Last updated: 2020-04-29
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Knit directory: Comparative_APA/analysis/
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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 19a855b | brimittleman | 2020-04-29 | hist mark plots and info with vars |
| html | fa67b41 | brimittleman | 2020-04-28 | Build site. |
| Rmd | e51455f | brimittleman | 2020-04-28 | add h3 and info with other vars |
library(tidyverse)── Attaching packages ────────────────────────────────────────────── tidyverse 1.2.1 ──✔ ggplot2 3.1.1 ✔ purrr 0.3.2
✔ tibble 2.1.1 ✔ dplyr 0.8.0.1
✔ tidyr 0.8.3 ✔ stringr 1.3.1
✔ readr 1.3.1 ✔ forcats 0.3.0 ── Conflicts ───────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()H3K36me3 is a mark associated with active transcription. I will see if this mark is different based on conservation of apa.
I will start by downloading data from encode for the LCLs. I will work with bam files so I can make deep tools plots and see if there is coverage at PAS.
mkdir ../data/H3K36me3https://www.encodeproject.org/experiments/ENCSR000DQT/ (GM06990 cell line)
https://www.encodeproject.org/experiments/ENCSR000DRW/ (GM12878)
https://www.encodeproject.org/experiments/ENCSR914AWT/ (GM23248)
https://www.encodeproject.org/experiments/ENCSR236YPE/ (GM23338)
Merge, sort, index these to use with deep tools.
mergeandsorth3k36me3.sh
#!/bin/bash
#SBATCH --job-name=mergeandsort_h3k36me3
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=mergeandsort_h3k36me3
#SBATCH --error=mergeandsort_h3k36me3
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END
source ~/activate_anaconda.sh
conda activate comp_threeprime_env
samtools merge ../data/H3K36me3/MergedIndiv.H3k36me3.bam ../data/H3K36me3/*.bam
samtools sort ../data/H3K36me3/MergedIndiv.H3k36me3.bam -o ../data/H3K36me3/MergedIndiv.H3k36me3.sort.bam
samtools index ../data/H3K36me3/MergedIndiv.H3k36me3.sort.bam sbatch mergeandsorth3k36me3.sh bam to bigwig and dt plot
#!/bin/bash
#SBATCH --job-name=H3K36me3DTplot.sh
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=H3K36me3DTplot.out
#SBATCH --error=H3K36me3DTplot.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END
source ~/activate_anaconda.sh
conda activate comp_threeprime_env
bamCoverage -b ../data/H3K36me3/MergedIndiv.H3k36me3.sort.bam -o ../data/H3K36me3/MergedIndiv.H3k36me3.sort.bw
computeMatrix scale-regions -S ../data/H3K36me3/MergedIndiv.H3k36me3.sort.bw -R ../data/PAS_doubleFilter/PAS_doublefilter_either_HumanCoordHummanUsage.sort.bed -b 500 -a 500 --skipZeros --transcript_id_designator 4 -out ../data/H3K36me3/H3k36me3PAS.gz
plotHeatmap --sortRegions descend -m ../data/H3K36me3/H3k36me3PAS.gz --plotTitle "PAS H3L36me3" --heatmapHeight 4 --colorMap magma --averageTypeSummaryPlot "mean" --startLabel "5'" --endLabel "3'" -out ../data/H3K36me3/H3k36me3PAS.pngH3K36me3DTplot.sh
Run this on distal 3’ UTRs PAS as well. I expect decrease after:
Look for most distal.
OverlapOrtho=read.table("../data/orthoUTR/FilteredPASOverlapOrthoUTR.text", header = T,stringsAsFactors = F)
OverlapOrtho_plus= OverlapOrtho %>% filter(strand=="+") %>% group_by(geneUTR) %>% arrange(desc(endpas)) %>% slice(1)%>% ungroup() %>% select(PAS)
OverlapOrtho_neg= OverlapOrtho %>% filter(strand=="-") %>% group_by(geneUTR) %>% arrange(startpas) %>% slice(1) %>% ungroup() %>% select(PAS)
Overlapetiher= OverlapOrtho_plus %>% bind_rows(OverlapOrtho_neg)
#filter bed
pasbed=read.table("../data/PAS_doubleFilter/PAS_doublefilter_either_HumanCoordHummanUsage.sort.bed", col.names = c("chr","start", "end", "PAS", "score", "strand"),stringsAsFactors = F) %>% inner_join(Overlapetiher,by="PAS")
write.table(pasbed, "../data/PAS_doubleFilter/DistalPAS_orthoUTR.bed", quote = F, col.names = F, row.names = F, sep="\t")
#!/bin/bash
#SBATCH --job-name=H3K36me3DTplot_distalPAS.sh
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=H3K36me3DTplot_distalPAS.out
#SBATCH --error=H3K36me3DTplot_distalPAS.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END
source ~/activate_anaconda.sh
conda activate comp_threeprime_env
computeMatrix scale-regions -S ../data/H3K36me3/MergedIndiv.H3k36me3.sort.bw -R ../data/PAS_doubleFilter/DistalPAS_orthoUTR.bed -b 2000 -a 2000 --skipZeros --transcript_id_designator 4 -out ../data/H3K36me3/H3k36me3distalPAS.gz
plotHeatmap --sortRegions descend -m ../data/H3K36me3/H3k36me3distalPAS.gz --plotTitle "Distal UTR PAS H3L36me3" --heatmapHeight 4 --colorMap magma --averageTypeSummaryPlot "mean" --startLabel "5'" --endLabel "3'" -out ../data/H3K36me3/H3k36me3distalPAS.png
computeMatrix scale-regions -S ../data/H3K36me3/MergedIndiv.H3k36me3.sort.bw -R ../data/PAS_doubleFilter/DistalPAS_orthoUTR.bed -b 10000 -a 10000 --skipZeros --transcript_id_designator 4 -out ../data/H3K36me3/H3k36me3distalPAS_longer.gz
plotHeatmap --sortRegions descend -m ../data/H3K36me3/H3k36me3distalPAS_longer.gz --plotTitle "Distal UTR PAS H3L36me3" --heatmapHeight 4 --colorMap magma --averageTypeSummaryPlot "mean" --startLabel "5'" --endLabel "3'" -out ../data/H3K36me3/H3k36me3distalPAS_longer.pngH3K36me3DTplot_distalPAS.sh
wider range for PAS
#!/bin/bash
#SBATCH --job-name=H3K36me3DTplotwide.sh
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=H3K36me3DTplotwide.out
#SBATCH --error=H3K36me3DTplotwide.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END
source ~/activate_anaconda.sh
conda activate comp_threeprime_env
computeMatrix scale-regions -S ../data/H3K36me3/MergedIndiv.H3k36me3.sort.bw -R ../data/PAS_doubleFilter/PAS_doublefilter_either_HumanCoordHummanUsage.sort.bed -b 2000 -a 2000 --skipZeros --transcript_id_designator 4 -out ../data/H3K36me3/H3k36me3PASwide.gz
plotHeatmap --sortRegions descend -m ../data/H3K36me3/H3k36me3PASwide.gz --plotTitle "PAS H3L36me3" --heatmapHeight 4 --colorMap magma --averageTypeSummaryPlot "mean" --startLabel "5'" --endLabel "3'" -out ../data/H3K36me3/H3k36me3PASwide.pngLook at the level of h3k36me3 across full transcripts.
H3K36me3DTplot_transcript.sh
#!/bin/bash
#SBATCH --job-name=H3K36me3DTplot_transcript.sh
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=H3K36me3DTplot_transcript.out
#SBATCH --error=H3K36me3DTplot_transcript.err
#SBATCH --partition=broadwl
#SBATCH --mem=36G
#SBATCH --mail-type=END
source ~/activate_anaconda.sh
conda activate comp_threeprime_env
computeMatrix scale-regions -S ../data/H3K36me3/MergedIndiv.H3k36me3.sort.bw -R /project2/gilad/briana/genome_anotation_data/hg38_refseq_anno/hg38_ncbiRefseq_transcripts.sort.bed -b 10000 -a 10000 --skipZeros --transcript_id_designator 4 -out ../data/H3K36me3/H3k36me3PASTranscript.gz
plotHeatmap --sortRegions descend -m ../data/H3K36me3/H3k36me3PASTranscript.gz --plotTitle "Transcript H3L36me3" --heatmapHeight 4 --colorMap magma --averageTypeSummaryPlot "mean" --startLabel "TSS" --endLabel "TES" -out ../data/H3K36me3/H3k36me3PASTranscript.pngUse H3K9me3 as a control:
H3K9me3
download
https://www.encodeproject.org/experiments/ENCSR000AOX/ (GM12878)
https://www.encodeproject.org/experiments/ENCSR673OPX/ ( GM23248)
H3K9me3_processandDT.sh
#!/bin/bash
#SBATCH --job-name=H3K9me3_processandDT
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=mergeandsort_H3K9me3
#SBATCH --error=mergeandsort_H3K9me3
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END
source ~/activate_anaconda.sh
conda activate comp_threeprime_env
samtools merge ../data/H3K36me3/MergedIndiv.H3K9me3.bam ../data/H3K36me3/*H3K9me3.bam
samtools sort ../data/H3K36me3/MergedIndiv.H3K9me3.bam -o ../data/H3K36me3/MergedIndiv.H3K9me3.sort.bam
samtools index ../data/H3K36me3/MergedIndiv.H3K9me3.sort.bam
bamCoverage -b ../data/H3K36me3/MergedIndiv.H3K9me3.sort.bam -o ../data/H3K36me3/MergedIndiv.H3K9me3.sort.bw
computeMatrix scale-regions -S ../data/H3K36me3/MergedIndiv.H3K9me3.sort.bw -R ../data/PAS_doubleFilter/PAS_doublefilter_either_HumanCoordHummanUsage.sort.bed -b 500 -a 500 --skipZeros --transcript_id_designator 4 -out ../data/H3K36me3/H3K9me3PAS.gz
plotHeatmap --sortRegions descend -m ../data/H3K36me3/H3K9me3PAS.gz --plotTitle "PAS H3K9me3" --heatmapHeight 4 --colorMap magma --averageTypeSummaryPlot "mean" --startLabel "5'" --endLabel "3'" -out ../data/H3K36me3/H3K9me3PAS.png
computeMatrix scale-regions -S ../data/H3K36me3/MergedIndiv.H3K9me3.sort.bw -R ../data/PAS_doubleFilter/PAS_doublefilter_either_HumanCoordHummanUsage.sort.bed -b 2000 -a 2000 --skipZeros --transcript_id_designator 4 -out ../data/H3K36me3/H3K9me3PASwide.gz
plotHeatmap --sortRegions descend -m ../data/H3K36me3/H3K9me3PASwide.gz --plotTitle "PAS H3K9me3" --heatmapHeight 4 --colorMap magma --averageTypeSummaryPlot "mean" --startLabel "5'" --endLabel "3'" -out ../data/H3K36me3/H3K9me3PASwide.png
computeMatrix scale-regions -S ../data/H3K36me3/MergedIndiv.H3K9me3.sort.bw -R ../data/PAS_doubleFilter/DistalPAS_orthoUTR.bed -b 10000 -a 10000 --skipZeros --transcript_id_designator 4 -out ../data/H3K36me3/H3K9me3distalPAS_longer.gz
plotHeatmap --sortRegions descend -m ../data/H3K36me3/H3K9me3distalPAS_longer.gz --plotTitle "Distal UTR PAS H3K9me3" --heatmapHeight 4 --colorMap magma --averageTypeSummaryPlot "mean" --startLabel "5'" --endLabel "3'" -out ../data/H3K36me3/H3K9me3distalPAS_longer.png
computeMatrix scale-regions -S ../data/H3K36me3/MergedIndiv.H3K9me3.sort.bw -R ../data/PAS_doubleFilter/PAS_doublefilter_either_HumanCoordHummanUsage.sort.bed -b 2000 -a 2000 --skipZeros --transcript_id_designator 4 -out ../data/H3K36me3/H3K9me3PASwide.gz
plotHeatmap --sortRegions descend -m ../data/H3K36me3/H3K9me3PASwide.gz --plotTitle "PAS H3K9me3" --heatmapHeight 4 --colorMap magma --averageTypeSummaryPlot "mean" --startLabel "5'" --endLabel "3'" -out ../data/H3K36me3/H3K9me3PASwide.png
H3K9me3DTplot_transcript.sh
#!/bin/bash
#SBATCH --job-name=H3K9me3DTplot_transcript.sh
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=H3K9me3DTplot_transcript.out
#SBATCH --error=H3K9me3DTplot_transcript.err
#SBATCH --partition=broadwl
#SBATCH --mem=36G
#SBATCH --mail-type=END
source ~/activate_anaconda.sh
conda activate comp_threeprime_env
computeMatrix scale-regions -S ../data/H3K36me3/MergedIndiv.H3K9me3.sort.bw -R /project2/gilad/briana/genome_anotation_data/hg38_refseq_anno/hg38_ncbiRefseq_transcripts.sort.bed -b 10000 -a 10000 --skipZeros --transcript_id_designator 4 -out ../data/H3K36me3/H3K9me3PASTranscript.gz
plotHeatmap --sortRegions descend -m ../data/H3K36me3/H3K9me3PASTranscript.gz --plotTitle "Transcript H3K9me3" --heatmapHeight 4 --colorMap magma --averageTypeSummaryPlot "mean" --startLabel "TSS" --endLabel "TES" -out ../data/H3K36me3/H3K9me3PASTranscript.pngNow I can look at PAS by if they are differentially used and see if the signatures are different:
PASmeta=read.table("../data/PAS_doubleFilter/PAS_doublefilter_either_HumanCoordHummanUsage.sort.bed", col.names = c("chr","start", "end", "PAS", "score", "strand"),stringsAsFactors = F)
DiffIso=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt",header = T,stringsAsFactors = F) %>% inner_join(PASmeta, by=c("chr", "start", "end"))
DiffIsoyes= DiffIso %>% filter(SigPAU2=="Yes") %>% select(PAS)
DiffIsono= DiffIso %>% filter(SigPAU2=="No") %>% select(PAS)
PASmeta_yes= PASmeta %>% inner_join(DiffIsoyes, by="PAS")
write.table(PASmeta_yes, "../data/PAS_doubleFilter/PAS_diffUsed.bed", quote = F, col.names = F, row.names = F, sep="\t")
PASmeta_no= PASmeta %>% inner_join(DiffIsono, by="PAS")
write.table(PASmeta_no, "../data/PAS_doubleFilter/PAS_NOTdiffUsed.bed", quote = F, col.names = F, row.names = F, sep="\t")H3K36me3DTplot_DiffIso.sh
#!/bin/bash
#SBATCH --job-name=H3K36me3DTplot_DiffIso.sh
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=H3K36me3DTplot_DiffIso.out
#SBATCH --error=H3K36me3DTplot_DiffIso.err
#SBATCH --partition=bigmem2
#SBATCH --mem=64G
#SBATCH --mail-type=END
source ~/activate_anaconda.sh
conda activate comp_threeprime_env
computeMatrix scale-regions -S ../data/H3K36me3/MergedIndiv.H3k36me3.sort.bw -R ../data/PAS_doubleFilter/PAS_diffUsed.bed -b 1000 -a 1000 --skipZeros --transcript_id_designator 4 -out ../data/H3K36me3/H3k36me3PASDiffUsed.gz
plotHeatmap --sortRegions descend -m ../data/H3K36me3/H3k36me3PASDiffUsed.gz --plotTitle "Diff Used PAS H3K36me3" --heatmapHeight 4 --colorMap magma --averageTypeSummaryPlot "mean" --startLabel "5'" --endLabel "3'" -out ../data/H3K36me3/H3k36me3PASDiffUsed.png
computeMatrix scale-regions -S ../data/H3K36me3/MergedIndiv.H3k36me3.sort.bw -R ../data/PAS_doubleFilter/PAS_NOTdiffUsed.bed -b 1000 -a 1000 --skipZeros --transcript_id_designator 4 -out ../data/H3K36me3/H3k36me3PASNOTDiffUsed.gz
plotHeatmap --sortRegions descend -m ../data/H3K36me3/H3k36me3PASNOTDiffUsed.gz --plotTitle "Conserved PAS H3K36me3" --heatmapHeight 4 --colorMap magma --averageTypeSummaryPlot "mean" --startLabel "5'" --endLabel "3'" -out ../data/H3K36me3/H3k36me3PASNOTDiffUsed.pngHuman Specific, Chimp Specific
PAS=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F)
HumanSpec= PAS %>% filter(Chimp==0)
ChimpSpec= PAS %>% filter(Human==0)
PASmetaHS= PASmeta %>% filter(PAS %in% HumanSpec$PAS)
PASmetaCS= PASmeta %>% filter(PAS %in% ChimpSpec$PAS)
write.table(PASmetaHS, "../data/PAS_doubleFilter/PAS_HumanSpecifc.bed", quote = F, col.names = F, row.names = F, sep="\t")
write.table(PASmetaCS, "../data/PAS_doubleFilter/PAS_ChimpSpecifc.bed", quote = F, col.names = F, row.names = F, sep="\t")H3K36me3DTplot_Specific.sh
#!/bin/bash
#SBATCH --job-name=H3K36me3DTplot_Specific.sh
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=H3K36me3DTplot_Specific.out
#SBATCH --error=H3K36me3DTplot_Specific.err
#SBATCH --partition=bigmem2
#SBATCH --mem=64G
#SBATCH --mail-type=END
source ~/activate_anaconda.sh
conda activate comp_threeprime_env
computeMatrix scale-regions -S ../data/H3K36me3/MergedIndiv.H3k36me3.sort.bw -R ../data/PAS_doubleFilter/PAS_HumanSpecifc.bed -b 1000 -a 1000 --skipZeros --transcript_id_designator 4 -out ../data/H3K36me3/H3k36me3HumanSpec.gz
plotHeatmap --sortRegions descend -m ../data/H3K36me3/H3k36me3HumanSpec.gz --plotTitle "Human Specific H3K36me3" --heatmapHeight 4 --colorMap magma --averageTypeSummaryPlot "mean" --startLabel "5'" --endLabel "3'" -out ../data/H3K36me3/H3k36me3HumanSpec.png
computeMatrix scale-regions -S ../data/H3K36me3/MergedIndiv.H3k36me3.sort.bw -R ../data/PAS_doubleFilter/PAS_ChimpSpecifc.bed -b 1000 -a 1000 --skipZeros --transcript_id_designator 4 -out ../data/H3K36me3/H3k36me3ChimpSpec.gz
plotHeatmap --sortRegions descend -m ../data/H3K36me3/H3k36me3ChimpSpec.gz --plotTitle "Chimp Specific H3K36me3" --heatmapHeight 4 --colorMap magma --averageTypeSummaryPlot "mean" --startLabel "5'" --endLabel "3'" -out ../data/H3K36me3/H3k36me3ChimpSpec.png
sessionInfo()R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] forcats_0.3.0 stringr_1.3.1 dplyr_0.8.0.1 purrr_0.3.2
[5] readr_1.3.1 tidyr_0.8.3 tibble_2.1.1 ggplot2_3.1.1
[9] tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.4.6 cellranger_1.1.0 plyr_1.8.4 compiler_3.5.1
[5] pillar_1.3.1 later_0.7.5 git2r_0.26.1 workflowr_1.6.0
[9] tools_3.5.1 digest_0.6.18 lubridate_1.7.4 jsonlite_1.6
[13] evaluate_0.12 nlme_3.1-137 gtable_0.2.0 lattice_0.20-38
[17] pkgconfig_2.0.2 rlang_0.4.0 cli_1.1.0 rstudioapi_0.10
[21] yaml_2.2.0 haven_1.1.2 withr_2.1.2 xml2_1.2.0
[25] httr_1.3.1 knitr_1.20 hms_0.4.2 generics_0.0.2
[29] fs_1.3.1 rprojroot_1.3-2 grid_3.5.1 tidyselect_0.2.5
[33] glue_1.3.0 R6_2.3.0 readxl_1.1.0 rmarkdown_1.10
[37] modelr_0.1.2 magrittr_1.5 whisker_0.3-2 backports_1.1.2
[41] scales_1.0.0 promises_1.0.1 htmltools_0.3.6 rvest_0.3.2
[45] assertthat_0.2.0 colorspace_1.3-2 httpuv_1.4.5 stringi_1.2.4
[49] lazyeval_0.2.1 munsell_0.5.0 broom_0.5.1 crayon_1.3.4