Establish Ortho 3’ UTRs
Briana Mittleman
4/8/2020
Last updated: 2020-04-08
Checks: 7 0
Knit directory: Comparative_APA/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 2f5d4d5 | brimittleman | 2020-04-08 | change number and add 3; utr |
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()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.bedLook 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.bedThere 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.bedLooks 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.bedoverlap 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.bedmetaPAS=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] 190 Overlapping %>% filter(loc!="utr3") %>% nrow()[1] 431 Overlappingfilt= Overlapping%>% filter(gene == geneUTR)190 of these are in different genes. check these.
431 not annotated as UTR3 (31 diff gene)
NotUTRanno= Overlapping %>% filter(loc!="utr3") %>% filter(gene == geneUTR)
nrow(NotUTRanno)[1] 400ggplot(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] 0.2656902nrow(Overlapping)/nrow(metaPAS %>% filter(loc=="utr3"))[1] 0.6850219almost 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] 7023nrow(OverlappingGenes %>% filter(nPAS>1))[1] 3142OverlappingGenes$nPAS= as.factor(OverlappingGenes$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 7023 genes. Thats pretty good. There are 3142 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 1973 0.630
2 Yes 1159 0.37037% 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 549 0.327
2 Yes 1129 0.6731678 sig ar in the 3’ UTR
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