 0.35 0.05 0.20 0.45 0.20 0.10 0.40 0.40
#> C 0.3 0.15 0.25 0.00 1 1 0 0 0 0.10 0.65 0.70 0.45 0.25 0.10 0.25 0.25 0.10
#> G 0.4 0.05 0.50 0.65 0 0 0 1 1 0.00 0.00 0.05 0.05 0.15 0.05 0.20 0.05 0.15
#> T 0.3 0.30 0.05 0.00 0 0 0 0 0 0.35 0.00 0.20 0.30 0.15 0.65 0.45 0.30 0.35
#> G N N
#> A 0.25 0.50 0.30
#> C 0.10 0.25 0.25
#> G 0.55 0.15 0.45
#> T 0.10 0.10 0.00This cleaned-up version of the FlyFactorSurvey data is packaged with
memes in
system.file("extdata/flyFactorSurvey_cleaned.meme", package = "memes").
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] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] universalmotif_1.23.8 magrittr_2.0.3 memes_1.15.0
#> [4] rmarkdown_2.28
#>
#> loaded via a namespace (and not attached):
#> [1] tidyselect_1.2.1 dplyr_1.1.4
#> [3] farver_2.1.2 R.utils_2.12.3
#> [5] Biostrings_2.75.0 bitops_1.0-9
#> [7] fastmap_1.2.0 RCurl_1.98-1.16
#> [9] GenomicAlignments_1.41.0 XML_3.99-0.17
#> [11] digest_0.6.37 lifecycle_1.0.4
#> [13] waldo_0.5.3 compiler_4.4.1
#> [15] rlang_1.1.4 sass_0.4.9
#> [17] tools_4.4.1 utf8_1.2.4
#> [19] yaml_2.3.10 data.table_1.16.2
#> [21] rtracklayer_1.65.0 knitr_1.48
#> [23] S4Arrays_1.5.11 curl_5.2.3
#> [25] splitstackshape_1.4.8 DelayedArray_0.33.1
#> [27] abind_1.4-8 pkgload_1.4.0
#> [29] BiocParallel_1.41.0 withr_3.0.2
#> [31] purrr_1.0.2 BiocGenerics_0.53.0
#> [33] sys_3.4.3 desc_1.4.3
#> [35] R.oo_1.26.0 grid_4.4.1
#> [37] stats4_4.4.1 fansi_1.0.6
#> [39] MotifDb_1.47.0 colorspace_2.1-1
#> [41] ggplot2_3.5.1 scales_1.3.0
#> [43] MASS_7.3-61 SummarizedExperiment_1.35.5
#> [45] cli_3.6.3 cmdfun_1.0.2
#> [47] crayon_1.5.3 generics_0.1.3
#> [49] httr_1.4.7 tzdb_0.4.0
#> [51] rjson_0.2.23 cachem_1.1.0
#> [53] ggseqlogo_0.2 zlibbioc_1.51.2
#> [55] parallel_4.4.1 XVector_0.45.0
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