Last updated: 2020-04-20
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Knit directory: Comparative_APA/analysis/
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library(tidyverse)
── Attaching packages ────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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── Conflicts ───────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
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I want to get a list of ortho 3’ UTRs. This is important because I want to look at the differentially used PAS and see if they make longer or shorter 3’ UTRs. This could point to regulatory variation.
I will start with the ortho exon file. Convert these to bed files to be able to merge
grep three_prime_utr /project2/gilad/kenneth/OrthoExonPartialMapping/human.noM.gtf > ../data/OrthoExonBed/Human3UTR.gtf
grep three_prime_utr /project2/gilad/kenneth/OrthoExonPartialMapping/chimp.noM.gtf > ../data/OrthoExonBed/Chimp3UTR.gtf
python SAF2Bed.py ../data/OrthoExonBed/Human3UTR.gtf ../data/OrthoExonBed/Human3UTR.bed
python SAF2Bed.py ../data/OrthoExonBed/Chimp3UTR.gtf ../data/OrthoExonBed/Chimp3UTR.bed
sort -k1,1 -k2,2n ../data/OrthoExonBed/Human3UTR.bed > ../data/OrthoExonBed/Human3UTR.sort.bed
sort -k1,1 -k2,2n ../data/OrthoExonBed/Chimp3UTR.bed > ../data/OrthoExonBed/Chimp3UTR.sort.bed
#mrge by strand and gene
bedtools merge -s -i ../data/OrthoExonBed/Human3UTR.sort.bed -c 4,5,6 -o distinct,mean,distinct > ../data/OrthoExonBed/Human3UTR.merged.sort.bed
bedtools merge -s -i ../data/OrthoExonBed/Chimp3UTR.sort.bed -c 4,5,6 -o distinct,mean,distinct > ../data/OrthoExonBed/Chimp3UTR.merged.sort.bed
Look at the human one and the annotation in humans:
mkdir ../data/orthoUTR
grep utr3 ../../genome_anotation_data/hg38_refseq_anno/hg38_ncbiRefseq_Formatted_Allannotation_noSNO.Resort.bed > ../data/orthoUTR/g38_ncbiRefseq_Formatted_Allannotation_UTR3.bed
There are a lot of exons in this set that look like they are not utrs.
For this analsis. I need one 3’ UTR per gene. I can look at the most distal one first.
I can do this here with if else statements:
strand: max start
strand:
humanMergeutr= read.table("../data/OrthoExonBed/Human3UTR.merged.sort.bed", col.names = c('chr','start','end','gene','score','strand'), stringsAsFactors = F) %>% group_by(gene)
humanMergeutrpos= humanMergeutr %>% filter(strand=="+") %>% group_by(gene) %>% top_n(1,start)
humanMergeutrneg= humanMergeutr %>% filter(strand=="-") %>% group_by(gene) %>% top_n(-1,start)
humanDistalboth=humanMergeutrpos %>% bind_rows(humanMergeutrneg)
write.table(humanDistalboth, "../data/orthoUTR/HumanDistal3UTR.bed", col.names = F, row.names = F, quote = F, sep="\t")
sort -k1,1 -k2,2n ../data/orthoUTR/HumanDistal3UTR.bed > ../data/orthoUTR/HumanDistal3UTR.sort.bed
Looks like that worked.
Do it with an intercect with the anno and see if the results are similar:
bedtools intersect -s -wa -a ../data/OrthoExonBed/Human3UTR.merged.sort.bed -b ../data/orthoUTR/g38_ncbiRefseq_Formatted_Allannotation_UTR3.bed > ../data/OrthoExonBed/Human3UTR.merged.sort.overlapHumanannno.bed
overlap has 135340 17680
I will now work with the most distal PAS. I want to see how many of the PAS fall in these regions.
bedtools intersect -s -wa -a ../data/PAS_doubleFilter/PAS_doublefilter_either_HumanCoordHummanUsage.bed -b ../data/orthoUTR/HumanDistal3UTR.sort.bed > ../data/orthoUTR/PASOverlapinDistal3UTR.bed
bedtools intersect -s -wa -wb -a ../data/PAS_doubleFilter/PAS_doublefilter_either_HumanCoordHummanUsage.bed -b ../data/orthoUTR/HumanDistal3UTR.sort.bed > ../data/orthoUTR/PASOverlapinDistal3UTR_bothWritten.bed
metaPAS=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", stringsAsFactors = F, header = T) %>% select(gene, PAS, loc)
Overlapping= read.table("../data/orthoUTR/PASOverlapinDistal3UTR_bothWritten.bed", col.names = c("chrpas", "startpas", "endpas","PAS", "humanusage", "strandpas", "chrutr", "startutr", "endutr","geneUTR", "score","strand"),stringsAsFactors = F) %>% select(-strandpas, -chrutr, -startutr, -endutr) %>% inner_join(metaPAS, by=c("PAS"))
Overlapping %>% filter(gene != geneUTR) %>% nrow()
[1] 184
Overlapping %>% filter(loc!="utr3") %>% nrow()
[1] 405
Overlappingfilt= Overlapping%>% filter(gene == geneUTR)
184 of these are in different genes. check these.
405 not annotated as UTR3
NotUTRanno= Overlapping %>% filter(loc!="utr3") %>% filter(gene == geneUTR)
nrow(NotUTRanno)
[1] 376
ggplot(NotUTRanno,aes(x=loc, fill=loc) ) + geom_histogram(stat="count") + scale_fill_brewer(palette = "Dark2") + labs(title="Location of 400 PAS overlapping PAS but not annotated 3' UTR")
Warning: Ignoring unknown parameters: binwidth, bins, pad
This is good. It looks like most of the time the problem is just that the ortho utr extends the original annotation. We call it an end or cds.
nrow(Overlapping)/nrow(metaPAS)
[1] 0.2739917
nrow(Overlapping)/nrow(metaPAS %>% filter(loc=="utr3"))
[1] 0.6861297
almost 70% of the 3’ UTR pas are represented.
Look at how many genes are represented
OverlappingGenes= Overlapping %>% filter(gene == geneUTR) %>% group_by(gene) %>% summarise(nPAS=n())
nrow(OverlappingGenes)
[1] 6931
nrow(OverlappingGenes %>% filter(nPAS>1))
[1] 3098
OverlappingGenes$nPAS= as.factor(OverlappingGenes$nPAS)
ggplot(OverlappingGenes, aes(x=nPAS)) + geom_histogram(stat="count") + labs(x="Number of PAS", y="Genes", title="Number of PAS per gene with PAS in ortho 3' UTR")
Warning: Ignoring unknown parameters: binwidth, bins, pad
There is at least one pas in 6931 genes. Thats pretty good. There are 3098 with at least 2.
Look to see if these represent any of those differenctially used:
PASGene=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt",stringsAsFactors = F, header = T) %>% select(PAS, chr, start, end,loc)
DiffUsed=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt",header = T,stringsAsFactors = F) %>% inner_join(PASGene, by=c("chr",'start','end')) %>% mutate(OrthoUTR=ifelse(PAS %in% Overlappingfilt$PAS, "Yes","No"))
DiffUsed_sig =DiffUsed %>% filter(SigPAU2=="Yes")
ggplot(DiffUsed_sig,aes(x=OrthoUTR)) +geom_histogram(stat="count")
Warning: Ignoring unknown parameters: binwidth, bins, pad
Proportion:
DiffUsed_sig %>% group_by(OrthoUTR) %>% summarise(nEach=n()) %>% mutate(prop=nEach/nrow(DiffUsed_sig))
# A tibble: 2 x 3
OrthoUTR nEach prop
<chr> <int> <dbl>
1 No 1485 0.634
2 Yes 857 0.366
37% of the differentially used are in the 3’ UTR.
Filter to utr
DiffUsed_sig %>% filter(loc=="utr3")%>%group_by(OrthoUTR) %>% summarise(nEach=n()) %>% mutate(prop=nEach/nrow(DiffUsed_sig%>% filter(loc=="utr3")))
# A tibble: 2 x 3
OrthoUTR nEach prop
<chr> <int> <dbl>
1 No 446 0.348
2 Yes 834 0.652
834 sig are in the 3’ UTR.
This seems like an ok way to do this. Write out the filtered overlap to use in the next analysis. THerea are about 7K genes in this set
write.table(Overlappingfilt, "../data/orthoUTR/FilteredPASOverlapOrthoUTR.text",col.names = T, quote = F, row.names = F)
Now that I have a set of orthologous 3’ UTRs, I wiil look at enrichment of reads at these locations in human and chimp.
../data/orthoUTR/HumanDistal3UTR.sort.bed
To do this I will want to lift these to chimp for the chimp anaylsis:
liftOver ../data/orthoUTR/HumanDistal3UTR.sort.bed ../data/chainFiles/hg38ToPanTro6.over.chain ../data/orthoUTR/ChimpDistal3UTR.bed ../data/orthoUTR/ChimpDistal3UTR.unlift
sort -k1,1 -k2,2n ../data/orthoUTR/ChimpDistal3UTR.bed > ../data/orthoUTR/ChimpDistal3UTR.sort.bed
#!/bin/bash
#SBATCH --job-name=NuclearDTUTR
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=NuclearDTUTRt.out
#SBATCH --error=NuclearDTUTR.err
#SBATCH --partition=broadwl
#SBATCH --mem=50G
#SBATCH --mail-type=END
source ~/activate_anaconda.sh
conda activate comp_threeprime_env
computeMatrix scale-regions -S ../Human/data/mergedbw_byFrac/human_Nuclear.SamplesMerged.sort.bw -R ../data/orthoUTR/HumanDistal3UTR.sort.bed -b 500 -a 500 --skipZeros --transcript_id_designator 4 -out ../data/orthoUTR/HumanNuclear_UTR.gz
plotHeatmap --sortRegions descend -m ../data/orthoUTR/HumanNuclear_UTR.gz --plotTitle "Human" --heatmapHeight 4 --colorMap magma --averageTypeSummaryPlot "mean" --startLabel "5'" --endLabel "3'" -out ../data/orthoUTR/Human_UTR.png
computeMatrix scale-regions -S ../Chimp/data/mergedbw_byFrac/chimp_Nuclear.SamplesMerged.sort.bw -R ../data/orthoUTR/ChimpDistal3UTR.sort.bed -b 500 -a 500 --skipZeros --transcript_id_designator 4 -out ../data/orthoUTR/ChimpNuclear_UTR.gz
plotHeatmap --sortRegions descend -m ../data/orthoUTR/ChimpNuclear_UTR.gz --plotTitle "Chimp" --heatmapHeight 4 --colorMap magma --averageTypeSummaryPlot "mean" --startLabel "5'" --endLabel "3'" -out ../data/orthoUTR/Chimp_UTR.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] tidyselect_0.2.5 haven_1.1.2 lattice_0.20-38
[4] colorspace_1.3-2 generics_0.0.2 htmltools_0.3.6
[7] yaml_2.2.0 utf8_1.1.4 rlang_0.4.0
[10] later_0.7.5 pillar_1.3.1 glue_1.3.0
[13] withr_2.1.2 RColorBrewer_1.1-2 modelr_0.1.2
[16] readxl_1.1.0 plyr_1.8.4 munsell_0.5.0
[19] gtable_0.2.0 workflowr_1.6.0 cellranger_1.1.0
[22] rvest_0.3.2 evaluate_0.12 labeling_0.3
[25] knitr_1.20 httpuv_1.4.5 fansi_0.4.0
[28] broom_0.5.1 Rcpp_1.0.2 promises_1.0.1
[31] scales_1.0.0 backports_1.1.2 jsonlite_1.6
[34] fs_1.3.1 hms_0.4.2 digest_0.6.18
[37] stringi_1.2.4 grid_3.5.1 rprojroot_1.3-2
[40] cli_1.1.0 tools_3.5.1 magrittr_1.5
[43] lazyeval_0.2.1 crayon_1.3.4 whisker_0.3-2
[46] pkgconfig_2.0.2 xml2_1.2.0 lubridate_1.7.4
[49] assertthat_0.2.0 rmarkdown_1.10 httr_1.3.1
[52] rstudioapi_0.10 R6_2.3.0 nlme_3.1-137
[55] git2r_0.26.1 compiler_3.5.1