#4 Changes in 3D chormatin structure

---

Details

• Original publication:

Ramos-Rodríguez, M., Raurell-Vila, H., Colli, M.L. et al. The impact of proinflammatory cytokines on the β-cell regulatory landscape provides insights into the genetics of type 1 diabetes. Nat Genet. 51, 1588–1595 (2019) https://doi.org/10.1038/s41588-019-0524-6

• Contents: Analyses and figures contained in this document correspond to the following figures/sections of the original publication:

  • Results: “Changes in islet 3D chromatin structure”.
  • Figure 3: “Cytokine exposure induces changes in human islet 3D chromatin structure”. Panels a to c.
  • Extended Data Figure 5: “3D chromatin changes induced by exposure of human islets to pro-inflammatory cytokines”. Panels b to d.

---

Process and calculate differential contacts at regulatory elements

Rscript code/CYT_UMI4C_processing.R

devtools::load_all("~/tools/umi4cCatcheR/")
library(umi4cPackage)
conf <- "../data/CYT/UMI4C/conf/"
umi4cPackage::p4cLoadConfFiles(conf)

df <- read.delim("../data/CYT/UMI4C/UMI4C_promoters_views.tsv", stringsAsFactors=T, header=T)

tracks <- gtrack.ls()

for (i in 1:nrow(df)) {
  sel <- tracks[grepl(df$bait[i], tracks) &
                   grepl("_m_", tracks)]
  
  res <- process4CProfiles(treat_name=sel[2],
                         ctrl_name=sel[1],
                         scope=1e6,
                         min_win_mols=50,
                         name_bait=df$bait[i])
  
  diff <- diffContacts(res,
                       times_mean=20,
                       exclude_viewpoint=3e3,
                       adj.threshold=0.05,
                       adj.method="none",
                       min_mols_test=0,
                       min_odds_ratio=1)

  save(res, diff,
       file=file.path(out_dir"UMI4C_norm_results_", df$bait[i], ".rda"))
}

Plot UMI-4C at up-regulated gene promoters

## RE ---------------------------------------------------------------
load("../data/CYT/REs/REs_hi_fc1_padj0.05_granges_subgroup.rda")

## Genes ------------------------------------------------------------
load("../data/CYT//RNA/diffAnalysis/RNA_hi_GRangesBatch.rda")
res.gr$type[res.gr$baseMean<=1] <- "not-expressed"
res.gr <- res.gr[res.gr$gene_biotype=="protein_coding",]

col.df <- data.frame("type"=c(names(pals$differential), "not-expressed"),
                     "color"=c(pals$differential, "black"),
                     stringsAsFactors = FALSE)
col.df$color[grep("grey", col.df$color)] <- "grey39"
mcols(res.gr) <- dplyr::left_join(data.frame(mcols(res.gr)[,c(1:2,10)]),
                                      col.df)
colnames(mcols(res.gr))[1] <- "ensembl_gene_id"

mcol.color=4
mcol.name=2
mcol.ensembl=1

df <- read.delim("../data/CYT/UMI4C/UMI4C_promoters_views.tsv", stringsAsFactors=T, header=T)

for (i in 1:length(df$bait)) {
  load(file.path(out_dir, paste0("UMI4C_norm_results_", df$bait[i], ".rda")))
  
  xlim <- c(df$start[i], df$end[i])
  region <- GRanges(seqnames=paste0(seqnames(res$bait)),
                    ranges=IRanges(start=xlim[1],
                                   end=xlim[2]))
  
  ### Genes ------------------
  plot.genes <- plotGenes(genes=res.gr[res.gr$type!="not-expressed",],
                          which=region,
                          mcol.color=mcol.color,
                          mcol.name=mcol.name,
                          mcol.ensembl=mcol.ensembl)
  
  g.plots <-
    ggplot() +
    plot.genes +
    xlim(xlim) +
    scale_fill_manual(values=pals$differential,
                       name="Gene expression",
                       labels=c(gained="Up-regulated",
                                lost="Down-regulated",
                                stable="Equal-regulated")) + 
    scale_color_manual(values=pals$differential,
                       name="Gene expression",
                       labels=c(gained="Up-regulated",
                                lost="Down-regulated",
                                stable="Equal-regulated")) + 
    themeXblank() +
    themeYblank()
  
  
  ### UMI-4C ------------------
  umi <-
    ggplot(res$norm_trend,
           aes(start, trend)) +
    geom_ribbon(aes(ymin=devM, ymax=devP, group=interaction(group, sample)),
                color=NA, fill="light grey") +
    geom_line(aes(color=sample, group=interaction(group, sample)),
              lwd=0.7) +
    annotate("point", x=start(res$bait), y=df$ymax[i], pch=25, fill="black",
             size=3) +
    annotate("text", x=start(res$bait), y=df$ymax[i]-0.2, label=df$bait[i],
             size=3) +
    scale_color_manual(values=c(ctrl="#1f78b4", treat="#d95f02"),
                       labels=c("CYT", "CTRL"), name="") +
    scale_y_continuous(name="# UMI-4C contacts",
                       limits=c(0, df$ymax[i]),
                       breaks=scales::pretty_breaks(),
                       expand=c(0,0)) +
    xlim(xlim) +
    theme(legend.position="right")
  
  ### RE ------------------
  re.sel <- as.data.frame(subsetByOverlaps(resize(re, 3e3, fix="center"),
                                           region))
  
  ire <-
    ggplot(re.sel) +
    geom_rect(aes(xmin=start, xmax=end, ymin=0, ymax=1, fill=h3k27ac.log2FoldChange)) +
    scale_fill_gradient2(low="dark grey",
                         mid="grey",
                        high="dark green",
                        name=expression("H3K27ac "*log[2]*" FC"),
                        breaks=scales::pretty_breaks(n=3),
                        midpoint=0,
                        guide = guide_colorbar(direction = "horizontal",
                                               title.position="top")) +
    scale_x_continuous(name=paste("Coordinates", seqnames(region), "(Mb)"),
                       breaks=scales::pretty_breaks(),
                       limits=xlim,
                       labels=function(x) round(x/1e6, 2),
                       expand=c(0,0))
  
  
  ### Get legends -----------
  gene.leg <- get_legend(g.plots)
  umi.leg <- get_legend(umi)
  diff.leg <- get_legend(diff$plot)
  ire.leg <- get_legend(ire)
  
  
  legends <- plot_grid(gene.leg, umi.leg, diff.leg, ire.leg, ncol=1)
  ### Grid ------------------
  p <- 
    plot_grid(g.plots + theme(plot.margin = margin(1,0,0,0, "cm"),
                              legend.position = "none"),
            umi + themeXblank() + theme(legend.position = "none"), 
            diff$plot + xlim(xlim) + themeXblank() + themeYblank() + theme(legend.position = "none"),
            ire + themeYblank() + theme(legend.position = "none"),
            ncol=1, 
            rel_heights = c(0.25, 0.45, 0.1, 0.2),
            align="v")
  
  plot <- 
    plot_grid(p, 
              legends,
              ncol=2, rel_widths=c(0.7, 0.3))
  
  print(plot)
}

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Plot zoom-ins

TNFSF10

load(file.path(out_dir, "UMI4C_norm_results_TNFSF10.rda"))

wins <- as.character(diff$results$id[diff$results$sign=="yes"])

coord <- diff$results[diff$results$id %in% wins, c(8:9,1)]
reg <- GRanges(paste0(as.character(seqnames(res$bait)),
                        ":", min(coord$start), "-", max(coord$end)))

tracks <- c(list.files("../data/CYT/ATAC/Visualization",
                       pattern=".bw",
                       full.names=T),
            list.files("../data/CYT/H3K27ac/Visualization",
                       pattern=".bw",
                       full.names=T))

tracks <- tracks[!grepl("[[:digit:]]_", tracks) &
                   grepl("hi_", tracks)]

## ATAC-seq -------------------
sm.at <- 20

ctrl.at <- rtracklayer::import(tracks[grepl("ATAC", tracks) & grepl("ctrl", tracks)],
                               which=reg)
score(ctrl.at) <- zoo::rollmean(score(ctrl.at), sm.at,
                                fill=c(NA, NA, NA))

cyt.at <- rtracklayer::import(tracks[grepl("ATAC", tracks) & grepl("cyt", tracks)],
                               which=reg)
score(cyt.at) <- zoo::rollmean(score(cyt.at), sm.at,
                               fill=c(NA, NA, NA))

## H3K27ac -------------------
sm.ac <- 20
ctrl.ac <- rtracklayer::import(tracks[grepl("H3K27ac", tracks) & grepl("ctrl", tracks)],
                               which=reg)
score(ctrl.ac) <- zoo::rollmean(score(ctrl.ac), sm.ac,
                                fill=c(NA, NA, NA))

cyt.ac <- rtracklayer::import(tracks[grepl("H3K27ac", tracks) & grepl("cyt", tracks)],
                               which=reg)
score(cyt.ac) <- zoo::rollmean(score(cyt.ac), sm.ac,
                               fill=c(NA, NA, NA))

##--------------------
## Plot
##--------------------
xlims <- c(start(ranges(reg)),
           end(ranges(reg)))

ctrl.at.p <-
  ggplot(data.frame(ctrl.at)) +
  geom_area(aes(x=start,
                y=score),
            fill=pals$treatment["ctrl"]) +
  scale_y_continuous(name="", 
                     limits=c(0,30),
                     expand=c(0,0),
                     breaks=c(0,30)) +
  xlim(xlims) +
  themeXblank() +
  theme(plot.margin=unit(c(0.5,0,0,0), "cm"))
    

cyt.at.p <-
  ggplot(data.frame(cyt.at)) +
  geom_area(aes(x=start,
                y=score),
            fill=pals$treatment["cyt"]) +
  scale_y_continuous(name="", 
                     limits=c(0,30),
                     expand=c(0,0),
                     breaks=c(0,30)) +
  xlim(xlims) +
  themeXblank() +
  theme(plot.margin=unit(c(0.5,0,0,0), "cm"))
  

ctrl.ac.p <-
  ggplot(data.frame(ctrl.ac)) +
  geom_area(aes(x=start,
                y=score),
            fill=pals$treatment["ctrl"]) +
  scale_y_continuous(name="", 
                     limits=c(0,60),
                     expand=c(0,0),
                     breaks=c(0,60)) +
  xlim(xlims) +
  themeXblank() +
  theme(plot.margin=unit(c(0.5,0,0,0), "cm"))
    

cyt.ac.p <-
  ggplot(data.frame(cyt.ac)) +
  geom_area(aes(x=start,
                y=score),
            fill=pals$treatment["cyt"]) +
  scale_y_continuous(name="", 
                     limits=c(0,60),
                     expand=c(0,0),
                     breaks=c(0,60)) +
  xlim(xlims) +
  themeXblank() +
  theme(plot.margin=unit(c(0.5,0,0,0), "cm"))

plot_grid(ctrl.at.p,
          cyt.at.p,
          ctrl.ac.p,
          cyt.ac.p,
          align="v",
          ncol=1)

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Version	Author	Date
90643f9	mireia-bioinfo	2020-08-31

GBP1

Region 1

load(file.path(out_dir, "UMI4C_norm_results_GBP1.rda"))

wins <- as.character(diff$results$id[diff$results$sign=="yes"])[2]
wins <- c(wins, "window_117")
coord <- diff$results[diff$results$id %in% wins, c(8:9,1)]
reg <- GRanges(paste0(as.character(seqnames(res$bait)),
                        ":", min(coord$start), "-", max(coord$end)))

tracks <- c(list.files("../data/CYT/ATAC/Visualization",
                       pattern=".bw",
                       full.names=T),
            list.files("../data/CYT/H3K27ac/Visualization",
                       pattern=".bw",
                       full.names=T))

tracks <- tracks[!grepl("[[:digit:]]_", tracks) &
                   grepl("hi_", tracks)]

## ATAC-seq -------------------
sm.at <- 20

ctrl.at <- rtracklayer::import(tracks[grepl("ATAC", tracks) & grepl("ctrl", tracks)],
                               which=reg)
score(ctrl.at) <- zoo::rollmean(score(ctrl.at), sm.at,
                                fill=c(NA, NA, NA))

cyt.at <- rtracklayer::import(tracks[grepl("ATAC", tracks) & grepl("cyt", tracks)],
                               which=reg)
score(cyt.at) <- zoo::rollmean(score(cyt.at), sm.at,
                               fill=c(NA, NA, NA))

## H3K27ac -------------------
sm.ac <- 20
ctrl.ac <- rtracklayer::import(tracks[grepl("H3K27ac", tracks) & grepl("ctrl", tracks)],
                               which=reg)
score(ctrl.ac) <- zoo::rollmean(score(ctrl.ac), sm.ac,
                                fill=c(NA, NA, NA))

cyt.ac <- rtracklayer::import(tracks[grepl("H3K27ac", tracks) & grepl("cyt", tracks)],
                               which=reg)
score(cyt.ac) <- zoo::rollmean(score(cyt.ac), sm.ac,
                               fill=c(NA, NA, NA))

##--------------------
## Plot
##--------------------
xlims <- c(start(ranges(reg)),
           end(ranges(reg)))

ctrl.at.p <-
  ggplot(data.frame(ctrl.at)) +
  geom_area(aes(x=start,
                y=score),
            fill=pals$treatment["ctrl"]) +
  scale_y_continuous(name="", 
                     limits=c(0,15),
                     expand=c(0,0),
                     breaks=c(0,15)) +
  xlim(xlims) +
  themeXblank() +
  theme(plot.margin=unit(c(0.5,0,0,0), "cm"))
    

cyt.at.p <-
  ggplot(data.frame(cyt.at)) +
  geom_area(aes(x=start,
                y=score),
            fill=pals$treatment["cyt"]) +
  scale_y_continuous(name="", 
                     limits=c(0,15),
                     expand=c(0,0),
                     breaks=c(0,15)) +
  xlim(xlims) +
  themeXblank() +
  theme(plot.margin=unit(c(0.5,0,0,0), "cm"))
  

ctrl.ac.p <-
  ggplot(data.frame(ctrl.ac)) +
  geom_area(aes(x=start,
                y=score),
            fill=pals$treatment["ctrl"]) +
  scale_y_continuous(name="", 
                     limits=c(0,10),
                     expand=c(0,0),
                     breaks=c(0,10)) +
  xlim(xlims) +
  themeXblank() +
  theme(plot.margin=unit(c(0.5,0,0,0), "cm"))
    

cyt.ac.p <-
  ggplot(data.frame(cyt.ac)) +
  geom_area(aes(x=start,
                y=score),
            fill=pals$treatment["cyt"]) +
  scale_y_continuous(name="", 
                     limits=c(0,10),
                     expand=c(0,0),
                     breaks=c(0,10)) +
  xlim(xlims) +
  themeXblank() +
  theme(plot.margin=unit(c(0.5,0,0,0), "cm"))

plot_grid(ctrl.at.p,
          cyt.at.p,
          ctrl.ac.p,
          cyt.ac.p,
          align="v",
          ncol=1)

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90643f9	mireia-bioinfo	2020-08-31

Region 2

load(file.path(out_dir, "UMI4C_norm_results_GBP1.rda"))

wins <- as.character(diff$results$id[diff$results$sign=="yes"])[3]

coord <- diff$results[diff$results$id %in% wins, c(8:9,1)]
reg <- GRanges(paste0(as.character(seqnames(res$bait)),
                        ":", min(coord$start), "-", max(coord$end)))

tracks <- c(list.files("../data/CYT/ATAC/Visualization",
                       pattern=".bw",
                       full.names=T),
            list.files("../data/CYT/H3K27ac/Visualization",
                       pattern=".bw",
                       full.names=T))

tracks <- tracks[!grepl("[[:digit:]]_", tracks) &
                   grepl("hi_", tracks)]

## ATAC-seq -------------------
sm.at <- 20

ctrl.at <- rtracklayer::import(tracks[grepl("ATAC", tracks) & grepl("ctrl", tracks)],
                               which=reg)
score(ctrl.at) <- zoo::rollmean(score(ctrl.at), sm.at,
                                fill=c(NA, NA, NA))

cyt.at <- rtracklayer::import(tracks[grepl("ATAC", tracks) & grepl("cyt", tracks)],
                               which=reg)
score(cyt.at) <- zoo::rollmean(score(cyt.at), sm.at,
                               fill=c(NA, NA, NA))

## H3K27ac -------------------
sm.ac <- 20
ctrl.ac <- rtracklayer::import(tracks[grepl("H3K27ac", tracks) & grepl("ctrl", tracks)],
                               which=reg)
score(ctrl.ac) <- zoo::rollmean(score(ctrl.ac), sm.ac,
                                fill=c(NA, NA, NA))

cyt.ac <- rtracklayer::import(tracks[grepl("H3K27ac", tracks) & grepl("cyt", tracks)],
                               which=reg)
score(cyt.ac) <- zoo::rollmean(score(cyt.ac), sm.ac,
                               fill=c(NA, NA, NA))

##--------------------
## Plot
##--------------------
xlims <- c(start(ranges(reg)),
           end(ranges(reg)))

ctrl.at.p <-
  ggplot(data.frame(ctrl.at)) +
  geom_area(aes(x=start,
                y=score),
            fill=pals$treatment["ctrl"]) +
  scale_y_continuous(name="", 
                     limits=c(0,40),
                     expand=c(0,0),
                     breaks=c(0,40)) +
  xlim(xlims) +
  themeXblank() +
  theme(plot.margin=unit(c(0.5,0,0,0), "cm"))
    

cyt.at.p <-
  ggplot(data.frame(cyt.at)) +
  geom_area(aes(x=start,
                y=score),
            fill=pals$treatment["cyt"]) +
  scale_y_continuous(name="", 
                     limits=c(0,40),
                     expand=c(0,0),
                     breaks=c(0,40)) +
  xlim(xlims) +
  themeXblank() +
  theme(plot.margin=unit(c(0.5,0,0,0), "cm"))
  

ctrl.ac.p <-
  ggplot(data.frame(ctrl.ac)) +
  geom_area(aes(x=start,
                y=score),
            fill=pals$treatment["ctrl"]) +
  scale_y_continuous(name="", 
                     limits=c(0,60),
                     expand=c(0,0),
                     breaks=c(0,60)) +
  xlim(xlims) +
  themeXblank() +
  theme(plot.margin=unit(c(0.5,0,0,0), "cm"))
    

cyt.ac.p <-
  ggplot(data.frame(cyt.ac)) +
  geom_area(aes(x=start,
                y=score),
            fill=pals$treatment["cyt"]) +
  scale_y_continuous(name="", 
                     limits=c(0,60),
                     expand=c(0,0),
                     breaks=c(0,60)) +
  xlim(xlims) +
  themeXblank() +
  theme(plot.margin=unit(c(0.5,0,0,0), "cm"))

plot_grid(ctrl.at.p,
          cyt.at.p,
          ctrl.ac.p,
          cyt.ac.p,
          align="v",
          ncol=1)

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Version	Author	Date
90643f9	mireia-bioinfo	2020-08-31

Region 3

load(file.path(out_dir, "UMI4C_norm_results_GBP1.rda"))

wins <- as.character(diff$results$id[diff$results$sign=="yes"])[4]

coord <- diff$results[diff$results$id %in% wins, c(8:9,1)]
reg <- GRanges(paste0(as.character(seqnames(res$bait)),
                        ":", min(coord$start), "-", max(coord$end)))

tracks <- c(list.files("../data/CYT/ATAC/Visualization",
                       pattern=".bw",
                       full.names=T),
            list.files("../data/CYT/H3K27ac/Visualization",
                       pattern=".bw",
                       full.names=T))

tracks <- tracks[!grepl("[[:digit:]]_", tracks) &
                   grepl("hi_", tracks)]

## ATAC-seq -------------------
sm.at <- 20

ctrl.at <- rtracklayer::import(tracks[grepl("ATAC", tracks) & grepl("ctrl", tracks)],
                               which=reg)
score(ctrl.at) <- zoo::rollmean(score(ctrl.at), sm.at,
                                fill=c(NA, NA, NA))

cyt.at <- rtracklayer::import(tracks[grepl("ATAC", tracks) & grepl("cyt", tracks)],
                               which=reg)
score(cyt.at) <- zoo::rollmean(score(cyt.at), sm.at,
                               fill=c(NA, NA, NA))

## H3K27ac -------------------
sm.ac <- 20
ctrl.ac <- rtracklayer::import(tracks[grepl("H3K27ac", tracks) & grepl("ctrl", tracks)],
                               which=reg)
score(ctrl.ac) <- zoo::rollmean(score(ctrl.ac), sm.ac,
                                fill=c(NA, NA, NA))

cyt.ac <- rtracklayer::import(tracks[grepl("H3K27ac", tracks) & grepl("cyt", tracks)],
                               which=reg)
score(cyt.ac) <- zoo::rollmean(score(cyt.ac), sm.ac,
                               fill=c(NA, NA, NA))

##--------------------
## Plot
##--------------------
xlims <- c(start(ranges(reg)),
           end(ranges(reg)))

ctrl.at.p <-
  ggplot(data.frame(ctrl.at)) +
  geom_area(aes(x=start,
                y=score),
            fill=pals$treatment["ctrl"]) +
  scale_y_continuous(name="", 
                     limits=c(0,60),
                     expand=c(0,0),
                     breaks=c(0,60)) +
  xlim(xlims) +
  themeXblank() +
  theme(plot.margin=unit(c(0.5,0,0,0), "cm"))
    

cyt.at.p <-
  ggplot(data.frame(cyt.at)) +
  geom_area(aes(x=start,
                y=score),
            fill=pals$treatment["cyt"]) +
  scale_y_continuous(name="", 
                     limits=c(0,60),
                     expand=c(0,0),
                     breaks=c(0,60)) +
  xlim(xlims) +
  themeXblank() +
  theme(plot.margin=unit(c(0.5,0,0,0), "cm"))
  

ctrl.ac.p <-
  ggplot(data.frame(ctrl.ac)) +
  geom_area(aes(x=start,
                y=score),
            fill=pals$treatment["ctrl"]) +
  scale_y_continuous(name="", 
                     limits=c(0,50),
                     expand=c(0,0),
                     breaks=c(0,50)) +
  xlim(xlims) +
  themeXblank() +
  theme(plot.margin=unit(c(0.5,0,0,0), "cm"))
    

cyt.ac.p <-
  ggplot(data.frame(cyt.ac)) +
  geom_area(aes(x=start,
                y=score),
            fill=pals$treatment["cyt"]) +
  scale_y_continuous(name="", 
                     limits=c(0,50),
                     expand=c(0,0),
                     breaks=c(0,50)) +
  xlim(xlims) +
  themeXblank() +
  theme(plot.margin=unit(c(0.5,0,0,0), "cm"))

plot_grid(ctrl.at.p,
          cyt.at.p,
          ctrl.ac.p,
          cyt.ac.p,
          align="v",
          ncol=1)

Past versions of gbp-3-plot-1.png

Version	Author	Date
90643f9	mireia-bioinfo	2020-08-31

Distribution of UMI-4C contact changes at promoters of up-regulated genes

files <- list.files(out_dir,
                    pattern="test",
                    full.names=T)
test.all <- data.frame()
for (i in files) {
  load(i)
  
  test <- test[,c(4,21,25)]
  name <- pipelineNGS::getNameFromPath(i, prefix="UMI4C_test_promoters_",
                                       suffix=".rda")
  test$bait <- name
  test.all <- rbind(test.all, test)
}

test.all <- test.all[!grepl("Other", test.all$subgroup2),]

ggplot(test.all,
       aes(subgroup2, log2_foldChange)) +
  geom_hline(yintercept=0, lty=2, color="grey") + 
  geom_boxplot(aes(color=subgroup2), notch=T, lwd=1) +
  scale_color_manual(values=pals$re) +
  scale_x_discrete(name="RE type", 
                   labels=function(x) paste0(x, "s")) +
  ylab(expression(Log[2]~FC~UMI4C~contacts)) +
  theme(legend.position="none",
        axis.text.x=element_text(angle=30, hjust=1))

Past versions of unnamed-chunk-2-1.png

Version	Author	Date
90643f9	mireia-bioinfo	2020-08-31

Session information

sessionInfo()

R version 4.0.2 (2020-06-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.1 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=es_ES.UTF-8        LC_COLLATE=C              
 [5] LC_MONETARY=es_ES.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=es_ES.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=es_ES.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] umi4cCatcheR_0.0.0.9000 RColorBrewer_1.1-2      GenomicRanges_1.41.5   
 [4] GenomeInfoDb_1.25.8     IRanges_2.23.10         S4Vectors_0.27.12      
 [7] BiocGenerics_0.35.4     cowplot_1.0.0           ggplot2_3.3.2          
[10] dplyr_1.0.1             workflowr_1.6.2        

loaded via a namespace (and not attached):
 [1] bitops_1.0-6                matrixStats_0.56.0         
 [3] fs_1.5.0                    usethis_1.6.1              
 [5] devtools_2.3.1              rprojroot_1.3-2            
 [7] tools_4.0.2                 backports_1.1.8            
 [9] R6_2.4.1                    colorspace_1.4-1           
[11] withr_2.2.0                 tidyselect_1.1.0           
[13] gridExtra_2.3               prettyunits_1.1.1          
[15] processx_3.4.3              compiler_4.0.2             
[17] git2r_0.27.1                cli_2.0.2                  
[19] Biobase_2.49.0              desc_1.2.0                 
[21] DelayedArray_0.15.7         rtracklayer_1.49.4         
[23] labeling_0.3                bookdown_0.20              
[25] scales_1.1.1                callr_3.4.3                
[27] askpass_1.1                 stringr_1.4.0              
[29] digest_0.6.25               Rsamtools_2.5.3            
[31] rmarkdown_2.3               XVector_0.29.3             
[33] pkgconfig_2.0.3             htmltools_0.5.0            
[35] sessioninfo_1.1.1           rlang_0.4.7                
[37] rstudioapi_0.11             generics_0.0.2             
[39] farver_2.0.3                zoo_1.8-8                  
[41] BiocParallel_1.23.2         RCurl_1.98-1.2             
[43] magrittr_1.5                GenomeInfoDbData_1.2.3     
[45] Matrix_1.2-18               Rcpp_1.0.5                 
[47] munsell_0.5.0               fansi_0.4.1                
[49] lifecycle_0.2.0             stringi_1.4.6              
[51] whisker_0.4                 yaml_2.2.1                 
[53] SummarizedExperiment_1.19.6 zlibbioc_1.35.0            
[55] pkgbuild_1.1.0              grid_4.0.2                 
[57] promises_1.1.1              crayon_1.3.4               
[59] lattice_0.20-41             Biostrings_2.57.2          
[61] magick_2.4.0                knitr_1.29                 
[63] ps_1.3.3                    pillar_1.4.6               
[65] pkgload_1.1.0               XML_3.99-0.5               
[67] glue_1.4.1                  evaluate_0.14              
[69] pdftools_2.3.1              qpdf_1.1                   
[71] remotes_2.2.0               vctrs_0.3.2                
[73] httpuv_1.5.4                testthat_2.3.2             
[75] gtable_0.3.0                purrr_0.3.4                
[77] assertthat_0.2.1            xfun_0.16                  
[79] later_1.1.0.1               tibble_3.0.3               
[81] GenomicAlignments_1.25.3    memoise_1.1.0              
[83] ellipsis_0.3.1