rTRM: an R package for the identification of transcription regulatory modules (TRMs)
24 October 2023
Package
rTRM 1.40.0
Contents
1 Introduction
Transcription factors (TFs) bind to short motifs in the DNA and regulate the expression of target genes in a cell type and time dependent fashion. TFs do so by cooperating with other TFs in what it is called Transcriptional Regulatory Modules (TRMs). These TRMs contain different TFs and form a combinatorial code that explains TF specificity. We have implemented a method for the identification of TRMs that integrates information about binding locations from a single ChIP-seq experiment, computational estimation of TF binding, gene expression and protein-protein interaction (PPI) data (Diez, Hutchins, and Miranda-Saavedra 2014); see workflow figure). rTRM implements the methods required for the integration of PPI information (step 4 in workflow). To do so, rTRM tries to identify TFs that are bound to a target TF (the one with experimental evidence- i.e. ChIP-seq data) either directly or through a bridge protein. This package has been used to identify cell-type independent and dependent TRMs associated with Stat3 functions (Hutchins et al. 2013). Also, it has been used to identify TRMs in embryonic and hematopoietic stem cells as part of the publication presenting the methodology (Diez, Hutchins, and Miranda-Saavedra 2014). Here we present the basic capabilities of rTRM with a naive example, a case study showing the identification of Sox2 related TRM in ESCs as performed in the paper describing rTRM (Diez, Hutchins, and Miranda-Saavedra 2014), and a complete workflow in R using the PWMEnrich package for the motif enrichment step.
Figure 1: Workflow for the identification of TRMs
Steps 1-3 can be performed with standard Bioconductor approaches. rTRM implements a method to perform step 4.
2 Minimal example
In this minimal example a dummy network is search to identify TRMs focused around a target node, N6, with query nodes being N7, N12 and N28. By default findTRM find nodes separated a max distance of 0 (i.e. nodes directly connected). We change this with parameter max.bridge = 1. Because node N28 is separated by two other nodes from the target node N6, it is not included in the predicted TRM. By default findTRM returns an object of class igraph, which can be used with plotTRM, plotTRMlegend and other rTRM functions. However, it is possible to directly obtain a graphNEL object (from the Bioconductor package graph), setting type = graphNEL. Of course it is possible to also use the igraph.to.graphNEL function in the igraph package to transform an igraph object into a graphNEL object.
# load the rTRM package
library(rTRM)
# load network example.
load(system.file(package = "rTRM", "extra/example.rda"))
# plot network
plot(g, vertex.size = 20, vertex.label.cex = .8, layout = layout.graphopt)## This graph was created by an old(er) igraph version.
## Call upgrade_graph() on it to use with the current igraph version
## For now we convert it on the fly...
Figure 2: Identification of a TRM 1from a test network (left)
In the resulting TRM (right) dark blue indicates the target node light blue are query nodes and white nodes are bridge nodes
# define target and query nodes:
target <- "N6"
query <- c("N7", "N12", "N28")
# find TRM:
s <- findTRM(g, target = target, query = query, method = "nsa", max.bridge = 1)## removing: 4 nodes out of 8 [keeping 4 nodes]# annotate nodes:
V(s)$color <- "white"
V(s)[ name %in% query]$color <- "steelblue2"
V(s)[ name %in% target]$color <- "steelblue4"
# plot:
plot(s,vertex.size=20,vertex.label.cex=.8)
Figure 3: Identification of a TRM 1from a test network (left)
In the resulting TRM (right) dark blue indicates the target node light blue are query nodes and white nodes are bridge nodes
3 Introduction to the rTRM package
rTRM relies on a series of optimizations. For example in the publication we used PWMs for vertebrate species compiled from different sources. This assumes the binding specificities of TFs will be conserved on all these species. Recent comparison between mouse and human PWMs suggests that this is the case for most TFs Jolma et al. (2013). rTRM also relies on protein-protein interaction data, and so provides utilities to download data from the BioGRID database (see below). As some of these functionalities are further integrated with existing Bioconductor functionality they may be defunct in the future.
3.1 Database
Information about TFs, including Position Specific Weight (PWMs) matrices, mapping to Entrez Gene identifiers, orthologs in mouse and human and other annotations are stored as a SQLite database. rTRM provides a basic API for accessing the data. Below there are some examples.
To obtain PWMs:
pwm <- getMatrices()
head(pwm, 1)## $MA0009.1
## 1 2 3 4 5 6 7 8 9 10 11
## A 0.05 0.025 1 0 0 0 0 0.00 0.025 1 0.775
## C 0.70 0.025 0 0 0 0 0 0.05 0.175 0 0.125
## G 0.20 0.000 0 1 1 0 1 0.00 0.700 0 0.000
## T 0.05 0.950 0 0 0 1 0 0.95 0.100 0 0.100To get annotations:
ann <- getAnnotations()
head(ann)## row_names pwm_id symbol family domain binding source
## 1 1 MA0009.1 T T monomer jaspar
## 2 2 MA0059.1 MYC::MAX Helix-Loop-Helix dimer jaspar
## 3 3 MA0146.1 Zfx BetaBetaAlpha-zinc finger monomer jaspar
## 4 4 MA0132.1 Pdx1 Homeo monomer jaspar
## 5 5 MA0162.1 Egr1 BetaBetaAlpha-zinc finger monomer jaspar
## 6 6 MA0093.1 USF1 Helix-Loop-Helix monomer jaspar
## note
## 1 2010
## 2 2010
## 3 2010
## 4 2010
## 5 2010
## 6 2010To get map of TFs to genes:
map <- getMaps()
head(map)## row_names pwm_id entrezgene organism
## 1 1 MA0009.1 20997 mouse
## 2 2 MA0059.1 4609 human
## 3 3 MA0059.1 4149 human
## 4 4 MA0146.1 22764 mouse
## 5 5 MA0132.1 18609 mouse
## 6 6 MA0162.1 13653 mouseTo get map of TFs to ortholog genes:
o <- getOrthologs(organism = "mouse")
head(o)## row_names entrezgene organism map_entrezgene map_organism
## 1 775 20997 mouse 20997 mouse
## 2 776 4609 human 107771 mouse
## 3 777 4149 human 17187 mouse
## 4 778 22764 mouse 22764 mouse
## 5 779 18609 mouse 18609 mouse
## 6 780 13653 mouse 13653 mouseIt is possible to map motif ids to entrezgene ids in the target organism (only between human and mouse). This is useful when all the information about existing PWMs is desired, as some TF binding affinities have only been studied in one organism.
getOrthologFromMatrix("MA0009.1", organism="human")## [1] "6862"getOrthologFromMatrix("MA0009.1", organism="mouse")## [1] "20997"3.2 Interactome data
rTRM requires information about protein-protein interactions (PPIs) for its predictions and includes interactome (PPI network) data from the BioGRID database (???). Currently mouse and human interactomes are supported. The networks are provided as an igraph object. To access the data use:
# check statistics about the network.
biogrid_mm()## Mouse PPI network data of class igraph## This graph was created by an old(er) igraph version.
## Call upgrade_graph() on it to use with the current igraph version
## For now we convert it on the fly...## Number of nodes: 5315## Number of edges: 11695## Source: The BioGRID (http://www.thebiogrid.org)## Release: 3.4.128## Downloaded: 2015-09-17## Use data(biogrid_mm) to load it# load mouse PPI network:
data(biogrid_mm)The amount of available PPI data increases rapidly so it is desirable to have a way to access the newest data conveniently. rTRM includes support for direct download and processing of PPI data from the BioGRID database. The PPI network is stored as an igraph object that can be readily used with rTRM or stored for later use. Below there is an example of the BioGRID database update procedure.
# obtain dataset.
db <- getBiogridData() # retrieves latest release.
# db = getBiogridData("3.2.96") # to get a specific release.
# check release:
db$release
db$data
# process PPI data for different organisms (currently supported human and mouse):
biogrid_hs <- processBiogrid(db, org = "human")
biogrid_mm <- processBiogrid(db, org = "mouse")PPI data from other databases could be used as long as it is formatted as an igraph object with the name attribute containing entrezgene identifiers and the label attribute containing the symbol.
3.3 Using PSICQUIC package to obtain protein-protein interactions
One possibility available from Bioconductor is to use the package PSICQUIC to obtain PPI data. PSICQUIC provides access to different databases of PPIs, including BioGRID and STRINGS, and databases of cellular networks like KEGG or Reactome. For example, to obtain the human BioGRID data (NOTE: named BioGrid in PSICQUIC):
library(PSICQUIC)
psicquic <- PSICQUIC()
providers(psicquic)
# obtain BioGrid human PPIs (as data.frame):
tbl <- interactions(psicquic, species="9606",provider="BioGrid")
# the target and source node information needs to be polished (i.e. must be Entrez gene id only)
biogrid_hs <- data.frame(source=tbl$A,target=tbl$B)
biogrid_hs$source <- sub(".*locuslink:(.*)\\|BIOGRID:.*","\\1", biogrid_hs$source)
biogrid_hs$target <- sub(".*locuslink:(.*)\\|BIOGRID:.*","\\1", biogrid_hs$target)
# create graph.
library(igraph)
biogrid_hs <- graph.data.frame(biogrid_hs,directed=FALSE)
biogrid_hs <- simplify(biogrid_hs)
# annotate with symbols.
library(org.Hs.eg.db)
V(biogrid_hs)$label <- select(org.Hs.eg.db,keys=V(biogrid_hs)$name,columns=c("SYMBOL"))$SYMBOL4 Case study: TRM associated with Sox2 in embryonic stem cells (ESCs)
Sox2 is a TF involved in the determination and maintainance of pluripotency in embryonic stem cells (ESCs). Sox2 forms a transcriptional regulatory module with Nanog and Pou5f1 (Oct4), and together determine ESCs phenotype. Other TFs important to this process are Erssb and Klf4. In this case study we want to identify TRMs associated with Sox2. ChIP-seq data for Sox2 was obtained from Chen et al. (2008) and motif enrichment analysis performed with HOMER Heinz et al. (2010), followed by matching against our library of PWMs using TOMTOM Gupta et al. (2007). The starting dataset is the TOMTOM output file with the motifs enriched in the Sox2 binding regions.
# read motif enrichment results.
motif_file <- system.file("extra/sox2_motif_list.rda", package = "rTRM")
load(motif_file)
length(motif_list)## [1] 177head(motif_list)## [1] "MA0039.2" "MA0071.1" "MA0075.1" "MA0077.1" "MA0078.1" "MA0112.2"First, we read the motifs and convert them into gene identifiers (i.e. Entrez Gene identifier). To do this we use the function getOrthologFromMatrix, which takes a list of motif identifiers and the target organism as parameters. The function returns a list with the Entrez Gene ids.
# get the corresponding gene.
tfs_list <- getOrthologFromMatrix(motif_list, organism = "mouse")
tfs_list <- unique(unlist(tfs_list, use.names = FALSE))
length(tfs_list)## [1] 98head(tfs_list)## [1] "18609" "20682" "13983" "20665" "18291" "18227"Next, we need a list of genes expressed in ESC. For this, the dataset was obtained from GEO (GSE27708; Ho et al. (2011)) and processed using the custom CDFs from the BrainArray project Dai et al. (2005) and the rma function from the package affy Gautier et al. (2004). Genes not expressed were filtered by removing all genes with log2 expression < 5 in all samples.
# load expression data.
eg_esc_file <- system.file("extra/ESC-expressed.txt", package = "rTRM")
eg_esc <- scan(eg_esc_file, what = "")
length(eg_esc)## [1] 8734head(eg_esc)## [1] "100008567" "100017" "100019" "100037258" "100038489" "100039781"tfs_list_esc <- tfs_list[tfs_list %in% eg_esc]
length(tfs_list_esc)## [1] 22head(tfs_list_esc)## [1] "26380" "18999" "20674" "16600" "26379" "13984"Next, we load the PPI network and filter out potential degree outliers and proteins not expressed in the paired expression data.
# load and process PPI data.
biogrid_mm()## Mouse PPI network data of class igraph## This graph was created by an old(er) igraph version.
## Call upgrade_graph() on it to use with the current igraph version
## For now we convert it on the fly...## Number of nodes: 5315## Number of edges: 11695## Source: The BioGRID (http://www.thebiogrid.org)## Release: 3.4.128## Downloaded: 2015-09-17## Use data(biogrid_mm) to load itdata(biogrid_mm)
ppi <- biogrid_mm
vcount(ppi)## This graph was created by an old(er) igraph version.
## Call upgrade_graph() on it to use with the current igraph version
## For now we convert it on the fly...## [1] 5315ecount(ppi)## [1] 11695# remove outliers.
f <- c("Ubc", "Sumo1", "Sumo2", "Sumo3")
f <- select(org.Mm.eg.db, keys = f, columns = "ENTREZID", keytype = "SYMBOL")$ENTREZID## 'select()' returned 1:1 mapping between keys and columnsf## [1] "22190" "22218" "170930" "20610"ppi <- removeVertices(ppi, f)
vcount(ppi)## [1] 4984ecount(ppi)## [1] 11081# filter by expression.
ppi_esc <- induced.subgraph(ppi, V(ppi)[ name %in% eg_esc ])
vcount(ppi_esc)## [1] 3109ecount(ppi_esc)## [1] 4889# ensure a single component.
ppi_esc <- getLargestComp(ppi_esc)
vcount(ppi_esc)## [1] 2576ecount(ppi_esc)## [1] 4851To identify TRMs we define a target TF (the one the ChIP-seq data comes from) and some query TFs (the ones with enriched binding sites in the neighborhood of the target TF).
# define target.
target <- select(org.Mm.eg.db,keys="Sox2",columns="ENTREZID",keytype="SYMBOL")$ENTREZID## 'select()' returned 1:1 mapping between keys and columnstarget## [1] "20674"# find TRM.
s <- findTRM(ppi_esc, target, tfs_list_esc, method = "nsa", max.bridge = 1)## 11 query nodes NOT FOUND in network-- removed## removing: 320 nodes out of 328 [keeping 8 nodes]vcount(s)## [1] 8ecount(s)## [1] 15Finally, we layout the network using a customized concentric layout and plot the network and the legend.
# generate layout (order by cluster, then label)
cl <- getConcentricList(s, target, tfs_list_esc)
l <- layout.concentric(s, cl, order = "label")
# plot TRM.
plotTRM(s, layout = l, vertex.cex = 15, label.cex = .8)
Figure 4: Sox2 specific TRM in ESCs
plotTRMlegend(s, title = "ESC Sox2 TRM", cex = .8)
Figure 5: Sox2 specific TRM in ESCs
5 A complete workflow in R
In this section we will identify Sox2 TRMs using a workflow performed completely in R. For this the MotifDb package will be used to obtain the information about PWMs, and PWMEnrich package for identifying enriched motifs. PWMEnrich requires the computation of background models and the enrichment analysis per se, which are computational intensive. Therefore these steps were not run during the compilation of this vignette.
The first step is to retrieve a set of PWMs. Here we will use the MotifDb package available in Bioconductor. We will use only mouse PWMs (i.e. PWMs for the target organism). It could be possible to use matrices from other species but then the user has to obtain the orthologs in the target organism (e.g. using getOrthologsFromBiomart in rTRM or using the Biomart package directly).
library(rTRM)
library(BSgenome.Mmusculus.UCSC.mm8.masked) # Sox2 peaks found against mm8## Loading required package: BSgenome## Loading required package: GenomeInfoDb## Loading required package: GenomicRanges## Loading required package: Biostrings## Loading required package: XVector##
## Attaching package: 'Biostrings'## The following object is masked from 'package:base':
##
## strsplit## Loading required package: BiocIO## Loading required package: rtracklayer##
## Attaching package: 'rtracklayer'## The following object is masked from 'package:BiocIO':
##
## FileForFormat## The following object is masked from 'package:igraph':
##
## blocks## Loading required package: BSgenome.Mmusculus.UCSC.mm8library(PWMEnrich)
registerCoresPWMEnrich(1) # register number of cores for parallelization in PWMEnrich
library(MotifDb)## See system.file("LICENSE", package="MotifDb") for use restrictions.# select mouse PWMs:
sel.mm <- values(MotifDb)$organism %in% c("Mmusculus")
pwm.mm <- MotifDb[sel.mm]The matrices need to be passed as counts, that is the PFM need to be converted to counts. The easiest way is to multiply by 100 and round the results. We also need to convert it to integer.
# generate logn background model of PWMs:
p <- as.list(pwm.mm)
p <- lapply(p, function(x) round(x * 100))
p <- lapply(p, function(x) t(apply(x, 1, as.integer)))With the PFMs we compute the background model using the makeBackground() function from the PWMEnrich package, which returns the corresponding PWMs. This requires a list with the PFMs as counts, the organisms to obtain the sequences to compute the background and the type of background model (here “logn” model is used).
pwm_logn <- makeBackground(p, Mmusculus, type = "logn")Next we read the peak information from the Sox2 Chip-seq data. This is the original coordinates obtained from Chen et al. (2008), which were obtained for Mus musculus (mm8) genome. The function getSequencesFromGenome() is an utility wrapper to getSeq() that facilitates appending a label to the sequences’ ids. PWMEnrich requires sequences the same size or longer to the motifs so we check what is the largest motif and filter the sequences accordingly.
sox2_bed <- read.table(system.file("extra/ESC_Sox2_peaks.txt", package = "rTRM"))
colnames(sox2_bed) <- c("chr", "start", "end")
sox2_seq <- getSequencesFromGenome(sox2_bed, Mmusculus, append.id="Sox2")
# PWMEnrich throws an error if the sequences are shorter than the motifs so we filter those sequences.
min.width <- max(sapply(p, ncol))
sox2_seq_filter <- sox2_seq[width(sox2_seq) >= min.width]Next, enrichment is computed with the sequences and the PWMs with the background model as parameters.
# find enrichment:
sox2_enr <- motifEnrichment(sox2_seq_filter, pwms=pwm_logn, group.only=TRUE)Next, retrieve the enriched motifs by choosing an appropriate cutoff. Here a raw.score of > 5 is used. Then, using the annotations in the MotifDb dataset, we can obtain the Entrezgene ids associated with the enriched TF motifs.
res <- groupReport(sox2_enr)
plot(density(res$raw.score),main="",log="x",xlab="log(raw.score)")## Warning in xy.coords(x, y, xlabel, ylabel, log): 2 x values <= 0 omitted from
## logarithmic plotabline(v=log2(5),col="red")
mtext(text="log2(5)",at=log2(5),side=3,cex=.8,col="red")
Figure 6: Density of log2(raw.score) for group
The selected cutoff is indicated with a red line.
res.gene <- unique(values(MotifDb[res$id[res$raw.score > 5]])$geneId)
res.gene <- unique(na.omit(res.gene))Then proceed with the same steps as in the Use Case example shown in the previous section. The resulting TRM is similar (~85% of edges shared) to the one in the Use Case, which used HOMER for motif enrichment. Differences may be to different approaches to determine the background. HOMER uses random sets of sequences with similar composition to the ChIP-seq peaks provided to generate the background. For PWMEnrich we generated a background using promoter sequences, defined as 2000 bp upstream of the transcription start site (TSS) of all genes in the genome. Generally, using different strategies for enrichment will tend to produce slightly different TRMs.
data(biogrid_mm)
ppi <- biogrid_mm
vcount(ppi)## This graph was created by an old(er) igraph version.
## Call upgrade_graph() on it to use with the current igraph version
## For now we convert it on the fly...## [1] 5315ecount(ppi)## [1] 11695f <- c("Ubc", "Sumo1", "Sumo2", "Sumo3")
f <- select(org.Mm.eg.db,keys=f,columns="ENTREZID",keytype="SYMBOL")$ENTREZID## 'select()' returned 1:1 mapping between keys and columnsppi <- removeVertices(ppi, f)
vcount(ppi)## [1] 4984ecount(ppi)## [1] 11081# filter by expression.
eg_esc <- scan(system.file("extra/ESC-expressed.txt", package = "rTRM"), what = "")
ppi_esc <- induced.subgraph(ppi, V(ppi)[ name %in% eg_esc ])
vcount(ppi_esc)## [1] 3109ecount(ppi_esc)## [1] 4889# ensure a single component.
ppi_esc <- getLargestComp(ppi_esc)
vcount(ppi_esc)## [1] 2576ecount(ppi_esc)## [1] 4851sox2.gene <- select(org.Mm.eg.db,keys="Sox2",columns="ENTREZID",keytype="SYMBOL")$ENTREZID## 'select()' returned 1:1 mapping between keys and columnssox2_trm <- findTRM(ppi_esc, target=sox2.gene, query = res.gene)## 18 query nodes NOT FOUND in network-- removed## removing: 346 nodes out of 357 [keeping 11 nodes]cl <- getConcentricList(sox2_trm, t=sox2.gene,e=res.gene)
l <- layout.concentric(sox2_trm, concentric=cl, order="label")
plotTRM(sox2_trm, layout = l, vertex.cex = 15, label.cex = .8)
Figure 7: Sox2 TRM identified using PWMEnrich for the motif enrichment
plotTRMlegend(sox2_trm, title = "ESC Sox2 TRM", cex = .8)
Figure 8: Sox2 TRM identified using PWMEnrich for the motif enrichment
We next compare the similarity between the TRM identified using motifs enriched as identified with HOMER and those identified with PWMEnrich. As shown in the heatmap, both methods return similar results.
m <- getSimilarityMatrix(list(PWMEnrich = sox2_trm, HOMER = s))
m## PWMEnrich HOMER
## PWMEnrich 100.00000 83.33333
## HOMER 83.33333 100.00000d <- as.data.frame.table(m)
g <- ggplot(d, aes(x = Var1, y = Var2, fill = Freq)) +
geom_tile() +
scale_fill_gradient2(
limit = c(0, 100),
low = "white",
mid = "darkblue",
high = "orange",
guide = guide_legend("similarity", reverse = TRUE),
midpoint = 50
) +
labs(x = NULL, y = NULL) +
theme(aspect.ratio = 1,
axis.text.x = element_text(
angle = 90,
vjust = .5,
hjust = 1
))6 Ploting parameters
The most important parameter determining the appearance of your network will be the layout. When networks contain many nodes and edges are difficult to interpret. rTRM implements two igraph layouts that try improve the visualization and interpretation of the identified TRMs. The layout layout.concentric is a circular layout with multiple concentric layers that places the target TFs in the center, the enriched (or query) TFs in the outer circle and the bridge TFs in the middle circle. Another layut is layout.arc that tries to mimic the layout presented in the rTRM description (Fig. 1). In this case all nodes are plotted in a liner layout, with the targets in the center, and the enriched (query) nodes at each side. Those enriched nodes connected directly to any of the target nodes are placed in the left side. Those connected through a bridge node are placed in the right side, with the bridge node placed in between. The following figure compares the concentric layout obtained in the previous section with a layout using the layout.arc function.
plotTRM(sox2_trm, layout = l, vertex.cex = 15, label.cex = .7)
Figure 9: Sox2 TRM obtained with PWMEnrich workflow and layout.concentric is shown in the left
Same TRM with layout.arc is shown in the right.
l=layout.arc(sox2_trm,target=sox2.gene,query=res.gene)
plotTRM(sox2_trm, layout=l,vertex.cex=15,label.cex=.7)
Figure 10: Sox2 TRM obtained with PWMEnrich workflow and layout.concentric is shown in the left
Same TRM with layout.arc is shown in the right.
7 Citation
If you use rTRM in your research please include the following reference:
citation(package = "rTRM")## To cite rTRM in publications use:
##
## Diego Diez, Andrew P. Hutchins and Diego Miranda-Saavedra. Systematic
## identification of transcriptional regulatory modules from
## protein-protein interaction networks. Nucleic Acids Research, 2014.
## 42 (1) e6.
##
## A BibTeX entry for LaTeX users is
##
## @Article{,
## title = {Systematic identification of transcriptional regulatory modules from protein-protein interaction networks},
## author = {Diego Diez and Andrew P. Hutchins and Diego Miranda-Saavedra},
## year = {2014},
## journal = {Nucleic Acids Research},
## volume = {42},
## number = {1},
## pages = {e6-e6},
## doi = {10.1093/nar/gkt913},
## }8 Session Information
sessionInfo()## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.18-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB 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: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] MotifDb_1.44.0
## [2] PWMEnrich_4.38.0
## [3] BSgenome.Mmusculus.UCSC.mm8.masked_1.3.99
## [4] BSgenome.Mmusculus.UCSC.mm8_1.4.0
## [5] BSgenome_1.70.0
## [6] rtracklayer_1.62.0
## [7] BiocIO_1.12.0
## [8] Biostrings_2.70.0
## [9] XVector_0.42.0
## [10] GenomicRanges_1.54.0
## [11] GenomeInfoDb_1.38.0
## [12] org.Mm.eg.db_3.18.0
## [13] AnnotationDbi_1.64.0
## [14] IRanges_2.36.0
## [15] S4Vectors_0.40.0
## [16] Biobase_2.62.0
## [17] BiocGenerics_0.48.0
## [18] rTRM_1.40.0
## [19] igraph_1.5.1
## [20] knitr_1.44
## [21] ggplot2_3.4.4
## [22] BiocStyle_2.30.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.0 farver_2.1.1
## [3] dplyr_1.1.3 blob_1.2.4
## [5] bitops_1.0-7 fastmap_1.1.1
## [7] RCurl_1.98-1.12 GenomicAlignments_1.38.0
## [9] XML_3.99-0.14 digest_0.6.33
## [11] lifecycle_1.0.3 KEGGREST_1.42.0
## [13] gdata_3.0.0 RSQLite_2.3.1
## [15] magrittr_2.0.3 compiler_4.3.1
## [17] rlang_1.1.1 sass_0.4.7
## [19] tools_4.3.1 utf8_1.2.4
## [21] yaml_2.3.7 data.table_1.14.8
## [23] S4Arrays_1.2.0 bit_4.0.5
## [25] splitstackshape_1.4.8 DelayedArray_0.28.0
## [27] abind_1.4-5 BiocParallel_1.36.0
## [29] withr_2.5.1 grid_4.3.1
## [31] fansi_1.0.5 colorspace_2.1-0
## [33] gtools_3.9.4 scales_1.2.1
## [35] SummarizedExperiment_1.32.0 cli_3.6.1
## [37] rmarkdown_2.25 crayon_1.5.2
## [39] generics_0.1.3 httr_1.4.7
## [41] rjson_0.2.21 DBI_1.1.3
## [43] cachem_1.0.8 zlibbioc_1.48.0
## [45] parallel_4.3.1 BiocManager_1.30.22
## [47] restfulr_0.0.15 matrixStats_1.0.0
## [49] vctrs_0.6.4 Matrix_1.6-1.1
## [51] jsonlite_1.8.7 bookdown_0.36
## [53] bit64_4.0.5 seqLogo_1.68.0
## [55] magick_2.8.1 evd_2.3-6.1
## [57] jquerylib_0.1.4 glue_1.6.2
## [59] codetools_0.2-19 gtable_0.3.4
## [61] munsell_0.5.0 tibble_3.2.1
## [63] pillar_1.9.0 htmltools_0.5.6.1
## [65] GenomeInfoDbData_1.2.11 R6_2.5.1
## [67] evaluate_0.22 lattice_0.22-5
## [69] png_0.1-8 Rsamtools_2.18.0
## [71] memoise_2.0.1 bslib_0.5.1
## [73] Rcpp_1.0.11 SparseArray_1.2.0
## [75] xfun_0.40 MatrixGenerics_1.14.0
## [77] pkgconfig_2.0.3References
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