Last updated: 2020-04-20

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
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Rmd 2f5d4d5 brimittleman 2020-04-08 change number and add 3; utr 
library(tidyverse)
── Attaching packages ────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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── 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.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

Version Author Date
c4f1c7f brimittleman 2020-04-10
4c131c9 brimittleman 2020-04-08

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

Version Author Date
c4f1c7f brimittleman 2020-04-10
4c131c9 brimittleman 2020-04-08

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

Version Author Date
c4f1c7f brimittleman 2020-04-10
4c131c9 brimittleman 2020-04-08

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