TEs and my data

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Last updated: 2024-09-23

Checks: 7 0

Knit directory: ATAC_learning/

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Ignored files:
    Ignored:    .RData
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    data/ACresp_SNP_table.csv
    Ignored:    data/ARR_SNP_table.csv
    Ignored:    data/All_merged_peaks.tsv
    Ignored:    data/CAD_gwas_dataframe.RDS
    Ignored:    data/CTX_SNP_table.csv
    Ignored:    data/Collapsed_expressed_NG_peak_table.csv
    Ignored:    data/DEG_toplist_sep_n45.RDS
    Ignored:    data/FRiP_first_run.txt
    Ignored:    data/Final_four_data/
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    Ignored:    data/GO_KEGG_analysis/
    Ignored:    data/HF_SNP_table.csv
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    Ignored:    data/Ind1_TSS_peaks.RDS
    Ignored:    data/Ind1_firstfragment_files.txt
    Ignored:    data/Ind1_fragment_files.txt
    Ignored:    data/Ind1_peaks_list.RDS
    Ignored:    data/Ind1_summary.txt
    Ignored:    data/Ind2_TSS_peaks.RDS
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    Ignored:    data/Ind2_peaks_list.RDS
    Ignored:    data/Ind2_summary.txt
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    Ignored:    data/Ind4_peaks_list.RDS
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    Ignored:    data/Ind5_peaks_list.RDS
    Ignored:    data/Ind5_summary.txt
    Ignored:    data/Ind6_TSS_peaks.RDS
    Ignored:    data/Ind6_fragment_files.txt
    Ignored:    data/Ind6_peaks_list.RDS
    Ignored:    data/Ind6_summary.txt
    Ignored:    data/Knowles_4.RDS
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    Ignored:    data/SNP_supp_schneider.RDS
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    Ignored:    data/all_TSSE_scores.RDS
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    Ignored:    trimmed_Ind1_75DA24h_S7.nodup.splited.bam/

Untracked files:
    Untracked:  DOX_DAR_assess.Rmd
    Untracked:  EAR_2_plot.pdf
    Untracked:  ESR_1_plot.pdf
    Untracked:  Firstcorr plotATAC.pdf
    Untracked:  IND1_2_3_6_corrplot.pdf
    Untracked:  LR_3_plot.pdf
    Untracked:  NR_1_plot.pdf
    Untracked:  analysis/LFC_corr.Rmd
    Untracked:  analysis/ReHeat_analysis.Rmd
    Untracked:  analysis/TE_analysis_old.Rmd
    Untracked:  analysis/my_hc_filt_counts.csv
    Untracked:  analysis/nucleosome_explore.Rmd
    Untracked:  code/IGV_snapshot_code.R
    Untracked:  code/LongDARlist.R
    Untracked:  code/TSSE.R
    Untracked:  code/just_for_Fun.R
    Untracked:  code/toplist_assembly.R
    Untracked:  lcpm_filtered_corplot.pdf
    Untracked:  log2cpmfragcount.pdf
    Untracked:  output/cormotif_probability_45_list.csv
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    Untracked:  splited/
    Untracked:  trimmed_Ind1_75DA24h_S7.nodup.fragment.size.distribution.pdf
    Untracked:  trimmed_Ind1_75DA3h_S1.nodup.fragment.size.distribution.pdf

Unstaged changes:
    Modified:   analysis/CorMotif_data_n45.Rmd
    Modified:   analysis/Enhancer_files_ff.Rmd
    Modified:   analysis/Enrichment_motif.Rmd
    Modified:   analysis/GO_KEGG_analysis.Rmd
    Modified:   analysis/Peak_calling.Rmd
    Modified:   analysis/Raodah.Rmd
    Modified:   analysis/Raodah_mycount.Rmd
    Modified:   analysis/Smaller_set_DAR.Rmd
    Modified:   analysis/TE_analysis.Rmd
    Modified:   analysis/index.Rmd

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/TE_analysis_ff.Rmd) and HTML (docs/TE_analysis_ff.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	365e557	reneeisnowhere	2024-09-23	updated group of eight based on 4 individuals
html	23374bd	reneeisnowhere	2024-09-13	Build site.
Rmd	617aa1e	reneeisnowhere	2024-09-13	updated peaklist filtering fixed
Rmd	ff2f378	reneeisnowhere	2024-09-12	fixing additional calculation problems
Rmd	b7f8719	reneeisnowhere	2024-09-12	fixing additional calculation problems
Rmd	1ae2a63	reneeisnowhere	2024-09-10	updates
html	6d65dd6	reneeisnowhere	2024-09-09	Build site.
Rmd	cc49b3b	reneeisnowhere	2024-09-09	updated with new peak file
Rmd	6e09040	reneeisnowhere	2024-09-08	updated with new peaks

---

library(tidyverse)
library(kableExtra)
library(broom)
library(RColorBrewer)
library(ChIPseeker)
library("TxDb.Hsapiens.UCSC.hg38.knownGene")
library("org.Hs.eg.db")
library(rtracklayer)
library(edgeR)
library(ggfortify)
library(limma)
library(readr)
library(BiocGenerics)
library(gridExtra)
library(VennDiagram)
library(scales)
library(BiocParallel)
library(ggpubr)
library(devtools)
library(biomaRt)
library(eulerr)
library(smplot2)
library(genomation)
library(ggsignif)
library(plyranges)
library(ggrepel)

loading data

This is where I pull in the repeatmasker file taken from UCSC genomebrowser, the peaks assigned with the closest expressed genes as ‘neargenes’ by TSS, the same peaks list, only condensing to unique peaks (some are assigned to two neargene), I call the collapsed_peaks and the peaks assigned to each MRC (EAR, etc…).
With the TSS data and the collapsed data, I made granges objects. I also separate out the LINEs, SINEs, LTRs, DNAs, and Retroposons from the repeatmasker to make granges objects from those sets.

repeatmasker <- read.delim("data/other_papers/repeatmasker.tsv")

### With new TSS_ngdata frame
TSS_NG_data <- read_delim("data/Final_four_data/TSS_assigned_NG.txt", 
    delim = "\t", escape_double = FALSE, 
    trim_ws = TRUE)
Collapsed_peaks <- read_delim("data/Final_four_data/collapsed_new_peaks.txt",
                              delim = "\t", 
                              escape_double = FALSE, 
                              trim_ws = TRUE)

reClass_list <- repeatmasker %>% 
  distinct(repClass)

Line_repeats <- repeatmasker %>% 
  dplyr::filter(repClass == "LINE") %>% 
 makeGRangesFromDataFrame(., keep.extra.columns = TRUE, seqnames.field = "genoName", start.field = "genoStart", end.field = "genoEnd",starts.in.df.are.0based=TRUE)

Sine_repeats <- repeatmasker %>% 
  dplyr::filter(repClass == "SINE") %>% 
 makeGRangesFromDataFrame(., keep.extra.columns = TRUE, seqnames.field = "genoName", start.field = "genoStart", end.field = "genoEnd",starts.in.df.are.0based=TRUE)

LTR_repeats <- repeatmasker %>% 
  dplyr::filter(repClass == "LTR") %>% 
 makeGRangesFromDataFrame(., keep.extra.columns = TRUE, seqnames.field = "genoName", start.field = "genoStart", end.field = "genoEnd",starts.in.df.are.0based=TRUE)

DNA_repeats <- repeatmasker %>% 
  dplyr::filter(repClass == "DNA") %>% 
 makeGRangesFromDataFrame(., keep.extra.columns = TRUE, seqnames.field = "genoName", start.field = "genoStart", end.field = "genoEnd",starts.in.df.are.0based=TRUE)

retroposon_repeats <- repeatmasker %>% 
  dplyr::filter(repClass == "Retroposon") %>% 
 makeGRangesFromDataFrame(., keep.extra.columns = TRUE, seqnames.field = "genoName", start.field = "genoStart", end.field = "genoEnd",starts.in.df.are.0based=TRUE)


all_TEs_gr <- repeatmasker %>% 
 makeGRangesFromDataFrame(., keep.extra.columns = TRUE, seqnames.field = "genoName", start.field = "genoStart", end.field = "genoEnd",starts.in.df.are.0based=TRUE)


peakAnnoList_ff_motif <- readRDS("data/Final_four_data/peakAnnoList_ff_motif.RDS")

background_peaks <- as.data.frame(peakAnnoList_ff_motif$background) 
EAR_df <- as.data.frame(peakAnnoList_ff_motif$EAR)
# EAR_df_gr <-  EAR_df %>%  GRanges()
ESR_df <- as.data.frame(peakAnnoList_ff_motif$ESR)
# ESR_df_gr <-ESR_df %>%  GRanges()

LR_df <- as.data.frame(peakAnnoList_ff_motif$LR)
# LR_df_gr <-LR_df %>%  GRanges()

NR_df <- as.data.frame(peakAnnoList_ff_motif$NR)
# NR_df_gr <-NR_df %>%  GRanges()

TSS_data_gr <- TSS_NG_data %>% 
  # dplyr::filter(chr != "chrX") %>%
  dplyr::filter(chr != "chrY") %>%
  dplyr::filter(Peakid %in% background_peaks$Peakid) %>% 
  GRanges()

Col_TSS_data_gr <- Collapsed_peaks %>% 
  # dplyr::filter(chr != "chrX") %>%
  dplyr::filter(chr != "chrY") %>%
  dplyr::filter(Peakid %in% background_peaks$Peakid) %>% 
  GRanges()

this code contains the fill functions for each of the plots that needed similar colors.

# scale fill repeat, 2nd set ----------------------------------------------
rep_other_names<- repeatmasker %>% 
  distinct(repClass) %>% 
  rbind("Other")

scale_fill_repeat <-  function(...){
  ggplot2:::manual_scale(
    'fill', 
    values = setNames(c( "#8DD3C7",
                         "#FFFFB3",
                         "#BEBADA" ,
                         "#FB8072",
                         "#80B1D3",
                         "#FDB462",
                         "#B3DE69",
                         "#FCCDE5",
                         "#D9D9D9",
                         "#BC80BD",
                         "#CCEBC5",
                         "pink4",
                         "cornflowerblue",
                         "chocolate",
                         "brown",
                         "green",
                         "yellow4",
                         "purple",
                         "darkorchid4",
                         "coral4",
                         "darkolivegreen4",
                         "darkorange",
                         "darkgrey"), unique(rep_other_names$repClass)), 
    ...
  )
}


# scale fill LTRs ---------------------------------------------------------

LTR_df <- LTR_repeats %>% 
  as.data.frame() %>% 
  mutate(repFamily=factor(repFamily))


scale_fill_LTRs <-  function(...){
  ggplot2:::manual_scale(
    'fill', 
    values = setNames(c( "#8DD3C7",
                         "#FFFFB3",
                         "#BEBADA" ,
                         "#FB8072",
                         "#80B1D3",
                         "#FDB462",
                         "#B3DE69",
                         "#FCCDE5",
                         "#D9D9D9",
                         "#BC80BD",
                         "#CCEBC5",
                         "pink4",
                         "cornflowerblue",
                         "chocolate",
                         "brown",
                         "green",
                         "yellow4",
                         "purple",
                         "darkorchid4",
                         "coral4",
                         "darkolivegreen4",
                         "darkorange"), unique(LTR_df$repFamily)), 
    ...
  )
}



scale_fill_DNA_family <-  function(...){
  ggplot2:::manual_scale(
    'fill', 
    values = setNames(c( "#8DD3C7", "#FFFFB3", "#BEBADA" ,"#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5", "purple4"), unique(DNA_family$repFamily)), 
    ...
  )
}


# # scale fill repeat first -------------------------------------------------
# 
# scale_fill_repeat <-  function(...){
#   ggplot2:::manual_scale(
#     'fill', 
#     values = setNames(c( "#8DD3C7",
#                          "#FFFFB3",
#                          "#BEBADA" ,
#                          "#FB8072",
#                          "#80B1D3",
#                          "#FDB462",
#                          "#B3DE69",
#                          "#FCCDE5",
#                          "#D9D9D9",
#                          "#BC80BD",
#                          "#CCEBC5",
#                          "pink4",
#                          "cornflowerblue",
#                          "chocolate",
#                          "brown",
#                          "green",
#                          "yellow4",
#                          "purple",
#                          "darkorchid4",
#                          "coral4",
#                          "darkolivegreen4",
#                          "darkorange"), unique(repeatmasker$repClass)), 
#     ...
#   )
# }

# scale lines -------------------------------------------------------------
Line_df <- Line_repeats %>% 
  as.data.frame() %>% 
  mutate(repFamily=factor(repFamily))


scale_fill_lines <-  function(...){
  ggplot2:::manual_scale(
    'fill', 
    values = setNames(c( "#8DD3C7",
                         "#FFFFB3",
                         "#BEBADA" ,
                         "#FB8072",
                         "#80B1D3",
                         "#FDB462",
                         "#B3DE69",
                         "#FCCDE5",
                         "#D9D9D9",
                         "#BC80BD",
                         "#CCEBC5",
                         "pink4",
                         "cornflowerblue",
                         "chocolate",
                         "brown",
                         "green",
                         "yellow4",
                         "purple",
                         "darkorchid4",
                         "coral4",
                         "darkolivegreen4",
                         "darkorange"), unique(Line_df$repFamily)), 
    ...
  )
}


# scale fill L2 family ----------------------------------------------------
L2_line_df<- Line_df %>% 
  dplyr::filter(repFamily=="L2")


scale_fill_L2 <-  function(...){
  ggplot2:::manual_scale(
    'fill', 
    values = setNames(c( "#8DD3C7",
                         "#FFFFB3",
                         "#BEBADA" ,
                         "#FB8072",
                         "#80B1D3",
                         "#FDB462",
                         "#B3DE69",
                         "#FCCDE5",
                         "#D9D9D9",
                         "#BC80BD",
                         "#CCEBC5",
                         "pink4",
                         "cornflowerblue",
                         "chocolate",
                         "brown",
                         "green",
                         "yellow4",
                         "purple",
                         "darkorchid4",
                         "coral4",
                         "darkolivegreen4",
                         "darkorange"), unique(L2_line_df$repName)), 
    ...
  )
}

# scale fill sines --------------------------------------------------------
Sine_df <- Sine_repeats %>% 
  as.data.frame() %>% 
  mutate(repFamily=factor(repFamily))


scale_fill_sines <-  function(...){
  ggplot2:::manual_scale(
    'fill', 
    values = setNames(c( "#8DD3C7",
                         "#FFFFB3",
                         "#BEBADA" ,
                         "#FB8072",
                         "#80B1D3",
                         "#FDB462",
                         "#B3DE69",
                         "#FCCDE5",
                         "#D9D9D9",
                         "#BC80BD",
                         "#CCEBC5",
                         "pink4",
                         "cornflowerblue",
                         "chocolate",
                         "brown",
                         "green",
                         "yellow4",
                         "purple",
                         "darkorchid4",
                         "coral4",
                         "darkolivegreen4",
                         "darkorange"), unique(Sine_df$repFamily)), 
    ...
  )
}


# scale fill DNAs ---------------------------------------------------------
DNA_df <- DNA_repeats %>% 
  as.data.frame() %>% 
  mutate(repFamily=factor(repFamily))


scale_fill_DNAs <-  function(...){
  ggplot2:::manual_scale(
    'fill', 
    values = setNames(c( "#8DD3C7",
                         "#FFFFB3",
                         "#BEBADA" ,
                         "#FB8072",
                         "#80B1D3",
                         "#FDB462",
                         "#B3DE69",
                         "#FCCDE5",
                         "#D9D9D9",
                         "#BC80BD",
                         "#CCEBC5",
                         "pink4",
                         "cornflowerblue",
                         "chocolate",
                         "brown",
                         "green",
                         "yellow4",
                         "purple",
                         "darkorchid4",
                         "coral4",
                         "darkolivegreen4",
                         "darkorange",
                         "blue",
                         "grey",
                         "lightgrey"), unique(DNA_df$repFamily)), 
    ...
  )
}


# scale fill retroposons --------------------------------------------------

retroposon_df <- retroposon_repeats %>% 
  as.data.frame() %>% 
  mutate(repName=factor(repName))

scale_fill_retroposons <-  function(...){
  ggplot2:::manual_scale(
    'fill', 
    values = setNames(c( "#8DD3C7",
                         "#FFFFB3",
                         "#BEBADA" ,
                         "#FB8072",
                         "#80B1D3",
                         "#FDB462",
                         "#B3DE69",
                         "#FCCDE5",
                         "#D9D9D9",
                         "#BC80BD",
                         "#CCEBC5",
                         "pink4",
                         "cornflowerblue",
                         "chocolate",
                         "brown",
                         "green",
                         "yellow4",
                         "purple",
                         "darkorchid4",
                         "coral4",
                         "darkolivegreen4",
                         "darkorange"), unique(retroposon_df$repName)), 
    ...
  )
}

TE distrubution:

The code below uses the TE data sets to create dataframes that contain all peaks that overlap a TE. Additionally, I wanted to know size distribution of the overlaps(width) between TEs and my peaks, the size of the TEs and their size distribution, and the size distribution of my peaks. This was to determine a cutoff across TEs to minimize size bias. We used this data to apply a inclusion stringency cutoff for TEs of >50% of the full TE needed to be covered by a peak to call the peak “TE-containing”. In the plots, the median of each density distribution is shown by the dotted line.

all_TEs_gr %>% 
  as.data.frame() %>% 
  mutate(repClass=factor(repClass)) %>% 
  dplyr::filter(repClass=="LINE") %>% 
  ggplot(., aes(x=width))+
    geom_density(aes(fill=repClass, alpha = 0.5))+
  geom_vline(aes(xintercept = median(width)), linetype = 2)+
  theme_classic()+
  ggtitle("Distribution of lengths of all LINEs across human genome",subtitle = " limited x axis")+
  coord_cartesian(xlim= c(0,2500))

Past versions of "data about TEs-1.png"

Version	Author	Date
6d65dd6	reneeisnowhere	2024-09-09

all_TEs_gr %>% 
  as.data.frame() %>% 
  mutate(repClass=factor(repClass)) %>% 
  dplyr::filter(repClass=="SINE") %>% 
  ggplot(., aes(x=width))+
    geom_density(aes(fill=repClass, alpha = 0.5))+
   geom_vline(aes(xintercept = median(width)), linetype = 2)+
  theme_classic()+
  ggtitle("Distribution of lengths of all SINEs across human genome")

Past versions of "data about TEs-2.png"

Version	Author	Date
6d65dd6	reneeisnowhere	2024-09-09

all_TEs_gr %>% 
  as.data.frame() %>% 
  mutate(repClass=factor(repClass)) %>% 
  dplyr::filter(repClass=="LTR") %>% 
  ggplot(., aes(x=width))+
    geom_density(aes(fill=repClass, alpha = 0.5))+
   geom_vline(aes(xintercept = median(width)), linetype = 2)+
  theme_classic()+
  ggtitle("Distribution of lengths of all LTRs across human genome",subtitle = " limited x axis")+
  coord_cartesian(xlim= c(0,2500))

Past versions of "data about TEs-3.png"

Version	Author	Date
6d65dd6	reneeisnowhere	2024-09-09

all_TEs_gr %>% 
  as.data.frame() %>% 
  mutate(repClass=factor(repClass)) %>% 
  dplyr::filter(repClass=="DNA") %>% 
  ggplot(., aes(x=width))+
    geom_density(aes(fill=repClass, alpha = 0.5))+
   geom_vline(aes(xintercept = median(width)), linetype = 2)+
  theme_classic()+
  ggtitle("Distribution of lengths of all DNA-TEs across human genome",subtitle = " limited x axis")+
  coord_cartesian(xlim= c(0,2500))

Past versions of "data about TEs-4.png"

Version	Author	Date
6d65dd6	reneeisnowhere	2024-09-09

all_TEs_gr %>% 
  as.data.frame() %>% 
  mutate(repClass=factor(repClass)) %>% 
  dplyr::filter(repClass=="Retroposon") %>% 
  ggplot(., aes(x=width))+
    geom_density(aes(fill=repClass,alpha = 0.5))+
   geom_vline(aes(xintercept = median(width)), linetype = 2)+
  theme_classic()+
  ggtitle("Distribution of lengths of all Retroposons across human genome",subtitle = " limited x axis")+
   coord_cartesian(xlim= c(0,2500))

Past versions of "data about TEs-5.png"

Version	Author	Date
6d65dd6	reneeisnowhere	2024-09-09

all_TEs_gr %>% 
  as.data.frame() %>% 
  dplyr::select(repClass, width) %>% 
  # dplyr::filter(repClass=="LTR") %>%
  group_by(repClass) %>% 
  summarise(med.width=median(width),mean.width=mean(width)) %>% 
  kable(., caption="Table 1: Summary of mean and median length of each TE Class") %>% 
  kable_paper("striped", full_width = TRUE) %>%
  kable_styling(full_width = FALSE, font_size = 14)

Table 1: Summary of mean and median length of each TE Class

repClass	med.width	mean.width
DNA	155.0	210.61264
DNA?	125.0	137.59542
LINE	219.0	421.99752
LTR	329.0	371.36349
LTR?	170.0	208.63493
Low_complexity	44.0	61.55669
RC	171.0	210.89231
RC?	144.0	150.47242
RNA	165.0	163.94730
Retroposon	646.0	776.24171
SINE	258.0	221.43075
SINE?	83.5	74.34211
Satellite	496.0	8741.48243
Simple_repeat	36.0	55.83912
Unknown	119.0	136.70621
rRNA	86.0	140.04608
scRNA	99.0	96.07143
snRNA	77.5	79.82394
srpRNA	136.0	169.81834
tRNA	69.0	60.12893

Col_TSS_data_gr %>% 
  as.data.frame %>% 
   ggplot(., aes(x=width))+
    geom_density(color="darkblue",fill="lightblue",aes(alpha = 0.5))+
  geom_vline(data=Col_TSS_data_gr$width., aes(xintercept = median(width)), linetype = 2)+
  theme_classic()+
  ggtitle("Distribution of peak length (bps) across all experimentally derived peaks",subtitle = " limited x axis")+
  coord_cartesian(xlim= c(0,2500))

Past versions of "data about TEs-6.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

fullDF_overlap <- join_overlap_intersect(TSS_data_gr,all_TEs_gr)

fullDF_overlap %>% 
  as.data.frame() %>% 
  group_by(repClass) %>%  
  tally %>% 
  kable(., caption="Table 2: Count of peaks by TE class; overlap 1 bp or greater") %>% 
  kable_paper("striped", full_width = TRUE) %>%
  kable_styling(full_width = FALSE, font_size = 14)

subsetByOverlaps(TSS_data_gr,all_TEs_gr) %>% as.data.frame %>% 
   ggplot(., aes(x=width))+
    geom_density(color="darkblue",fill="lightblue",aes(alpha = 0.5))+
   geom_vline(aes(xintercept = median(width)), linetype = 2)+
  theme_classic()+
  ggtitle("Distribution of overlaps between all TEs and all my peaks",subtitle = " limited x axis")+
  coord_cartesian(xlim= c(0,2500))

### This is how I subset only those peaks who cover >50% of TEs
# hits <- findOverlaps(TSS_data_gr,all_TEs_gr)
# overlaps <- pintersect(TSS_data_gr[queryHits(hits)], all_TEs_gr[subjectHits(hits)])
# percentOverlap <- width(overlaps) / width(all_TEs_gr[subjectHits(hits)])
# hits <- hits[percentOverlap > 0.5]
# ### THis actually did not work well---I ended up losing data.  What I wanted was a data frame that had metadata from both the TE overlap and the peak metadata, so I could easily sort and manipulation.  
# testingol <- TSS_data_gr[queryHits(hits)]
# testingol %>% as.data.frame() %>% 
#   left_join(., (fullDF_overlap %>% as.data.frame(.)), by =c("seqnames"="seqnames","start"="start","end"="end","Peakid"="Peakid", "NG_start"="NG_start", "end_position"="end_position", "entrezgene_id"="entrezgene_id", "ensembl_gene_id"="ensembl_gene_id","dist_to_NG"="dist_to_NG", "width"="width", "strand"="strand", "hgnc_symbol" = "hgnc_symbol")) %>% 
#   group_by(repClass) %>% 
#   tally %>% 
#   kable(., caption=" Table 3: Count of peaks by TE class; overlap> 50%") %>% 
#   kable_paper("striped", full_width = TRUE) %>%
#   kable_styling(full_width = FALSE, font_size = 14)

The first dataframe to subset peaks that overlap >50% of a TE, was using the full neargene dataframe, where a peak is listed more than once because it was assigned more than one neargene ( one-to-many relationships). I changed the code to use the ‘collapsed’ data frame. This means the data frame was simplified to only include peaks one time, but those peaks that were assigned to more than one neargene had the assigned neargenes condensed and separated by a comma into the same column to create a one-to-one relationship dataframe. (yes, wordy I know)

######################################################
all_TEs_gr$TE_width <- width(all_TEs_gr)
Col_TSS_data_gr$peak_width <- width(Col_TSS_data_gr)
Col_fullDF_overlap <- join_overlap_intersect(Col_TSS_data_gr,all_TEs_gr)
Col_fullDF_overlap %>% 
  as.data.frame() %>% 
  group_by(repClass) %>%  
  tally %>% 
  kable(., caption=" Table 2: Count of peaks by TE class; overlap at least 1 bp; using one:one df ") %>% 
  kable_paper("striped", full_width = TRUE) %>%
  kable_styling(full_width = FALSE, font_size = 14)

Table 2: Count of peaks by TE class; overlap at least 1 bp; using one:one df

repClass	n
DNA	16960
DNA?	138
LINE	40440
LTR	27844
LTR?	257
Low_complexity	5731
RC	52
RC?	9
RNA	28
Retroposon	295
SINE	52755
SINE?	1
Satellite	191
Simple_repeat	30184
Unknown	288
rRNA	54
scRNA	32
snRNA	144
srpRNA	44
tRNA	302

Col_fullDF_overlap %>% 
   as.data.frame %>% 
  mutate(per_ol= width/TE_width) %>% 
  dplyr::filter(per_ol>0.5) %>%
  group_by(repClass) %>% 
  tally() %>% 
  kable(., caption=" Table 3:Count of peaks by TE class; overlap of >50% of TE; newway ") %>% 
  kable_paper("striped", full_width = TRUE) %>%
  kable_styling(full_width = FALSE, font_size = 14)

Table 3:Count of peaks by TE class; overlap of >50% of TE; newway

repClass	n
DNA	11821
DNA?	118
LINE	25548
LTR	18403
LTR?	199
Low_complexity	5305
RC	41
RC?	7
RNA	22
Retroposon	81
SINE	31936
SINE?	1
Satellite	81
Simple_repeat	28143
Unknown	242
rRNA	44
scRNA	24
snRNA	124
srpRNA	31
tRNA	291

Filter_TE_list <- Col_fullDF_overlap %>% 
   as.data.frame %>% 
  mutate(per_ol= width/TE_width) 
  # dplyr::filter(per_ol>0.5)

Unique_peak_overlap <- Col_fullDF_overlap %>%
  as.data.frame() %>%
  distinct(Peakid)

peak_overlap_50unique <-  Filter_TE_list %>%
   dplyr::filter(per_ol>0.5) %>% 
  distinct(Peakid)

Note that the counts of peaks total to more than the total number of peaks that overlap a TE because many peaks overlap multiple elements (generally the very small TEs).

A summary of numbers of peaks is below:

• Total number of peaks = 172481
• Total number of peaks overlapping at least 1 TE = 104149
  
• Total number of peaks overlapping by >50% TE length = 81185
  note these numbers include peaks that are not classified by an MRC.

Tables 3 above used the better way of sub-setting (keeping all metadata organized vs the sub-setting code I found on the internet).

Distribution of TE overlaps by peaks according to class

Below are plots of the distribution of overlapping widths between peaks and TEs. The first plot is all TE Class, and the 2nd plot limits the classes to LINEs, SINEs, LTRs, DNAs, and Retroposons.

Col_fullDF_overlap %>%  
  as.data.frame %>% 
  ggplot(., aes(x=width, fill=repClass))+
    geom_density(color="darkblue",aes(alpha = 0.5))+
  theme_classic()+
  ggtitle("Distribution of all overlapping widths in my data",subtitle = " limited x axis")+
  coord_cartesian(xlim= c(0,750))+
  scale_fill_repeat()

Past versions of "another set of plots-1.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

Col_fullDF_overlap %>%  
  as.data.frame %>% 
  dplyr::filter(repClass=="LINE"|repClass=="SINE"|repClass =="LTR"|repClass=="DNA"|repClass =="Retroposon") %>% 
  ggplot(., aes(x=width, fill=repClass))+
    geom_density(color="darkblue",aes(alpha = 0.5))+
  theme_classic()+
  ggtitle("Distribution of all overlapping peak-TE widths",subtitle = "Just LINE, SINE,LTR, DNA, Retroposons; limited x axis")+
  coord_cartesian(xlim= c(0,1550))+
  scale_fill_repeat()

Past versions of "another set of plots-2.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

Getting the numbers of peaks containing an overlap of > 50% of the TE required creation of a data frame. The first step of the dataframe was to use all high confidence Peaks and join with the TE_Overlapping dataframe.

Peak_TE_overlapbreakdown <-  Col_TSS_data_gr %>% 
  as.data.frame %>%
  distinct(Peakid) %>%
  left_join(.,(Col_fullDF_overlap %>% as.data.frame)) %>%
  dplyr::select(Peakid, repName:repFamily,TE_width,width) %>% 
  left_join(.,(TSS_data_gr %>% as.data.frame), by=c("Peakid"="Peakid")) %>% 
  mutate(mrc=if_else(Peakid %in% NR_df$Peakid, "NR",
                    if_else(Peakid %in% ESR_df$Peakid,"ESR",
                       if_else(Peakid %in% LR_df$Peakid,"LR",
  if_else(Peakid %in% EAR_df$Peakid, "EAR", "not_mrc"))))) %>%   mutate(per_ol= width.x/TE_width)

Peak_TE_overlapbreakdown %>% 
  mutate(TEstatus=if_else(is.na(repClass),"not_TE_peak","TE_peak")) %>%
distinct(Peakid,.keep_all = TRUE) %>% 
  group_by(mrc,TEstatus) %>% tally() 

# A tibble: 10 × 3
# Groups:   mrc [5]
   mrc     TEstatus        n
   <chr>   <chr>       <int>
 1 EAR     TE_peak      4858
 2 EAR     not_TE_peak  2596
 3 ESR     TE_peak     10256
 4 ESR     not_TE_peak  5525
 5 LR      TE_peak     29288
 6 LR      not_TE_peak 13321
 7 NR      TE_peak     57561
 8 NR      not_TE_peak 28767
 9 not_mrc TE_peak      2186
10 not_mrc not_TE_peak  1199

# TE_mrc_status_list %>% 
#   distinct(Peakid, .keep_all = TRUE) %>% 
#   group_by(TEstatus, mrc) %>%  
#   tally #%>% 
#   dplyr::filter(n>=4&mrc=="EAR")
  
  
  # Col_fullDF_cug_overlap %>% as.data.frame() %>% group_by(Peakid) %>% tally %>% dplyr::filter(n>=4)

This increased the number of rows from 172481 to 232225 indicating many peaks contained more than one TE. The column per_ol is representing the proportion of TE covered within the peak. I next decided to only count a peak to contain a TE, if it covered >50% of the length of the TE.

per_cov=0.5
TE_mrc_status_list <- Peak_TE_overlapbreakdown %>%
   mutate(repClass=if_else(per_ol>per_cov, repClass,                          if_else(per_ol<per_cov,NA,repClass))) %>%
  mutate(TEstatus=if_else(is.na(repClass),"not_TE_peak","TE_peak")) %>%
  dplyr::select(Peakid,repName,repClass,repFamily,TEstatus, mrc, per_ol) 
 
TE_mrc_status_list%>% 
   distinct(Peakid,TEstatus,.keep_all = TRUE) %>%
   mutate(mrc="all_peaks")%>% 
   rbind((TE_mrc_status_list %>% distinct(Peakid,TEstatus,.keep_all = TRUE))) %>%
   mutate(repClass=factor(repClass)) %>%
   mutate(TEstatus=factor(TEstatus, levels = c("TE_peak","not_TE_peak")))%>% 
   dplyr::filter(mrc != "not_mrc") %>% 
  mutate(mrc=factor(mrc, levels = c("EAR","ESR", "LR", "NR","all_peaks"))) %>% 
     ggplot(., aes(x=mrc, fill= TEstatus))+
  geom_bar(position="fill", col="black")+ 
   theme_classic()+
  ggtitle(paste("TE status by MRC and Family using the ",per_cov*100,"% cutoff"))

Past versions of "Making of 4group TE dataframe-1.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13

The table below represents the numbers for each of the four groups when I include the >50% stringency cutoff filter.

per_cov=0.5
 
TE_mrc_status_list %>% 
   distinct(Peakid,.keep_all = TRUE) %>%
   mutate(mrc="all_peaks")%>% 
   rbind((TE_mrc_status_list %>% distinct(Peakid,.keep_all = TRUE))) %>%
   mutate(repClass=factor(repClass)) %>%
   mutate(TEstatus=factor(TEstatus, levels = c("TE_peak","not_TE_peak")))%>% 
   dplyr::filter(mrc != "not_mrc") %>% 
  mutate(mrc=factor(mrc, levels = c("EAR","ESR", "LR", "NR","all_peaks"))) %>%
  group_by(mrc, TEstatus) %>% 
  count() %>% 
  pivot_wider(., id_cols = mrc, names_from = TEstatus, values_from = n) %>% 
     rowwise() %>% 
     mutate(summary= sum(c_across(TE_peak:not_TE_peak))) %>% 
     ungroup() %>% 
     pivot_longer(., cols= c(TE_peak, not_TE_peak), names_to = c("TEstatus"), values_to = "n") %>% 
     mutate(percent_mrc= n/summary*100) %>% 
      kable(., caption="Table 4: Summary of peak numbers overlapping and not overlapping TEs by each basic MRC using strigency cutoff of 50%.") %>% 
  kable_paper("striped", full_width = TRUE) %>%
  kable_styling(full_width = FALSE, font_size = 14)

Table 4: Summary of peak numbers overlapping and not overlapping TEs by each basic MRC using strigency cutoff of 50%.

mrc	summary	TEstatus	n	percent_mrc
EAR	7454	TE_peak	3126	41.93721
EAR	7454	not_TE_peak	4328	58.06279
ESR	15781	TE_peak	6594	41.78442
ESR	15781	not_TE_peak	9187	58.21558
LR	42609	TE_peak	18855	44.25121
LR	42609	not_TE_peak	23754	55.74879
NR	86328	TE_peak	37456	43.38801
NR	86328	not_TE_peak	48872	56.61199
all_peaks	155557	TE_peak	67439	43.35324
all_peaks	155557	not_TE_peak	88118	56.64676

Problem with summary of TE ## Human genome TE breakdown

I first wanted to know the distribution of TEs across the human genome compared to each other. Below are pie plots with all classes from repeatmasker, and then pie plots with ONLY the LINES, SINES, LTRs, DNAs, and Retroposon classes.

repeatmasker  %>% 
   mutate(repClass=factor(repClass)) %>% 
count(repClass) %>% 
   mutate(perc= n/sum(n)) %>% 
   arrange(desc(n)) %>% 
  mutate(repClass = fct_rev(fct_inorder(repClass))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
   ggplot(., aes(x = "", y = n, fill = repClass)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repClass,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle("Human genome TE breakdown", subtitle=paste(length(repeatmasker$milliIns)))+
  scale_fill_repeat()

Past versions of "repeatmasker genome breakdown-1.png"

Version	Author	Date
6d65dd6	reneeisnowhere	2024-09-09

LiSiLTDNRe <- repeatmasker %>%
  dplyr::filter(repClass=="LINE"|repClass=="SINE"|repClass=="LTR"|repClass=="DNA"|repClass=="Retroposon")


repeatmasker  %>% 
   mutate(repClass=factor(repClass)) %>% 
  dplyr::filter(repClass=="LINE"|repClass=="SINE"|repClass=="LTR"|repClass=="DNA"|repClass=="Retroposon") %>% 
count(repClass) %>% 
   mutate(perc= n/sum(n)) %>% 
   arrange(desc(n)) %>% 
  mutate(repClass = fct_rev(fct_inorder(repClass))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
   ggplot(., aes(x = "", y = n, fill = repClass)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repClass,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle("Human genome TE breakdown LINE/SINE/LTR/DNA/Retroposon only", subtitle=paste(length(LiSiLTDNRe$milliIns)))+
  scale_fill_repeat()

Past versions of "repeatmasker genome breakdown-2.png"

Version	Author	Date
6d65dd6	reneeisnowhere	2024-09-09

repeatmasker  %>% 
  mutate(repClass_org=repClass) %>% 
   mutate(repClass=factor(repClass)) %>% 
   mutate(repClass=if_else(##relable repClass with other
    repClass_org=="LINE", repClass_org,
    if_else(repClass_org=="SINE",repClass_org,
            if_else(repClass_org=="LTR", repClass_org, 
                    if_else(repClass_org=="DNA", repClass_org,                            if_else(repClass_org=="Retroposon",repClass_org,"Other")))))) %>% 
  # dplyr::filter(repClass=="LINE"|repClass=="SINE"|repClass=="LTR"|repClass=="DNA"|repClass=="Retroposon") %>% 
count(repClass) %>% 
   mutate(perc= n/sum(n)) %>% 
   arrange(desc(n)) %>% 
  mutate(repClass = fct_rev(fct_inorder(repClass))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
   ggplot(., aes(x = "", y = n, fill = repClass)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repClass,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle("Human genome TE breakdown LINE/SINE/LTR/DNA/Retroposon focused with other", subtitle=paste(length(repeatmasker$milliIns)))+
  scale_fill_repeat()

Past versions of "repeatmasker genome breakdown-3.png"

Version	Author	Date
6d65dd6	reneeisnowhere	2024-09-09

# saveRDS(TE_mrc_status_list,"data/TE_info/TE_mrc_status_list.RDS")
# TE_ALL_count <- TE_mrc_status_list %>%
#   dplyr::filter(TEstatus =="TE_peak") %>% 
#   dplyr::filter(mrc!="not_mrc") %>%
#   distinct(Peakid) %>% 
#     count
# TE_ALL_count_filt <- TE_mrc_status_list %>%
#   dplyr::filter(repClass=="LINE"|repClass=="SINE"|repClass=="LTR"|repClass=="DNA"|repClass=="Retroposon") %>% 
#   dplyr::filter(TEstatus =="TE_peak") %>% 
#   dplyr::filter(mrc!="not_mrc") %>% 
#   distinct(Peakid) %>% 
#     count
TE_50_count <- TE_mrc_status_list %>%
  dplyr::filter(TEstatus =="TE_peak") %>% 
  dplyr::filter(mrc!="not_mrc") %>% 
  distinct(Peakid) %>% 
    count
TE_50_count_filt <- TE_mrc_status_list %>%
   dplyr::filter(repClass=="LINE"|repClass=="SINE"|repClass=="LTR"|repClass=="DNA"|repClass=="Retroposon") %>% 
  # dplyr::filter(per_ol>per_cov) %>% 
  dplyr::filter(TEstatus =="TE_peak") %>% 
  dplyr::filter(mrc!="not_mrc") %>% 
  distinct(Peakid) %>% 
    count

TE_mrc_status_list %>% 
   mutate(TEstatus=if_else(is.na(repClass),"not_TE_peak","TE_peak")) %>%
   mutate(repClass=factor(repClass)) %>% 
   dplyr::filter(repClass=="LINE"|repClass=="SINE"|repClass=="LTR"|repClass=="DNA"|repClass=="Retroposon") %>% 
  # group_by(repClass) %>% 
  dplyr::filter(TEstatus =="TE_peak") %>% 
   count(repClass) %>% 
  mutate(perc= n/sum(n)) %>% 
   arrange(desc(n)) %>% 
  mutate(repClass = fct_rev(fct_inorder(repClass))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repClass)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
   geom_label_repel(aes(label = paste0(repClass,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35)+
  # geom_label_repel(aes(label = paste(n,"\n", repClass)),
  #                    position = position_stack(vjust = .3),
  #                    show.legend = FALSE,max.overlaps = 50) +
  theme_void()+
  ggtitle("TE breakdown of Just LINEs, SINEs, LTRs, DNAs, and Retroposons",subtitle = paste(TE_50_count_filt$n))+
  scale_fill_repeat()

Past versions of "TE in all peaks-1.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

TE_mrc_status_list %>% 
   mutate(TEstatus=if_else(is.na(repClass),"not_TE_peak","TE_peak")) %>%
   mutate(repClass=factor(repClass)) %>% 
  distinct() %>% 
  dplyr::filter(TEstatus =="TE_peak") %>% 
   count(repClass) %>% 
  mutate(perc= n/sum(n)) %>% 
   arrange(desc(n)) %>% 
  mutate(repClass = fct_rev(fct_inorder(repClass))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repClass)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repClass,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
    # geom_label_repel(aes(label = paste(n,"\n", repClass)),
    #                  position = position_stack(vjust = .3),
    #                  show.legend = FALSE,max.overlaps = 50) +
  theme_void()+
  ggtitle("TE breakdown of all peaks using 50% cutoff",subtitle = paste(TE_50_count$n))+
  scale_fill_repeat()

Past versions of "TE in all peaks-2.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

TE_mrc_status_list %>% 
   mutate(TEstatus=if_else(is.na(repClass),"not_TE_peak","TE_peak")) %>%
   mutate(repClass_org=repClass) %>% 
   mutate(repClass=factor(repClass)) %>% 
   mutate(repClass=if_else(##relable repClass with other
    repClass_org=="LINE", repClass_org,
    if_else(repClass_org=="SINE",repClass_org,
            if_else(repClass_org=="LTR", repClass_org, 
                    if_else(repClass_org=="DNA", repClass_org,
                            if_else(repClass_org=="Retroposon",repClass_org,"Other")))))) %>% 
  distinct() %>% 
  dplyr::filter(TEstatus =="TE_peak") %>%
  count(repClass) %>% 
  mutate(perc= n/sum(n)) %>% 
   arrange(desc(n)) %>% 
  mutate(repClass = fct_rev(fct_inorder(repClass))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repClass)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repClass,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  # geom_label_repel(aes(label = paste(n,"\n", repClass)),
  #                    position = position_stack(vjust = .3),
  #                    show.legend = FALSE,max.overlaps = 50) +
  theme_void()+
  ggtitle("TE breakdown of all peaks using 50% cutoff",subtitle = paste(TE_50_count_filt$n, "peaks that contain LINEs, SINEs, LTRs, DNAs, or Retroposons"))+
  scale_fill_repeat()

Past versions of "TE in all peaks-3.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

### note the change in data frame Renee, this is where you flipped back to the original without filters.
Peak_TE_overlapbreakdown%>% 
  mutate(TEstatus=if_else(is.na(repClass),"not_TE_peak","TE_peak")) %>%
  dplyr::filter(TEstatus=="TE_peak") %>% 
  distinct() %>% 
  mutate(repClass=factor(repClass)) %>% 
  ggplot(., aes(x=width.x))+
  geom_density(aes(fill=repClass, alpha = 0.5))+
  theme_classic()+
  ggtitle("Distribution of all overlapping peak-TE widths")+
  scale_fill_repeat()

Past versions of "TEs in all peaks-1.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13

Peak_TE_overlapbreakdown%>% 
  mutate(TEstatus=if_else(is.na(repClass),"not_TE_peak","TE_peak")) %>%
  dplyr::filter(TEstatus=="TE_peak") %>% 
  distinct() %>% 
  mutate(repClass=factor(repClass)) %>% 
  dplyr::filter(repClass=="LINE"|repClass=="SINE"|repClass=="LTR"|repClass=="DNA"|repClass=="Retroposon") %>% 
  ggplot(., aes(x=width.x))+
  geom_density(aes(fill=repClass, alpha = 0.5))+
  theme_classic()+
  ggtitle("Distribution of all overlapping peak-TE widths >50%")+
  scale_fill_repeat()

Past versions of "TEs in all peaks-2.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13

Peak_TE_overlapbreakdown%>%
  mutate(repClass=if_else(per_ol>per_cov, repClass,                          if_else(per_ol<per_cov,NA,repClass))) %>%
  mutate(TEstatus=if_else(is.na(repClass),"not_TE_peak","TE_peak")) %>%
  dplyr::filter(TEstatus=="TE_peak") %>% 
  distinct() %>% 
  mutate(repClass=factor(repClass)) %>%
  ggplot(., aes(x=width.x))+
  geom_density(aes(fill=repClass, alpha = 0.5))+
  theme_classic()+
  ggtitle("Distribution of all overlapping peak-TE widths >50% of TE length")+
  scale_fill_repeat()

Past versions of "TEs in all peaks-3.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13

Peak_TE_overlapbreakdown %>% 
  mutate(repClass=if_else(per_ol>per_cov, repClass,                          if_else(per_ol<per_cov,NA,repClass))) %>%
  mutate(TEstatus=if_else(is.na(repClass),"not_TE_peak","TE_peak")) %>%
   mutate(TEstatus=if_else(is.na(repClass),"not_TE_peak","TE_peak")) %>%
  distinct() %>% 
  dplyr::filter(TEstatus=="TE_peak") %>% 
  mutate(repClass=factor(repClass)) %>% 
  dplyr::filter(repClass=="LINE"|repClass=="SINE"|repClass=="LTR"|repClass=="DNA"|repClass=="Retroposon") %>% 
  ggplot(., aes(x=width.x))+
  geom_density(aes(fill=repClass, alpha = 0.5))+
  scale_fill_repeat()+
  theme_classic()+
  ggtitle("Filtered Distribution of all overlapping peak-TE widths >50 TE length%")

Past versions of "TEs in all peaks-4.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13

Line repeats

Line_df %>% 
  count(repFamily) %>% 
  mutate(perc= n/sum(n)) %>% 
  arrange(desc(n)) %>% 
  mutate(repFamily = fct_rev(fct_inorder(repFamily))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repFamily)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repFamily,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle("Human genome LINE breakdown", subtitle=paste(length(Line_df$milliIns)))+
  scale_fill_lines()

Past versions of "LINEclass breakdown-1.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

TE_LINE_count <- TE_mrc_status_list %>% 
  dplyr::filter(TEstatus =="TE_peak"&repClass=="LINE") %>% 
  count

TE_mrc_status_list %>% 
  dplyr::filter(repClass == "LINE") %>%
  mutate(repFamily=factor(repFamily)) %>% 
  count(repFamily) %>%
  mutate(perc= n/sum(n)) %>% 
  arrange(desc(n)) %>% 
  mutate(repFamily = fct_rev(fct_inorder(repFamily))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repFamily)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repFamily,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle(paste0("LINE breakdown of peaks ",per_cov),subtitle=paste(TE_LINE_count$n))+
  scale_fill_lines()

Past versions of "LINEclass breakdown-2.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

EAR_LINE_count <- TE_mrc_status_list %>% 
  dplyr::filter(mrc =="EAR"&repClass=="LINE") %>% 
  count
ESR_LINE_count <- TE_mrc_status_list %>% 
  dplyr::filter(mrc =="ESR"&repClass=="LINE") %>% 
  count
LR_LINE_count <- TE_mrc_status_list %>% 
  dplyr::filter(mrc =="LR"&repClass=="LINE") %>% 
  count
NR_LINE_count <- TE_mrc_status_list %>% 
  dplyr::filter(mrc =="NR"&repClass=="LINE") %>% 
  count

TE_mrc_status_list %>% 
  dplyr::filter(mrc =="EAR"&repClass=="LINE") %>% 
  mutate(repFamily=factor(repFamily)) %>% 
  # group_by(repFamily) %>% 
  count(repFamily) %>% 
  mutate(perc= n/sum(n)) %>% 
  arrange(desc(n)) %>% 
  mutate(repFamily = fct_rev(fct_inorder(repFamily))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repFamily)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repFamily,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle(paste0("EAR LINE breakdown of peaks ",per_cov),subtitle=paste(EAR_LINE_count$n))+
  scale_fill_lines()

Past versions of "LINEclass breakdown-3.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

TE_mrc_status_list %>% 
  dplyr::filter(mrc =="ESR"&repClass=="LINE") %>% 
  mutate(repFamily=factor(repFamily)) %>% 
  count(repFamily) %>% 
  mutate(perc= n/sum(n)) %>% 
  arrange(desc(n)) %>% 
  mutate(repFamily = fct_rev(fct_inorder(repFamily))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repFamily)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repFamily,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle(paste0("ESR LINE breakdown of peaks ",per_cov),subtitle=paste(ESR_LINE_count$n))+
  scale_fill_lines()

Past versions of "LINEclass breakdown-4.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

TE_mrc_status_list %>% 
  dplyr::filter(mrc =="LR"&repClass=="LINE") %>% 
  mutate(repFamily=factor(repFamily)) %>% 
  count(repFamily) %>% 
  mutate(perc= n/sum(n)) %>% 
  arrange(desc(n)) %>% 
  mutate(repFamily = fct_rev(fct_inorder(repFamily))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repFamily)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repFamily,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle(paste0("LR LINE breakdown of peaks ",per_cov),subtitle=paste(LR_LINE_count$n))+
  scale_fill_lines()

Past versions of "LINEclass breakdown-5.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

TE_mrc_status_list %>% 
  dplyr::filter(mrc =="NR"&repClass=="LINE") %>% 
  mutate(repFamily=factor(repFamily)) %>% 
  count(repFamily) %>% 
  mutate(perc= n/sum(n)) %>% 
  arrange(desc(n)) %>% 
  mutate(repFamily = fct_rev(fct_inorder(repFamily))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repFamily)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repFamily,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle(paste0("NR LINE breakdown of peaks ",per_cov),subtitle=paste(NR_LINE_count$n))+
  scale_fill_lines()

Past versions of "LINEclass breakdown-6.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

all_L2_count <- TE_mrc_status_list %>% 
  dplyr::filter(repClass=="LINE"&repFamily=="L2") %>% 
  tally
EAR_L2_count <- TE_mrc_status_list %>% 
  dplyr::filter(repClass=="LINE"&repFamily=="L2", mrc=="EAR") %>% 
  tally
ESR_L2_count <- TE_mrc_status_list %>% 
  dplyr::filter(repClass=="LINE"&repFamily=="L2", mrc=="ESR") %>% 
  tally
LR_L2_count <- TE_mrc_status_list %>% 
  dplyr::filter(repClass=="LINE"&repFamily=="L2", mrc=="LR") %>% 
  tally
NR_L2_count <- TE_mrc_status_list %>% 
  dplyr::filter(repClass=="LINE"&repFamily=="L2", mrc=="NR") %>% 
  tally


TE_mrc_status_list %>% 
  dplyr::filter(repClass=="LINE"&repFamily=="L2")%>% 
  mutate(repName=factor(repName)) %>% 
  # group_by(repName) %>% 
  count(repName) %>% 
  mutate(perc= n/sum(n)) %>% 
  arrange(desc(n)) %>% 
  mutate(repName = fct_rev(fct_inorder(repName))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repName)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repName,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  # geom_label_repel(aes(label = n),
  #                    position = position_stack(vjust = .3),
  #                    show.legend = FALSE,max.overlaps = 50) +
  theme_void()+
  ggtitle(paste0("LINE-L2 breakdown for all peaks ",per_cov),subtitle=paste(all_L2_count," total LINEs"))+
  scale_fill_L2()

Past versions of L2breakdown-1.png

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

TE_mrc_status_list %>% 
  dplyr::filter(mrc =="EAR"&repClass=="LINE"&repFamily=="L2")%>% 
  mutate(repName=factor(repName)) %>% 
  # group_by(repName) %>% 
  count(repName) %>%
  mutate(perc= n/sum(n)) %>% 
  arrange(desc(n)) %>% 
  mutate(repName = fct_rev(fct_inorder(repName))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repName)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  # geom_label_repel(aes(label = n),
  #                    position = position_stack(vjust = .3),
  #                    show.legend = FALSE,max.overlaps = 50) +
  geom_label_repel(aes(label = paste0(repName,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle(paste0("LINE-L2 breakdown for EAR ",per_cov),subtitle=paste(EAR_L2_count$n," total L2s"))+
  scale_fill_L2()

Past versions of L2breakdown-2.png

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

TE_mrc_status_list %>% 
  dplyr::filter(mrc =="ESR"&repClass=="LINE"&repFamily=="L2")%>% 
  mutate(repName=factor(repName)) %>% 
  # group_by(repName) %>% 
  count(repName) %>% 
  mutate(perc= n/sum(n)) %>% 
  arrange(desc(n)) %>% 
  mutate(repName = fct_rev(fct_inorder(repName))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repName)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repName,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle(paste0("LINE-L2 breakdown for ESR ",per_cov),subtitle=paste(ESR_L2_count$n," total L2s"))+
  scale_fill_L2()

Past versions of L2breakdown-3.png

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

TE_mrc_status_list %>% 
  dplyr::filter(mrc =="LR"&repClass=="LINE"&repFamily=="L2")%>% 
  mutate(repName=factor(repName)) %>% 
  count(repName) %>% 
  mutate(perc= n/sum(n)) %>% 
  arrange(desc(n)) %>% 
  mutate(repName = fct_rev(fct_inorder(repName))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repName)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repName,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle(paste0("LINE-L2 breakdown for LR ",per_cov),subtitle=paste(LR_L2_count$n," total L2s"))+
  scale_fill_L2()

Past versions of L2breakdown-4.png

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

TE_mrc_status_list %>% 
  dplyr::filter(mrc =="NR"&repClass=="LINE"&repFamily=="L2")%>% 
  mutate(repName=factor(repName)) %>% 
  count(repName) %>% 
  mutate(perc= n/sum(n)) %>% 
  arrange(desc(n)) %>% 
  mutate(repName = fct_rev(fct_inorder(repName))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repName)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repName,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle(paste0("LINE-L2 breakdown for NR ",per_cov),subtitle=paste(NR_L2_count$n," total L2s"))+
  scale_fill_L2()

Past versions of L2breakdown-5.png

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

Sine repeats

Sine_df%>% 
count(repFamily) %>% 
   mutate(perc= n/sum(n)) %>% 
   arrange(desc(n)) %>% 
  mutate(repFamily = fct_rev(fct_inorder(repFamily))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
   ggplot(., aes(x = "", y = n, fill = repFamily)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repFamily,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle("Human genome SINE breakdown", subtitle=paste(length(Sine_df$milliIns)))+
  scale_fill_sines()

Past versions of "Sineclass breakdown-1.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

TE_SINE_count <- TE_mrc_status_list %>% 
  dplyr::filter(TEstatus =="TE_peak"&repClass=="SINE") %>% 
  count

TE_mrc_status_list %>% 
  dplyr::filter(repClass == "SINE") %>% 
   mutate(repFamily=factor(repFamily)) %>% 
     # group_by(repFamily) %>% 
  count(repFamily) %>% 
   mutate(perc= n/sum(n)) %>% 
   arrange(desc(n)) %>% 
  mutate(repFamily = fct_rev(fct_inorder(repFamily))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repFamily)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repFamily,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
   # geom_label_repel(aes(label = n),
   #                   position = position_stack(vjust = .3),
   #                   show.legend = FALSE,max.overlaps = 50) +
  theme_void()+
  ggtitle(paste0("SINE breakdown of peaks ",per_cov),subtitle=paste(TE_SINE_count$n," total SINEs found"))+
  scale_fill_sines()

Past versions of "Sineclass breakdown-2.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

EAR_SINE_count <- TE_mrc_status_list %>% 
  dplyr::filter(mrc =="EAR"&repClass=="SINE") %>% 
  count
ESR_SINE_count <- TE_mrc_status_list %>% 
  dplyr::filter(mrc =="ESR"&repClass=="SINE") %>% 
  count
LR_SINE_count <- TE_mrc_status_list %>% 
  dplyr::filter(mrc =="LR"&repClass=="SINE") %>% 
  count
NR_SINE_count <- TE_mrc_status_list %>% 
  dplyr::filter(mrc =="NR"&repClass=="SINE") %>% 
  count

TE_mrc_status_list %>% 
  dplyr::filter(mrc =="EAR"&repClass=="SINE") %>% 
  mutate(repFamily=factor(repFamily)) %>% 
  # group_by(repFamily) %>% 
  count(repFamily) %>% 
  mutate(perc= n/sum(n)) %>% 
  arrange(desc(n)) %>% 
  mutate(repFamily = fct_rev(fct_inorder(repFamily))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repFamily)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  # geom_label_repel(aes(label = n),
  #                    position = position_stack(vjust = .3),
  #                    show.legend = FALSE,max.overlaps = 50) +
  geom_label_repel(aes(label = paste0(repFamily,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle(paste0("EAR SINE breakdown of peaks ",per_cov),subtitle=paste(EAR_SINE_count$n))+
  scale_fill_sines()

Past versions of "Sineclass breakdown-3.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

TE_mrc_status_list %>% 
  dplyr::filter(mrc =="ESR"&repClass=="SINE") %>% 
  mutate(repFamily=factor(repFamily)) %>% 
  # group_by(repFamily) %>% 
  count(repFamily) %>% 
  mutate(perc= n/sum(n)) %>% 
  arrange(desc(n)) %>% 
  mutate(repFamily = fct_rev(fct_inorder(repFamily))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repFamily)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repFamily,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  # geom_label_repel(aes(label = n),
  #                    position = position_stack(vjust = .3),
  # 
  theme_void()+
  ggtitle(paste0("ESR SINE breakdown of peaks ",per_cov),subtitle=paste(ESR_SINE_count$n))+
  scale_fill_sines()

Past versions of "Sineclass breakdown-4.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

TE_mrc_status_list %>% 
  dplyr::filter(mrc =="LR"&repClass=="SINE") %>% 
  mutate(repFamily=factor(repFamily)) %>% 
  # group_by(repFamily) %>% 
  count(repFamily) %>% 
  mutate(perc= n/sum(n)) %>% 
  arrange(desc(n)) %>% 
  mutate(repFamily = fct_rev(fct_inorder(repFamily))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repFamily)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  # geom_label_repel(aes(label = n),
  #                    position = position_stack(vjust = .3),
  #                    show.legend = FALSE,max.overlaps = 50) +
  geom_label_repel(aes(label = paste0(repFamily,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle(paste0("LR SINE breakdown of peaks ",per_cov),subtitle=paste(LR_SINE_count$n))+
  scale_fill_sines()

Past versions of "Sineclass breakdown-5.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

TE_mrc_status_list %>% 
  dplyr::filter(mrc =="NR"&repClass=="SINE") %>% 
  mutate(repFamily=factor(repFamily)) %>% 
  # group_by(repFamily) %>% 
  count(repFamily) %>% 
  mutate(perc= n/sum(n)) %>% 
  arrange(desc(n)) %>% 
  mutate(repFamily = fct_rev(fct_inorder(repFamily))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repFamily)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  # geom_label_repel(aes(label = n),
  #                    position = position_stack(vjust = .3),
  #                    show.legend = FALSE,max.overlaps = 50) +
  geom_label_repel(aes(label = paste0(repFamily,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle(paste0("NR SINE breakdown of peaks ",per_cov),subtitle=paste(NR_SINE_count$n))+
  scale_fill_sines()

Past versions of "Sineclass breakdown-6.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

LTR repeats

per_cov <- 0.5


LTR_df%>% 
  count(repFamily) %>% 
  mutate(perc= n/sum(n)) %>% 
  arrange(desc(n)) %>% 
  mutate(repFamily = fct_rev(fct_inorder(repFamily))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
   ggplot(., aes(x = "", y = n, fill = repFamily)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repFamily,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle("Human genome LTR breakdown", subtitle=paste(length(LTR_df$milliIns)))+
  scale_fill_LTRs()

Past versions of "LTR breakdown-1.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

LTR_count <- TE_mrc_status_list %>% 
  dplyr::filter(repClass=="LTR") %>% 
  count
EAR_LTR_count <- TE_mrc_status_list %>% 
  dplyr::filter(mrc =="EAR"&repClass=="LTR") %>% 
  count
ESR_LTR_count <- TE_mrc_status_list %>% 
  dplyr::filter(mrc =="ESR"&repClass=="LTR") %>% 
  count
LR_LTR_count <- TE_mrc_status_list %>% 
  dplyr::filter(mrc =="LR"&repClass=="LTR") %>% 
  count
NR_LTR_count <- TE_mrc_status_list %>% 
  dplyr::filter(mrc =="NR"&repClass=="LTR") %>% 
  count

TE_mrc_status_list %>% 
  dplyr::filter(repClass=="LTR") %>% 
  mutate(repFamily=factor(repFamily)) %>% 
  # group_by(repFamily) %>% 
  count(repFamily) %>% 
  mutate(perc= n/sum(n)) %>% 
  arrange(desc(n)) %>% 
  mutate(repFamily = fct_rev(fct_inorder(repFamily))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repFamily)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repFamily,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle(paste("All peaks LTR breakdown of peaks",per_cov),subtitle=paste(LTR_count$n))+
  scale_fill_LTRs()

Past versions of "LTR breakdown-2.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

TE_mrc_status_list %>% 
  dplyr::filter(mrc =="EAR"&repClass=="LTR") %>% 
  mutate(repFamily=factor(repFamily)) %>% 
  # group_by(repFamily) %>% 
  count(repFamily) %>% 
  mutate(perc= n/sum(n)) %>% 
  arrange(desc(n)) %>% 
  mutate(repFamily = fct_rev(fct_inorder(repFamily))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repFamily)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repFamily,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle(paste("EAR LTR breakdown of peaks",per_cov),subtitle=paste(EAR_LTR_count$n))+
  scale_fill_LTRs()

Past versions of "LTR breakdown-3.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

TE_mrc_status_list %>% 
  dplyr::filter(mrc =="ESR"&repClass=="LTR") %>% 
  mutate(repFamily=factor(repFamily)) %>% 
  # group_by(repFamily) %>% 
  count(repFamily) %>% 
  mutate(perc= n/sum(n)) %>% 
  arrange(desc(n)) %>% 
  mutate(repFamily = fct_rev(fct_inorder(repFamily))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repFamily)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repFamily,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle(paste("ESR LTR breakdown of peaks",per_cov),subtitle=paste(ESR_LTR_count$n))+
  scale_fill_LTRs()

Past versions of "LTR breakdown-4.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

TE_mrc_status_list %>% 
  dplyr::filter(mrc =="LR"&repClass=="LTR") %>% 
  mutate(repFamily=factor(repFamily)) %>% 
  # group_by(repFamily) %>% 
  count(repFamily) %>% 
  mutate(perc= n/sum(n)) %>% 
  arrange(desc(n)) %>% 
  mutate(repFamily = fct_rev(fct_inorder(repFamily))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repFamily)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repFamily,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle(paste("LR LTR breakdown of peaks ",per_cov),subtitle=paste(LR_LTR_count$n))+
  scale_fill_LTRs()

Past versions of "LTR breakdown-5.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

TE_mrc_status_list %>% 
  dplyr::filter(mrc =="NR"&repClass=="LTR") %>% 
  mutate(repFamily=factor(repFamily)) %>% 
  # group_by(repFamily) %>% 
  count(repFamily) %>% 
  mutate(perc= n/sum(n)) %>% 
  arrange(desc(n)) %>% 
  mutate(repFamily = fct_rev(fct_inorder(repFamily))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repFamily)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repFamily,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle(paste("NR LTR breakdown of peaks", per_cov),subtitle=paste(NR_LTR_count$n))+
  scale_fill_LTRs()

Past versions of "LTR breakdown-6.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

DNA TEs

per_cov <- 0.5

DNA_df%>% 
count(repFamily) %>% 
   mutate(perc= n/sum(n)) %>% 
   arrange(desc(n)) %>% 
  mutate(repFamily = fct_rev(fct_inorder(repFamily))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
   ggplot(., aes(x = "", y = n, fill = repFamily)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repFamily,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 50) +
  theme_void()+
  ggtitle("Human genome DNA breakdown", subtitle=paste(length(DNA_df$milliIns)))+
  scale_fill_DNAs()

Past versions of "DNA breakdown-1.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

TE_DNA_count <- TE_mrc_status_list %>% 
  dplyr::filter(TEstatus =="TE_peak"&repClass=="DNA") %>% 
  count

TE_mrc_status_list %>% 
  dplyr::filter(repClass == "DNA") %>% 
   mutate(repFamily=factor(repFamily)) %>% 
     # group_by(repFamily) %>% 
  count(repFamily) %>% 
   mutate(perc= n/sum(n)) %>% 
   arrange(desc(n)) %>% 
  mutate(repFamily = fct_rev(fct_inorder(repFamily))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repFamily)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
   # geom_label(aes(label = repClass),
   #          position = position_stack(vjust = .8)) +
  # geom_label(aes(label=repClass, y=text_y))
  geom_label_repel(aes(label = paste0(repFamily,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle(paste("DNA breakdown of peaks", per_cov),subtitle=paste(TE_DNA_count$n))+
  scale_fill_DNAs()

Past versions of "DNA breakdown-2.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

EAR_DNA_count <- TE_mrc_status_list %>% 
  dplyr::filter(mrc =="EAR"&repClass=="DNA") %>% 
  count
ESR_DNA_count <- TE_mrc_status_list %>% 
  dplyr::filter(mrc =="ESR"&repClass=="DNA") %>% 
  count
LR_DNA_count <- TE_mrc_status_list %>% 
  dplyr::filter(mrc =="LR"&repClass=="DNA") %>% 
  count
NR_DNA_count <- TE_mrc_status_list %>% 
  dplyr::filter(mrc =="NR"&repClass=="DNA") %>% 
  count


TE_mrc_status_list %>% 
  dplyr::filter(mrc =="EAR"&repClass=="DNA") %>% 
  mutate(repFamily=factor(repFamily)) %>% 
  # group_by(repFamily) %>% 
  count(repFamily) %>% 
  mutate(perc= n/sum(n)) %>% 
  arrange(desc(n)) %>% 
  mutate(repFamily = fct_rev(fct_inorder(repFamily))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repFamily)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  # geom_label_repel(aes(label = n),
  #                    position = position_stack(vjust = .3),
  #                    show.legend = FALSE,max.overlaps = 50) +
  geom_label_repel(aes(label = paste0(repFamily,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle(paste("EAR DNA breakdown of peaks",per_cov),subtitle=paste(EAR_DNA_count$n))+
  scale_fill_DNAs()

Past versions of "DNA breakdown-3.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

TE_mrc_status_list %>% 
  dplyr::filter(mrc =="ESR"&repClass=="DNA") %>% 
  mutate(repFamily=factor(repFamily)) %>% 
  # group_by(repFamily) %>% 
  count(repFamily) %>% 
  mutate(perc= n/sum(n)) %>% 
  arrange(desc(n)) %>% 
  mutate(repFamily = fct_rev(fct_inorder(repFamily))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repFamily)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repFamily,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle(paste("ESR DNA breakdown of peaks",per_cov),subtitle=paste(ESR_DNA_count$n))+
  scale_fill_DNAs()

Past versions of "DNA breakdown-4.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

TE_mrc_status_list %>% 
  dplyr::filter(mrc =="LR"&repClass=="DNA") %>% 
  mutate(repFamily=factor(repFamily)) %>% 
  # group_by(repFamily) %>% 
  count(repFamily) %>% 
  mutate(perc= n/sum(n)) %>% 
  arrange(desc(n)) %>% 
  mutate(repFamily = fct_rev(fct_inorder(repFamily))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repFamily)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repFamily,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle(paste("LR DNA breakdown of peaks",per_cov),subtitle=paste(LR_DNA_count$n))+
  scale_fill_DNAs()

Past versions of "DNA breakdown-5.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

TE_mrc_status_list %>% 
  dplyr::filter(mrc =="NR"&repClass=="DNA") %>% 
  mutate(repFamily=factor(repFamily)) %>% 
  # group_by(repFamily) %>% 
  count(repFamily) %>% 
  mutate(perc= n/sum(n)) %>% 
  arrange(desc(n)) %>% 
  mutate(repFamily = fct_rev(fct_inorder(repFamily))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repFamily)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repFamily,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle(paste("NR DNA breakdown of peaks", per_cov),subtitle=paste(NR_DNA_count$n))+
  scale_fill_DNAs()

Past versions of "DNA breakdown-6.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

retroposon

per_cov <- 0.5

retroposon_df%>% 
  count(repName) %>% 
  mutate(perc= n/sum(n)) %>% 
  arrange(desc(n)) %>% 
  mutate(repName = fct_rev(fct_inorder(repName))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repName)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label =  paste0(repName ,"\n", sprintf("%.2f", perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle("Human genome retroposon breakdown", subtitle= paste( length(retroposon_df$milliIns)))+
  scale_fill_retroposons()

Past versions of "retroposon breakdown-1.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

TE_retroposon_count <- TE_mrc_status_list %>% 
  dplyr::filter(TEstatus =="TE_peak"&repClass=="Retroposon") %>% 
  count

TE_mrc_status_list %>% 
  dplyr::filter(repClass == "Retroposon") %>% 
   mutate(repName=factor(repName)) %>% 
     # group_by(repName) %>% 
  count(repName) %>% 
   mutate(perc= n/sum(n)) %>% 
   arrange(desc(n)) %>% 
  mutate(repName = fct_rev(fct_inorder(repName))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repName)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
   # geom_label(aes(label = repClass),
   #          position = position_stack(vjust = .8)) +
  geom_label_repel(aes(label = paste0(repName,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle(paste("retroposon breakdown of peaks",per_cov),subtitle=paste(TE_retroposon_count$n))+
  scale_fill_retroposons()

Past versions of "retroposon breakdown-2.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

EAR_Retroposon_count <- TE_mrc_status_list %>% 
  dplyr::filter(mrc =="EAR"&repClass=="Retroposon") %>% 
  count
ESR_Retroposon_count <- TE_mrc_status_list %>% 
  dplyr::filter(mrc =="ESR"&repClass=="Retroposon") %>% 
  count
LR_Retroposon_count <- TE_mrc_status_list %>% 
  dplyr::filter(mrc =="LR"&repClass=="Retroposon") %>% 
  count
NR_Retroposon_count <- TE_mrc_status_list %>% 
  dplyr::filter(mrc =="NR"&repClass=="Retroposon") %>% 
  count


TE_mrc_status_list %>% 
  dplyr::filter(mrc =="ESR"&repClass=="Retroposon") %>% 
  mutate(repName=factor(repName)) %>% 
  # group_by(repName) %>% 
  count(repName) %>% 
  mutate(perc= n/sum(n)) %>% 
  arrange(desc(n)) %>% 
   mutate(repName = fct_rev(fct_inorder(repName))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repName)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repName,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle(paste("ESR Retroposon breakdown of peaks",per_cov),subtitle=paste(ESR_Retroposon_count$n))+
  scale_fill_retroposons()

Past versions of "retroposon breakdown-3.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

TE_mrc_status_list %>% 
  dplyr::filter(mrc =="LR"&repClass=="Retroposon") %>% 
  mutate(repName=factor(repName)) %>% 
  # group_by(repName) %>% 
  count(repName) %>% 
  mutate(perc= n/sum(n)) %>% 
  arrange(desc(n)) %>% 
   mutate(repName = fct_rev(fct_inorder(repName))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repName)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repName,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle(paste("LR Retroposon breakdown of peaks",per_cov),subtitle=paste(LR_Retroposon_count$n))+
  scale_fill_retroposons()

Past versions of "retroposon breakdown-4.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

TE_mrc_status_list %>% 
  dplyr::filter(mrc =="NR"&repClass=="Retroposon") %>% 
  mutate(repName=factor(repName)) %>% 
  # group_by(repName) %>% 
  count(repName) %>% 
  mutate(perc= n/sum(n)) %>% 
  arrange(desc(n)) %>% 
   mutate(repName = fct_rev(fct_inorder(repName))) %>% 
  mutate(text_y = cumsum(n) - n/2) %>% 
  ggplot(., aes(x = "", y = n, fill = repName)) +
  geom_col(color = "black") +
  coord_polar(theta = "y", start = 0)+
  geom_label_repel(aes(label = paste0(repName,"\n",sprintf("%.2f",perc*100),"%")),
                     position = position_stack(vjust = .5),
                     force=.9,show.legend = FALSE,max.overlaps = 35) +
  theme_void()+
  ggtitle(paste("NR Retroposon breakdown of peaks",per_cov),subtitle=paste(NR_Retroposon_count$n))+
  scale_fill_retroposons()

Past versions of "retroposon breakdown-5.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

TE by response cluster bargraph summaries

TE_mrc_status_list %>% 
   mutate(repClass_org = repClass) %>% #copy repClass for storage
  mutate(repClass=if_else(##relable repClass with other
    repClass_org=="LINE", repClass_org,
    if_else(repClass_org=="SINE",repClass_org,
            if_else(repClass_org=="LTR", repClass_org, 
                    if_else(repClass_org=="DNA", repClass_org,
                          if_else(repClass_org=="Retroposon",repClass_org,"Other")))))) %>% 
  dplyr::select(Peakid,repName,repClass,repFamily,TEstatus, mrc, per_ol) %>%
  distinct(Peakid, .keep_all = TRUE) %>% 
  mutate(mrc="all_peaks") %>% 
  rbind((TE_mrc_status_list %>% distinct(Peakid,.keep_all = TRUE))) %>%
  mutate(repClass=factor(repClass)) %>%
  mutate(TEstatus=factor(TEstatus, levels = c("TE_peak","not_TE_peak")))%>% 
  dplyr::filter(mrc != "not_mrc") %>% 
  # dplyr::filter(is.na(per_ol)| per_ol>per_cov) %>%
  mutate(mrc=factor(mrc, levels = c("EAR","ESR", "LR", "NR","all_peaks"))) %>% 
  ggplot(., aes(x=mrc, fill= TEstatus))+
  geom_bar(position="fill", col="black")+
  theme_classic()+
  ggtitle(paste("TE status by MRC and Family",">", per_cov*100,"%"))

Past versions of "TE status old clusters-1.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

# TE_counts_nofilt <- TE_mrc_status_list %>% 
#    mutate(repClass_org = repClass) %>% #copy repClass for storage
#   mutate(repClass=if_else(##relable repClass with other
#     repClass_org=="LINE", repClass_org,if_else(repClass_org=="SINE",repClass_org,if_else(repClass_org=="LTR", repClass_org, if_else(repClass_org=="DNA", repClass_org, if_else(repClass_org=="Retroposon",repClass_org,if_else(is.na(repClass_org),repClass_org,"Other"))))))) %>% 
#    dplyr::select(Peakid,repName,repClass,repFamily,TEstatus, mrc, per_ol) %>% distinct(Peakid, .keep_all = TRUE) %>% 
#    mutate(mrc="all_peaks") %>% 
#   rbind((TE_mrc_status_list %>% distinct(Peakid,.keep_all = TRUE))) %>% 
#   # mutate(repClass=factor(repClass)) %>%
#    mutate(TEstatus=factor(TEstatus, levels = c("TE_peak","not_TE_peak")))%>% 
#    dplyr::filter(mrc != "not_mrc") %>% 
#   mutate(mrc=factor(mrc, levels = c("EAR","ESR", "LR", "NR","all_peaks"))) %>% 
#      group_by(mrc, TEstatus) %>% 
#      count() %>% 
#      pivot_wider(., id_cols = mrc, names_from = TEstatus, values_from = n) %>% 
#      rowwise() %>% 
#      mutate(summary= sum(c_across(TE_peak:not_TE_peak))) %>% 
#      ungroup() %>% 
#      pivot_longer(., cols= c(TE_peak, not_TE_peak), names_to = c("TEstatus"), values_to = "n") %>% 
#      mutate(percent_mrc= n/summary*100)

# notinterested_list <- c("Simple_repeat","Satellite","Low_complexity","DNA?","snRNA","tRNA","Unknown","RC","LTR?","srpRNA","scRNA","rRNA","RC?","SINE?")

Lines_Etc <-   TE_mrc_status_list %>%
  mutate(repClass_org = repClass) %>%  
  # dplyr::filter(!repClass %in% notinterested_list) %>% 
  mutate(repClass=if_else(##relable repClass with other
    repClass_org=="LINE", repClass_org,
    if_else(repClass_org=="SINE",repClass_org,
            if_else(repClass_org=="LTR", repClass_org, 
                    if_else(repClass_org=="DNA", repClass_org,
              if_else(repClass_org=="Retroposon",repClass_org,
                if_else(is.na(repClass_org),repClass_org,"Other"))))))) %>% 
   dplyr::select(Peakid,repName,repClass,repFamily,TEstatus, mrc, per_ol)%>% 
    distinct(Peakid,mrc, .keep_all = TRUE) %>% 
   mutate(mrc="all_peaks")

LiSiLTDNRe_TE <-TE_mrc_status_list %>% 
   mutate(repClass_org = repClass) %>% 
   mutate(repClass=if_else(##relable repClass with other
    repClass_org=="LINE", repClass_org,
    if_else(repClass_org=="SINE",repClass_org,
            if_else(repClass_org=="LTR", repClass_org, 
                    if_else(repClass_org=="DNA", repClass_org,
        if_else(repClass_org=="Retroposon",repClass_org,
                if_else(is.na(repClass_org),repClass_org,"Other"))))))) %>% 
   dplyr::select(Peakid,repName,repClass,repFamily,TEstatus, mrc, per_ol) %>%
  distinct(Peakid, .keep_all = TRUE) %>% 
  rbind(Lines_Etc) %>% 
  mutate(repClass=factor(repClass)) %>%
  mutate(TEstatus=factor(TEstatus, levels = c("TE_peak","not_TE_peak")))%>%
  dplyr::filter(mrc != "not_mrc") %>% 
  mutate(mrc=factor(mrc, levels = c("EAR","ESR", "LR", "NR","all_peaks"))) %>% 
  group_by(mrc, TEstatus) %>% 
  count() %>% 
  pivot_wider(., id_cols = mrc, names_from = TEstatus, values_from = n) %>% 
  rowwise() %>% 
  mutate(summary= sum(c_across(TE_peak:not_TE_peak))) %>% 
  ungroup() %>% 
  pivot_longer(., cols= c(TE_peak, not_TE_peak), names_to = c("TEstatus"), values_to = "n") %>% 
  mutate(percent_mrc= n/summary*100)

LiSiLTDNRe_TE %>% 
  kable(., caption="Table 5: Summary of peak numbers overlapping and not overlapping TEs by each basic MRC in TE_peak count") %>% 
  kable_paper("striped", full_width = TRUE) %>%
  kable_styling(full_width = FALSE, font_size = 14) %>% 
  scroll_box(height = "500px")

Table 5: Summary of peak numbers overlapping and not overlapping TEs by each basic MRC in TE_peak count

mrc	summary	TEstatus	n	percent_mrc
EAR	7454	TE_peak	3126	41.93721
EAR	7454	not_TE_peak	4328	58.06279
ESR	15781	TE_peak	6594	41.78442
ESR	15781	not_TE_peak	9187	58.21558
LR	42609	TE_peak	18855	44.25121
LR	42609	not_TE_peak	23754	55.74879
NR	86328	TE_peak	37456	43.38801
NR	86328	not_TE_peak	48872	56.61199
all_peaks	155557	TE_peak	67439	43.35324
all_peaks	155557	not_TE_peak	88118	56.64676

# TE_mrc_status_list %>% 
#    mutate(repClass_org = repClass) %>% 
#     mutate(repClass=if_else(##relable repClass with other
#     repClass_org=="LINE", repClass_org,
#     if_else(repClass_org=="SINE",repClass_org,
#             if_else(repClass_org=="LTR", repClass_org, 
#                     if_else(repClass_org=="DNA", repClass_org,                            if_else(repClass_org=="Retroposon",repClass_org,"Other")))))) %>% 
#    dplyr::select(Peakid,repName,repClass,repFamily,TEstatus, mrc, per_ol) %>%
#   distinct(Peakid, .keep_all = TRUE) %>% 
#   rbind(Lines_Etc) %>% 
#   dplyr::filter(is.na(per_ol)| per_ol > per_cov) %>% 
#    mutate(repClass=factor(repClass)) %>% 
#    mutate(TEstatus=factor(TEstatus, levels = c("TE_peak","not_TE_peak")))%>% 
#    dplyr::filter(mrc != "not_mrc") %>% 
#   mutate(mrc=factor(mrc, levels = c("EAR","ESR", "LR", "NR","all_peaks"))) %>% 
#   ggplot(., aes(x=mrc, fill= TEstatus))+
#   geom_bar(position="fill", col="black")+
#   theme_classic()+
#   ggtitle(paste("TE status by MRC and Family", per_cov), subtitle = "Just LINEs, SINEs,LTRs, DNAs, Retroposons, and all peaks in the MRC families")

Chi square tests

# ##complete counts()
# print("Chi square tests without filtering")
# chitest_LRvNRTE_nf <- matrix(c(TE_counts_nofilt$n[5],TE_counts_nofilt$n[6],TE_counts_nofilt$n[7],TE_counts_nofilt$n[8]), ncol=2, byrow=TRUE)
# chisq.test(chitest_LRvNRTE_nf)
# 
# chitest_EARvNRTE_nf <- matrix(c(TE_counts_nofilt$n[1],TE_counts_nofilt$n[2],TE_counts_nofilt$n[7],TE_counts_nofilt$n[8]), ncol=2, byrow=TRUE)
# chisq.test(chitest_EARvNRTE_nf)
# 
# chitest_ESRvNRTE_nf <- matrix(c(TE_counts_nofilt$n[3],TE_counts_nofilt$n[4],TE_counts_nofilt$n[7],TE_counts_nofilt$n[8]), ncol=2, byrow=TRUE)
# chisq.test(chitest_ESRvNRTE_nf)

### just subsets

# print("chi test on subsets without >50% overlap cut off")
# chitest_LRvNRTE_nc <- matrix(c(LiSiLTDNRe_TE_no_cut$n[5],LiSiLTDNRe_TE_no_cut$n[6],LiSiLTDNRe_TE_no_cut$n[7],LiSiLTDNRe_TE_no_cut$n[8]), ncol=2, byrow=TRUE)
# chisq.test(chitest_LRvNRTE_nc)
# 
# chitest_EARvNRTE_nc <- matrix(c(LiSiLTDNRe_TE_no_cut$n[1],LiSiLTDNRe_TE_no_cut$n[2],LiSiLTDNRe_TE_no_cut$n[7],LiSiLTDNRe_TE_no_cut$n[8]), ncol=2, byrow=TRUE)
# chisq.test(chitest_EARvNRTE_nc)
# 
# chitest_ESRvNRTE_nc <- matrix(c(LiSiLTDNRe_TE_no_cut$n[3],LiSiLTDNRe_TE_no_cut$n[4],LiSiLTDNRe_TE_no_cut$n[7],LiSiLTDNRe_TE_no_cut$n[8]), ncol=2, byrow=TRUE)
# chisq.test(chitest_ESRvNRTE_nc)



##### JUst using the .5 overlap cutoff
print(" chi tests using >50% cutoff")

[1] " chi tests using >50% cutoff"

chitest_LRvNRTE <- matrix(c(LiSiLTDNRe_TE$n[5],LiSiLTDNRe_TE$n[6],LiSiLTDNRe_TE$n[7],LiSiLTDNRe_TE$n[8]), ncol=2, byrow=TRUE)
chisq.test(chitest_LRvNRTE)

    Pearson's Chi-squared test with Yates' continuity correction

data:  chitest_LRvNRTE
X-squared = 8.6061, df = 1, p-value = 0.00335

chitest_EARvNRTE <- matrix(c(LiSiLTDNRe_TE$n[1],LiSiLTDNRe_TE$n[2],LiSiLTDNRe_TE$n[7],LiSiLTDNRe_TE$n[8]), ncol=2, byrow=TRUE)
chisq.test(chitest_EARvNRTE)

    Pearson's Chi-squared test with Yates' continuity correction

data:  chitest_EARvNRTE
X-squared = 5.8244, df = 1, p-value = 0.01581

chitest_ESRvNRTE <- matrix(c(LiSiLTDNRe_TE$n[3],LiSiLTDNRe_TE$n[4],LiSiLTDNRe_TE$n[7],LiSiLTDNRe_TE$n[8]), ncol=2, byrow=TRUE)
chisq.test(chitest_ESRvNRTE)

    Pearson's Chi-squared test with Yates' continuity correction

data:  chitest_ESRvNRTE
X-squared = 13.922, df = 1, p-value = 0.0001906

Breakdown of Classes by 4 member clusters

per_cov <- 0.5
 
# ggline_df <-   Line_repeats %>%
#   as.data.frame() %>% 
#  tidyr::unite(Peakid,seqnames:end, sep= ".") %>% 
#   dplyr::select(Peakid,repName,repClass, repFamily) %>% 
#   mutate(TEstatus ="TE_peak", mrc="h.genome", per_ol = "NA") %>% 
  ggline_df <-   TE_mrc_status_list %>%  
             mutate(TEstatus= if_else(is.na(repClass), "not_TE_peak","TE_peak")) %>%
             dplyr::filter(repClass=="LINE") %>%
  distinct(Peakid, TEstatus,repClass,.keep_all = TRUE) %>% 
  mutate(mrc="all_peaks")

ggsine_df <-TE_mrc_status_list %>%  
             mutate(TEstatus= if_else(is.na(repClass), "not_TE_peak","TE_peak")) %>%
             dplyr::filter(repClass=="SINE") %>%
  distinct(Peakid, TEstatus,repClass,.keep_all = TRUE) %>% 
  mutate(mrc="all_peaks")

 #  Sine_repeats %>% 
 #  as.data.frame() %>% 
 # tidyr::unite(Peakid,seqnames:end, sep= ".") %>% 
 #  dplyr::select(Peakid,repName,repClass, repFamily) %>% 
 #  mutate(TEstatus ="TE_peak", mrc="h.genome",per_ol = "NA") %>% 
 #    rbind((TE_mrc_status_list %>%  
 #             mutate(TEstatus= if_else(is.na(repClass), "not_TE_peak","TE_peak")) %>%
 #             dplyr::filter(repClass=="SINE"))) %>%
 #  mutate(mrc="all_peaks")

ggLTR_df <- TE_mrc_status_list %>%  
             mutate(TEstatus= if_else(is.na(repClass), "not_TE_peak","TE_peak")) %>%
             dplyr::filter(repClass=="LTR") %>%
  distinct(Peakid, TEstatus,repClass,.keep_all = TRUE) %>% 
  mutate(mrc="all_peaks")

 #  LTR_repeats %>% 
 #  as.data.frame() %>% 
 # tidyr::unite(Peakid,seqnames:end, sep= ".") %>% 
 #  dplyr::select(Peakid,repName,repClass, repFamily) %>% 
 #  mutate(TEstatus ="TE_peak", mrc="h.genome",per_ol = "NA")%>% 
 #    rbind((TE_mrc_status_list %>%  
 #             mutate(TEstatus= if_else(is.na(repClass), "not_TE_peak","TE_peak")) %>%
 #             dplyr::filter(repClass=="LTR"))) %>%
 #  mutate(mrc="all_peaks")


ggDNA_df <-TE_mrc_status_list %>%  
             mutate(TEstatus= if_else(is.na(repClass), "not_TE_peak","TE_peak")) %>%
             dplyr::filter(repClass=="DNA") %>%
  distinct(Peakid, TEstatus,repClass,.keep_all = TRUE) %>% 
  mutate(mrc="all_peaks")

 #  DNA_repeats %>% 
 #  as.data.frame() %>% 
 # tidyr::unite(Peakid,seqnames:end, sep= ".") %>% 
 #  dplyr::select(Peakid,repName,repClass, repFamily) %>% 
 #  mutate(TEstatus ="TE_peak", mrc="h.genome",per_ol = "NA")%>% 
 #     rbind((TE_mrc_status_list %>%  
 #             mutate(TEstatus= if_else(is.na(repClass), "not_TE_peak","TE_peak")) %>%
 #             dplyr::filter(repClass=="DNA"))) %>%
 #  mutate(mrc="all_peaks")

ggretroposon_df <-TE_mrc_status_list %>%  
             mutate(TEstatus= if_else(is.na(repClass), "not_TE_peak","TE_peak")) %>%
             dplyr::filter(repClass=="Retroposons") %>%
  distinct(Peakid, TEstatus,repClass,.keep_all = TRUE) %>% 
  mutate(mrc="all_peaks")

  # retroposon_repeats %>% 
 #  as.data.frame() %>% 
 # tidyr::unite(Peakid,seqnames:end, sep= ".") %>% 
 #  dplyr::select(Peakid,repName,repClass, repFamily) %>% 
 #  mutate(TEstatus ="TE_peak", mrc="h.genome",per_ol = "NA")%>% 
 #     rbind((TE_mrc_status_list %>%  
 #             mutate(TEstatus= if_else(is.na(repClass), "not_TE_peak","TE_peak")) %>%
 #             dplyr::filter(repClass=="Retroposon"))) %>%
 #  mutate(mrc="all_peaks")


plot1 <- TE_mrc_status_list %>% 
  dplyr::filter(repClass=="LINE") %>% 
  dplyr::filter(mrc != "not_mrc") %>% 
  rbind(., ggline_df) %>% 
  mutate(repFamily=factor(repFamily)) %>% 
  mutate(mrc=factor(mrc, levels = c("EAR","ESR", "LR", "NR","all_peaks","h.genome"))) %>% 
  ggplot(., aes(x=mrc, fill= repFamily))+
  geom_bar(position="fill", col="black")+
  theme_bw()+
  ggtitle(paste("LINE breakdown by MRC and Family", per_cov))+
  scale_fill_lines()
plot1

Past versions of "breakdown by cluster-1.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

plot2 <- TE_mrc_status_list %>% 
  dplyr::filter(repClass=="SINE") %>% 
  dplyr::filter(mrc != "not_mrc") %>% 
   rbind(., ggsine_df) %>% 
  mutate(repFamily=factor(repFamily)) %>% 
  mutate(mrc=factor(mrc, levels = c("EAR","ESR", "LR", "NR","all_peaks","h.genome"))) %>% 
  ggplot(., aes(x=mrc, fill= repFamily))+
  geom_bar(position="fill", col="black")+
  theme_bw()+
  ggtitle(paste("SINE breakdown by MRC and Family",per_cov))+
  scale_fill_sines()
plot2

Past versions of "breakdown by cluster-2.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

TE_mrc_status_list %>% 
  dplyr::filter(repClass=="LTR") %>% 
  dplyr::filter(mrc != "not_mrc") %>% 
   rbind(., ggLTR_df) %>% 
  mutate(repFamily=factor(repFamily)) %>% 
  mutate(mrc=factor(mrc, levels = c("EAR","ESR", "LR", "NR","all_peaks","h.genome"))) %>% 
  ggplot(., aes(x=mrc, fill= repFamily))+
  geom_bar(position="fill", col="black")+
  theme_bw()+
  ggtitle(paste("LTR breakdown by MRC and Family",per_cov))+
  scale_fill_LTRs()

Past versions of "breakdown by cluster-3.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

TE_mrc_status_list %>% 
  dplyr::filter(repClass=="DNA") %>% 
  dplyr::filter(mrc != "not_mrc") %>% 
   rbind(., ggDNA_df) %>% 
  mutate(repFamily=factor(repFamily)) %>% 
  mutate(mrc=factor(mrc, levels = c("EAR","ESR", "LR", "NR","all_peaks","h.genome"))) %>% 
  ggplot(., aes(x=mrc, fill= repFamily))+
  geom_bar(position="fill", col="black")+
  theme_classic()+
  ggtitle(paste("DNA breakdown by MRC and Family",per_cov))+
  scale_fill_DNAs()

Past versions of "breakdown by cluster-4.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

TE_mrc_status_list %>% 
  dplyr::filter(repClass=="Retroposon") %>% 
  dplyr::filter(mrc != "not_mrc") %>% 
   rbind(., ggretroposon_df) %>% 
  mutate(repName=factor(repName)) %>% 
  mutate(mrc=factor(mrc, levels = c("EAR","ESR", "LR", "NR","all_peaks","h.genome"))) %>% 
  ggplot(., aes(x=mrc, fill= repName))+
  geom_bar(position="fill", col="black")+
  theme_classic()+
  ggtitle(paste("Retroposon breakdown by MRC and Family",per_cov))+
  scale_fill_retroposons()

Past versions of "breakdown by cluster-5.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

Adding in the new categories

Here I break up the categories using median LFC

• EAR_open = median 3 hour LFC > 0
  
• EAR_close = median 3 hour LFC < 0
• ESR_A = median 3 hour > 0 and median 24 hour >0
• ESR_B = median 3 hour < 0 and median 24 hour <0
• ESR_C = median 3 hour > 0 and median 24 hour <0
• ESR_D = median 3 hour < 0 and median 24 hour >0
• LR_open = median 24 hour LFC > 0
  
• LR_close = median 24 hour LFC < 0
  
• NR = all NR classified peaks

per_cov=0.5
median_24_lfc <- read_csv("data/Final_four_data/median_24_lfc.csv") 
median_3_lfc <- read_csv("data/Final_four_data/median_3_lfc.csv")

open_3med <- median_3_lfc %>% 
  dplyr::filter(med_3h_lfc > 0)

close_3med <- median_3_lfc %>% 
  dplyr::filter(med_3h_lfc < 0)

open_24med <- median_24_lfc %>% 
  dplyr::filter(med_24h_lfc > 0)

close_24med <- median_24_lfc %>% 
  dplyr::filter(med_24h_lfc < 0)

medA <- median_3_lfc %>% 
  left_join(median_24_lfc, by=c("peak"="peak")) %>% 
  dplyr::filter(med_3h_lfc > 0 & med_24h_lfc>0)

medB <- median_3_lfc %>% 
  left_join(median_24_lfc, by=c("peak"="peak")) %>% 
  dplyr::filter(med_3h_lfc < 0 & med_24h_lfc < 0)
 
medC <- median_3_lfc %>% 
  left_join(median_24_lfc, by=c("peak"="peak")) %>% 
  dplyr::filter(med_3h_lfc > 0& med_24h_lfc <0)
  

medD <- median_3_lfc %>% 
 left_join(median_24_lfc, by=c("peak"="peak"))%>% 
  dplyr::filter(med_3h_lfc < 0 & med_24h_lfc > 0)
 

EAR_open <- EAR_df %>%
  dplyr::filter(Peakid %in% open_3med$peak)
  
EAR_open_gr <- EAR_open %>% GRanges()

EAR_close <- EAR_df %>%
  dplyr::filter(Peakid %in% close_3med$peak) 

EAR_close_gr <- EAR_close %>% GRanges()

LR_open <- LR_df %>%
  dplyr::filter(Peakid %in% open_24med$peak) 

LR_open_gr <- LR_open %>% GRanges()

LR_close <- LR_df %>%
  dplyr::filter(Peakid %in% close_24med$peak) 

LR_close_gr <- LR_close %>% GRanges()

NR_gr <- NR_df %>% 
   GRanges()

ESR_open <- ESR_df %>% 
  dplyr::filter(Peakid %in% medA$peak)  
 
ESR_open_gr <- ESR_open %>% GRanges()

ESR_close <- ESR_df %>% 
  dplyr::filter(Peakid %in% medB$peak)  

ESR_close_gr <- ESR_close %>% GRanges()

ESR_C <- ESR_df %>% 
  dplyr::filter(Peakid %in% medC$peak) 

ESR_D <- ESR_df %>% 
  dplyr::filter(Peakid %in% medD$peak) 


ESR_OC <- ESR_C %>% 
  rbind(ESR_D)
ESR_OC_gr <- ESR_OC %>% GRanges()

Eight_group_TE <-  Col_TSS_data_gr %>% 
  as.data.frame %>% 
  dplyr::select(Peakid) %>% 
  left_join(.,(Col_fullDF_overlap %>% 
                 as.data.frame)) %>% 
   # mutate(TEstatus=if_else(is.na(repClass),"not_TE_peak","TE_peak")) %>% 
   mutate(mrc=if_else(Peakid %in% EAR_open$Peakid, "EAR_open",
                      if_else(Peakid %in% EAR_close$Peakid, "EAR_close",
                              if_else(Peakid %in% ESR_open$Peakid,"ESR_open",
                                      if_else(Peakid %in% ESR_close$Peakid,"ESR_close",
                                              if_else(Peakid %in% ESR_OC$Peakid,"ESR_OC",
                                                              if_else(Peakid %in% LR_open$Peakid, "LR_open",
                                                                      if_else(Peakid %in% LR_close$Peakid, "LR_close",
                                     if_else(Peakid %in% NR_df$Peakid, "NR", "not_mrc"))))))))) %>%  
                                       mutate(per_ol= width/TE_width) %>% 
  mutate(repClass_org=repClass) %>% 
  mutate(repClass=if_else(per_ol>per_cov, repClass,                          if_else(per_ol<per_cov,NA,repClass))) %>%
  mutate(TEstatus=if_else(is.na(repClass),"not_TE_peak","TE_peak")) %>%
  mutate(repClass=factor(repClass)) %>%
  mutate(repClass=if_else(##relable repClass with other
    repClass_org=="LINE", repClass_org,
    if_else(repClass_org=="SINE",repClass_org,
            if_else(repClass_org=="LTR", repClass_org, 
                    if_else(repClass_org=="DNA", repClass_org,
                            if_else(repClass_org=="Retroposon",repClass_org,
                                    if_else(is.na(repClass_org), repClass_org, "Other"))))))) %>% 
  dplyr::select(Peakid, repName,repClass,repClass_org, repFamily, width, TEstatus, mrc, per_ol)


Eight_group_TE %>% distinct(Peakid,.keep_all = TRUE) %>% 
  group_by(mrc, TEstatus) %>% 
  tally

# A tibble: 18 × 3
# Groups:   mrc [9]
   mrc       TEstatus        n
   <chr>     <chr>       <int>
 1 EAR_close TE_peak      1728
 2 EAR_close not_TE_peak  2568
 3 EAR_open  TE_peak      1398
 4 EAR_open  not_TE_peak  1760
 5 ESR_OC    TE_peak       496
 6 ESR_OC    not_TE_peak   664
 7 ESR_close TE_peak      3657
 8 ESR_close not_TE_peak  5692
 9 ESR_open  TE_peak      2441
10 ESR_open  not_TE_peak  2831
11 LR_close  TE_peak      5833
12 LR_close  not_TE_peak  8239
13 LR_open   TE_peak     13022
14 LR_open   not_TE_peak 15515
15 NR        TE_peak     37456
16 NR        not_TE_peak 48872
17 not_mrc   TE_peak      1408
18 not_mrc   not_TE_peak  1977

EAR

ESR

Past versions of ESR-te-1.png

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13

Past versions of ESR-te-2.png

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13

Past versions of ESR-te-3.png

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13

LR

Past versions of LRTE-1.png

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13

Past versions of LRTE-2.png

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13

NR

Past versions of NR-te-1.png

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13

sub-set breakdown with new classes

This section reclassifies any TE that is NOT a LINE, SINE, LTR, DNA, or retroposon as “other”. Still using > 50% coverage cutoff

per_cov <- 0.5
subsetall_df <-  Eight_group_TE %>% 
  dplyr::filter(mrc != "not_mrc") %>%
  mutate(mrc="all_peaks") 

# h.genome_df <- LiSiLTDNRe %>% 
h.genome_df <- repeatmasker %>% 
  mutate(repClass_org = repClass) %>% #copy repClass for storage
  mutate(repClass=if_else(##relable repClass with other
    repClass_org=="LINE", repClass_org,if_else(repClass_org=="SINE",repClass_org,if_else(repClass_org=="LTR", repClass_org, if_else(repClass_org=="DNA", repClass_org, if_else(repClass_org=="Retroposon",repClass_org,"Other")))))) %>% 
  mutate(Peakid=paste0(rownames(.),"_TE")) %>% 
  dplyr::select(Peakid,repName,repClass, repFamily,repClass_org) %>%
   mutate(TEstatus ="TE_peak", mrc="h.genome",per_ol = "NA", width="NA")


Eight_group_TE %>%
  dplyr::filter(mrc != "not_mrc") %>%
  # dplyr::filter(per_ol>per_cov) %>% 
  rbind(subsetall_df) %>%
  mutate(mrc=factor(mrc, levels = c("EAR_open","EAR_close","ESR_open", "ESR_close","ESR_OC", "LR_open","LR_close","NR","all_peaks"))) %>%
  dplyr::filter(TEstatus=="TE_peak") %>% 
  ggplot(., aes(x=mrc, fill= repClass))+
  geom_bar(position="fill", col="black")+
  theme_bw()+
  ggtitle(paste("Repeat breakdown across eight clusters", per_cov))+
  scale_fill_repeat()

Past versions of "breakdown by specific cluster-1.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

Now I will display without the “other” and regraph with new groups

Eight_group_TE %>%
  dplyr::filter(mrc != "not_mrc") %>%
  dplyr::filter(per_ol>per_cov) %>% 
  rbind(subsetall_df) %>%
  mutate(mrc=factor(mrc, levels = c("EAR_open","EAR_close","ESR_open", "ESR_close","ESR_OC", "LR_open","LR_close","NR","all_peaks"))) %>%
  dplyr::filter(TEstatus=="TE_peak") %>% 
  dplyr::filter(is.na(repClass)|repClass != "Other") %>% 
  ggplot(., aes(x=mrc, fill= repClass))+
  geom_bar(position="fill", col="black")+
  theme_bw()+
   ggtitle(paste("Repeat breakdown across eight clusters without 'Other'", per_cov))+
  scale_fill_repeat()

Past versions of "eight group breakdown-1.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

Eight_group_TE %>% 
   mutate(repClass_org = repClass) %>% 
  # mutate(repClass=if_else(per_ol>0.5, repClass,NA)) %>% 
   dplyr::select(Peakid,repName,repClass,repFamily,repClass_org,width,TEstatus, mrc, per_ol)%>%
  distinct(Peakid, .keep_all = TRUE)%>% 
  rbind((subsetall_df %>% distinct(Peakid,TEstatus,.keep_all = TRUE))) %>% 
    mutate(repClass=if_else(per_ol>0.5, repClass,NA)) %>% 
   mutate(TEstatus= if_else(is.na(repClass), "not_TE_peak","TE_peak")) %>% 
  mutate(repClass=factor(repClass)) %>% 
  mutate(TEstatus=factor(TEstatus, levels = c("TE_peak","not_TE_peak")))%>% 
  dplyr::filter(mrc != "not_mrc") %>% 
  mutate(mrc=factor(mrc, levels = c("EAR_open","EAR_close","ESR_open", "ESR_close","ESR_OC", "LR_open","LR_close","NR","all_peaks"))) %>%
  dplyr::filter(is.na(repClass)|repClass != "Other") %>% 
  ggplot(., aes(x=mrc, fill= TEstatus))+
  geom_bar(position="fill", col="black")+
  theme_classic()+
  ggtitle(paste("TE status for eight MRCs and Family > 50 %"))

Past versions of "TEs by without other-1.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13

Eight_group_TE %>% 
  mutate(repClass_org = repClass) %>% 
  mutate(repClass=if_else(per_ol>0.5, repClass,NA)) %>% 
   dplyr::select(Peakid,repName,repClass,repFamily,repClass_org,width,TEstatus, mrc, per_ol)%>%
  distinct(Peakid, .keep_all = TRUE)%>% 
  rbind((subsetall_df %>% distinct(Peakid,TEstatus, .keep_all = TRUE))) %>% 
    mutate(repClass=if_else(per_ol>0.5, repClass,NA)) %>% 
   mutate(TEstatus= if_else(is.na(repClass), "not_TE_peak","TE_peak")) %>%
  mutate(repClass=factor(repClass)) %>% 
  dplyr::filter(mrc != "not_mrc") %>%
  dplyr::filter(is.na(repClass)|repClass != "Other") %>% 
  group_by(TEstatus, mrc) %>% 
  tally %>% 
  pivot_wider(., id_cols = mrc, names_from = TEstatus, values_from = n) %>% 
  rowwise() %>% 
  mutate(perc_TE= (TE_peak/sum(c_across(TE_peak:not_TE_peak)))*100) %>% 
  kable(., caption = "Unique Peak TE status counts for each using >50% of TE LINEs,SINEs, LTRs,DNAs,and Retroposons") %>% 
  kable_paper("striped", full_width = TRUE) %>%
  kable_styling(full_width = FALSE, font_size = 14)%>% 
  scroll_box(height = "500px")

Unique Peak TE status counts for each using >50% of TE LINEs,SINEs, LTRs,DNAs,and Retroposons

mrc	TE_peak	not_TE_peak	perc_TE
EAR_close	1415	2574	35.47255
EAR_open	878	1761	33.27018
ESR_OC	433	667	39.36364
ESR_close	2739	5698	32.46415
ESR_open	1648	2841	36.71196
LR_close	4581	8251	35.69981
LR_open	10659	15560	40.65372
NR	26417	48978	35.03813
all_peaks	59855	97170	38.11813

Eight_group_TE %>% 
   mutate(repClass_org = repClass) %>% 
  # mutate(repClass=if_else(per_ol>0.5, repClass,NA)) %>% 
   dplyr::select(Peakid,repName,repClass,repFamily,repClass_org,width,TEstatus, mrc, per_ol)%>%
  distinct(Peakid, .keep_all = TRUE)%>% 
  rbind(subsetall_df ) %>% 
    mutate(repClass=if_else(per_ol>0.5, repClass,NA)) %>% 
   mutate(TEstatus= if_else(is.na(repClass), "not_TE_peak","TE_peak")) %>% 
  mutate(repClass=factor(repClass)) %>% 
  mutate(TEstatus=factor(TEstatus, levels = c("TE_peak","not_TE_peak")))%>% 
  dplyr::filter(mrc != "not_mrc") %>% 
  mutate(mrc=factor(mrc, levels = c("EAR_open","EAR_close","ESR_open", "ESR_close","ESR_OC", "LR_open","LR_close","NR","all_peaks"))) %>%
  ggplot(., aes(x=mrc, fill= TEstatus))+
  geom_bar(position="fill", col="black")+
  theme_classic()+
  ggtitle(paste("TE status for eight MRCs and Family > 50 %"))

Past versions of "with out the other-1.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13

Eight_group_TE %>% 
  mutate(repClass_org = repClass) %>% 
  mutate(repClass=if_else(per_ol>0.5, repClass,NA)) %>% 
   dplyr::select(Peakid,repName,repClass,repFamily,repClass_org,width,TEstatus, mrc, per_ol)%>%
  distinct(Peakid, .keep_all = TRUE)%>% 
  rbind(subsetall_df) %>% 
    mutate(repClass=if_else(per_ol>0.5, repClass,NA)) %>% 
   mutate(TEstatus= if_else(is.na(repClass), "not_TE_peak","TE_peak")) %>%
  mutate(repClass=factor(repClass)) %>% 
  dplyr::filter(mrc != "not_mrc") %>% 
  group_by(TEstatus, mrc) %>% 
  tally %>% 
  pivot_wider(., id_cols = mrc, names_from = TEstatus, values_from = n) %>% 
  rowwise() %>% 
  mutate(perc_TE= (TE_peak/sum(c_across(TE_peak:not_TE_peak)))*100) %>% 
  kable(., caption = "Unique Peak TE status counts for each  using >50% of TE") %>% 
  kable_paper("striped", full_width = TRUE) %>%
  kable_styling(full_width = FALSE, font_size = 14)%>% 
  scroll_box(height = "500px")

Unique Peak TE status counts for each using >50% of TE

mrc	TE_peak	not_TE_peak	perc_TE
EAR_close	1722	2574	40.08380
EAR_open	1397	1761	44.23686
ESR_OC	493	667	42.50000
ESR_close	3651	5698	39.05231
ESR_open	2431	2841	46.11153
LR_close	5821	8251	41.36583
LR_open	12977	15560	45.47430
NR	37350	48978	43.26522
all_peaks	120003	102381	53.96207

Class_status_df <-
  Eight_group_TE %>% 
  dplyr::filter(mrc != "not_mrc") %>%
    # mutate(mrc="all_peaks") %>% 
  # rbind(subsetall_df) %>% 
   mutate(Sine_status = if_else(is.na(repClass),"not_sine",
                               if_else(repClass=="SINE","sine_peak", "not_sine"))) %>% 
   mutate(Line_status = if_else(is.na(repClass),"not_line",
                                if_else(repClass=="LINE","line_peak", "not_line"))) %>%
   mutate(LTR_status = if_else(is.na(repClass),"not_LTR",
                               if_else(repClass=="LTR","LTR_peak", "not_LTR"))) %>% 
   mutate(DNA_status = if_else(is.na(repClass),"not_DNA",
                                if_else(repClass=="DNA","DNA_peak", "not_DNA"))) %>% 
   mutate(Retro_status = if_else(is.na(repClass)&is.na(per_ol),"not_Retro",
                                if_else(repClass=="Retroposon","Retro_peak", "not_Retro"))) %>% 
     # mutate(TEstatus=if_else(is.na(repClass),"not_TE_peak","TE_peak")) %>%
     mutate(TEstatus=factor(TEstatus, levels = c("TE_peak","not_TE_peak")))%>% 
   # dplyr::filter(mrc != "not_mrc") %>%
 mutate(Sine_status=factor(Sine_status, levels = c("sine_peak","not_sine")),
       Line_status=factor(Line_status, levels =c("line_peak","not_line")),
       LTR_status=factor(LTR_status, levels =c("LTR_peak","not_LTR")),
       DNA_status=factor(DNA_status, levels =c("DNA_peak","not_DNA")),
       Retro_status=factor(Retro_status, levels =c("Retro_peak","not_Retro"))) %>% 
  # dplyr::filter(per_ol>per_cov|is.na(per_ol)) %>% 
   mutate(mrc=factor(mrc, levels = c("EAR_open","EAR_close","ESR_open", "ESR_close","ESR_OC", "LR_open","LR_close","NR","all_peaks")))

Class_status_df  %>% distinct(Peakid,.keep_all = TRUE) %>% 
  group_by(mrc, TEstatus) %>% 
  tally

# A tibble: 16 × 3
# Groups:   mrc [8]
   mrc       TEstatus        n
   <fct>     <fct>       <int>
 1 EAR_open  TE_peak      1398
 2 EAR_open  not_TE_peak  1760
 3 EAR_close TE_peak      1728
 4 EAR_close not_TE_peak  2568
 5 ESR_open  TE_peak      2441
 6 ESR_open  not_TE_peak  2831
 7 ESR_close TE_peak      3657
 8 ESR_close not_TE_peak  5692
 9 ESR_OC    TE_peak       496
10 ESR_OC    not_TE_peak   664
11 LR_open   TE_peak     13022
12 LR_open   not_TE_peak 15515
13 LR_close  TE_peak      5833
14 LR_close  not_TE_peak  8239
15 NR        TE_peak     37456
16 NR        not_TE_peak 48872

Sine status

Class_status_df %>% 
  distinct(Peakid, mrc, Sine_status) %>% 
  ggplot(., aes(x=mrc, fill= Sine_status))+
  geom_bar(position="fill", col="black")+
  theme_classic()+
  ggtitle("Sine_status")

Past versions of "status plots sine-1.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13

Class_status_df %>% 
  distinct(Peakid, mrc, Sine_status) %>% 
  group_by(mrc,Sine_status) %>% 
  tally %>% 
  pivot_wider(., id_cols = mrc, names_from = Sine_status, values_from = n) %>% 
  mutate(Per_peak = sine_peak/(not_sine+sine_peak)*100) %>% 
  kable(., caption= "Sine status of unique Peaks") %>% 
  kable_paper("striped", full_width = TRUE) %>%
  kable_styling(full_width = FALSE, font_size = 14) 

Sine status of unique Peaks

mrc	sine_peak	not_sine	Per_peak
EAR_open	800	2785	22.31520
EAR_close	1232	3638	25.29774
ESR_open	1379	4605	23.04479
ESR_close	2458	8046	23.40061
ESR_OC	388	956	28.86905
LR_open	8817	24248	26.66566
LR_close	3626	12252	22.83663
NR	23227	74915	23.66673

Line status

Class_status_df %>% 
  distinct(Peakid, mrc, Line_status) %>% 
  ggplot(., aes(x=mrc, fill= Line_status))+
  geom_bar(position="fill", col="black")+
  theme_classic()+
  ggtitle("Line_status")

Past versions of "status plots lines-1.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13

Class_status_df %>% 
  distinct(Peakid, mrc, Line_status) %>% 
  group_by(mrc,Line_status) %>% 
  tally %>% 
  pivot_wider(., id_cols = mrc, names_from = Line_status, values_from = n) %>% 
  mutate(Per_peak = line_peak/(not_line+line_peak)*100) %>% 
  kable(., caption= "Line status of unique Peaks") %>% 
  kable_paper("striped", full_width = TRUE) %>%
  kable_styling(full_width = FALSE, font_size = 14) 

Line status of unique Peaks

mrc	line_peak	not_line	Per_peak
EAR_open	603	2882	17.30273
EAR_close	950	3763	20.15701
ESR_open	1126	4749	19.16596
ESR_close	1939	8278	18.97817
ESR_OC	276	1023	21.24711
LR_open	7051	25214	21.85340
LR_close	3240	12339	20.79723
NR	18221	77137	19.10799

LTR status

Class_status_df %>% 
  distinct(Peakid, mrc,LTR_status) %>% 
  ggplot(., aes(x=mrc, fill= LTR_status))+
  geom_bar(position="fill", col="black")+
  theme_classic()+
  ggtitle("LTR_status")

Past versions of "status plots ltrs-1.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13

Class_status_df %>% 
  distinct(Peakid, mrc, LTR_status) %>% 
  group_by(mrc,LTR_status) %>% 
  tally %>% 
  pivot_wider(., id_cols = mrc, names_from = LTR_status, values_from = n) %>% 
  mutate(Per_peak = LTR_peak/(not_LTR+LTR_peak)*100) %>% 
  kable(., caption= "LTR status of unique Peaks") %>% 
  kable_paper("striped", full_width = TRUE) %>%
  kable_styling(full_width = FALSE, font_size = 14) 

LTR status of unique Peaks

mrc	LTR_peak	not_LTR	Per_peak
EAR_open	356	2972	10.69712
EAR_close	674	3905	14.71937
ESR_open	621	4969	11.10912
ESR_close	1264	8614	12.79611
ESR_OC	198	1053	15.82734
LR_open	4871	26033	15.76171
LR_close	2242	12784	14.92080
NR	12012	79598	13.11211

DNA status

Class_status_df %>% 
  distinct(Peakid, mrc, DNA_status) %>% 
  ggplot(., aes(x=mrc, fill= DNA_status))+
  geom_bar(position="fill", col="black")+
  theme_classic()+
  ggtitle("DNA_status")

Past versions of "status plots dna-1.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13

Class_status_df %>% 
  distinct(Peakid, mrc, DNA_status) %>% 
  group_by(mrc,DNA_status) %>% 
  tally %>% 
  pivot_wider(., id_cols = mrc, names_from = DNA_status, values_from = n) %>% 
  mutate(Per_peak = DNA_peak/(not_DNA+DNA_peak)*100) %>% 
  kable(., caption= "DNA status of unique Peaks") %>% 
  kable_paper("striped", full_width = TRUE) %>%
  kable_styling(full_width = FALSE, font_size = 14) 

DNA status of unique Peaks

mrc	DNA_peak	not_DNA	Per_peak
EAR_open	269	3045	8.117079
EAR_close	363	4137	8.066667
ESR_open	486	5091	8.714363
ESR_close	777	9017	7.933429
ESR_OC	124	1112	10.032362
LR_open	3311	27192	10.854670
LR_close	1363	13484	9.180306
NR	8093	83036	8.880817

Retroposon status

Class_status_df %>% 
  distinct(Peakid, mrc, Retro_status) %>% 
  ggplot(., aes(x=mrc, fill= Retro_status))+
  geom_bar(position="fill", col="black")+
  theme_classic()+
  ggtitle("Retroposon status")

Past versions of "status plots Retroposons-1.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13

Class_status_df %>% 
  distinct(Peakid, mrc, Retro_status) %>% 
  group_by(mrc,Retro_status) %>% 
  tally %>% 
  pivot_wider(., id_cols = mrc, names_from = Retro_status, values_from = n) %>% 
  mutate(Per_peak = Retro_peak/(not_Retro+Retro_peak)*100) %>% 
  kable(., caption= "Retro status of unique Peaks") %>% 
  kable_paper("striped", full_width = TRUE) %>%
  kable_styling(full_width = FALSE, font_size = 14) 

Retro status of unique Peaks

mrc	Retro_peak	not_Retro	Per_peak
EAR_open	21	3149	0.6624606
EAR_close	7	4292	0.1628286
ESR_open	40	5260	0.7547170
ESR_close	11	9346	0.1175590
ESR_OC	2	1159	0.1722653
LR_open	36	28529	0.1260284
LR_close	18	14067	0.1277955
NR	148	86286	0.1712289

This code is for Chi analysis

####### LOoKing at the results from TE counts across clusters##### with cutoff

chi_TE_Eight <- Eight_group_TE %>% 
  # dplyr::filter(is.na(per_ol)|per_ol>.5) %>% 
  dplyr::select(Peakid,TEstatus, mrc)%>% 
  distinct(Peakid, .keep_all = TRUE) %>% 
  group_by(mrc,TEstatus) %>% 
  tally %>% 
  ungroup() %>% 
  pivot_wider(., id_cols = mrc, names_from = TEstatus, values_from = n) %>% 
  column_to_rownames("mrc") 

chi_TE_nine_mat <- chi_TE_Eight %>% as.matrix

EARopenvNR <- matrix(c(chi_TE_nine_mat[2,1],chi_TE_nine_mat[2,2],chi_TE_nine_mat[8,1],chi_TE_nine_mat[8,2]),ncol=2, byrow=TRUE)%>% chisq.test(.)

EARclosevNR <- matrix(c(chi_TE_nine_mat[1,1],chi_TE_nine_mat[1,2],chi_TE_nine_mat[8,1],chi_TE_nine_mat[8,2]),ncol=2, byrow=TRUE)%>% chisq.test(.)

ESRopenvNR <- matrix(c(chi_TE_nine_mat[5,1],chi_TE_nine_mat[5,2],chi_TE_nine_mat[8,1],chi_TE_nine_mat[8,2]),ncol=2, byrow=TRUE)%>% chisq.test(.)

ESRclosevNR <- matrix(c(chi_TE_nine_mat[4,1],chi_TE_nine_mat[4,2],chi_TE_nine_mat[8,1],chi_TE_nine_mat[8,2]),ncol=2, byrow=TRUE)%>% chisq.test(.)

ESROCvNR <- matrix(c(chi_TE_nine_mat[3,1],chi_TE_nine_mat[3,2],chi_TE_nine_mat[8,1],chi_TE_nine_mat[8,2]),ncol=2, byrow=TRUE)%>% chisq.test(.)


LRopenvNR <- matrix(c(chi_TE_nine_mat[7,1],chi_TE_nine_mat[7,2],chi_TE_nine_mat[8,1],chi_TE_nine_mat[8,2]),ncol=2, byrow=TRUE) %>% chisq.test(.)

LRclosevNR <- matrix(c(chi_TE_nine_mat[6,1],chi_TE_nine_mat[6,2],chi_TE_nine_mat[8,1],chi_TE_nine_mat[8,2]),ncol=2, byrow=TRUE)%>% chisq.test(.)

pvalue <- c(EARclosevNR$p.value, 
            EARopenvNR$p.value,
            ESROCvNR$p.value,
            ESRclosevNR$p.value,
            ESRopenvNR$p.value,
            LRclosevNR$p.value, 
            LRopenvNR$p.value, 
            "na",
            "not checked")

chi_TE_Eight %>% 
  cbind(pvalue) %>% 
  mutate(pvalue= as.numeric(pvalue)) %>% 
  mutate(signif=if_else(pvalue<0.005,"***",if_else(pvalue<0.01,"**",if_else(pvalue<0.05,"*","ns")))) %>% 
  mutate(per_TE=TE_peak/(TE_peak+not_TE_peak)*100) %>% 
  mutate(per_TE=sprintf("%.2f%% ",per_TE)) %>% 
    kable(., caption = "Unique Peak TE status with all TEs for each MRC, >50% TE coverage") %>% 
   kable_paper("striped", full_width = TRUE) %>%
  kable_styling(full_width = FALSE, font_size = 14) %>% 
  scroll_box(height = "500px")

Unique Peak TE status with all TEs for each MRC, >50% TE coverage

	TE_peak	not_TE_peak	pvalue	signif	per_TE
EAR_close	1728	2568	0.0000469	***	40.22%
EAR_open	1398	1760	0.3359162	ns	44.27%
ESR_OC	496	664	0.6892784	ns	42.76%
ESR_close	3657	5692	0.0000000	***	39.12%
ESR_open	2441	2831	0.0000367	***	46.30%
LR_close	5833	8239	0.0000176	***	41.45%
LR_open	13022	15515	0.0000000	***	45.63%
NR	37456	48872	NA	NA	43.39%
not_mrc	1408	1977	NA	NA	41.60%

sub-Class breakdown

per_cov <- 0.5
 
ggline_df <-Line_repeats %>% 
  as.data.frame() %>% 
 tidyr::unite(Peakid,seqnames:end, sep= ".") %>% 
  dplyr::select(Peakid,repName,repClass, repFamily,width) %>%
   mutate(repClass_org=repClass) %>% 
   mutate(repClass=if_else(##relable repClass with other
    repClass_org=="LINE", repClass_org,
    if_else(repClass_org=="SINE",repClass_org,
            if_else(repClass_org=="LTR", repClass_org, 
                    if_else(repClass_org=="DNA", repClass_org,
                            if_else(repClass_org=="Retroposon",repClass_org,
                                    if_else(is.na(repClass_org), repClass_org,"Other"))))))) %>% 
  mutate(TEstatus ="TE_peak", mrc="h.genome", per_ol = "NA") %>% 
    rbind(Eight_group_TE %>% dplyr::filter(repClass=="LINE") %>% mutate(mrc="all_peaks") %>%  mutate(repClass_org=repClass)) 
  

ggsine_df <-
  Sine_repeats %>% 
  as.data.frame() %>% 
 tidyr::unite(Peakid,seqnames:end, sep= ".") %>% 
  dplyr::select(Peakid,repName,repClass, repFamily,width) %>% 
  mutate(TEstatus ="TE_peak", mrc="h.genome",per_ol = "NA") %>%
  mutate(repClass_org=repClass) %>% 
   mutate(repClass=if_else(##relable repClass with other
    repClass_org=="LINE", repClass_org,
    if_else(repClass_org=="SINE",repClass_org,
            if_else(repClass_org=="LTR", repClass_org, 
                    if_else(repClass_org=="DNA", repClass_org,
                            if_else(repClass_org=="Retroposon",repClass_org,
                                    if_else(is.na(repClass_org), repClass_org,"Other"))))))) %>% 
    rbind(Eight_group_TE %>% dplyr::filter(repClass=="SINE") %>% mutate(mrc="all_peaks") %>% mutate(repClass_org=repClass))

ggLTR_df <-LTR_repeats %>% 
   as.data.frame() %>% 
 tidyr::unite(Peakid,seqnames:end, sep= ".") %>% 
  dplyr::select(Peakid,repName,repClass, repFamily,width) %>% 
  mutate(TEstatus ="TE_peak", mrc="h.genome",per_ol = "NA") %>%
  mutate(repClass_org=repClass) %>% 
   mutate(repClass=if_else(##relable repClass with other
    repClass_org=="LINE", repClass_org,
    if_else(repClass_org=="SINE",repClass_org,
            if_else(repClass_org=="LTR", repClass_org, 
                    if_else(repClass_org=="DNA", repClass_org,
                            if_else(repClass_org=="Retroposon",repClass_org,
                                    if_else(is.na(repClass_org), repClass_org,"Other"))))))) %>% 
    rbind(Eight_group_TE %>% dplyr::filter(repClass=="LTR") %>% mutate(mrc="all_peaks") %>% mutate(repClass_org=repClass))


ggDNA_df <-DNA_repeats %>% 
 
  as.data.frame() %>% 
 tidyr::unite(Peakid,seqnames:end, sep= ".") %>% 
  dplyr::select(Peakid,repName,repClass, repFamily,width) %>% 
  mutate(TEstatus ="TE_peak", mrc="h.genome",per_ol = "NA") %>%
  mutate(repClass_org=repClass) %>% 
   mutate(repClass=if_else(##relable repClass with other
    repClass_org=="LINE", repClass_org,
    if_else(repClass_org=="SINE",repClass_org,
            if_else(repClass_org=="LTR", repClass_org, 
                    if_else(repClass_org=="DNA", repClass_org,
                            if_else(repClass_org=="Retroposon",repClass_org,
                                    if_else(is.na(repClass_org), repClass_org,"Other"))))))) %>% 
    rbind(Eight_group_TE %>% dplyr::filter(repClass=="DNA") %>% mutate(mrc="all_peaks") %>% mutate(repClass_org=repClass))


ggretroposon_df <-retroposon_repeats %>% 
 
  as.data.frame() %>% 
 tidyr::unite(Peakid,seqnames:end, sep= ".") %>% 
  dplyr::select(Peakid,repName,repClass, repFamily,width) %>% 
  mutate(TEstatus ="TE_peak", mrc="h.genome",per_ol = "NA") %>%
  mutate(repClass_org=repClass) %>% 
   mutate(repClass=if_else(##relable repClass with other
    repClass_org=="LINE", repClass_org,
    if_else(repClass_org=="SINE",repClass_org,
            if_else(repClass_org=="LTR", repClass_org, 
                    if_else(repClass_org=="DNA", repClass_org,
                            if_else(repClass_org=="Retroposon",repClass_org,
                                    if_else(is.na(repClass_org), repClass_org,"Other"))))))) %>% 
    rbind(Eight_group_TE %>% dplyr::filter(repClass=="Retroposon") %>% mutate(mrc="all_peaks") %>% mutate(repClass_org=repClass)) 

Eight group by subset TE type

eight_lines <- Eight_group_TE %>% 
    dplyr::filter(repClass=="LINE") %>% 
  # distinct(mrc)
  dplyr::filter(mrc != "not_mrc") %>% 
  rbind(., ggline_df) %>% 
  mutate(repFamily=factor(repFamily)) %>% 
  mutate(mrc=factor(mrc, levels = c("EAR_open","EAR_close","ESR_open", "ESR_close","ESR_OC", "LR_open","LR_close","NR","all_peaks","h.genome"))) %>%
  dplyr::filter(mrc != "h.genome")

eight_lines %>% 
  ggplot(., aes(x=mrc, fill= repFamily))+
  geom_bar(position="fill", col="black")+
  theme_bw()+
  ggtitle(paste("LINE breakdown by eight-clusters and Family", per_cov))+
  scale_fill_lines()

Past versions of "LinesLTRand eight-1.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

eight_lines %>% 
  group_by(mrc,repFamily) %>% 
  tally %>% 
  pivot_wider(., id_cols = mrc, names_from = repFamily, values_from = n) %>% 
  rowwise() %>% 
 mutate(total= sum(c_across("CR1":"RTE-X"),na.rm =TRUE)) %>% 
  kable(., caption="Breakdown of SINE counts by Family") %>% 
kable_paper("striped", full_width = TRUE) %>%
  kable_styling(full_width = FALSE, font_size = 14) %>% 
  scroll_box(height = "500px")

Breakdown of SINE counts by Family

mrc	CR1	Dong-R4	L1	L2	Penelope	RTE-BovB	RTE-X	total
EAR_open	39	1	253	427	1	9	9	739
EAR_close	81	1	348	633	NA	25	12	1100
ESR_open	112	NA	492	748	2	26	23	1403
ESR_close	165	NA	717	1285	1	49	16	2233
ESR_OC	24	1	110	179	NA	4	6	324
LR_open	710	7	2724	4849	9	142	128	8569
LR_close	237	3	1335	2058	1	81	62	3777
NR	1660	4	7395	11769	29	290	276	21423
all_peaks	3101	17	13664	22418	45	649	546	40440

eight_sines <- Eight_group_TE %>% 
  dplyr::filter(repClass=="SINE") %>% 
  dplyr::filter(mrc != "not_mrc") %>% 
  rbind(., ggsine_df) %>% 
  mutate(repFamily=factor(repFamily)) %>% 
 mutate(mrc=factor(mrc, levels = c("EAR_open","EAR_close","ESR_open", "ESR_close","ESR_OC", "LR_open","LR_close","NR","all_peaks","h.genome"))) %>%
  dplyr::filter(mrc != "h.genome") 

eight_sines %>% 
  ggplot(., aes(x=mrc, fill= repFamily))+
  geom_bar(position="fill", col="black")+
  theme_bw()+
  ggtitle(paste("SINE breakdown by breakdown by eight-clusters and Family",per_cov))+
  scale_fill_sines()

Past versions of "Sines and eight-1.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

eight_sines %>% 
  group_by(mrc,repFamily) %>% 
  tally %>% 
  pivot_wider(., id_cols = mrc, names_from = repFamily, values_from = n) %>% 
  rowwise() %>% 
 mutate(total= sum(c_across(1:6),na.rm =TRUE)) %>% 
  kable(., caption="Breakdown of SINE counts by Family") %>% 
kable_paper("striped", full_width = TRUE) %>%
  kable_styling(full_width = FALSE, font_size = 14)%>% 
  scroll_box(height = "500px")

Breakdown of SINE counts by Family

mrc	5S-Deu-L2	Alu	MIR	tRNA	tRNA-Deu	tRNA-RTE	total
EAR_open	4	459	530	4	1	5	1003
EAR_close	1	709	822	2	NA	11	1545
ESR_open	11	896	829	4	3	10	1753
ESR_close	10	1247	1727	11	NA	15	3010
ESR_OC	2	247	237	NA	NA	2	488
LR_open	42	5631	5179	32	12	100	10996
LR_close	35	1842	2440	13	7	30	4367
NR	127	12831	15289	84	30	171	28532
all_peaks	239	24295	27669	154	53	345	52755

eight_LTRs <- Eight_group_TE %>% 
  dplyr::filter(repClass=="LTR") %>% 
  dplyr::filter(mrc != "not_mrc") %>% 
  rbind(., ggLTR_df) %>% 
  mutate(repFamily=factor(repFamily)) %>% 
 mutate(mrc=factor(mrc, levels = c("EAR_open","EAR_close","ESR_open", "ESR_close","ESR_OC", "LR_open","LR_close","NR","all_peaks","h.genome"))) %>%
  dplyr::filter(mrc != "h.genome")

eight_LTRs %>% 
  ggplot(., aes(x=mrc, fill= repFamily))+
  geom_bar(position="fill", col="black")+
  theme_bw()+
  ggtitle(paste("LTR breakdown by breakdown by eight-clusters and Family",per_cov))+
  scale_fill_LTRs()

Past versions of "LTR and eight-1.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

eight_LTRs %>% 
  group_by(mrc,repFamily) %>% 
  tally %>% 
  pivot_wider(., id_cols = mrc, names_from = repFamily, values_from = n) %>% 
  rowwise() %>% 
 mutate(total= sum(c_across(1:9),na.rm =TRUE)) %>% 
  kable(., caption="Breakdown of LTR counts by Family") %>% 
kable_paper("striped", full_width = TRUE) %>%
  kable_styling(full_width = FALSE, font_size = 14)%>% 
  scroll_box(height = "500px")

Breakdown of LTR counts by Family

mrc	ERV1	ERVK	ERVL	ERVL-MaLR	ERVL?	Gypsy	Gypsy?	LTR	ERV1?	total
EAR_open	122	21	108	157	3	10	2	4	NA	427
EAR_close	201	23	202	335	4	15	5	2	NA	787
ESR_open	239	10	184	305	4	16	12	3	3	776
ESR_close	353	40	428	618	4	40	12	10	3	1508
ESR_OC	57	2	79	92	NA	7	7	NA	1	245
LR_open	1547	79	1602	2603	20	142	93	22	8	6116
LR_close	614	42	843	1046	10	69	40	16	4	2684
NR	4010	526	4215	5212	97	405	212	58	22	14757
all_peaks	7266	755	7821	10588	147	719	390	116	42	27844

eight_DNA <- Eight_group_TE %>% 
  dplyr::filter(repClass=="DNA") %>% 
  dplyr::filter(mrc != "not_mrc") %>% 
  rbind(., ggDNA_df) %>% 
  mutate(repFamily=factor(repFamily)) %>% 
mutate(mrc=factor(mrc, levels = c("EAR_open","EAR_close","ESR_open", "ESR_close","ESR_OC", "LR_open","LR_close","NR","all_peaks","h.genome"))) %>%
  dplyr::filter(mrc != "h.genome") 

eight_DNA %>% 
  ggplot(., aes(x=mrc, fill= repFamily))+
  geom_bar(position="fill", col="black")+
  theme_bw()+
  ggtitle(paste("DNA breakdown by breakdown by eight-clusters and Family",per_cov))+
  scale_fill_DNAs()

Past versions of "DNA and eight-1.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

eight_DNA %>% 
  group_by(mrc,repFamily) %>% 
  tally %>% 
  pivot_wider(., id_cols = mrc, names_from = repFamily, values_from = n) %>% 
  rowwise() %>% 
 mutate(total= sum(c_across(1:18),na.rm =TRUE)) %>% 
  kable(., caption="Breakdown of DNA counts by Family") %>% 
kable_paper("striped", full_width = TRUE) %>%
  kable_styling(full_width = FALSE, font_size = 14)%>% 
  scroll_box(height = "500px")

Breakdown of DNA counts by Family

mrc	DNA	hAT	hAT-Ac	hAT-Blackjack	hAT-Charlie	hAT-Tip100	PiggyBac	PiggyBac?	TcMar-Mariner	TcMar-Tc2	TcMar-Tigger	hAT-Tip100?	hAT?	MULE-MuDR	TcMar?	PIF-Harbinger	TcMar	TcMar-Pogo	total
EAR_open	2	11	6	11	165	31	1	1	5	1	67	NA	NA	NA	NA	NA	NA	NA	301
EAR_close	4	7	1	21	228	52	1	1	8	3	81	1	NA	NA	NA	NA	NA	NA	408
ESR_open	NA	14	10	27	262	65	6	NA	12	3	141	4	1	1	NA	NA	NA	NA	546
ESR_close	6	19	14	44	496	136	NA	NA	6	6	117	2	5	2	1	NA	NA	NA	854
ESR_OC	1	NA	4	2	75	12	NA	NA	4	5	32	NA	NA	NA	NA	1	NA	NA	136
LR_open	36	52	42	131	1873	370	16	5	55	34	1155	14	12	11	2	NA	1	NA	3809
LR_close	14	33	26	50	813	261	6	1	18	13	273	7	2	3	1	NA	1	NA	1522
NR	61	135	142	371	4804	1132	58	13	106	82	2023	65	25	11	8	2	1	1	9040
all_peaks	127	276	250	670	8893	2097	89	22	219	149	3979	95	45	29	13	3	3	1	16960

eight_retro <- Eight_group_TE %>% 
  dplyr::filter(repClass=="Retroposon") %>% 
  dplyr::filter(mrc != "not_mrc") %>% 
  rbind(., ggretroposon_df) %>% 
  mutate(repName=factor(repName)) %>% 
mutate(mrc=factor(mrc, levels = c("EAR_open","EAR_close","ESR_open", "ESR_close","ESR_OC", "LR_open","LR_close","NR","all_peaks","h.genome"))) %>%
  dplyr::filter(mrc != "h.genome") 

eight_retro %>% 
  ggplot(., aes(x=mrc, fill= repName))+
  geom_bar(position="fill", col="black")+
  theme_classic()+
  ggtitle(paste("Retroposon breakdown by by eight-clusters and Family",per_cov))+
  scale_fill_retroposons()

Past versions of "Retroposon and eight-1.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

eight_retro %>% 
  group_by(mrc,repName) %>% 
  tally %>% 
  pivot_wider(., id_cols = mrc, names_from = repName, values_from = n) %>% 
  rowwise() %>% 
 mutate(total= sum(c_across(1:6),na.rm =TRUE)) %>% 
  kable(., caption="Breakdown of Retroposon counts by Name") %>% 
kable_paper("striped", full_width = TRUE) %>%
  kable_styling(full_width = FALSE, font_size = 14)%>% 
  scroll_box(height = "500px")

Breakdown of Retroposon counts by Name

mrc	SVA_A	SVA_B	SVA_D	SVA_E	SVA_F	SVA_C	total
EAR_open	1	1	13	1	6	NA	22
EAR_close	1	2	3	1	NA	NA	7
ESR_open	5	1	23	1	8	3	41
ESR_close	3	2	4	1	1	NA	11
ESR_OC	NA	NA	1	NA	NA	1	2
LR_open	8	5	18	1	3	3	38
LR_close	7	3	2	3	1	2	18
NR	22	30	55	10	16	15	148
all_peaks	50	44	121	19	36	25	295

CG islands!

cpgislands_df <- read.delim("data/other_papers/cpg_islands.tsv")
cpg_cCREs_df <- read_delim("data/other_papers/cpg_cCREs.tsv", delim="\t")
# aligncre <- genomation::readBed("data/enhancerdata/ENCFF867HAD_ENCFF152PBB_ENCFF352YYH_ENCFF252IVK.7group.bed") %>% as.data.frame

CPG_promoters_gr <- cpg_cCREs_df %>% 
    dplyr::rename(.,"seqnames"=X1,"start"=X2,"end"=X3,"promotor_name"=X4,"length"=X5,"strand"=X6,"color"=X9) %>% 
  dplyr::filter(color ==25500) %>% 
  dplyr::select(seqnames:strand,color) %>% 
  GRanges() 


Peaks_v_cpgpromo <- join_overlap_intersect(CPG_promoters_gr,Col_TSS_data_gr) %>% as.data.frame

cpg_island_gr <- cpgislands_df %>% 
 makeGRangesFromDataFrame(., keep.extra.columns = TRUE, seqnames.field = "chrom", start.field = "chromStart", end.field = "chromEnd",starts.in.df.are.0based=TRUE)
Col_TSS_data_gr$peak_width <- width(Col_TSS_data_gr)
cpg_island_gr$cpg_width <- width(cpg_island_gr)

Col_fullDF_cug_overlap <- join_overlap_intersect(Col_TSS_data_gr,cpg_island_gr)
Col_fullDF_cug_overlap <-Col_fullDF_cug_overlap %>% 
  as.data.frame %>% 
  mutate(per_ol=width/cpg_width)


Col_fullDF_cug_overlap %>% 
  as.data.frame() %>% 
    # group_by(name) %>%
  distinct(Peakid) %>% 
  tally %>% 
  kable(., caption="Count of peaks with CG islands") %>% 
  kable_paper("striped", full_width = TRUE) %>%
  kable_styling(full_width = FALSE, font_size = 14)%>% 
  scroll_box(height = "500px")

Count of peaks with CG islands

n
18769

Col_fullDF_cug_overlap %>% 
  as.data.frame() %>% 
  dplyr::filter(per_ol>0.5) %>% 
  distinct(Peakid) %>% 
  tally %>% 
  kable(., caption="Count of peaks with >50% of CG islands") %>% 
  kable_paper("striped", full_width = TRUE) %>%
  kable_styling(full_width = FALSE, font_size = 14)%>% 
  scroll_box(height = "500px")

Count of peaks with >50% of CG islands

n
15310

# CUG_mrc_status_list <-
# Col_TSS_data_gr%>% 
#   as.data.frame() %>%
#   left_join(., (Col_fullDF_cug_overlap %>% as.data.frame(.)), by =c("seqnames"="seqnames","start"="start","end"="end","Peakid"="Peakid", "NCBI_gene"="NCBI_gene", "ensembl_ID"="ensembl_ID","dist_to_NG"="dist_to_NG",  "SYMBOL" = "SYMBOL", "peak_width"="peak_width")) %>% 
#   left_join(., (Peaks_v_cpgpromo %>% dplyr::select(promotor_name,Peakid,peak_width)), by=c("peak_width"="peak_width","Peakid"="Peakid")) %>% 
#   dplyr::select(Peakid, name,cpgNum:promotor_name) %>% 
#   mutate(cugstatus=if_else(is.na(cpgNum),"not_CGi_peak","CGi_peak")) %>% 
#   mutate(prom_status= if_else(is.na(promotor_name),"not_CpGpromo","CpGpromo")) %>% 
#    mutate(mrc=if_else(Peakid %in% EAR_df$id, "EAR",
#                      if_else(Peakid %in% ESR_df$id,"ESR",
#                              if_else(Peakid %in% LR_df$id,"LR",
#                                      if_else(Peakid %in% NR_df$id,"NR","not_mrc"))))) %>% distinct()
# 


CUG_mrc_nine_list <-
Col_TSS_data_gr%>% as.data.frame() %>%
  left_join(., (Col_fullDF_cug_overlap %>% as.data.frame(.)), by =c("seqnames"="seqnames","start"="start","end"="end","Peakid"="Peakid", "NCBI_gene"="NCBI_gene", "ensembl_ID"="ensembl_ID","dist_to_NG"="dist_to_NG",  "SYMBOL" = "SYMBOL", "peak_width"="peak_width")) %>% 
  left_join(., (Peaks_v_cpgpromo %>% dplyr::select(promotor_name,Peakid,peak_width)), by=c("peak_width"="peak_width","Peakid"="Peakid")) %>% 
  dplyr::select(Peakid, name,cpgNum:promotor_name) %>% 
  mutate(cugstatus=if_else(is.na(cpgNum),"not_CGi_peak","CGi_peak")) %>% 
  mutate(prom_status= if_else(is.na(promotor_name),"not_CpGpromo","CpGpromo")) %>% 
   mutate(mrc=if_else(Peakid %in% EAR_open$Peakid, "EAR_open",
                      if_else(Peakid %in% EAR_close$Peakid, "EAR_close",
                              if_else(Peakid %in% ESR_open$Peakid,"ESR_open",
                                      if_else(Peakid %in% ESR_close$Peakid,"ESR_close",
                                              if_else(Peakid %in% ESR_OC$Peakid,"ESR_OC",
                                                      if_else(Peakid %in% LR_open$Peakid,"LR_open",
                                                                      if_else(Peakid %in% LR_close$Peakid,"LR_close",
                                                                              if_else(Peakid %in% NR_df$Peakid,"NR","not_mrc"))))))))) %>%
  distinct()

# 
# CUG_mrc_status_list %>% 
#  group_by(cugstatus, mrc) %>% 
#   distinct(Peakid) %>% 
#  count %>% 
#   pivot_wider(., id_cols = mrc, names_from = cugstatus, values_from = n) %>% 
#   kable(., caption="Breakdown of CG islands overlap four groups") %>% 
# kable_paper("striped", full_width = TRUE) %>%
#   kable_styling(full_width = FALSE, font_size = 14)

# CUG_mrc_status_list %>% 
#   mutate(mrc=factor(mrc, levels = c("NR", "EAR", "ESR","LR","not_mrc"))) %>%
#   group_by(cugstatus, mrc) %>% 
#   ggplot(., aes(x = mrc,  fill = cugstatus)) +
#   geom_bar(position="fill",color = "black")+
#   theme_bw()+
#   ggtitle("CG islands by mrc")
          # subtitle=paste((CUG_mrc_status_list %>% dplyr::filter(cugstatus=="CGi_peak") %>% count(cugstatus))$n))
  
CUG_mrc_nine_list %>%
  distinct(Peakid,.keep_all = TRUE) %>%
  mutate(mrc="full_list") %>%
  rbind(., (CUG_mrc_nine_list %>% distinct(Peakid,.keep_all = TRUE))) %>%
   mutate(mrc=factor(mrc, levels=c("EAR_open","EAR_close","ESR_open","ESR_close","ESR_OC","LR_open","LR_close","NR","not-mrc","full_list"))) %>%
  dplyr::filter(mrc !="not_mrc") %>%
  group_by(cugstatus, mrc) %>%
  ggplot(., aes(x = mrc,  fill = cugstatus)) +
  geom_bar(position="fill",color = "black")+
  theme_bw()+
  ggtitle("CG islands by mrc")

Past versions of "cug development-1.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

# subtitle=paste((CUG_mrc_nine_list %>% dplyr::filter(cugstatus=="CGi_peak") %>% count(cugstatus))$n, "CG island peaks"))
    
CUG_mrc_nine_list %>% 
  distinct(Peakid,.keep_all = TRUE) %>%
  dplyr::filter(mrc != "not_mrc") %>% 
  mutate(mrc="full_list") %>% 
  rbind(., (CUG_mrc_nine_list %>% distinct(Peakid,.keep_all = TRUE))) %>% 
   mutate(mrc=factor(mrc, levels=c("EAR_open","EAR_close","ESR_open","ESR_close","ESR_OC","LR_open","LR_close","NR","not-mrc","full_list"))) %>%
  dplyr::filter(mrc !="not_mrc") %>% 
  group_by(cugstatus, mrc) %>% 
  tally %>% 
  pivot_wider(., id_cols = mrc, names_from = cugstatus, values_from = n) %>% 
  kable(., caption="Breakdown of CG islands overlap with not_mrc removed") %>% 
kable_paper("striped", full_width = TRUE) %>%
  kable_styling(full_width = FALSE, font_size = 14)%>% 
  scroll_box(height = "500px")

Breakdown of CG islands overlap with not_mrc removed

mrc	CGi_peak	not_CGi_peak
EAR_open	81	3077
EAR_close	17	4279
ESR_open	139	5133
ESR_close	71	9278
LR_open	307	28230
LR_close	102	13970
NR	1265	85063
full_list	1982	150190
ESR_OC	NA	1160

Col_fullDF_cug_overlap %>% 
        as.data.frame %>% 
        ggplot(., aes (x = width))+
        geom_density(color="darkblue",fill="lightblue",aes(alpha = 0.5))+
  theme_classic()+
  ggtitle("Distribution of CGisland overlap widths",subtitle = " limited x axis")+
  coord_cartesian(xlim= c(0,2500))

Past versions of "cug eight stuff-1.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

cpg_island_gr %>% 
  as.data.frame() %>% 
  ggplot(., aes (x = cpg_width))+
        geom_density(aes(alpha = 0.5))+
  theme_classic()+
  ggtitle("Distribution of overlap widths of CGislands ",subtitle = " limited x axis")+
  coord_cartesian(xlim= c(0,2500))

Past versions of "cug eight stuff-2.png"

Version	Author	Date
6d65dd6	reneeisnowhere	2024-09-09

# CUG_mrc_status_list %>% 
#    mutate(mrc="full_list") %>% 
#   rbind(., CUG_mrc_status_list) %>% 
#    mutate(mrc=factor(mrc, levels=c("EAR","ESR","LR","NR","not-mrc","full_list"))) %>% 
#   dplyr::filter(mrc !="not_mrc") %>% 
#   dplyr::filter(cugstatus=="CGi_peak") %>% 
#   ggplot(., aes(x = mrc,  fill = prom_status)) +
#   geom_bar(position="fill",color = "black")+
#   theme_bw()+
#   ggtitle("CG islands promotors vs non-promotor CpGi by mrc", subtitle=paste(length(CUG_mrc_status_list$Peakid )))


promo_count_list <- CUG_mrc_nine_list %>% 
  dplyr::filter(mrc !="not_mrc") %>% 
  dplyr::filter(cugstatus=="CGi_peak")  %>%
  group_by(prom_status) %>% 
  tally()
  

CUG_mrc_nine_list %>%
   mutate(mrc="full_list") %>%
  rbind(., CUG_mrc_nine_list) %>%
   mutate(mrc=factor(mrc, levels=c("EAR_open","EAR_close","ESR_open","ESR_close","ESR_OC","LR_open","LR_close","NR","not-mrc","full_list"))) %>%
  dplyr::filter(mrc !="not_mrc") %>%
  dplyr::filter(cugstatus=="CGi_peak") %>%
  ggplot(., aes(x = mrc,  fill = prom_status)) +
  geom_bar(position="fill",color = "black")+
  theme_bw()+
  ggtitle("CG islands promotors vs non-promotor CpGi by mrc", subtitle=paste(promo_count_list[1,1],promo_count_list[1,2],"\n", promo_count_list [2,1],promo_count_list [2,2]))

Past versions of "cug eight stuff-3.png"

Version	Author	Date
23374bd	reneeisnowhere	2024-09-13
6d65dd6	reneeisnowhere	2024-09-09

SNP

gwas_ACtox <- readRDS("data/gwas_1_dataframe.RDS")  
gwas_ARR <- readRDS("data/gwas_2_dataframe.RDS")
gwas_ACresp <- readRDS("data/gwas_3_dataframe.RDS")
gwas_HD <- readRDS("data/gwas_4_dataframe.RDS")
gwas_HF <- readRDS("data/gwas_5_dataframe.RDS")
gwas_CAD <- readRDS( "data/CAD_gwas_dataframe.RDS")
gwas_MI <- readRDS("data/MI_gwas.RDS")

gwas_snp_list <- gwas_ACtox %>%
  distinct(SNPS,.keep_all = TRUE) %>% 
  dplyr::select(CHR_ID, CHR_POS,SNPS) %>% 
  mutate(gwas="ACtox") %>% 
  rbind(gwas_ARR %>% 
          distinct(SNPS,.keep_all = TRUE) %>%
          dplyr::select(CHR_ID, CHR_POS,SNPS) %>% 
          mutate(gwas="ARR")) %>% 
  rbind(gwas_ACresp %>% 
          distinct(SNPS,.keep_all = TRUE) %>%
          dplyr::select(CHR_ID, CHR_POS,SNPS) %>% 
          mutate(gwas="ACresp")) %>% 
  rbind(gwas_HD %>% 
          distinct(SNPS,.keep_all = TRUE) %>%
          dplyr::select(CHR_ID, CHR_POS,SNPS) %>% 
          mutate(gwas="HD")) %>% 
  rbind(gwas_HF %>% 
          distinct(SNPS,.keep_all = TRUE) %>%
          dplyr::select(CHR_ID, CHR_POS,SNPS) %>% 
          mutate(gwas="HF")) %>% 
  rbind(gwas_CAD %>% 
          distinct(SNPS,.keep_all = TRUE) %>%
          dplyr::select(CHR_ID, CHR_POS,SNPS) %>% 
          mutate(gwas="CAD")) %>% 
  rbind(gwas_MI %>% 
          distinct(SNPS,.keep_all = TRUE) %>%
          dplyr::select(CHR_ID, CHR_POS,SNPS) %>% 
          mutate(gwas="MI")) %>% 
  separate_longer_delim(.,col= c(CHR_ID,CHR_POS,SNPS), delim= ";")  

gwas_snp_gr <- gwas_snp_list %>% 
   mutate(CHR_ID=as.numeric(CHR_ID), CHR_POS=as.numeric(CHR_POS)) %>% 
  na.omit() %>% 
   mutate(start=CHR_POS, end=CHR_POS, chr=paste0("chr",CHR_ID)) %>% 
  GRanges()

# rtracklayer::export.bed(gwas_snp_gr,con="data/full_bedfiles/GWAS_SNP.bed",format="bed")

findOverlaps(gwas_snp_gr, all_TEs_gr)

Hits object with 3432 hits and 0 metadata columns:
         queryHits subjectHits
         <integer>   <integer>
     [1]         1     4910496
     [2]         4     1047158
     [3]         7     4718029
     [4]        11      116853
     [5]        12     4826568
     ...       ...         ...
  [3428]      7804     5009192
  [3429]      7805     3904058
  [3430]      7807     4735830
  [3431]      7809     4615754
  [3432]      7812     1872218
  -------
  queryLength: 7816 / subjectLength: 5683690

test <- join_overlap_intersect(gwas_snp_gr, all_TEs_gr) %>% GRanges
##3413- (#3432 after separate)

test_new <-  test %>% 
   as.data.frame %>% 
   dplyr::select(seqnames:gwas,repName:repFamily) %>% 
   GRanges()
peaks <-
  Collapsed_peaks %>% 
    dplyr::select(chr:Peakid) %>% 
    GRanges()
peak_test <- join_overlap_intersect(test_new, peaks)

# peak_test 135

Col_fullDF_overlap %>% 
  as.data.frame %>% 
  dplyr::select(seqnames:Peakid,repName:repFamily) %>% 
  GRanges() %>% 
  join_overlap_intersect(.,gwas_snp_gr)

GRanges object with 131 ranges and 8 metadata columns:
        seqnames    ranges strand |                 Peakid     repName
           <Rle> <IRanges>  <Rle> |            <character> <character>
    [1]     chr1  16012818      * | chr1.16012567.16013729        MIRb
    [2]     chr1 170224718      * | chr1.170224576.17022..       L1MA7
    [3]     chr1 170224718      * | chr1.170224576.17022..       L1MA7
    [4]    chr10  20953453      * | chr10.20953148.20953..       MLT1B
    [5]    chr10  20953453      * | chr10.20953148.20953..       MLT1B
    ...      ...       ...    ... .                    ...         ...
  [127]     chr8 124847608      * | chr8.124847104.12484..     MamTip2
  [128]     chr8 124847608      * | chr8.124847104.12484..     MamTip2
  [129]     chr9 107755513      * | chr9.107755276.10775..  Charlie13a
  [130]     chr9 107755513      * | chr9.107755276.10775..  Charlie13a
  [131]     chr9 123933491      * | chr9.123933461.12393..       MLT1J
           repClass   repFamily    CHR_ID   CHR_POS        SNPS        gwas
        <character> <character> <numeric> <numeric> <character> <character>
    [1]        SINE         MIR         1  16012818  rs10927886          HD
    [2]        LINE          L1         1 170224718  rs12122060         ARR
    [3]        LINE          L1         1 170224718  rs12122060          HD
    [4]         LTR   ERVL-MaLR        10  20953453   rs7910227         ARR
    [5]         LTR   ERVL-MaLR        10  20953453   rs7910227          HD
    ...         ...         ...       ...       ...         ...         ...
  [127]         DNA  hAT-Tip100         8 124847608  rs34866937          HD
  [128]         DNA  hAT-Tip100         8 124847608  rs34866937          HF
  [129]         DNA hAT-Charlie         9 107755513    rs944172          HD
  [130]         DNA hAT-Charlie         9 107755513    rs944172         CAD
  [131]         LTR   ERVL-MaLR         9 123933491  rs10818894      ACresp
  -------
  seqinfo: 23 sequences from an unspecified genome; no seqlengths

test2 <- join_overlap_intersect(peaks, gwas_snp_gr) 
  
test2 %>% 
   join_overlap_intersect(., all_TEs_gr) %>% 
  as.data.frame %>% 
  dplyr::select(seqnames:gwas,repName:repFamily) %>% 
  mutate(mrc=if_else(Peakid %in% EAR_open$Peakid, "EAR_open",
                      if_else(Peakid %in% EAR_close$Peakid, "EAR_close",
                              if_else(Peakid %in% ESR_open$Peakid,"ESR_open",
                                      if_else(Peakid %in% ESR_close$Peakid,"ESR_close",
                                              if_else(Peakid %in% ESR_OC$Peakid,"ESR_OC",
                                                      if_else(Peakid %in% LR_open$Peakid,"LR_open",
                                                                      if_else(Peakid %in% LR_close$Peakid,"LR_close",
                                                                              if_else(Peakid %in% NR_df$Peakid,"NR","not_mrc"))))))))) %>%
  # distinct(Peakid, .keep_all = TRUE) %>% 
  group_by(gwas,mrc) %>% 
  tally

# A tibble: 24 × 3
# Groups:   gwas [6]
   gwas   mrc           n
   <chr>  <chr>     <int>
 1 ACresp LR_open       2
 2 ARR    ESR_close     2
 3 ARR    LR_close      4
 4 ARR    LR_open       3
 5 ARR    NR           12
 6 ARR    not_mrc       1
 7 CAD    EAR_close     1
 8 CAD    EAR_open      1
 9 CAD    ESR_close     2
10 CAD    LR_close      4
# ℹ 14 more rows

SNP_df <- test2 %>% 
   join_overlap_intersect(., all_TEs_gr) %>% 
  as.data.frame %>% 
  dplyr::select(seqnames:gwas,repName:repFamily) %>% 
  mutate(mrc=if_else(Peakid %in% EAR_open$Peakid, "EAR_open",
                      if_else(Peakid %in% EAR_close$Peakid, "EAR_close",
                              if_else(Peakid %in% ESR_open$Peakid,"ESR_open",
                                      if_else(Peakid %in% ESR_close$Peakid,"ESR_close",
                                              if_else(Peakid %in% ESR_OC$Peakid,"ESR_OC",
                                                      if_else(Peakid %in% LR_open$Peakid,"LR_open",
                                                                      if_else(Peakid %in% LR_close$Peakid,"LR_close",
                                                                              if_else(Peakid %in% NR_df$Peakid,"NR","not_mrc"))))))))) 

SNP_df %>%  group_by(Peakid) %>%
    summarise(snp_id=paste(unique(SNPS), collapse = ","),
              gwas=paste(unique(gwas),collapse=","),
              repName = paste(unique(repName),collapse=","),
              mrc =paste(unique(mrc),collapse=","),
              repFamily= paste(unique(repFamily),collapse = ","),
              location_chr= paste(unique(CHR_ID),collapse = ",") ,
              location= paste(unique(CHR_POS),collapse = ",") ,
              ) %>% 
  kable(., caption = "The output of all SNPs and their SNP category followed by location and peak") %>% 
  
  kable_paper("striped", full_width = TRUE) %>%
  kable_styling(full_width = FALSE, font_size = 14) %>% 
  scroll_box(height = "500px")

The output of all SNPs and their SNP category followed by location and peak

Peakid	snp_id	gwas	repName	mrc	repFamily	location_chr	location
chr1.16012567.16013729	rs10927886	HD	MIRb	ESR_close	MIR	1	16012818
chr1.170224576.170224916	rs12122060	ARR,HD	L1MA7	LR_close	L1	1	170224718
chr1.55055575.55055793	rs472495	HD,CAD	MLT2D	not_mrc	ERVL	1	55055640
chr10.103712814.103713354	rs10509768	ARR,HD	MIRb	NR	MIR	10	103713072
chr10.121155583.121156319	rs17101521	HD,CAD	LTR16A2	NR	ERVL	10	121156039
chr10.20953148.20953475	rs7910227	ARR,HD	MLT1B	LR_close	ERVL-MaLR	10	20953453
chr11.100753128.100753902	rs7947761	HD,CAD	L1PBa	NR	L1	11	100753868
chr11.118911506.118912630	rs11606719	HD,CAD	MIRb	NR	MIR	11	118911765
chr11.128759383.128760154	rs672149	HD	L2	NR	L2	11	128759487
chr11.19988504.19989024	rs4757877,rs2625322	ARR,HD	AmnSINE1	LR_close	5S-Deu-L2	11	19988745,19988805
chr11.90455310.90455981	rs534128177	HD,CAD	LTR14B	NR	ERVK	11	90455679
chr11.9748359.9748943	rs472109	HD,CAD	LTR41	LR_close	ERVL	11	9748771
chr12.106865628.106866040	rs7977247	HD,HF	L2a	LR_close	L2	12	106865692
chr12.109538192.109538640	rs75524776	HD,CAD	MIRb	LR_close	MIR	12	109538471
chr12.111171885.111172119	rs3809297	ARR,HD	(TGCGTG)n	not_mrc	Simple_repeat	12	111171923
chr12.111569574.111570394	rs653178	HD,MI	MER3	NR	hAT-Charlie	12	111569952
chr12.20004923.20005338	rs11044977	HD,MI	L2a	NR	L2	12	20005226
chr12.24561685.24562685	rs2291437	ARR,HD	(CCACCTC)n	NR	Simple_repeat	12	24562114
chr13.110301444.110302036	rs4773141	HD,CAD	L2	NR	L2	13	110302006
chr13.21536977.21537596	rs11841562	ARR,HD	L2	LR_open	L2	13	21537382
chr13.22794559.22794879	rs9506925	ARR,HD	OldhAT1	NR	hAT-Ac	13	22794804
chr13.30740138.30740672	rs3885907	ACresp	MIR	LR_open	MIR	13	30740318
chr14.58326948.58327320	rs2145598	HD,CAD	MER3	NR	hAT-Charlie	14	58327283
chr15.70171390.70172398	rs2415081	ARR,HD	MIRb	NR	MIR	15	70171653
chr15.90885954.90886257	rs4932373	HD,CAD	MIR	LR_close	MIR	15	90886057
chr17.17823556.17824258	rs12936927	HD,CAD	(CGCCC)n	ESR_close	Simple_repeat	17	17823651
chr17.2867997.2868307	rs12603284	ARR,HD	MamRTE1	NR	RTE-BovB	17	2868218
chr18.35089781.35090661	rs476348	HD	L1MB7	NR	L1	18	35090057
chr18.45565618.45565968	rs2852306	HD	MIR3	EAR_close	MIR	18	45565696
chr19.18478653.18479216	rs11670056	HD,CAD	MER20	LR_open	hAT-Charlie	19	18479133
chr19.41352378.41354231	rs1800470	HD,CAD	(CAGCAG)n	NR	Simple_repeat	19	41353016
chr2.25936965.25937466	rs6546620	ARR,HD	MIRb	NR	MIR	2	25937071
chr2.27262237.27263756	rs6759518	ARR,HD,HF,CAD	MIRc	NR	MIR	2	27263727
chr2.36922199.36922493	rs11124554	HD,HF	AluJr	NR	Alu	2	36922355
chr2.60391661.60392192	rs243071	HD,CAD	MIRb	LR_open	MIR	2	60391893
chr2.86367009.86367615	rs72926475	ARR,HD	MIR	ESR_close	MIR	2	86367364
chr21.14118943.14119390	rs57346421	HD,HF	HERV1_I-int	NR	ERV1	21	14119015
chr21.29162621.29163485	rs73193808	HD,CAD	AluSz6	NR	Alu	21	29162981
chr21.34746498.34746999	rs2834618	ARR,HD	MIRc	ESR_close	MIR	21	34746814
chr22.38435884.38436418	rs2267386	HD	MIR	NR	MIR	22	38436107
chr3.123019226.123019547	rs7632505	ARR,HD,HF,CAD	AluJb	NR	Alu	3	123019460
chr3.136350085.136351911	rs667920	HD,CAD	L1M4b	NR	L1	3	136350630
chr3.14216199.14216968	rs62232870	HD	MLT1K	NR	ERVL-MaLR	3	14216209
chr3.151483988.151484643	rs4387942	HD,CAD	L1PA7	ESR_close	L1	3	151484399
chr3.169478628.169479748	rs2421649	HD	L2b	NR	L2	3	169479545
chr3.57959935.57960395	rs1522387	HD	MLT1K	NR	ERVL-MaLR	3	57960369
chr4.148015916.148016422	rs10213171	ARR,HD	AluJr4	LR_open	Alu	4	148016386
chr4.168766280.168767270	rs7696431,rs869396	HD,CAD	L3	LR_open	CR1	4	168766574,168766849
chr4.76495482.76496240	rs2068063	HD,MI	L2	not_mrc	L2	4	76496050
chr5.115543502.115545233	rs13177180	HD	G-rich	NR	Low_complexity	5	115544896
chr5.135458929.135459424	rs899162	HD,CAD	L2c	NR	L2	5	135459219
chr5.138068694.138070268	rs141654122	ARR,HD	SVA_D	NR	SVA	5	138070140
chr5.52859657.52860199	rs73102285	HD,CAD	L3	NR	CR1	5	52859808
chr6.118252411.118253042	rs281868	ARR,HD	L2b	LR_open	L2	6	118252898
chr6.134047673.134047938	rs965652	HD,CAD	MamRep1894	NR	hAT	6	134047815
chr6.28269507.28270003	rs1225600	HD,CAD	MLT2B4	LR_open	ERVL	6	28269667
chr6.39166177.39166915	rs56336142	HD,CAD	MIRc	NR	MIR	6	39166323
chr7.100243501.100243833	rs117038461	HD,CAD	L1M5	EAR_close	L1	7	100243731
chr7.36459124.36459918	rs192407614	HD,CAD	LTR38B	EAR_open	ERV1	7	36459695
chr7.836506.836873	rs11768850	ARR,HD	MLT2B4	NR	ERVL	7	836590
chr7.92620778.92621787	rs42044	ARR,HD	AluJb	NR	Alu	7	92620826
chr7.93713592.93714106	rs376825901	HD,CAD	Tigger1	NR	TcMar-Tigger	7	93714028
chr7.99822872.99823551	rs62471956	HD,CAD	MER52A	LR_close	ERV1	7	99823462
chr8.124847104.124848853	rs35006907,rs34866937	ARR,HD,HF	MamTip2	NR	hAT-Tip100	8	124847575,124847608
chr8.20007264.20008372	rs2083636,rs894211	HD,CAD	MER34A,LTR48	NR	ERV1	8	20007752,20008236
chr9.107755276.107755816	rs944172	HD,CAD	Charlie13a	LR_open	hAT-Charlie	9	107755513
chr9.123933461.123933812	rs10818894	ACresp	MLT1J	LR_open	ERVL-MaLR	9	123933491

# SNP_df %>%  group_by(Peakid) %>%
#     summarise(snp_id=paste(unique(SNPS), collapse = ","),
#               gwas=paste(unique(gwas),collapse=","),
#               repName = paste(unique(repName),collapse=","),
#               mrc =paste(unique(mrc),collapse=","),
#               repFamily= paste(unique(repFamily),collapse = ","),
#               location_chr= paste(unique(CHR_ID),collapse = ";") ,
#               location= paste(unique(CHR_POS),collapse = ";") ,
#               ) %>%
# write.csv(.,"data/SNP_GWAS_PEAK_MRC_id.csv")
# rtracklayer::export(ESR_open,con = "data/Final_four_data/meme_bed/ESR_open.bed", format="bed", ignore.strand = FALSE)
# 
# rtracklayer::export(ESR_close,con = "data/Final_four_data/meme_bed/ESR_close.bed", format="bed", ignore.strand = FALSE)
# 
# rtracklayer::export(ESR_OC,con = "data/Final_four_data/meme_bed/ESR_OC.bed", format="bed", ignore.strand = FALSE)
# rtracklayer::export(EAR_open,con = "data/Final_four_data/meme_bed/EAR_open.bed", format="bed", ignore.strand = FALSE)
# rtracklayer::export(EAR_close,con = "data/Final_four_data/meme_bed/EAR_close.bed", format="bed", ignore.strand = FALSE)
# 
# rtracklayer::export(LR_open,con = "data/Final_four_data/meme_bed/LR_open.bed", format="bed", ignore.strand = FALSE)
# rtracklayer::export(LR_close,con = "data/Final_four_data/meme_bed/LR_close.bed", format="bed", ignore.strand = FALSE)
# 
# rtracklayer::export(NR_df,con = "data/Final_four_data/meme_bed/NR_df.bed", format="bed", ignore.strand = FALSE)

SNP annotation file

SNPanno <- 
  test2 %>% 
  as.data.frame() %>% 
  dplyr::select(Peakid,SNPS,gwas) %>% 
 mutate(mrc=if_else(Peakid %in% EAR_open$Peakid, "EAR_open",
                      if_else(Peakid %in% EAR_close$Peakid, "EAR_close",
                              if_else(Peakid %in% ESR_open$Peakid,"ESR_open",
                                      if_else(Peakid %in% ESR_close$Peakid,"ESR_close",
                                              if_else(Peakid %in% ESR_OC$Peakid,"ESR_OC",
                                                      if_else(Peakid %in% LR_open$Peakid,"LR_open",
                                                                      if_else(Peakid %in% LR_close$Peakid,"LR_close",
                                                                              if_else(Peakid %in% NR_df$Peakid,"NR","not_mrc"))))))))) %>%
  left_join(Col_fullDF_cug_overlap) %>% 
  mutate(has_cpg= if_else(is.na(name),"not_CpGi","CpGi")) %>% 
  dplyr::select(Peakid:mrc,has_cpg,name,per_ol) %>% 
  dplyr::rename("CpG"="has_cpg","cpg_per_ol"="per_ol","CpG_name"="name") %>%
  left_join(., Peaks_v_cpgpromo, by=c("Peakid"="Peakid"))%>% 
    dplyr::select(Peakid:cpg_per_ol,promotor_name) %>% 
    mutate(CpG_promo=if_else(is.na(promotor_name),"not_CpGpromo","CpGpromo")) %>% 
  left_join(.,Filter_TE_list, by=c(Peakid="Peakid"))%>% 
  dplyr::select(SNPS, Peakid,gwas:CpG_promo,repName:repFamily, per_ol) %>% 
  dplyr::rename("TE_per_ol"="per_ol") %>% 
    group_by(SNPS) %>% 
    summarise(Peakid=paste(unique(Peakid), collapse = ","),
              gwas=paste(unique(gwas),collapse=","),
              mrc=paste(unique(mrc),collapse=","),
               CpG=paste(unique(CpG),collapse=","),
               CpG_name=paste(unique(CpG_name),collapse=","),
               cpg_per_ol=paste(unique(cpg_per_ol),collapse=","),
               promotor_name=paste(unique(promotor_name),collapse=","),
               CpG_promo=paste(unique(CpG_promo),collapse=","),
               repName=paste(unique(repName),collapse=","),
               repClass=paste(unique(repClass),collapse=","),
               repFamily=paste(unique(repFamily),collapse=","),
               TE_per_ol=paste(unique(TE_per_ol),collapse=";"))
              
# write.csv(SNPanno,"data/Final_four_data/annotated_gwas_SNPS.csv")

SNPanno %>% 
  kable(., caption = "SNPs and their annotations from my data") %>% 
  kable_paper("striped", full_width = TRUE) %>%
  kable_styling(full_width = FALSE, font_size = 14) %>% 
  scroll_box(height = "500px")

SNPs and their annotations from my data

SNPS	Peakid	gwas	mrc	CpG	CpG_name	cpg_per_ol	promotor_name	CpG_promo	repName	repClass	repFamily	TE_per_ol
rs10083696	chr15.78830933.78832028	HD,CAD	LR_open	CpGi	CpG: 122	0.692356285533797	NA	not_CpGpromo	NA	NA	NA	NA
rs10083697	chr15.78830933.78832028	HD,CAD	LR_open	CpGi	CpG: 122	0.692356285533797	NA	not_CpGpromo	NA	NA	NA	NA
rs10121140	chr9.14292492.14292732	HD,CAD	not_mrc	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs10213171	chr4.148015916.148016422	ARR,HD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	AluJr4	SINE	Alu	0.673400673400673
rs10213376	chr4.148015002.148015341	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	(TCTCCTG)n,MIR3	Simple_repeat,SINE	Simple_repeat,MIR	1;0.135714285714286
rs1032763	chr5.17118580.17119099	HD,CAD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs10456100	chr6.39214962.39215733	HD,CAD	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	MIR3	SINE	MIR	0.820809248554913
rs1049334	chr7.116560073.116560553	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs10509768	chr10.103712814.103713354	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	MIRb,MIR3	SINE	MIR	1;0.0384615384615385
rs1052586	chr17.46940653.46941215	HD,MI	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs10734649	chr11.9758098.9760569	HD,CAD	NR	CpGi	CpG: 72	1	NA	not_CpGpromo	(GTGGC)n,MIR,GA-rich,L2c	Simple_repeat,SINE,Low_complexity,LINE	Simple_repeat,MIR,Low_complexity,L2	1
rs10786662	chr10.102229686.102230890	ARR,HD	NR	CpGi	CpG: 141	0.268564356435644	NA	not_CpGpromo	MIR3,(GGC)n,(GCCCGG)n	SINE,Simple_repeat	MIR,Simple_repeat	1
rs10811650	chr9.22067279.22067652	HD,CAD	not_mrc	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs10818576	chr9.121650071.121650712	HD,CAD	ESR_close	not_CpGi	NA	NA	NA	not_CpGpromo	MER30,MIR3	DNA,SINE	hAT-Charlie,MIR	0.256198347107438;1
rs10818579	chr9.121651049.121653070	HD,MI	NR	CpGi	CpG: 78	1	EH38E2724036	CpGpromo	GA-rich,MIRb,L3	Low_complexity,SINE,LINE	Low_complexity,MIR,CR1	1
rs10818894	chr9.123933461.123933812	ACresp	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	MLT1J	LTR	ERVL-MaLR	0.698412698412698
rs10821415	chr9.94950339.94951513	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	MIRc,MIRb	SINE	MIR	0.746543778801843;1;0.633484162895928
rs10824026	chr10.73660865.73661568	ARR,HD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	AluY,L1MA4A,AluSz	SINE,LINE	Alu,L1	0.342948717948718;1;0.164634146341463
rs10871753	chr18.58289003.58291154	HD,HF	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	L2a,MLT1I	LINE,LTR	L2,ERVL-MaLR	1
rs10927886	chr1.16012567.16013729	HD	ESR_close	not_CpGi	NA	NA	NA	not_CpGpromo	MIRb,MIR3,L2b	SINE,LINE	MIR,L2	1;0.686567164179104
rs10931898	chr2.200305915.200307710	ARR,HD	NR	CpGi	CpG: 150	0.954334365325077	EH38E2065239	CpGpromo	(GGCG)n,(CGGCCC)n,(CGCCCG)n	Simple_repeat	Simple_repeat	1
rs11038225	chr11.44955010.44955513	HD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	MIRb	SINE	MIR	0.121848739495798
rs11044977	chr12.20004923.20005338	HD,MI	NR	not_CpGi	NA	NA	NA	not_CpGpromo	MER85,L2a	DNA,LINE	PiggyBac,L2	1
rs11054833	chr12.12349737.12350701	HD,CAD	NR	CpGi	CpG: 61	1	EH38E1593399	CpGpromo	L1ME4b	LINE	L1	1
rs11124554	chr2.36922199.36922493	HD,HF	NR	not_CpGi	NA	NA	NA	not_CpGpromo	L1MC4,AluJr	LINE,SINE	L1,Alu	0.0381231671554252;0.842622950819672
rs11180610	chr12.75646380.75646944	HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	Tigger19b	DNA	TcMar-Tigger	0.802083333333333
rs1122608	chr19.11052376.11052994	HD,CAD,MI	NR	not_CpGi	NA	NA	NA	not_CpGpromo	AluJr	SINE	Alu	0.244897959183673
rs11235604	chr11.72821745.72822900	HD,CAD	NR	CpGi	CpG: 111	0.847676419965577	NA	not_CpGpromo	NA	NA	NA	NA
rs11242465	chr5.139426247.139426836	HD,HF	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	Alu,AluSz6	SINE	Alu	1;0.72463768115942
rs112577387	chr4.22625204.22625843	HD,HF	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	MIRc	SINE	MIR	0.811188811188811
rs112941079	chr5.9544368.9546915	HD,CAD	NR	CpGi	CpG: 138,CpG: 25	1	NA	not_CpGpromo	(GGAGCGG)n,(CCG)n,(GCCCC)n	Simple_repeat	Simple_repeat	1
rs112941127	chr2.217868752.217869273	HD,CAD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	MIRb	SINE	MIR	0.220883534136546
rs112949822	chr5.108747709.108749572	HD,CAD	NR	CpGi	CpG: 110	1	NA	not_CpGpromo	L2c	LINE	L2	1
rs113140904	chr5.9544368.9546915	HD,CAD	NR	CpGi	CpG: 138,CpG: 25	1	NA	not_CpGpromo	(GGAGCGG)n,(CCG)n,(GCCCC)n	Simple_repeat	Simple_repeat	1
rs113452171	chr7.91784539.91784870	HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs113716316	chr1.27601871.27602613	HD,CAD,MI	NR	not_CpGi	NA	NA	NA	not_CpGpromo	(CCAGC)n,(TCCCGC)n	Simple_repeat	Simple_repeat	1
rs113819537	chr12.26194928.26196665	ARR,HD,HF	NR	CpGi	CpG: 74	1	EH38E1600228	CpGpromo	AluJr,(CACC)n,(CTG)n	SINE,Simple_repeat	Alu,Simple_repeat	0.229452054794521;1
rs113920486	chr10.52539479.52540028	HD	ESR_open	not_CpGi	NA	NA	NA	not_CpGpromo	MamGypLTR1d	LTR	Gypsy	0.175572519083969
rs11398961	chr15.78830933.78832028	HD,CAD	LR_open	CpGi	CpG: 122	0.692356285533797	NA	not_CpGpromo	NA	NA	NA	NA
rs114192718	chr2.128027137.128028647	HD,CAD	NR	CpGi	CpG: 165	0.86441647597254	EH38E2031716,EH38E2031718	CpGpromo	(CGG)n,(CCGC)n,G-rich	Simple_repeat,Low_complexity	Simple_repeat,Low_complexity	1;0.780487804878049
rs11465228	chr5.157575011.157576352	HD,CAD	NR	CpGi	CpG: 95	1	EH38E2424756	CpGpromo	MIR3,(AG)n,(T)n	SINE,Simple_repeat	MIR,Simple_repeat	0.191489361702128;1
rs114782882	chr12.75895398.75895669	HD,HF	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs11554495	chr12.52903311.52905516	HD	NR	CpGi	CpG: 41	1	NA	not_CpGpromo	MIRb,(CCACC)n,(A)n	SINE,Simple_repeat	MIR,Simple_repeat	0.311827956989247;1
rs11591147	chr1.55039222.55040233	HD,CAD,MI	NR	CpGi	CpG: 85	0.88586387434555	EH38E1349458,EH38E1349459	CpGpromo	(GCT)n,MIR3	Simple_repeat,SINE	Simple_repeat,MIR	1
rs11606719	chr11.118911506.118912630	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	MIRb	SINE	MIR	1
rs11631816	chr15.73353612.73354309	HD,CAD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs11642015	chr16.53768577.53768944	HD,HF	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	UCON8	DNA	DNA	1
rs11643207	chr16.75463626.75465107	HD	NR	CpGi	CpG: 113	1	EH38E1828262,EH38E1828263,EH38E1828264	CpGpromo	NA	NA	NA	NA
rs11670056	chr19.18478653.18479216	HD,CAD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	MER20	DNA	hAT-Charlie	0.552941176470588
rs11677932	chr2.237315068.237315792	HD,CAD	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs116843064	chr19.8363631.8364760	HD,CAD,MI	NR	CpGi	CpG: 52	1	NA	not_CpGpromo	AluSz,(CCCCGAAT)n	SINE,Simple_repeat	Alu,Simple_repeat	0.869747899159664;1
rs117038461	chr7.100243501.100243833	HD,CAD	EAR_close	not_CpGi	NA	NA	NA	not_CpGpromo	MIRc,L1M5	SINE,LINE	MIR,L1	0.521276595744681;1
rs11705555	chr22.27810496.27811281	HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	(TGCACA)n	Simple_repeat	Simple_repeat	0.95
rs11713141	chr3.138347976.138349043	HD,CAD	NR	CpGi	CpG: 132	0.979225684608121	EH38E2241845,EH38E2241846	CpGpromo	NA	NA	NA	NA
rs117299725	chr9.76808694.76808955	ACtox,ACresp,HD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs11759102	chr6.19836789.19839260	HD,CAD	NR	CpGi	CpG: 247	0.997826086956522	EH38E2452004	CpGpromo	AmnSINE2	SINE	tRNA-Deu	1
rs11768850	chr7.836506.836873	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	MLT2B4	LTR	ERVL	0.651612903225806
rs11838267	chr12.7068459.7069322	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	MIRb	SINE	MIR	1
rs11838776	chr13.110387344.110389032	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs11841562	chr13.21536977.21537596	ARR,HD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	L2,AluSq2	LINE,SINE	L2,Alu	0.176470588235294;1;0.440251572327044
rs12122060	chr1.170224576.170224916	ARR,HD	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	MER53,MIRb,L1MA7	DNA,SINE,LINE	hAT,MIR,L1	0.0173913043478261;1;0.288571428571429
rs12150051	chr17.12584681.12585327	HD,CAD	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	L1M7	LINE	L1	0.331269349845201
rs12155623	chr8.48898785.48899940	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	(CA)n	Simple_repeat	Simple_repeat	0.787037037037037
rs1225600	chr6.28269507.28270003	HD,CAD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	MLT2B4,AluSc8	LTR,SINE	ERVL,Alu	0.82565130260521;0.259259259259259
rs12440045	chr15.41490412.41490650	HD,CAD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs1250258	chr2.215435176.215436848	HD,MI	NR	CpGi	CpG: 43	1	EH38E2072836	CpGpromo	(CCCG)n,G-rich	Simple_repeat,Low_complexity	Simple_repeat,Low_complexity	1
rs1250259	chr2.215435176.215436848	HD,CAD	NR	CpGi	CpG: 43	1	EH38E2072836	CpGpromo	(CCCG)n,G-rich	Simple_repeat,Low_complexity	Simple_repeat,Low_complexity	1
rs1250566	chr10.79286266.79286796	HD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs12603284	chr17.2867997.2868307	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	MamRTE1	LINE	RTE-BovB	1
rs12640611	chr4.10102598.10102907	ARR,HD	ESR_open	not_CpGi	NA	NA	NA	not_CpGpromo	MSTD,MIRb	LTR,SINE	ERVL-MaLR,MIR	0.197707736389685;1
rs12712649	chr2.39631404.39632344	HD,CAD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	MER5A,MLT1O	DNA,LTR	hAT-Charlie,ERVL-MaLR	1;0.738095238095238
rs12724121	chr1.236688731.236689166	HD,HF	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs12740374	chr1.109274953.109276202	HD,CAD,MI	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	(TCCTC)n	Simple_repeat	Simple_repeat	1
rs12893887	chr14.99644968.99645471	HD,CAD	NR	CpGi	CpG: 93	0.641221374045801	EH38E1741629,EH38E1741630	CpGpromo	NA	NA	NA	NA
rs12906125	chr15.90884159.90884726	HD,MI	not_mrc	CpGi	CpG: 45	1	EH38E1787901	CpGpromo	G-rich	Low_complexity	Low_complexity	1
rs12936927	chr17.17823556.17824258	HD,CAD	ESR_close	CpGi	CpG: 47	0.967213114754098	NA	not_CpGpromo	(CGCCC)n,MIRb	Simple_repeat,SINE	Simple_repeat,MIR	1
rs12980942	chr19.41324959.41326640	HD,CAD	ESR_close	not_CpGi	NA	NA	NA	not_CpGpromo	(CTCC)n,(CCCTG)n	Simple_repeat	Simple_repeat	1
rs12983897	chr19.17747415.17747946	HD,MI	LR_close	CpGi	CpG: 64	0.738461538461539	EH38E1943617	CpGpromo	MIRb	SINE	MIR	0.0130718954248366
rs13176353	chr5.1800893.1802029	HD	NR	CpGi	CpG: 185	0.367839607201309	EH38E2353141	CpGpromo	NA	NA	NA	NA
rs13177180	chr5.115543502.115545233	HD	NR	CpGi	CpG: 65	1	EH38E2400588	CpGpromo	G-rich,THE1B	Low_complexity,LTR	Low_complexity,ERVL-MaLR	1;0.304093567251462
rs1333042	chr9.22102751.22103879	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	MIR3,(TG)n	SINE,Simple_repeat	MIR,Simple_repeat	1
rs13346603	chr19.41439501.41440141	HD,HF	NR	CpGi	CpG: 18	1	NA	not_CpGpromo	AluJr	SINE	Alu	0.148936170212766
rs13402621	chr2.43231283.43231596	HD,MI	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs141654122	chr5.138068694.138070268	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	MIR3,U6,MamTip2,AluSp,SVA_D	SINE,snRNA,DNA,Retroposon	MIR,snRNA,hAT-Tip100,Alu,SVA	1;0.177004538577912
rs145306069	chr1.203795403.203796608	HD,CAD	NR	CpGi	CpG: 57	1	EH38E1413938	CpGpromo	NA	NA	NA	NA
rs1468498	chr10.114306394.114306913	HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs147288039	chr9.95006342.95007055	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	AluSc8	SINE	Alu	0.516778523489933
rs147631684	chr16.83599141.83599350	ACtox,ACresp,HD	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	MLT1K	LTR	ERVL-MaLR	0.0246045694200351
rs148241618	chr18.45879810.45880598	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs148416395	chr22.46417011.46418228	HD,HF	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	MIRc,MIRb,AluSx1	SINE	MIR,Alu	1;0.0946372239747634
rs1522387	chr3.57959935.57960395	HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	MLT1K	LTR	ERVL-MaLR	0.677083333333333
rs1522388	chr3.57959935.57960395	HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	MLT1K	LTR	ERVL-MaLR	0.677083333333333
rs1537372	chr9.22102751.22103879	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	MIR3,(TG)n	SINE,Simple_repeat	MIR,Simple_repeat	1
rs1537373	chr9.22102751.22103879	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	MIR3,(TG)n	SINE,Simple_repeat	MIR,Simple_repeat	1
rs16866933	chr2.179701478.179702086	ARR,HD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	MIR	SINE	MIR	0.367741935483871
rs170041	chr17.2266828.2267276	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	AluJb	SINE	Alu	0.222996515679443
rs17101521	chr10.121155583.121156319	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	L3,LTR16A2	LINE,LTR	CR1,ERVL	0.363247863247863;1
rs17118812	chr5.140322792.140323772	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs17228212	chr15.67165920.67166678	HD,CAD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	L2b	LINE	L2	1
rs17375901	chr1.11792123.11792871	ARR,HD	ESR_close	not_CpGi	NA	NA	NA	not_CpGpromo	L2c,MIRc	LINE,SINE	L2,MIR	0.3625;0.56
rs17458018	chr2.215420519.215420812	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs175040	chr14.75002369.75003794	HD,CAD	NR	CpGi	CpG: 53	1	EH38E1728281	CpGpromo	NA	NA	NA	NA
rs17608766	chr17.46935408.46936158	HD,CAD	EAR_close	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs1769758	chr10.79139073.79139940	ARR,HD	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs17782904	chr18.44733224.44733992	HD,CAD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs1788826	chr18.23574050.23574545	HD,HF	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs1800449	chr5.122076732.122078641	HD,CAD	LR_open	CpGi	CpG: 137	1	NA	not_CpGpromo	NA	NA	NA	NA
rs1800470	chr19.41352378.41354231	HD,CAD	NR	CpGi	CpG: 139	1	EH38E1954757,EH38E1954758	CpGpromo	(CAGCAG)n,(TCC)n	Simple_repeat	Simple_repeat	1
rs1800797	chr7.22726398.22726697	HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs1800978	chr9.104903653.104904005	HD,CAD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs1822273	chr11.19988504.19989024	ARR,HD	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	AmnSINE1	SINE	5S-Deu-L2	1
rs190258023	chr22.46417011.46418228	HD,HF	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	MIRc,MIRb,AluSx1	SINE	MIR,Alu	1;0.0946372239747634
rs191615952	chr19.2235309.2237165	HD,CAD	NR	CpGi	CpG: 157	1	EH38E1932153,EH38E1932154	CpGpromo	MLT1C,AluJr	LTR,SINE	ERVL-MaLR,Alu	0.0374149659863946;0.102040816326531
rs192407614	chr7.36459124.36459918	HD,CAD	EAR_open	not_CpGi	NA	NA	NA	not_CpGpromo	LTR38B	LTR	ERV1	1
rs1926032	chr10.103069381.103070029	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	L1ME3A	LINE	L1	0.2
rs1962412	chr17.48892152.48893499	HD,CAD	NR	CpGi	CpG: 35	1	EH38E1868507	CpGpromo	AluSx	SINE	Alu	0.0487012987012987
rs2052923	chr2.43184483.43184778	HD,CAD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	MIRb,Cheshire	SINE,DNA	MIR,hAT-Charlie	1;0.100917431192661
rs2068063	chr4.76495482.76496240	HD,MI	not_mrc	not_CpGi	NA	NA	NA	not_CpGpromo	LTR16A1,L2	LTR,LINE	ERVL,L2	0.337264150943396;0.889583333333333
rs2071502	chr17.7510984.7512510	ARR,HD	ESR_open	not_CpGi	NA	NA	NA	not_CpGpromo	AluSx	SINE	Alu	0.902654867256637
rs2073533	chr7.13989380.13991798	HD,CAD	NR	CpGi	CpG: 18,CpG: 21	1	EH38E2535294	CpGpromo	(AACA)n	Simple_repeat	Simple_repeat	1
rs2076380	chr20.38164364.38165984	HD	NR	CpGi	CpG: 35	1	EH38E2111384	CpGpromo	MIRc,(AC)n	SINE,Simple_repeat	MIR,Simple_repeat	1
rs2083636	chr8.20007264.20008372	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	LTR32,MER34A,LTR48	LTR	ERVL,ERV1	0.875294117647059;1
rs2099684	chr1.161530078.161531531	HD	NR	CpGi	CpG: 32	1	NA	not_CpGpromo	tRNA-Leu-CTG,tRNA-Gly-GGA,(TTCC)n,Charlie5	tRNA,Simple_repeat,DNA	tRNA,Simple_repeat,hAT-Charlie	1;0.204301075268817
rs2145274	chr20.6590860.6591642	ARR,HD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	Penelope1_Vert	LINE	Penelope	1
rs2145598	chr14.58326948.58327320	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	L2c,MER3	LINE,DNA	L2,hAT-Charlie	0.219123505976096;1
rs216172	chr17.2222861.2223544	HD,CAD	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs2220127	chr2.178846382.178846751	HD,HF	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	MIRc	SINE	MIR	0.38655462184874
rs2220427	chr4.110793293.110793943	ARR,HD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs2267386	chr22.38435884.38436418	HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	MIR	SINE	MIR	1
rs2269001	chr7.150954727.150956459	ARR,HD	ESR_close	CpGi	CpG: 23	1	EH38E2600998	CpGpromo	NA	NA	NA	NA
rs2281727	chr17.2214040.2216112	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	(AC)n,GA-rich	Simple_repeat,Low_complexity	Simple_repeat,Low_complexity	1
rs2286466	chr16.1963596.1965464	ARR,HD	NR	CpGi	CpG: 122	1	EH38E1796197,EH38E1796198,EH38E1796199	CpGpromo	AluSx3,(CCCCGG)n,(CTGACCC)n	SINE,Simple_repeat	Alu,Simple_repeat	0.0114068441064639;1
rs2291437	chr12.24561685.24562685	ARR,HD	NR	CpGi	CpG: 92	0.495515695067265	EH38E1599170,EH38E1599171	CpGpromo	G-rich,(CCACCTC)n,(TG)n,(TCGC)n	Low_complexity,Simple_repeat	Low_complexity,Simple_repeat	1
rs2306363	chr11.65637814.65638912	HD,CAD	NR	CpGi	CpG: 36	1	EH38E1545418	CpGpromo	MIRb	SINE	MIR	0.252873563218391
rs2306374	chr3.138400627.138401136	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs2359171	chr16.73018778.73020183	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	(GCGTGT)n	Simple_repeat	Simple_repeat	1
rs2410859	chr17.75844492.75845572	HD,CAD	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	(GTGGG)n	Simple_repeat	Simple_repeat	1
rs2415081	chr15.70171390.70172398	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	MER102c,MIRb,MIR3	DNA,SINE	hAT-Charlie,MIR	0.788793103448276;1
rs2421649	chr3.169478628.169479748	HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	L2b,(A)n	LINE,Simple_repeat	L2,Simple_repeat	1
rs243071	chr2.60391661.60392192	HD,CAD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	MIRb	SINE	MIR	1
rs2452600	chr4.94575665.94576013	HD,CAD	EAR_open	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs2493298	chr1.3408398.3409463	HD,CAD	LR_open	CpGi	CpG: 37	1	NA	not_CpGpromo	NA	NA	NA	NA
rs2507	chr15.73983047.73983757	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	(CCTTC)n	Simple_repeat	Simple_repeat	1
rs2540949	chr2.65055695.65057220	ARR,HD	NR	CpGi	CpG: 132	1	EH38E2004398	CpGpromo	NA	NA	NA	NA
rs2595104	chr4.110631655.110632575	ARR,HD	NR	CpGi	CpG: 100	0.576480990274094	NA	not_CpGpromo	NA	NA	NA	NA
rs2625322	chr11.19988504.19989024	ARR,HD	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	AmnSINE1	SINE	5S-Deu-L2	1
rs2660739	chr3.78644690.78645022	HD	not_mrc	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs281868	chr6.118252411.118253042	ARR,HD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	L2a,L2b	LINE	L2	0.492152466367713;0.26080476900149
rs2834618	chr21.34746498.34746999	ARR,HD	ESR_close	not_CpGi	NA	NA	NA	not_CpGpromo	AluSx,MIRc,(CTCC)n	SINE,Simple_repeat	Alu,MIR,Simple_repeat	0.201320132013201;1;0.526315789473684
rs2852306	chr18.45565618.45565968	HD	EAR_close	not_CpGi	NA	NA	NA	not_CpGpromo	MIR3	SINE	MIR	0.614814814814815
rs295114	chr2.200330546.200331118	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs3176326	chr6.36678380.36679788	ARR,HD,HF	ESR_open	CpGi	CpG: 204	0.594777127420081	EH38E2463466	CpGpromo	NA	NA	NA	NA
rs3213420	chr16.72008437.72009100	HD,CAD	LR_close	CpGi	CpG: 50	1	EH38E1826130	CpGpromo	NA	NA	NA	NA
rs326	chr8.19961869.19963021	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs328	chr8.19961869.19963021	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs337711	chr5.114412835.114413212	ARR,HD	not_mrc	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs34292822	chr1.154839629.154839995	ARR,HD	ESR_close	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs34774090	chr19.11142349.11143639	HD,CAD	EAR_close	not_CpGi	NA	NA	NA	not_CpGpromo	MIR1_Amn,G-rich,MER20	SINE,Low_complexity,DNA	MIR,Low_complexity,hAT-Charlie	1
rs34866937	chr8.124847104.124848853	HD,HF	NR	not_CpGi	NA	NA	NA	not_CpGpromo	MamTip2,AluSx	DNA,SINE	hAT-Tip100,Alu	1
rs34871776	chr3.12773796.12774087	HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs34969716	chr6.18209665.18210014	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	AluJr,MER5B	SINE,DNA	Alu,hAT-Charlie	0.636904761904762;1
rs35006907	chr8.124847104.124848853	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	MamTip2,AluSx	DNA,SINE	hAT-Tip100,Alu	1
rs35176054	chr10.103720444.103721025	ARR,HD	ESR_close	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs35267671	chr1.37931148.37932171	HD,MI	NR	CpGi	CpG: 53	1	EH38E1338382	CpGpromo	MIR3	SINE	MIR	1
rs35620480	chr8.11641900.11643102	ARR,HD	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	MLT1K,MIRb,(C)n	LTR,SINE,Simple_repeat	ERVL-MaLR,MIR,Simple_repeat	0.588832487309645;1
rs35946663	chr14.96362201.96364292	HD	NR	CpGi	CpG: 101	1	EH38E1739803,EH38E1739804	CpGpromo	(CGGCTC)n,MIR	Simple_repeat,SINE	Simple_repeat,MIR	1;0.248780487804878
rs360153	chr11.9740217.9740734	HD,CAD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs360801	chr2.62727824.62728333	HD,MI	NR	not_CpGi	NA	NA	NA	not_CpGpromo	LTR35,L1MB8	LTR,LINE	ERV1,L1	0.886917960088692;0.107569721115538
rs3731748	chr2.178547784.178549172	ARR,HD	ESR_OC	not_CpGi	NA	NA	NA	not_CpGpromo	OldhAT1	DNA	hAT-Ac	1
rs3734634	chr6.125789773.125791822	HD	NR	CpGi	CpG: 107	1	EH38E2500543,EH38E2500544	CpGpromo	(CCT)n,(CGC)n	Simple_repeat	Simple_repeat	1
rs376825901	chr7.93713592.93714106	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	Tigger1	DNA	TcMar-Tigger	0.213959285417532
rs3787662	chr21.29151834.29152614	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	L2b	LINE	L2	1
rs3803802	chr17.7718045.7718289	ARR,HD,HF,CAD	EAR_open	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs3807989	chr7.116545872.116546373	ARR,HD	ESR_close	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs3809297	chr12.111171885.111172119	ARR,HD	not_mrc	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs3813127	chr18.22457586.22457998	HD,CAD	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	MIRb	SINE	MIR	0.418439716312057
rs3814864	chr14.72893758.72895206	ARR,HD	NR	CpGi	CpG: 165	0.218648018648019	EH38E1726798,EH38E1726799	CpGpromo	GA-rich	Low_complexity	Low_complexity	1
rs3822127	chr4.173529475.173530537	ARR,HD	EAR_open	CpGi	CpG: 120	0.593113141250878	EH38E2344608,EH38E2344610	CpGpromo	(CCGGCT)n,(CCCTC)n	Simple_repeat	Simple_repeat	1
rs3822259	chr4.10115366.10117581	ARR,HD	NR	CpGi	CpG: 136	1	EH38E2280769,EH38E2280771,EH38E2280772	CpGpromo	MIRc,5S,(GGCAG)n,G-rich,(CCGGCC)n	SINE,rRNA,Simple_repeat,Low_complexity	MIR,rRNA,Simple_repeat,Low_complexity	1
rs3853445	chr4.110840197.110840561	ARR,HD	not_mrc	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs3885907	chr13.30740138.30740672	ACresp	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	L3,MIR,(TC)n,L2	LINE,SINE,Simple_repeat	CR1,MIR,Simple_repeat,L2	0.385869565217391;1;0.27319587628866
rs3895874	chr17.48970223.48970597	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs3904323	chr13.22794559.22794879	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	OldhAT1	DNA	hAT-Ac	1
rs42044	chr7.92620778.92621787	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	AluJb,MamRTE1	SINE,LINE	Alu,RTE-BovB	0.506802721088435;0.298449612403101
rs4234323	chr3.151479569.151479846	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs4234324	chr3.151479569.151479846	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs4387942	chr3.151483988.151484643	HD,CAD	ESR_close	not_CpGi	NA	NA	NA	not_CpGpromo	L1PA7	LINE	L1	0.101516558341071
rs472109	chr11.9748359.9748943	HD,CAD	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	LTR41,MIRb	LTR,SINE	ERVL,MIR	0.61864406779661;1;0.703703703703704
rs472495	chr1.55055575.55055793	HD,CAD	not_mrc	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs4757877	chr11.19988504.19989024	ARR,HD	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	AmnSINE1	SINE	5S-Deu-L2	1
rs476348	chr18.35089781.35090661	HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	L1MB7,L1MA6,AluSx1	LINE,SINE	L1,Alu	0.478102189781022;1;0.67948717948718
rs4773140	chr13.110301444.110302036	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	L2	LINE	L2	0.210682492581602
rs4773141	chr13.110301444.110302036	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	L2	LINE	L2	0.210682492581602
rs4773144	chr13.110305804.110309074	HD,CAD	NR	CpGi	CpG: 178,CpG: 19	1	EH38E1697729,EH38E1697730	CpGpromo	(AAGAAC)n,(GGC)n	Simple_repeat	Simple_repeat	1
rs4871397	chr8.123622558.123623378	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs4894803	chr3.172082306.172082889	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	MIR3	SINE	MIR	1
rs4896104	chr6.134797479.134798124	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs4932373	chr15.90885954.90886257	HD,CAD	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	MIR,MER58A	SINE,DNA	MIR,hAT-Charlie	1;0.0588235294117647
rs494207	chr10.44245450.44245918	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	L1MB2	LINE	L1	0.566343042071197
rs4951261	chr1.205748526.205750556	ARR,HD	NR	CpGi	CpG: 100	1	NA	not_CpGpromo	NA	NA	NA	NA
rs4977575	chr9.22124697.22124840	HD,CAD	not_mrc	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs4999127	chr1.154741375.154741854	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs518594	chr10.44261009.44262169	HD,CAD	ESR_close	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs523297	chr10.44261009.44262169	HD,CAD	ESR_close	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs534128177	chr11.90455310.90455981	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	L1PA16,LTR14B	LINE,LTR	L1,ERVK	0.0403825717321998;1;0.10625
rs55734480	chr7.14331921.14332483	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	(GTTT)n,MIR,MER113	Simple_repeat,SINE,DNA	Simple_repeat,MIR,hAT-Charlie	1;0.00966183574879227
rs56210063	chr11.8767540.8768078	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	L2a,L3	LINE	L2,CR1	0.422077922077922;1
rs56281979	chr3.14232390.14233136	HD,HF	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	MIR3,L3,AluSz6	SINE,LINE	MIR,CR1,Alu	0.72;1;0.130281690140845
rs56336142	chr6.39166177.39166915	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	MIRc	SINE	MIR	1
rs564427867	chr1.55039222.55040233	HD,CAD	NR	CpGi	CpG: 85	0.88586387434555	EH38E1349458,EH38E1349459	CpGpromo	(GCT)n,MIR3	Simple_repeat,SINE	Simple_repeat,MIR	1
rs57346421	chr21.14118943.14119390	HD,HF	NR	not_CpGi	NA	NA	NA	not_CpGpromo	HERV1_I-int	LTR	ERV1	0.0510483135824977
rs582384	chr2.45668749.45669787	HD,CAD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	MIRc,L1M5,AluSx1	SINE,LINE	MIR,L1,Alu	0.260869565217391;1;0.196013289036545
rs585967	chr2.21047256.21047719	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	(CA)n	Simple_repeat	Simple_repeat	1
rs5867305	chr5.36156365.36157193	HD,CAD	EAR_open	not_CpGi	NA	NA	NA	not_CpGpromo	MER5A,MER112	DNA	hAT-Charlie	1
rs587606498	chr1.121213617.121213779	HD,HF	not_mrc	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs588136	chr15.58437614.58438322	HD,CAD	not_mrc	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs590121	chr11.75561990.75564409	HD,CAD	NR	CpGi	CpG: 53	0.873873873873874	EH38E1553275	CpGpromo	G-rich	Low_complexity	Low_complexity	1
rs60280851	chr15.68666479.68666914	HD	not_mrc	not_CpGi	NA	NA	NA	not_CpGpromo	5S	rRNA	rRNA	1
rs604723	chr11.100739719.100740030	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs62042066	chr16.86664502.86665119	HD,MI	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	AmnSINE1,LTR16A2	SINE,LTR	5S-Deu-L2,ERVL	1
rs62139062	chr2.65271531.65272160	HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	AluSp	SINE	Alu	0.385665529010239
rs62232870	chr3.14216199.14216968	HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	MLT1K,MER117,MIRb	LTR,DNA,SINE	ERVL-MaLR,hAT-Charlie,MIR	0.767313019390582;1;0.248587570621469
rs62471956	chr7.99822872.99823551	HD,CAD	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	MER52A	LTR	ERV1	0.416922133660331
rs62568141	chr9.77011996.77012519	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs629301	chr1.109274953.109276202	ARR,HD,HF,CAD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	(TCCTC)n	Simple_repeat	Simple_repeat	1
rs6426551	chr1.226353539.226354600	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	LTR78,Charlie4z,MLT1L	LTR,DNA	ERV1,hAT-Charlie,ERVL-MaLR	0.652284263959391;1;0.0392156862745098
rs646776	chr1.109274953.109276202	HD,CAD,MI	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	(TCCTC)n	Simple_repeat	Simple_repeat	1
rs653178	chr12.111569574.111570394	HD,MI	NR	not_CpGi	NA	NA	NA	not_CpGpromo	AluJb,(CAC)n,MER3,L3,L1MC5	SINE,Simple_repeat,DNA,LINE	Alu,Simple_repeat,hAT-Charlie,CR1,L1	0.534798534798535;1;0.768707482993197
rs6542647	chr2.4898614.4899128	HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	L1PB3,MER113B	LINE,DNA	L1,hAT-Charlie	0.0321543408360129;1
rs6546620	chr2.25936965.25937466	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	MIRb,(GGCAGT)n	SINE,Simple_repeat	MIR,Simple_repeat	0.691176470588235;0.0487804878048781
rs6597292	chr6.7974642.7975583	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs6598541	chr15.98727491.98728563	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	(T)n,LTR12F	Simple_repeat,LTR	Simple_repeat,ERV1	1
rs660240	chr1.109274953.109276202	HD,HF,CAD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	(TCCTC)n	Simple_repeat	Simple_repeat	1
rs667920	chr3.136350085.136351911	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	AluSz,L1M4b,L1MA9,L1M4a2	SINE,LINE	Alu,L1	0.25;1;0.300291545189504
rs6702619	chr1.99580395.99580980	HD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs672149	chr11.128759383.128760154	HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	L2	LINE	L2	0.927433628318584
rs6730157	chr2.135149084.135149706	ARR,HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	MIR,AluY	SINE	MIR,Alu	1;0.152542372881356
rs6759518	chr2.27262237.27263756	ARR,HD,HF,CAD	NR	CpGi	CpG: 257	0.479192938209332	EH38E1982668	CpGpromo	MIRc	SINE	MIR	0.84297520661157
rs6801957	chr3.38725644.38726351	ARR,HD	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	MIRb	SINE	MIR	0.15668202764977
rs6807275	chr3.14232390.14233136	HD	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	MIR3,L3,AluSz6	SINE,LINE	MIR,CR1,Alu	0.72;1;0.130281690140845
rs6909752	chr6.22612314.22612576	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs6982502	chr8.125466567.125467336	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs7090277	chr10.12234804.12236216	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	AluSp,MER117	SINE,DNA	Alu,hAT-Charlie	0.678787878787879;1
rs7115242	chr11.117037235.117037719	ARR,HD,HF,CAD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs7157599	chr14.100158874.100160055	ARR,HD	NR	CpGi	CpG: 131	0.939579684763573	EH38E1742143	CpGpromo	G-rich	Low_complexity	Low_complexity	1
rs7165042	chr15.78830933.78832028	HD,CAD,MI	LR_open	CpGi	CpG: 122	0.692356285533797	NA	not_CpGpromo	NA	NA	NA	NA
rs7165081	chr15.78830933.78832028	HD,CAD	LR_open	CpGi	CpG: 122	0.692356285533797	NA	not_CpGpromo	NA	NA	NA	NA
rs7165733	chr15.78830933.78832028	HD,CAD	LR_open	CpGi	CpG: 122	0.692356285533797	NA	not_CpGpromo	NA	NA	NA	NA
rs7166764	chr15.78830933.78832028	HD,CAD	LR_open	CpGi	CpG: 122	0.692356285533797	NA	not_CpGpromo	NA	NA	NA	NA
rs7172038	chr15.73374622.73375198	ARR,HD	ESR_close	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs7178084	chr15.73375634.73376745	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	L2c,(CAAGCCC)n	LINE,Simple_repeat	L2,Simple_repeat	1
rs7181432	chr15.78830933.78832028	HD,CAD	LR_open	CpGi	CpG: 122	0.692356285533797	NA	not_CpGpromo	NA	NA	NA	NA
rs7182103	chr15.78830933.78832028	HD,CAD	LR_open	CpGi	CpG: 122	0.692356285533797	NA	not_CpGpromo	NA	NA	NA	NA
rs7182529	chr15.78830933.78832028	HD,CAD	LR_open	CpGi	CpG: 122	0.692356285533797	NA	not_CpGpromo	NA	NA	NA	NA
rs7182716	chr15.78830933.78832028	HD,CAD	LR_open	CpGi	CpG: 122	0.692356285533797	NA	not_CpGpromo	NA	NA	NA	NA
rs7183206	chr15.73375634.73376745	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	L2c,(CAAGCCC)n	LINE,Simple_repeat	L2,Simple_repeat	1
rs7189462	chr16.81873989.81874357	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	MER102a	DNA	hAT-Charlie	1
rs7197197	chr16.72877300.72877905	ARR,HD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	(TGT)n	Simple_repeat	Simple_repeat	1
rs7234864	chr18.60067561.60068151	HD,HF	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	L2c	LINE	L2	0.0103092783505155
rs7246865	chr19.17107804.17108454	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	MLT1C,MER20	LTR,DNA	ERVL-MaLR,hAT-Charlie	0.321867321867322;0.541666666666667
rs72654473	chr19.44911105.44911471	HD,CAD	ESR_close	not_CpGi	NA	NA	NA	not_CpGpromo	MIRc	SINE	MIR	0.393939393939394
rs72658867	chr19.11119936.11120563	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	AluSq,(AAAC)n	SINE,Simple_repeat	Alu,Simple_repeat	0.135231316725979;1
rs72664335	chr1.56521199.56521723	HD,CAD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	AluSz6,(TG)n,FLAM_C,(CTCC)n	SINE,Simple_repeat	Alu,Simple_repeat	0.205787781350482;1;0.219178082191781
rs72671655	chr8.105335411.105336052	HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs72700114	chr1.170224576.170224916	ARR,HD	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	MER53,MIRb,L1MA7	DNA,SINE,LINE	hAT,MIR,L1	0.0173913043478261;1;0.288571428571429
rs72926475	chr2.86367009.86367615	ARR,HD	ESR_close	not_CpGi	NA	NA	NA	not_CpGpromo	MIR	SINE	MIR	0.407608695652174;1
rs72935945	chr6.110334421.110335161	HD,CAD	ESR_open	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs729743	chr17.80795708.80796334	HD,CAD	ESR_close	not_CpGi	NA	NA	NA	not_CpGpromo	THE1D,MLT2A2	LTR	ERVL-MaLR,ERVL	1
rs73045269	chr19.41318885.41319481	HD,CAD	ESR_open	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs73102285	chr5.52859657.52860199	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	L3	LINE	CR1	0.763713080168776
rs73123536	chr4.22630084.22630437	HD,HF	not_mrc	not_CpGi	NA	NA	NA	not_CpGpromo	MIRc	SINE	MIR	0.32258064516129
rs73193808	chr21.29162621.29163485	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	AluSq2,AluSz6	SINE	Alu	0.263333333333333;1
rs7333991	chr13.110455785.110456535	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs733701	chr6.39203639.39204133	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	MER5B	DNA	hAT-Charlie	1
rs7403708	chr15.78830933.78832028	HD,CAD	LR_open	CpGi	CpG: 122	0.692356285533797	NA	not_CpGpromo	NA	NA	NA	NA
rs74181299	chr2.65055695.65057220	ARR,HD	NR	CpGi	CpG: 132	1	EH38E2004398	CpGpromo	NA	NA	NA	NA
rs7433206	chr3.38615932.38616880	HD	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs7440763	chr4.155512047.155512545	HD,CAD	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	Charlie4z	DNA	hAT-Charlie	0.42483660130719
rs7486169	chr12.74338024.74338393	HD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs75112503	chr11.110366109.110367343	HD,MI	NR	not_CpGi	NA	NA	NA	not_CpGpromo	AluY,L1MB3,L2b,AluSx	SINE,LINE	Alu,L1,L2	0.277227722772277;1;0.598425196850394
rs75190942	chr11.128894447.128895096	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs7529220	chr1.21955557.21956277	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	MIRb	SINE	MIR	1
rs75524776	chr12.109538192.109538640	HD,CAD	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	MIRb	SINE	MIR	1
rs7568458	chr2.85560977.85561973	HD,CAD	NR	CpGi	CpG: 37	1	EH38E2013886	CpGpromo	AluJr	SINE	Alu	0.0138888888888889
rs7580831	chr2.237310830.237311051	HD,CAD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs759098931	chr14.99644968.99645471	HD,CAD	NR	CpGi	CpG: 93	0.641221374045801	EH38E1741629,EH38E1741630	CpGpromo	NA	NA	NA	NA
rs7604403	chr2.111897773.111899676	HD,CAD	NR	CpGi	CpG: 137	1	EH38E2024982	CpGpromo	AluSc	SINE	Alu	0.533783783783784
rs76064792	chr16.719886.721785	HD,HF	LR_close	CpGi	CpG: 169	0.757477243172952	EH38E1795024,EH38E1795025	CpGpromo	(GCAGG)n	Simple_repeat	Simple_repeat	1
rs76097649	chr11.128894447.128895096	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs7612445	chr3.179454943.179455262	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs7617480	chr3.49173142.49174143	HD,HF	ESR_close	not_CpGi	NA	NA	NA	not_CpGpromo	(CCATCT)n	Simple_repeat	Simple_repeat	1
rs7617773	chr3.48151277.48152429	HD,CAD	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	MamTip1	DNA	hAT-Tip100	1
rs7632505	chr3.123019226.123019547	ARR,HD,HF,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	MIR,AluJb	SINE	MIR,Alu	0.771929824561403;0.829787234042553
rs7633770	chr3.46646587.46647198	HD,CAD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	MER74A	LTR	ERVL	0.641025641025641
rs76600480	chr7.128909544.128910271	HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	L2	LINE	L2	0.1875
rs7690530	chr4.40629425.40630609	HD,CAD	NR	CpGi	CpG: 66	1	EH38E2292822	CpGpromo	G-rich	Low_complexity	Low_complexity	1
rs7696431	chr4.168766280.168767270	HD,CAD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	MER41A,L3,MIRc	LTR,LINE,SINE	ERV1,CR1,MIR	0.842105263157895;1;0.883018867924528
rs77316573	chr16.2214289.2215681	ARR,HD	LR_close	CpGi	CpG: 162	0.75131926121372	NA	not_CpGpromo	(CCG)n,L1ME3,AluY	Simple_repeat,LINE,SINE	Simple_repeat,L1,Alu	1;0.205787781350482
rs7873551	chr9.116482574.116483181	HD,CAD	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs7910227	chr10.20953148.20953475	ARR,HD	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	MLT1B	LTR	ERVL-MaLR	0.824120603015075
rs7947761	chr11.100753128.100753902	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	L1PBa	LINE	L1	0.395206527281999
rs79661299	chr6.42087754.42088697	HD,HF	NR	not_CpGi	NA	NA	NA	not_CpGpromo	MIRb,AluSc	SINE	MIR,Alu	0.706666666666667;1
rs79717953	chr6.73694726.73695115	HD,CAD,MI	EAR_open	not_CpGi	NA	NA	NA	not_CpGpromo	MER5A1	DNA	hAT-Charlie	0.264367816091954
rs7977247	chr12.106865628.106866040	HD,HF	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	L2a	LINE	L2	0.568870523415978
rs79825511	chr11.69700398.69700987	HD	ESR_close	not_CpGi	NA	NA	NA	not_CpGpromo	MER113B,(CCAGG)n	DNA,Simple_repeat	hAT-Charlie,Simple_repeat	0.713235294117647;1
rs8003602	chr14.99682572.99684938	HD,CAD	NR	CpGi	CpG: 127	1	EH38E1741669,EH38E1741670	CpGpromo	MER50,MamGypLTR3,MamGypLTR2b,(CCCGG)n,G-rich,MIR	LTR,Simple_repeat,Low_complexity,SINE	ERV1,Gypsy,Simple_repeat,Low_complexity,MIR	0.0142857142857143;1
rs8096658	chr18.79396485.79396813	ARR,HD	NR	CpGi	CpG: 544	0.0474336793540946	NA	not_CpGpromo	NA	NA	NA	NA
rs869396	chr4.168766280.168767270	HD,CAD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	MER41A,L3,MIRc	LTR,LINE,SINE	ERV1,CR1,MIR	0.842105263157895;1;0.883018867924528
rs880315	chr1.10736684.10737808	ARR,HD	ESR_close	not_CpGi	NA	NA	NA	not_CpGpromo	L2c	LINE	L2	0.931034482758621;1
rs894211	chr8.20007264.20008372	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	LTR32,MER34A,LTR48	LTR	ERVL,ERV1	0.875294117647059;1
rs899162	chr5.135458929.135459424	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	L2c	LINE	L2	1
rs899997	chr15.78726891.78727425	HD,CAD	ESR_close	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA
rs9388813	chr6.130602114.130602839	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	MamSINE1	SINE	tRNA-RTE	1
rs944172	chr9.107755276.107755816	HD,CAD	LR_open	not_CpGi	NA	NA	NA	not_CpGpromo	Charlie13a,MLT2C1	DNA,LTR	hAT-Charlie,ERVL	0.71658615136876;0.307420494699647
rs9469890	chr6.34796116.34796383	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	L2a	LINE	L2	0.528089887640449
rs9506925	chr13.22794559.22794879	ARR,HD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	OldhAT1	DNA	hAT-Ac	1
rs9556903	chr13.98207008.98208056	HD,CAD	LR_close	not_CpGi	NA	NA	NA	not_CpGpromo	(CA)n	Simple_repeat	Simple_repeat	1
rs965652	chr6.134047673.134047938	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	MamRep1894	DNA	hAT	1
rs9899183	chr17.7548665.7550717	ARR,HD	NR	CpGi	CpG: 50	1	EH38E1844469	CpGpromo	MER21A,G-rich,MIRc,MER94	LTR,Low_complexity,SINE,DNA	ERVL,Low_complexity,MIR,hAT-Blackjack	0.748815165876777;1
rs9906944	chr17.49012959.49014932	HD,CAD	NR	CpGi	CpG: 48,CpG: 18	1	EH38E1868609	CpGpromo	MIRc	SINE	MIR	1
rs9912587	chr17.43021036.43021565	HD,CAD	NR	not_CpGi	NA	NA	NA	not_CpGpromo	NA	NA	NA	NA

Peak annotation file

Session information

sessionInfo()

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 10 x64 (build 19045)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/Chicago
tzcode source: internal

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

other attached packages:
 [1] ggrepel_0.9.6                           
 [2] plyranges_1.24.0                        
 [3] ggsignif_0.6.4                          
 [4] genomation_1.36.0                       
 [5] smplot2_0.2.4                           
 [6] eulerr_7.0.2                            
 [7] biomaRt_2.60.1                          
 [8] devtools_2.4.5                          
 [9] usethis_3.0.0                           
[10] ggpubr_0.6.0                            
[11] BiocParallel_1.38.0                     
[12] scales_1.3.0                            
[13] VennDiagram_1.7.3                       
[14] futile.logger_1.4.3                     
[15] gridExtra_2.3                           
[16] ggfortify_0.4.17                        
[17] edgeR_4.2.1                             
[18] limma_3.60.4                            
[19] rtracklayer_1.64.0                      
[20] org.Hs.eg.db_3.19.1                     
[21] TxDb.Hsapiens.UCSC.hg38.knownGene_3.18.0
[22] GenomicFeatures_1.56.0                  
[23] AnnotationDbi_1.66.0                    
[24] Biobase_2.64.0                          
[25] GenomicRanges_1.56.1                    
[26] GenomeInfoDb_1.40.1                     
[27] IRanges_2.38.1                          
[28] S4Vectors_0.42.1                        
[29] BiocGenerics_0.50.0                     
[30] ChIPseeker_1.40.0                       
[31] RColorBrewer_1.1-3                      
[32] broom_1.0.6                             
[33] kableExtra_1.4.0                        
[34] lubridate_1.9.3                         
[35] forcats_1.0.0                           
[36] stringr_1.5.1                           
[37] dplyr_1.1.4                             
[38] purrr_1.0.2                             
[39] readr_2.1.5                             
[40] tidyr_1.3.1                             
[41] tibble_3.2.1                            
[42] ggplot2_3.5.1                           
[43] tidyverse_2.0.0                         
[44] workflowr_1.7.1                         

loaded via a namespace (and not attached):
  [1] fs_1.6.4                               
  [2] matrixStats_1.4.1                      
  [3] bitops_1.0-8                           
  [4] enrichplot_1.24.4                      
  [5] httr_1.4.7                             
  [6] profvis_0.3.8                          
  [7] tools_4.4.1                            
  [8] backports_1.5.0                        
  [9] utf8_1.2.4                             
 [10] R6_2.5.1                               
 [11] lazyeval_0.2.2                         
 [12] urlchecker_1.0.1                       
 [13] withr_3.0.1                            
 [14] prettyunits_1.2.0                      
 [15] cli_3.6.3                              
 [16] formatR_1.14                           
 [17] scatterpie_0.2.4                       
 [18] labeling_0.4.3                         
 [19] sass_0.4.9                             
 [20] Rsamtools_2.20.0                       
 [21] systemfonts_1.1.0                      
 [22] yulab.utils_0.1.7                      
 [23] foreign_0.8-87                         
 [24] DOSE_3.30.5                            
 [25] svglite_2.1.3                          
 [26] R.utils_2.12.3                         
 [27] sessioninfo_1.2.2                      
 [28] plotrix_3.8-4                          
 [29] BSgenome_1.72.0                        
 [30] pwr_1.3-0                              
 [31] impute_1.78.0                          
 [32] rstudioapi_0.16.0                      
 [33] RSQLite_2.3.7                          
 [34] generics_0.1.3                         
 [35] gridGraphics_0.5-1                     
 [36] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
 [37] BiocIO_1.14.0                          
 [38] vroom_1.6.5                            
 [39] gtools_3.9.5                           
 [40] car_3.1-2                              
 [41] GO.db_3.19.1                           
 [42] Matrix_1.7-0                           
 [43] fansi_1.0.6                            
 [44] abind_1.4-8                            
 [45] R.methodsS3_1.8.2                      
 [46] lifecycle_1.0.4                        
 [47] whisker_0.4.1                          
 [48] yaml_2.3.10                            
 [49] carData_3.0-5                          
 [50] SummarizedExperiment_1.34.0            
 [51] BiocFileCache_2.12.0                   
 [52] gplots_3.1.3.1                         
 [53] qvalue_2.36.0                          
 [54] SparseArray_1.4.8                      
 [55] blob_1.2.4                             
 [56] promises_1.3.0                         
 [57] crayon_1.5.3                           
 [58] miniUI_0.1.1.1                         
 [59] lattice_0.22-6                         
 [60] cowplot_1.1.3                          
 [61] KEGGREST_1.44.1                        
 [62] pillar_1.9.0                           
 [63] knitr_1.48                             
 [64] fgsea_1.30.0                           
 [65] rjson_0.2.23                           
 [66] boot_1.3-31                            
 [67] codetools_0.2-20                       
 [68] fastmatch_1.1-4                        
 [69] glue_1.7.0                             
 [70] getPass_0.2-4                          
 [71] ggfun_0.1.6                            
 [72] remotes_2.5.0                          
 [73] data.table_1.16.0                      
 [74] vctrs_0.6.5                            
 [75] png_0.1-8                              
 [76] treeio_1.28.0                          
 [77] gtable_0.3.5                           
 [78] cachem_1.1.0                           
 [79] xfun_0.47                              
 [80] S4Arrays_1.4.1                         
 [81] mime_0.12                              
 [82] tidygraph_1.3.1                        
 [83] statmod_1.5.0                          
 [84] ellipsis_0.3.2                         
 [85] nlme_3.1-166                           
 [86] ggtree_3.12.0                          
 [87] bit64_4.0.5                            
 [88] filelock_1.0.3                         
 [89] progress_1.2.3                         
 [90] rprojroot_2.0.4                        
 [91] bslib_0.8.0                            
 [92] rpart_4.1.23                           
 [93] KernSmooth_2.23-24                     
 [94] Hmisc_5.1-3                            
 [95] colorspace_2.1-1                       
 [96] DBI_1.2.3                              
 [97] seqPattern_1.36.0                      
 [98] nnet_7.3-19                            
 [99] tidyselect_1.2.1                       
[100] processx_3.8.4                         
[101] bit_4.0.5                              
[102] compiler_4.4.1                         
[103] curl_5.2.2                             
[104] git2r_0.33.0                           
[105] httr2_1.0.4                            
[106] htmlTable_2.4.3                        
[107] xml2_1.3.6                             
[108] DelayedArray_0.30.1                    
[109] shadowtext_0.1.4                       
[110] checkmate_2.3.2                        
[111] caTools_1.18.3                         
[112] callr_3.7.6                            
[113] rappdirs_0.3.3                         
[114] digest_0.6.37                          
[115] rmarkdown_2.28                         
[116] XVector_0.44.0                         
[117] base64enc_0.1-3                        
[118] htmltools_0.5.8.1                      
[119] pkgconfig_2.0.3                        
[120] MatrixGenerics_1.16.0                  
[121] highr_0.11                             
[122] dbplyr_2.5.0                           
[123] fastmap_1.2.0                          
[124] htmlwidgets_1.6.4                      
[125] rlang_1.1.4                            
[126] UCSC.utils_1.0.0                       
[127] shiny_1.9.1                            
[128] farver_2.1.2                           
[129] jquerylib_0.1.4                        
[130] zoo_1.8-12                             
[131] jsonlite_1.8.8                         
[132] GOSemSim_2.30.2                        
[133] R.oo_1.26.0                            
[134] RCurl_1.98-1.16                        
[135] magrittr_2.0.3                         
[136] Formula_1.2-5                          
[137] GenomeInfoDbData_1.2.12                
[138] ggplotify_0.1.2                        
[139] patchwork_1.3.0                        
[140] munsell_0.5.1                          
[141] Rcpp_1.0.13                            
[142] ape_5.8                                
[143] viridis_0.6.5                          
[144] stringi_1.8.4                          
[145] ggraph_2.2.1                           
[146] zlibbioc_1.50.0                        
[147] MASS_7.3-61                            
[148] plyr_1.8.9                             
[149] pkgbuild_1.4.4                         
[150] parallel_4.4.1                         
[151] Biostrings_2.72.1                      
[152] graphlayouts_1.1.1                     
[153] splines_4.4.1                          
[154] hms_1.1.3                              
[155] locfit_1.5-9.10                        
[156] ps_1.8.0                               
[157] igraph_2.0.3                           
[158] reshape2_1.4.4                         
[159] pkgload_1.4.0                          
[160] futile.options_1.0.1                   
[161] XML_3.99-0.17                          
[162] evaluate_1.0.0                         
[163] lambda.r_1.2.4                         
[164] tzdb_0.4.0                             
[165] tweenr_2.0.3                           
[166] httpuv_1.6.15                          
[167] polyclip_1.10-7                        
[168] gridBase_0.4-7                         
[169] ggforce_0.4.2                          
[170] xtable_1.8-4                           
[171] restfulr_0.0.15                        
[172] tidytree_0.4.6                         
[173] rstatix_0.7.2                          
[174] later_1.3.2                            
[175] viridisLite_0.4.2                      
[176] aplot_0.2.3                            
[177] memoise_2.0.1                          
[178] GenomicAlignments_1.40.0               
[179] cluster_2.1.6                          
[180] timechange_0.3.0