Mouse and dunnart peak features
lecook
2022-03-16
Last updated: 2022-03-22
Checks: 7 0
Knit directory: chipseq-cross-species/
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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/mouse_dunnart_peak_features.Rmd) and HTML (docs/mouse_dunnart_peak_features.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | e189f4c | lecook | 2022-03-22 | wflow_publish(“analysis/mouse_dunnart_peak_features.Rmd”) |
| Rmd | a605c33 | lecook | 2022-03-16 | updated |
Set-up
# Load in libraries
library(ChIPseeker)
library(GenomicFeatures)
library(ggplot2)
library(data.table)
library(dplyr)
plot_dir <- "output/plots/"
fullPeak_dir <- "output/peaks/"
annot_dir <- "output/annotations/"
filterPeaks_dir <- "output/filtered_peaks/"
## Set the fonts up so that each plot is the saved the same way.
font <- theme(axis.text.x = element_text(size = 25),
axis.text.y = element_text(size = 25),
axis.title.x = element_text(size = 25),
axis.title.y = element_text(size = 25),
legend.title = element_text(size = 25), legend.text = element_text(size = 25))Peak features
Peak counts
files =list.files(fullPeak_dir, pattern= "*5.narrowPeak|*only.narrowPeak|*downSampled.narrowPeak", full.names=T) # create list of files in directory
files = as.list(files)
data = lapply(files, function(x) fread(x, header=FALSE, sep="\t", quote = "", na.strings=c("", "NA")))
data[[1]]$mark = "H3K27ac"
data[[2]]$mark = "H3K27ac"
data[[3]]$mark = "H3K27ac"
data[[4]]$mark = "H3K27ac"
data[[5]]$mark = "H3K27ac"
data[[6]]$mark = "H3K27ac"
data[[7]]$mark = "H3K27ac"
data[[8]]$mark = "H3K27ac_H3K4me3"
data[[9]]$mark = "H3K27ac_H3K4me3"
data[[10]]$mark = "H3K27ac_H3K4me3"
data[[11]]$mark = "H3K27ac_H3K4me3"
data[[12]]$mark = "H3K27ac_H3K4me3"
data[[13]]$mark = "H3K27ac_H3K4me3"
data[[14]]$mark = "H3K4me3"
data[[15]]$mark = "H3K4me3"
data[[16]]$mark = "H3K4me3"
data[[17]]$mark = "H3K4me3"
data[[18]]$mark = "H3K4me3"
data[[19]]$mark = "H3K4me3"
data[[20]]$mark = "H3K27ac_H3K4me3"
data[[21]]$mark = "H3K4me3"
data[[1]]$sample = "E10.5"
data[[2]]$sample = "E11.5"
data[[3]]$sample = "E12.5"
data[[4]]$sample = "E13.5"
data[[5]]$sample = "E14.5"
data[[6]]$sample = "E15.5"
data[[7]]$sample = "dunnart"
data[[8]]$sample = "E10.5"
data[[9]]$sample = "E11.5"
data[[10]]$sample = "E12.5"
data[[11]]$sample = "E13.5"
data[[12]]$sample = "E14.5"
data[[13]]$sample = "E15.5"
data[[14]]$sample = "E10.5"
data[[15]]$sample = "E11.5"
data[[16]]$sample = "E12.5"
data[[17]]$sample = "E13.5"
data[[18]]$sample = "E14.5"
data[[19]]$sample = "E15.5"
data[[20]]$sample = "dunnart"
data[[21]]$sample = "dunnart"
## Plot stacked bar graph with number of peaks for each
combined.peaks <- rbindlist(data,)
combined.tbl <- with(combined.peaks, table(sample, mark))
combined.tbl <- as.data.frame(combined.tbl)
p <- ggplot(combined.tbl, aes(factor(sample), Freq, fill=mark)) +
geom_bar(position=position_stack(reverse = TRUE), stat="identity") +
theme_minimal() + ylab("Number of peaks") +
xlab("") + scale_color_manual(values = c("#ffd166", "#299d8f", "#244653")) + scale_fill_manual(values = c("#ffd166", "#299d8f", "#244653")) +
theme_bw()
p
pdf(file=paste0(plot_dir, "number_of_mouse_dunnart_peaks.pdf"), width = 10, height = 7)
print(p + font)
dev.off()svg
2 Peak lengths for H3K4me3 and H3K27ac
files =list.files(fullPeak_dir, pattern= "*enhancer_peaks.narrowPeak|*promoter_peaks.narrowPeak", full.names=T) # create list of files in directory
filenames <- sub('\\.narrowPeak$', '', basename(files))
files = as.list(files)
data = lapply(files, function(x) fread(x, header=FALSE, sep="\t", quote = "", na.strings=c("", "NA")))
names(data) <- filenames
df1 = Map(mutate, data[c(1,3,5,7,9,11,13)], cre = "enhancer")
df2 = Map(mutate, data[c(2,4,6,8,10,12,14)], cre = "promoter")
data = append(df1, df2)
df1 = Map(mutate, data[c(1,8)], group = "dunnart")
df2 = Map(mutate, data[c(2,9)], group = "E10.5")
df3 = Map(mutate, data[c(3,10)], group = "E11.5")
df4 = Map(mutate, data[c(4,11)], group = "E12.5")
df5 = Map(mutate, data[c(5,12)], group = "E13.5")
df6 = Map(mutate, data[c(6,13)], group = "E14.5")
df7 = Map(mutate, data[c(7,14)], group = "E15.5")
data <- append(df1, df2)
data <- append(data, df3)
data = append(data, df4)
data = append(data, df5)
data = append(data, df6)
data = append(data, df7)
data = rbindlist(data,)
data$length = data$V3 - data$V2
p = ggplot(data, aes(x=factor(group), y=log10(length), fill = cre)) + geom_violin(aes(fill=factor(cre)),
position = position_dodge(width=0.8)) +
geom_boxplot(aes(fill=factor(cre)),
width=.2,
outlier.shape = NA,
notch=FALSE,
position = position_dodge(width=0.8)) +
theme_bw() + xlab("") + ylab("Log10 Peak Length") + scale_color_manual(values = c("#efc769", "#1a6259")) +
scale_fill_manual(values = c("#ffe29f","#8db1ac"))
p
pdf(file=paste0(plot_dir, "mouse_dunnart_peak_lengths.pdf"), width = 10, height = 7)
print(p + font)
dev.off()svg
2 Plot peak intensity
p = ggplot(data, aes(x=factor(group), y=log10(V7), fill = cre)) + geom_violin(aes(fill=factor(cre)),
position = position_dodge(width=0.8)) +
geom_boxplot(aes(fill=factor(cre)),
width=.2,
outlier.shape = NA,
notch=FALSE,
position = position_dodge(width=0.8)) +
theme_bw() + xlab("") + ylab("Log10 Peak Length") + scale_color_manual(values = c("#efc769", "#1a6259")) +
scale_fill_manual(values = c("#ffe29f","#8db1ac"))
p
pdf(file=paste0(plot_dir, "mouse_dunnart_peak_intensity.pdf"), width = 10, height = 7)
print(p + font)
dev.off()svg
2 Annotate dunnart peaks
The easiest way to call the nearest genes for the peaks in the dunnart is to use the ChIPseeker package (Guangchuang Yu 2021) as it allows easy integration of non-model organism genomes and has well documented instructions on incorporating BYO genomes with the package. To use the ChIPseeker to annotate peaks, firstly a txdb is needed for the dunnart annotation file. A TxDb class is a container for storing transcript annotations. The dunnart genome doesn’t have a de novo annotation so instead the Tasmanian devil annotation (RefSeq) has been lifted over to the dunnart genome using LiftOff (https://github.com/agshumate/Liftoff).
Gene ID conversion tables
For downstream analyses, conversion tables between gene databases and between species is needed. This is because the ENSEMBL/ENTREZ IDs for the Tasmanian Devil have fewer links to databases such as GO terms etc. For this I have two conversion tables: 1. Converts Tasmanian devil RefSeq to Tasmanian Devil ENSEMBL IDs 2. Convert Tasmanian Devil ENSEMBL IDs to mouse ENSEMBL IDs
Additionally I have a list of background genes for calculating GO enrichment. This background list includes all devil genes that have an orthologous mouse gene ID in the ensembl database.
Annotation files
# Using ENSEMBL version 103 for both the mouse and devil to keep it consistent
## Annotation file for the mouse
mm10_txdb <- makeTxDbFromBiomart(biomart="ENSEMBL_MART_ENSEMBL",
dataset="mmusculus_gene_ensembl",
host="http://feb2021.archive.ensembl.org")
seqlevelsStyle(mm10_txdb) <- "UCSC"
## Make txdb for dunnart annotation file
smiCra_txdb <- makeTxDbFromGFF("data/genomic_data/Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr2.gff")
## Convert geneIDs
### Tables downloaded from biomart and collated
df2 <- read.table("output/annotations/devil_to_mouse_ensembl.txt", header=TRUE, sep="\t") ## conversion table for devil ENSEMBL to mouse ENSEMBL
df3 <- read.table("output/annotations/refseq_to_ensembl.txt", header=TRUE, sep="\t") ## convertsion table for devil refseq to devil ENSEMBL
bg = fread("output/annotations/go_background_orthologousENSEMBL_biomart.txt", header = FALSE)
bg = unlist(bg, use.names = FALSE)Annotate peak files with ChIPseeker
Mouse
## Anotate peak files
annotatePeaksmm10 <- function(peak, outFile){
# Annotate peak file based on mouse ENSEMBL annotation
peakAnno <- annotatePeak(peak, tssRegion = c(-3000, 3000), TxDb = mm10_txdb)
# Write annotation to file
write.table(peakAnno, outFile, sep="\t", quote=F, row.names=F)
}
annotatePeaksmm10(peak = paste0(fullPeak_dir, "E10.5_enhancer_peaks.narrowPeak"), outFile = paste0(annot_dir, "E10.5_enhancer_annotation.txt"))>> loading peak file... 2022-03-22 15:57:04
>> preparing features information... 2022-03-22 15:57:04
>> identifying nearest features... 2022-03-22 15:57:05
>> calculating distance from peak to TSS... 2022-03-22 15:57:06
>> assigning genomic annotation... 2022-03-22 15:57:06
>> assigning chromosome lengths 2022-03-22 15:57:24
>> done... 2022-03-22 15:57:24 annotatePeaksmm10(peak = paste0(fullPeak_dir, "E11.5_enhancer_peaks.narrowPeak"), outFile = paste0(annot_dir, "E11.5_enhancer_annotation.txt"))>> loading peak file... 2022-03-22 15:57:24
>> preparing features information... 2022-03-22 15:57:24
>> identifying nearest features... 2022-03-22 15:57:24
>> calculating distance from peak to TSS... 2022-03-22 15:57:25
>> assigning genomic annotation... 2022-03-22 15:57:25
>> assigning chromosome lengths 2022-03-22 15:57:27
>> done... 2022-03-22 15:57:27 annotatePeaksmm10(peak = paste0(fullPeak_dir, "E12.5_enhancer_peaks.narrowPeak"), outFile = paste0(annot_dir, "E12.5_enhancer_annotation.txt"))>> loading peak file... 2022-03-22 15:57:27
>> preparing features information... 2022-03-22 15:57:27
>> identifying nearest features... 2022-03-22 15:57:27
>> calculating distance from peak to TSS... 2022-03-22 15:57:28
>> assigning genomic annotation... 2022-03-22 15:57:28
>> assigning chromosome lengths 2022-03-22 15:57:30
>> done... 2022-03-22 15:57:31 annotatePeaksmm10(peak = paste0(fullPeak_dir, "E13.5_enhancer_peaks.narrowPeak"), outFile = paste0(annot_dir, "E13.5_enhancer_annotation.txt"))>> loading peak file... 2022-03-22 15:57:31
>> preparing features information... 2022-03-22 15:57:31
>> identifying nearest features... 2022-03-22 15:57:31
>> calculating distance from peak to TSS... 2022-03-22 15:57:31
>> assigning genomic annotation... 2022-03-22 15:57:31
>> assigning chromosome lengths 2022-03-22 15:57:34
>> done... 2022-03-22 15:57:34 annotatePeaksmm10(peak = paste0(fullPeak_dir, "E14.5_enhancer_peaks.narrowPeak"), outFile = paste0(annot_dir, "E14.5_enhancer_annotation.txt"))>> loading peak file... 2022-03-22 15:57:34
>> preparing features information... 2022-03-22 15:57:34
>> identifying nearest features... 2022-03-22 15:57:34
>> calculating distance from peak to TSS... 2022-03-22 15:57:34
>> assigning genomic annotation... 2022-03-22 15:57:34
>> assigning chromosome lengths 2022-03-22 15:57:37
>> done... 2022-03-22 15:57:37 annotatePeaksmm10(peak = paste0(fullPeak_dir, "E15.5_enhancer_peaks.narrowPeak"), outFile = paste0(annot_dir,"E15.5_enhancer_annotation.txt"))>> loading peak file... 2022-03-22 15:57:37
>> preparing features information... 2022-03-22 15:57:37
>> identifying nearest features... 2022-03-22 15:57:37
>> calculating distance from peak to TSS... 2022-03-22 15:57:37
>> assigning genomic annotation... 2022-03-22 15:57:37
>> assigning chromosome lengths 2022-03-22 15:57:40
>> done... 2022-03-22 15:57:40 annotatePeaksmm10(peak = paste0(fullPeak_dir, "E10.5_promoter_peaks.narrowPeak"), outFile = paste0(annot_dir,"E10.5_promoter_annotation.txt"))>> loading peak file... 2022-03-22 15:57:40
>> preparing features information... 2022-03-22 15:57:40
>> identifying nearest features... 2022-03-22 15:57:40
>> calculating distance from peak to TSS... 2022-03-22 15:57:41
>> assigning genomic annotation... 2022-03-22 15:57:41
>> assigning chromosome lengths 2022-03-22 15:57:44
>> done... 2022-03-22 15:57:44 annotatePeaksmm10(peak = paste0(fullPeak_dir, "E11.5_promoter_peaks.narrowPeak"), outFile = paste0(annot_dir,"E11.5_promoter_annotation.txt"))>> loading peak file... 2022-03-22 15:57:44
>> preparing features information... 2022-03-22 15:57:44
>> identifying nearest features... 2022-03-22 15:57:44
>> calculating distance from peak to TSS... 2022-03-22 15:57:45
>> assigning genomic annotation... 2022-03-22 15:57:45
>> assigning chromosome lengths 2022-03-22 15:57:48
>> done... 2022-03-22 15:57:48 annotatePeaksmm10(peak = paste0(fullPeak_dir, "E12.5_promoter_peaks.narrowPeak"), outFile = paste0(annot_dir,"E12.5_promoter_annotation.txt"))>> loading peak file... 2022-03-22 15:57:48
>> preparing features information... 2022-03-22 15:57:49
>> identifying nearest features... 2022-03-22 15:57:49
>> calculating distance from peak to TSS... 2022-03-22 15:57:49
>> assigning genomic annotation... 2022-03-22 15:57:49
>> assigning chromosome lengths 2022-03-22 15:57:52
>> done... 2022-03-22 15:57:52 annotatePeaksmm10(peak = paste0(fullPeak_dir, "E13.5_promoter_peaks.narrowPeak"), outFile = paste0(annot_dir,"E13.5_promoter_annotation.txt"))>> loading peak file... 2022-03-22 15:57:52
>> preparing features information... 2022-03-22 15:57:52
>> identifying nearest features... 2022-03-22 15:57:52
>> calculating distance from peak to TSS... 2022-03-22 15:57:53
>> assigning genomic annotation... 2022-03-22 15:57:53
>> assigning chromosome lengths 2022-03-22 15:57:56
>> done... 2022-03-22 15:57:56 annotatePeaksmm10(peak = paste0(fullPeak_dir, "E14.5_promoter_peaks.narrowPeak"), outFile = paste0(annot_dir,"E14.5_promoter_annotation.txt"))>> loading peak file... 2022-03-22 15:57:56
>> preparing features information... 2022-03-22 15:57:56
>> identifying nearest features... 2022-03-22 15:57:56
>> calculating distance from peak to TSS... 2022-03-22 15:57:57
>> assigning genomic annotation... 2022-03-22 15:57:57
>> assigning chromosome lengths 2022-03-22 15:58:00
>> done... 2022-03-22 15:58:00 annotatePeaksmm10(peak = paste0(fullPeak_dir, "E15.5_promoter_peaks.narrowPeak"), outFile = paste0(annot_dir,"E15.5_promoter_annotation.txt"))>> loading peak file... 2022-03-22 15:58:00
>> preparing features information... 2022-03-22 15:58:00
>> identifying nearest features... 2022-03-22 15:58:00
>> calculating distance from peak to TSS... 2022-03-22 15:58:01
>> assigning genomic annotation... 2022-03-22 15:58:01
>> assigning chromosome lengths 2022-03-22 15:58:04
>> done... 2022-03-22 15:58:04 Dunnart
annotatePeaks <- function(peak, outFile, outFile1, outFile2, GOenrich, kegg, backg){
# Annotate peak file based on dunnart GFF
peakAnno <- ChIPseeker::annotatePeak(peak = peak, tssRegion = c(-3000, 3000), TxDb = smiCra_txdb)
# Write annotation to file
write.table(peakAnno, file = paste0(annot_dir, outFile), sep = "\t", quote = F, row.names = F)
peakAnnoDF <- as.data.frame(peakAnno, row.names = NULL)
# # Convert refseq IDs and geneIDs to devil ensembl IDs
df2 <- read.table("output/annotations/devil_to_mouse_ensembl.txt", header=TRUE, sep="\t") ## conversion table for devil ENSEMBL to mouse ENSEMBL
df3 <- read.table("output/annotations/refseq_to_ensembl.txt", header=TRUE, sep="\t") ## convertsion table for devil refseq to devil ENSEMBL
peakAnnoDF$ensemblgeneID <- df2$Gene.stable.ID[match(unlist(peakAnnoDF$geneId), df2$Gene.name)]
peakAnnoDF$ensemblgeneID <- replace(peakAnnoDF$ensemblgeneID,is.na(peakAnnoDF$ensemblgeneID),"-")
peakAnnoDF$transcriptIdAltered <- gsub("\\..*","", peakAnnoDF$transcriptId)
peakAnnoDF$refseqID <- df3$Ensembl.Gene.ID[match(unlist(peakAnnoDF$transcriptIdAltered), df3$RefSeq.mRNA.Accession)]
peakAnnoDF$refseqID <- replace(peakAnnoDF$refseqID,is.na(peakAnnoDF$refseqID),"-")
peakAnnoDF$combined <- ifelse(peakAnnoDF$refseqID == "-", peakAnnoDF$ensemblgeneID, peakAnnoDF$refseqID)
peakAnnoDF$combined[peakAnnoDF$combined == as.character("-")] <- NA
peakAnnoDF <- peakAnnoDF[!is.na(peakAnnoDF$combined),]
write.table(peakAnnoDF, paste0(annot_dir, outFile2), sep = "\t", quote = F, row.names = F)
# # Convert devil ensembl to mouse ensembl
peakAnnoDF$mouseensembl <- df2$Mouse.gene.stable.ID[match(unlist(peakAnnoDF$combined), df2$Gene.stable.ID)]
# # Write annotation with converted IDs
peakAnnoDF$mouseensembl[peakAnnoDF$mouseensembl == as.character("")] <- NA
peakAnnoDF <- peakAnnoDF[!is.na(peakAnnoDF$mouseensembl),]
write.table(peakAnnoDF, paste0(annot_dir, outFile1), sep = "\t", quote = F, row.names = F)
}
### Enhancer-associated peaks annotation
annotatePeaks(peak = paste0(fullPeak_dir, "dunnart_downSampled_enhancer_peaks.narrowPeak"), outFile = "dunnart_downSampled_enhancer_annotation.txt", backg = unlist(fread("output/annotations/go_background_orthologousENSEMBL_biomart.txt", header = FALSE), use.names = FALSE),
outFile1 = "dunnart_downSampled_enhancer_annotationConvertedIDs.txt", outFile2 = "dunnart_enhancer_annotationConvertedIDs_t.devil.txt")>> loading peak file... 2022-03-22 15:58:04
>> preparing features information... 2022-03-22 15:58:04
>> identifying nearest features... 2022-03-22 15:58:05 Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
- in 'x': scaffold00197_pilon_pilon, scaffold00231_pilon_pilon, scaffold00251_pilon_pilon, scaffold00262_pilon_pilon, scaffold00282_pilon_pilon, scaffold00331_pilon_pilon, scaffold00370_pilon_pilon
- in 'y': scaffold00067_pilon_pilon, scaffold00108_pilon_pilon, scaffold00123_pilon_pilon, scaffold00129_pilon_pilon, scaffold00131_pilon_pilon, scaffold00145_pilon_pilon, scaffold00152_pilon_pilon, scaffold00156_pilon_pilon, scaffold00161_pilon_pilon, scaffold00162_pilon_pilon, scaffold00163_pilon_pilon, scaffold00169_pilon_pilon, scaffold00170_pilon_pilon, scaffold00172_pilon_pilon, scaffold00175_pilon_pilon, scaffold00176_pilon_pilon, scaffold00178_pilon_pilon, scaffold00181_pilon_pilon, scaffold00182_pilon_pilon, scaffold00184_pilon_pilon, scaffold00189_pilon_pilon, scaffold00192_pilon_pilon, scaffold00193_pilon_pilon, scaffold00194_pilon_pilon, scaffold00198_pilon_pilon, scaffold00199_pilon_pilon, scaffold00205_pilon_pilon, scaffold00207_pilon_pilon, scaffold00209_pilon_pilon, scaffold00213_pilon_pilon, scaffold00214_pilon_pilon, scaffold00215_pilon_pilon, scaffold00217_pilon_pilon, scaffold00218_pilon_pilon, scaffold00219_pilon_pilon, scaffold00221_pilon_pilon, scaffold00225_pilon_pilon, scaffold00227_pilon_pilon, scaffold00228_pilon_pilon, scaffold00229_pilon_pilon, scaffold00230_pilon_pilon, scaffold00234_pilon_pilon, scaffold00235_pilon_pilon, scaffold00236_pilon_pilon, scaffold00238_pilon_pilon, scaffold00240_pilon_pilon, scaffold00245_pilon_pilon, scaffold00247_pilon_pilon, scaffold00253_pilon_pilon, scaffold00255_pilon_pilon, scaffold00256_pilon_pilon, scaffold00257_pilon_pilon, scaffold00258_pilon_pilon, scaffold00267_pilon_pilon, scaffold00268_pilon_pilon, scaffold00269_pilon_pilon, scaffold00270_pilon_pilon, scaffold00273_pilon_pilon, scaffold00274_pilon_pilon, scaffold00276_pilon_pilon, scaffold00277_pilon_pilon, scaffold00278_pilon_pilon, scaffold00286_pilon_pilon, scaffold00288_pilon_pilon, scaffold00293_pilon_pilon, scaffold00294_pilon_pilon, scaffold00295_pilon_pilon, scaffold00298_pilon_pilon, scaffold00305_pilon_pilon, scaffold00311_pilon_pilon, scaffold00317_pilon_pilon, scaffold00322_pilon_pilon, scaffold00326_pilon_pilon, scaffold00349_pilon_pilon, scaffold00413_pilon_pilon, scaffold00426_pilon_pilon, scaffold00541_pilon_pilon, scaffold00556_pilon_pilon, scaffold00574_pilon_pilon, scaffold00593_pilon_pilon, scaffold00594_pilon_pilon
Make sure to always combine/compare objects based on the same reference
genome (use suppressWarnings() to suppress this warning).>> calculating distance from peak to TSS... 2022-03-22 15:58:05
>> assigning genomic annotation... 2022-03-22 15:58:05 Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
- in 'x': scaffold00197_pilon_pilon, scaffold00231_pilon_pilon, scaffold00251_pilon_pilon, scaffold00262_pilon_pilon, scaffold00282_pilon_pilon, scaffold00331_pilon_pilon, scaffold00370_pilon_pilon
- in 'y': scaffold00067_pilon_pilon, scaffold00108_pilon_pilon, scaffold00123_pilon_pilon, scaffold00129_pilon_pilon, scaffold00131_pilon_pilon, scaffold00145_pilon_pilon, scaffold00152_pilon_pilon, scaffold00156_pilon_pilon, scaffold00161_pilon_pilon, scaffold00162_pilon_pilon, scaffold00163_pilon_pilon, scaffold00169_pilon_pilon, scaffold00170_pilon_pilon, scaffold00172_pilon_pilon, scaffold00175_pilon_pilon, scaffold00176_pilon_pilon, scaffold00178_pilon_pilon, scaffold00181_pilon_pilon, scaffold00182_pilon_pilon, scaffold00184_pilon_pilon, scaffold00189_pilon_pilon, scaffold00192_pilon_pilon, scaffold00193_pilon_pilon, scaffold00194_pilon_pilon, scaffold00198_pilon_pilon, scaffold00199_pilon_pilon, scaffold00205_pilon_pilon, scaffold00207_pilon_pilon, scaffold00209_pilon_pilon, scaffold00213_pilon_pilon, scaffold00214_pilon_pilon, scaffold00215_pilon_pilon, scaffold00217_pilon_pilon, scaffold00218_pilon_pilon, scaffold00219_pilon_pilon, scaffold00221_pilon_pilon, scaffold00225_pilon_pilon, scaffold00227_pilon_pilon, scaffold00228_pilon_pilon, scaffold00229_pilon_pilon, scaffold00230_pilon_pilon, scaffold00234_pilon_pilon, scaffold00235_pilon_pilon, scaffold00236_pilon_pilon, scaffold00238_pilon_pilon, scaffold00240_pilon_pilon, scaffold00245_pilon_pilon, scaffold00247_pilon_pilon, scaffold00253_pilon_pilon, scaffold00255_pilon_pilon, scaffold00256_pilon_pilon, scaffold00257_pilon_pilon, scaffold00258_pilon_pilon, scaffold00267_pilon_pilon, scaffold00268_pilon_pilon, scaffold00269_pilon_pilon, scaffold00270_pilon_pilon, scaffold00273_pilon_pilon, scaffold00274_pilon_pilon, scaffold00276_pilon_pilon, scaffold00277_pilon_pilon, scaffold00278_pilon_pilon, scaffold00286_pilon_pilon, scaffold00288_pilon_pilon, scaffold00293_pilon_pilon, scaffold00294_pilon_pilon, scaffold00295_pilon_pilon, scaffold00298_pilon_pilon, scaffold00305_pilon_pilon, scaffold00311_pilon_pilon, scaffold00317_pilon_pilon, scaffold00322_pilon_pilon, scaffold00326_pilon_pilon, scaffold00349_pilon_pilon, scaffold00413_pilon_pilon, scaffold00426_pilon_pilon, scaffold00541_pilon_pilon, scaffold00556_pilon_pilon, scaffold00574_pilon_pilon, scaffold00593_pilon_pilon, scaffold00594_pilon_pilon
Make sure to always combine/compare objects based on the same reference
genome (use suppressWarnings() to suppress this warning).
Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
- in 'x': scaffold00197_pilon_pilon, scaffold00231_pilon_pilon, scaffold00251_pilon_pilon, scaffold00262_pilon_pilon, scaffold00282_pilon_pilon, scaffold00331_pilon_pilon, scaffold00370_pilon_pilon
- in 'y': scaffold00067_pilon_pilon, scaffold00108_pilon_pilon, scaffold00123_pilon_pilon, scaffold00129_pilon_pilon, scaffold00131_pilon_pilon, scaffold00145_pilon_pilon, scaffold00152_pilon_pilon, scaffold00156_pilon_pilon, scaffold00161_pilon_pilon, scaffold00162_pilon_pilon, scaffold00163_pilon_pilon, scaffold00169_pilon_pilon, scaffold00170_pilon_pilon, scaffold00172_pilon_pilon, scaffold00175_pilon_pilon, scaffold00176_pilon_pilon, scaffold00178_pilon_pilon, scaffold00181_pilon_pilon, scaffold00182_pilon_pilon, scaffold00184_pilon_pilon, scaffold00189_pilon_pilon, scaffold00192_pilon_pilon, scaffold00193_pilon_pilon, scaffold00194_pilon_pilon, scaffold00198_pilon_pilon, scaffold00199_pilon_pilon, scaffold00205_pilon_pilon, scaffold00207_pilon_pilon, scaffold00209_pilon_pilon, scaffold00213_pilon_pilon, scaffold00214_pilon_pilon, scaffold00215_pilon_pilon, scaffold00217_pilon_pilon, scaffold00218_pilon_pilon, scaffold00219_pilon_pilon, scaffold00221_pilon_pilon, scaffold00225_pilon_pilon, scaffold00227_pilon_pilon, scaffold00228_pilon_pilon, scaffold00229_pilon_pilon, scaffold00230_pilon_pilon, scaffold00234_pilon_pilon, scaffold00235_pilon_pilon, scaffold00236_pilon_pilon, scaffold00238_pilon_pilon, scaffold00240_pilon_pilon, scaffold00245_pilon_pilon, scaffold00247_pilon_pilon, scaffold00253_pilon_pilon, scaffold00255_pilon_pilon, scaffold00256_pilon_pilon, scaffold00257_pilon_pilon, scaffold00258_pilon_pilon, scaffold00267_pilon_pilon, scaffold00268_pilon_pilon, scaffold00269_pilon_pilon, scaffold00270_pilon_pilon, scaffold00273_pilon_pilon, scaffold00274_pilon_pilon, scaffold00276_pilon_pilon, scaffold00277_pilon_pilon, scaffold00278_pilon_pilon, scaffold00286_pilon_pilon, scaffold00288_pilon_pilon, scaffold00293_pilon_pilon, scaffold00294_pilon_pilon, scaffold00295_pilon_pilon, scaffold00298_pilon_pilon, scaffold00305_pilon_pilon, scaffold00311_pilon_pilon, scaffold00317_pilon_pilon, scaffold00322_pilon_pilon, scaffold00326_pilon_pilon, scaffold00349_pilon_pilon, scaffold00413_pilon_pilon, scaffold00426_pilon_pilon, scaffold00541_pilon_pilon, scaffold00556_pilon_pilon, scaffold00574_pilon_pilon, scaffold00593_pilon_pilon, scaffold00594_pilon_pilon
Make sure to always combine/compare objects based on the same reference
genome (use suppressWarnings() to suppress this warning).
Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
- in 'x': scaffold00197_pilon_pilon, scaffold00231_pilon_pilon, scaffold00251_pilon_pilon, scaffold00262_pilon_pilon, scaffold00282_pilon_pilon, scaffold00331_pilon_pilon, scaffold00370_pilon_pilon
- in 'y': scaffold00067_pilon_pilon, scaffold00108_pilon_pilon, scaffold00123_pilon_pilon, scaffold00129_pilon_pilon, scaffold00131_pilon_pilon, scaffold00145_pilon_pilon, scaffold00152_pilon_pilon, scaffold00156_pilon_pilon, scaffold00161_pilon_pilon, scaffold00162_pilon_pilon, scaffold00163_pilon_pilon, scaffold00169_pilon_pilon, scaffold00170_pilon_pilon, scaffold00172_pilon_pilon, scaffold00175_pilon_pilon, scaffold00176_pilon_pilon, scaffold00178_pilon_pilon, scaffold00181_pilon_pilon, scaffold00182_pilon_pilon, scaffold00184_pilon_pilon, scaffold00189_pilon_pilon, scaffold00192_pilon_pilon, scaffold00193_pilon_pilon, scaffold00194_pilon_pilon, scaffold00198_pilon_pilon, scaffold00199_pilon_pilon, scaffold00205_pilon_pilon, scaffold00207_pilon_pilon, scaffold00209_pilon_pilon, scaffold00213_pilon_pilon, scaffold00214_pilon_pilon, scaffold00215_pilon_pilon, scaffold00217_pilon_pilon, scaffold00218_pilon_pilon, scaffold00219_pilon_pilon, scaffold00221_pilon_pilon, scaffold00225_pilon_pilon, scaffold00227_pilon_pilon, scaffold00228_pilon_pilon, scaffold00229_pilon_pilon, scaffold00230_pilon_pilon, scaffold00234_pilon_pilon, scaffold00235_pilon_pilon, scaffold00236_pilon_pilon, scaffold00238_pilon_pilon, scaffold00240_pilon_pilon, scaffold00245_pilon_pilon, scaffold00247_pilon_pilon, scaffold00253_pilon_pilon, scaffold00255_pilon_pilon, scaffold00256_pilon_pilon, scaffold00257_pilon_pilon, scaffold00258_pilon_pilon, scaffold00267_pilon_pilon, scaffold00268_pilon_pilon, scaffold00269_pilon_pilon, scaffold00270_pilon_pilon, scaffold00273_pilon_pilon, scaffold00274_pilon_pilon, scaffold00276_pilon_pilon, scaffold00277_pilon_pilon, scaffold00278_pilon_pilon, scaffold00286_pilon_pilon, scaffold00288_pilon_pilon, scaffold00293_pilon_pilon, scaffold00294_pilon_pilon, scaffold00295_pilon_pilon, scaffold00298_pilon_pilon, scaffold00305_pilon_pilon, scaffold00311_pilon_pilon, scaffold00317_pilon_pilon, scaffold00322_pilon_pilon, scaffold00326_pilon_pilon, scaffold00349_pilon_pilon, scaffold00413_pilon_pilon, scaffold00426_pilon_pilon, scaffold00541_pilon_pilon, scaffold00556_pilon_pilon, scaffold00574_pilon_pilon, scaffold00593_pilon_pilon, scaffold00594_pilon_pilon
Make sure to always combine/compare objects based on the same reference
genome (use suppressWarnings() to suppress this warning).
Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
- in 'x': scaffold00197_pilon_pilon, scaffold00231_pilon_pilon, scaffold00251_pilon_pilon, scaffold00262_pilon_pilon, scaffold00282_pilon_pilon, scaffold00331_pilon_pilon, scaffold00370_pilon_pilon
- in 'y': scaffold00067_pilon_pilon, scaffold00108_pilon_pilon, scaffold00123_pilon_pilon, scaffold00129_pilon_pilon, scaffold00131_pilon_pilon, scaffold00145_pilon_pilon, scaffold00152_pilon_pilon, scaffold00156_pilon_pilon, scaffold00161_pilon_pilon, scaffold00162_pilon_pilon, scaffold00163_pilon_pilon, scaffold00169_pilon_pilon, scaffold00170_pilon_pilon, scaffold00172_pilon_pilon, scaffold00175_pilon_pilon, scaffold00176_pilon_pilon, scaffold00178_pilon_pilon, scaffold00181_pilon_pilon, scaffold00182_pilon_pilon, scaffold00184_pilon_pilon, scaffold00189_pilon_pilon, scaffold00192_pilon_pilon, scaffold00193_pilon_pilon, scaffold00194_pilon_pilon, scaffold00198_pilon_pilon, scaffold00199_pilon_pilon, scaffold00205_pilon_pilon, scaffold00207_pilon_pilon, scaffold00209_pilon_pilon, scaffold00213_pilon_pilon, scaffold00214_pilon_pilon, scaffold00215_pilon_pilon, scaffold00217_pilon_pilon, scaffold00218_pilon_pilon, scaffold00219_pilon_pilon, scaffold00221_pilon_pilon, scaffold00225_pilon_pilon, scaffold00227_pilon_pilon, scaffold00228_pilon_pilon, scaffold00229_pilon_pilon, scaffold00230_pilon_pilon, scaffold00234_pilon_pilon, scaffold00235_pilon_pilon, scaffold00236_pilon_pilon, scaffold00238_pilon_pilon, scaffold00240_pilon_pilon, scaffold00245_pilon_pilon, scaffold00247_pilon_pilon, scaffold00253_pilon_pilon, scaffold00255_pilon_pilon, scaffold00256_pilon_pilon, scaffold00257_pilon_pilon, scaffold00258_pilon_pilon, scaffold00267_pilon_pilon, scaffold00268_pilon_pilon, scaffold00269_pilon_pilon, scaffold00270_pilon_pilon, scaffold00273_pilon_pilon, scaffold00274_pilon_pilon, scaffold00276_pilon_pilon, scaffold00277_pilon_pilon, scaffold00278_pilon_pilon, scaffold00286_pilon_pilon, scaffold00288_pilon_pilon, scaffold00293_pilon_pilon, scaffold00294_pilon_pilon, scaffold00295_pilon_pilon, scaffold00298_pilon_pilon, scaffold00305_pilon_pilon, scaffold00311_pilon_pilon, scaffold00317_pilon_pilon, scaffold00322_pilon_pilon, scaffold00326_pilon_pilon, scaffold00349_pilon_pilon, scaffold00413_pilon_pilon, scaffold00426_pilon_pilon, scaffold00541_pilon_pilon, scaffold00556_pilon_pilon, scaffold00574_pilon_pilon, scaffold00593_pilon_pilon, scaffold00594_pilon_pilon
Make sure to always combine/compare objects based on the same reference
genome (use suppressWarnings() to suppress this warning).>> assigning chromosome lengths 2022-03-22 15:58:17
>> done... 2022-03-22 15:58:17 ### Promoter-associated peaks annotation
annotatePeaks(peak = paste0(fullPeak_dir, "dunnart_downSampled_promoter_peaks.narrowPeak"), outFile = "dunnart_downSampled_promoter_annotation.txt", backg = unlist(fread("output/annotations/go_background_orthologousENSEMBL_biomart.txt", header = FALSE), use.names = FALSE),
outFile1 = "dunnart_downSampled_promoter_annotationConvertedIDs.txt", outFile2 = "dunnart_downSampled_promoter_annotationConvertedIDs_t.devil.txt")>> loading peak file... 2022-03-22 15:58:18
>> preparing features information... 2022-03-22 15:58:18
>> identifying nearest features... 2022-03-22 15:58:18 Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
- in 'x': scaffold00139_pilon_pilon, scaffold00155_pilon_pilon, scaffold00177_pilon_pilon, scaffold00188_pilon_pilon, scaffold00197_pilon_pilon, scaffold00206_pilon_pilon, scaffold00222_pilon_pilon, scaffold00231_pilon_pilon, scaffold00239_pilon_pilon, scaffold00241_pilon_pilon, scaffold00244_pilon_pilon, scaffold00250_pilon_pilon, scaffold00251_pilon_pilon, scaffold00252_pilon_pilon, scaffold00259_pilon_pilon, scaffold00260_pilon_pilon, scaffold00282_pilon_pilon, scaffold00290_pilon_pilon, scaffold00319_pilon_pilon, scaffold00320_pilon_pilon, scaffold00321_pilon_pilon, scaffold00324_pilon_pilon, scaffold00335_pilon_pilon, scaffold00493_pilon_pilon
- in 'y': scaffold00198_pilon_pilon, scaffold00229_pilon_pilon, scaffold00238_pilon_pilon, scaffold00240_pilon_pilon, scaffold00256_pilon_pilon, scaffold00258_pilon_pilon, scaffold00276_pilon_pilon, scaffold00277_pilon_pilon, scaffold00294_pilon_pilon, scaffold00298_pilon_pilon, scaffold00305_pilon_pilon, scaffold00311_pilon_pilon, scaffold00317_pilon_pilon, scaffold00326_pilon_pilon, scaffold00349_pilon_pilon, scaffold00413_pilon_pilon, scaffold00424_pilon_pilon, scaffold00426_pilon_pilon, scaffold00541_pilon_pilon, scaffold00556_pilon_pilon, scaffold00574_pilon_pilon, scaffold00593_pilon_pilon, scaffold00594_pilon_pilon
Make sure to always combine/compare objects based on the same reference
genome (use suppressWarnings() to suppress this warning).>> calculating distance from peak to TSS... 2022-03-22 15:58:18
>> assigning genomic annotation... 2022-03-22 15:58:18 Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
- in 'x': scaffold00139_pilon_pilon, scaffold00155_pilon_pilon, scaffold00177_pilon_pilon, scaffold00188_pilon_pilon, scaffold00197_pilon_pilon, scaffold00206_pilon_pilon, scaffold00222_pilon_pilon, scaffold00231_pilon_pilon, scaffold00239_pilon_pilon, scaffold00241_pilon_pilon, scaffold00244_pilon_pilon, scaffold00250_pilon_pilon, scaffold00251_pilon_pilon, scaffold00252_pilon_pilon, scaffold00259_pilon_pilon, scaffold00260_pilon_pilon, scaffold00282_pilon_pilon, scaffold00290_pilon_pilon, scaffold00319_pilon_pilon, scaffold00320_pilon_pilon, scaffold00321_pilon_pilon, scaffold00324_pilon_pilon, scaffold00335_pilon_pilon, scaffold00493_pilon_pilon
- in 'y': scaffold00198_pilon_pilon, scaffold00229_pilon_pilon, scaffold00238_pilon_pilon, scaffold00240_pilon_pilon, scaffold00256_pilon_pilon, scaffold00258_pilon_pilon, scaffold00276_pilon_pilon, scaffold00277_pilon_pilon, scaffold00294_pilon_pilon, scaffold00298_pilon_pilon, scaffold00305_pilon_pilon, scaffold00311_pilon_pilon, scaffold00317_pilon_pilon, scaffold00326_pilon_pilon, scaffold00349_pilon_pilon, scaffold00413_pilon_pilon, scaffold00424_pilon_pilon, scaffold00426_pilon_pilon, scaffold00541_pilon_pilon, scaffold00556_pilon_pilon, scaffold00574_pilon_pilon, scaffold00593_pilon_pilon, scaffold00594_pilon_pilon
Make sure to always combine/compare objects based on the same reference
genome (use suppressWarnings() to suppress this warning).Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
- in 'x': scaffold00139_pilon_pilon, scaffold00155_pilon_pilon, scaffold00177_pilon_pilon, scaffold00188_pilon_pilon, scaffold00197_pilon_pilon, scaffold00206_pilon_pilon, scaffold00222_pilon_pilon, scaffold00231_pilon_pilon, scaffold00239_pilon_pilon, scaffold00241_pilon_pilon, scaffold00244_pilon_pilon, scaffold00250_pilon_pilon, scaffold00251_pilon_pilon, scaffold00252_pilon_pilon, scaffold00259_pilon_pilon, scaffold00260_pilon_pilon, scaffold00282_pilon_pilon, scaffold00290_pilon_pilon, scaffold00319_pilon_pilon, scaffold00320_pilon_pilon, scaffold00321_pilon_pilon, scaffold00324_pilon_pilon, scaffold00335_pilon_pilon, scaffold00493_pilon_pilon
- in 'y': scaffold00198_pilon_pilon, scaffold00229_pilon_pilon, scaffold00238_pilon_pilon, scaffold00240_pilon_pilon, scaffold00256_pilon_pilon, scaffold00258_pilon_pilon, scaffold00276_pilon_pilon, scaffold00277_pilon_pilon, scaffold00294_pilon_pilon, scaffold00298_pilon_pilon, scaffold00305_pilon_pilon, scaffold00311_pilon_pilon, scaffold00317_pilon_pilon, scaffold00326_pilon_pilon, scaffold00349_pilon_pilon, scaffold00413_pilon_pilon, scaffold00424_pilon_pilon, scaffold00426_pilon_pilon, scaffold00541_pilon_pilon, scaffold00556_pilon_pilon, scaffold00574_pilon_pilon, scaffold00593_pilon_pilon, scaffold00594_pilon_pilon
Make sure to always combine/compare objects based on the same reference
genome (use suppressWarnings() to suppress this warning).
Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
- in 'x': scaffold00139_pilon_pilon, scaffold00155_pilon_pilon, scaffold00177_pilon_pilon, scaffold00188_pilon_pilon, scaffold00197_pilon_pilon, scaffold00206_pilon_pilon, scaffold00222_pilon_pilon, scaffold00231_pilon_pilon, scaffold00239_pilon_pilon, scaffold00241_pilon_pilon, scaffold00244_pilon_pilon, scaffold00250_pilon_pilon, scaffold00251_pilon_pilon, scaffold00252_pilon_pilon, scaffold00259_pilon_pilon, scaffold00260_pilon_pilon, scaffold00282_pilon_pilon, scaffold00290_pilon_pilon, scaffold00319_pilon_pilon, scaffold00320_pilon_pilon, scaffold00321_pilon_pilon, scaffold00324_pilon_pilon, scaffold00335_pilon_pilon, scaffold00493_pilon_pilon
- in 'y': scaffold00198_pilon_pilon, scaffold00229_pilon_pilon, scaffold00238_pilon_pilon, scaffold00240_pilon_pilon, scaffold00256_pilon_pilon, scaffold00258_pilon_pilon, scaffold00276_pilon_pilon, scaffold00277_pilon_pilon, scaffold00294_pilon_pilon, scaffold00298_pilon_pilon, scaffold00305_pilon_pilon, scaffold00311_pilon_pilon, scaffold00317_pilon_pilon, scaffold00326_pilon_pilon, scaffold00349_pilon_pilon, scaffold00413_pilon_pilon, scaffold00424_pilon_pilon, scaffold00426_pilon_pilon, scaffold00541_pilon_pilon, scaffold00556_pilon_pilon, scaffold00574_pilon_pilon, scaffold00593_pilon_pilon, scaffold00594_pilon_pilon
Make sure to always combine/compare objects based on the same reference
genome (use suppressWarnings() to suppress this warning).
Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
- in 'x': scaffold00139_pilon_pilon, scaffold00155_pilon_pilon, scaffold00177_pilon_pilon, scaffold00188_pilon_pilon, scaffold00197_pilon_pilon, scaffold00206_pilon_pilon, scaffold00222_pilon_pilon, scaffold00231_pilon_pilon, scaffold00239_pilon_pilon, scaffold00241_pilon_pilon, scaffold00244_pilon_pilon, scaffold00250_pilon_pilon, scaffold00251_pilon_pilon, scaffold00252_pilon_pilon, scaffold00259_pilon_pilon, scaffold00260_pilon_pilon, scaffold00282_pilon_pilon, scaffold00290_pilon_pilon, scaffold00319_pilon_pilon, scaffold00320_pilon_pilon, scaffold00321_pilon_pilon, scaffold00324_pilon_pilon, scaffold00335_pilon_pilon, scaffold00493_pilon_pilon
- in 'y': scaffold00198_pilon_pilon, scaffold00229_pilon_pilon, scaffold00238_pilon_pilon, scaffold00240_pilon_pilon, scaffold00256_pilon_pilon, scaffold00258_pilon_pilon, scaffold00276_pilon_pilon, scaffold00277_pilon_pilon, scaffold00294_pilon_pilon, scaffold00298_pilon_pilon, scaffold00305_pilon_pilon, scaffold00311_pilon_pilon, scaffold00317_pilon_pilon, scaffold00326_pilon_pilon, scaffold00349_pilon_pilon, scaffold00413_pilon_pilon, scaffold00424_pilon_pilon, scaffold00426_pilon_pilon, scaffold00541_pilon_pilon, scaffold00556_pilon_pilon, scaffold00574_pilon_pilon, scaffold00593_pilon_pilon, scaffold00594_pilon_pilon
Make sure to always combine/compare objects based on the same reference
genome (use suppressWarnings() to suppress this warning).>> assigning chromosome lengths 2022-03-22 15:58:20
>> done... 2022-03-22 15:58:20 Distance to nearest TSS
Now see where the peaks are located in relation to the TSS. Promoters should be reasonably close to the TSS and enhancers more distal to the TSS. Plot distance to TSS for all unfiltered peaks
files =list.files(annot_dir, pattern= "*.5_enhancer_annotation.txt|*.5_promoter_annotation.txt|*downSampled_promoter_annotationConvertedIDs.txt|*downSampled_enhancer_annotationConvertedIDs.txt", full.names=T) # create list of files in directory
filenames <- sub('\\_annotation.txt$', '', basename(files))
filenames <- sub('\\_annotationConvertedIDs.txt$', '', basename(filenames))
files = as.list(files)
data = lapply(files, function(x) fread(x, header=TRUE, sep="\t", quote = "", na.strings=c("", "NA"))) # read in all files
names(data) <- filenames
df1 = Map(mutate, data[c(1,3,5,7,9,11,13)], cre = "enhancer")
df2 = Map(mutate, data[c(2,4,6,8,10,12,14)], cre = "promoter")
data = append(df1, df2)
df1 = Map(mutate, data[c(1,8)], group = "dunnart")
df2 = Map(mutate, data[c(2,9)], group = "E10.5")
df3 = Map(mutate, data[c(3,10)], group = "E11.5")
df4 = Map(mutate, data[c(4,11)], group = "E12.5")
df5 = Map(mutate, data[c(5,12)], group = "E13.5")
df6 = Map(mutate, data[c(6,13)], group = "E14.5")
df7 = Map(mutate, data[c(7,14)], group = "E15.5")
data <- append(df1, df2)
data <- append(data, df3)
data = append(data, df4)
data = append(data, df5)
data = append(data, df6)
data = append(data, df7)
data_add_distance = suppressWarnings(lapply(data, function(x) x[,log10_abs_dist:=log10(abs(distanceToTSS)+1)][,log10_abs_dist:=ifelse(distanceToTSS<0,-log10_abs_dist,log10_abs_dist)]))
data_subset = lapply(data_add_distance, function(x) x %>% select(cre, group, log10_abs_dist) )
data_bind = rbindlist(data_subset,)
enhancers <- data_bind[data_bind$cre == "enhancer"]
promoters <- data_bind[data_bind$cre == "promoter"]
p <- ggplot(enhancers, aes(x=log10_abs_dist, color = group)) +
geom_density() + scale_color_manual(values = c("#fbb03b", rep("#ffd166",7))) + scale_fill_manual(values = c("#fbb03b", rep("#ffd166",7))) + theme_bw() + scale_y_continuous(limits=c(0,0.33), labels = scales::percent) +
scale_x_continuous(limits=c(-8, 8))
p + ggtitle("enhancer distance to nearest TSS")
pdf(file = paste0(plot_dir, "mouse_dunnart_enhancer_distTSS.pdf"), width=10, height = 7)
print(p + font)
dev.off()svg
2 p <- ggplot(promoters, aes(x=log10_abs_dist, color = group)) +
geom_density() + scale_color_manual(values = c("#1a6259", rep("#2a9d8f",7))) + scale_fill_manual(values = c("#1a6259", rep("#2a9d8f",7))) + theme_bw() + scale_y_continuous(limits=c(0,0.8), labels = scales::percent) +
scale_x_continuous(limits=c(-8, 8))
p + ggtitle("promoter distance to nearest TSS")
pdf(file = paste0(plot_dir, "mouse_dunnart_promoter_distTSS.pdf"), width=10, height = 7)
print(p + font)
dev.off()svg
2
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux
Matrix products: default
BLAS/LAPACK: /usr/local/easybuild-2019/easybuild/software/compiler/gcc/10.2.0/openblas/0.3.12/lib/libopenblas_haswellp-r0.3.12.so
locale:
[1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
[5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
[7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] dplyr_1.0.8 data.table_1.14.0 ggplot2_3.3.3
[4] GenomicFeatures_1.46.5 AnnotationDbi_1.56.2 Biobase_2.54.0
[7] GenomicRanges_1.46.1 GenomeInfoDb_1.30.1 IRanges_2.28.0
[10] S4Vectors_0.32.3 BiocGenerics_0.40.0 ChIPseeker_1.30.3
[13] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] shadowtext_0.1.1
[2] fastmatch_1.1-0
[3] BiocFileCache_2.2.1
[4] plyr_1.8.6
[5] igraph_1.2.6
[6] lazyeval_0.2.2
[7] splines_4.1.0
[8] BiocParallel_1.28.3
[9] digest_0.6.27
[10] yulab.utils_0.0.4
[11] htmltools_0.5.1.1
[12] GOSemSim_2.20.0
[13] viridis_0.6.2
[14] GO.db_3.14.0
[15] fansi_0.5.0
[16] magrittr_2.0.1
[17] memoise_2.0.0
[18] Biostrings_2.62.0
[19] graphlayouts_0.7.1
[20] matrixStats_0.61.0
[21] enrichplot_1.14.2
[22] prettyunits_1.1.1
[23] colorspace_2.0-1
[24] blob_1.2.1
[25] rappdirs_0.3.3
[26] ggrepel_0.9.1
[27] xfun_0.23
[28] callr_3.7.0
[29] crayon_1.4.1
[30] RCurl_1.98-1.3
[31] jsonlite_1.7.2
[32] scatterpie_0.1.7
[33] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[34] ape_5.5
[35] glue_1.4.2
[36] polyclip_1.10-0
[37] gtable_0.3.0
[38] zlibbioc_1.40.0
[39] XVector_0.34.0
[40] DelayedArray_0.20.0
[41] scales_1.1.1
[42] DOSE_3.20.1
[43] DBI_1.1.1
[44] Rcpp_1.0.8.3
[45] plotrix_3.8-1
[46] viridisLite_0.4.0
[47] progress_1.2.2
[48] tidytree_0.3.9
[49] gridGraphics_0.5-1
[50] bit_4.0.4
[51] httr_1.4.2
[52] fgsea_1.20.0
[53] gplots_3.1.1
[54] RColorBrewer_1.1-2
[55] ellipsis_0.3.2
[56] pkgconfig_2.0.3
[57] XML_3.99-0.6
[58] farver_2.1.0
[59] sass_0.4.0
[60] dbplyr_2.1.1
[61] utf8_1.2.1
[62] labeling_0.4.2
[63] ggplotify_0.1.0
[64] tidyselect_1.1.1
[65] rlang_1.0.2
[66] reshape2_1.4.4
[67] later_1.2.0
[68] munsell_0.5.0
[69] tools_4.1.0
[70] cachem_1.0.5
[71] cli_2.5.0
[72] generics_0.1.0
[73] RSQLite_2.2.7
[74] evaluate_0.14
[75] stringr_1.4.0
[76] fastmap_1.1.0
[77] yaml_2.2.1
[78] ggtree_3.2.1
[79] processx_3.5.2
[80] knitr_1.33
[81] bit64_4.0.5
[82] fs_1.5.0
[83] tidygraph_1.2.0
[84] caTools_1.18.2
[85] purrr_0.3.4
[86] KEGGREST_1.34.0
[87] ggraph_2.0.5
[88] nlme_3.1-152
[89] whisker_0.4
[90] aplot_0.1.2
[91] DO.db_2.9
[92] xml2_1.3.2
[93] biomaRt_2.50.3
[94] compiler_4.1.0
[95] rstudioapi_0.13
[96] filelock_1.0.2
[97] curl_4.3.1
[98] png_0.1-7
[99] treeio_1.18.1
[100] tibble_3.1.2
[101] tweenr_1.0.2
[102] bslib_0.2.5.1
[103] stringi_1.6.2
[104] highr_0.9
[105] ps_1.6.0
[106] lattice_0.20-44
[107] Matrix_1.3-4
[108] vctrs_0.3.8
[109] pillar_1.6.1
[110] lifecycle_1.0.1
[111] jquerylib_0.1.4
[112] bitops_1.0-7
[113] patchwork_1.1.1
[114] httpuv_1.6.1
[115] rtracklayer_1.54.0
[116] qvalue_2.26.0
[117] R6_2.5.0
[118] BiocIO_1.4.0
[119] promises_1.2.0.1
[120] KernSmooth_2.23-20
[121] gridExtra_2.3
[122] gtools_3.8.2
[123] boot_1.3-28
[124] MASS_7.3-54
[125] assertthat_0.2.1
[126] SummarizedExperiment_1.24.0
[127] rprojroot_2.0.2
[128] rjson_0.2.20
[129] withr_2.4.2
[130] GenomicAlignments_1.30.0
[131] Rsamtools_2.10.0
[132] GenomeInfoDbData_1.2.7
[133] parallel_4.1.0
[134] hms_1.1.0
[135] grid_4.1.0
[136] ggfun_0.0.5
[137] tidyr_1.1.3
[138] rmarkdown_2.8
[139] MatrixGenerics_1.6.0
[140] git2r_0.28.0
[141] getPass_0.2-2
[142] ggforce_0.3.3
[143] restfulr_0.0.13