3’ UTR PAS enrichment

Last updated: 2020-01-30

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/LDregress.Rmd
    Modified:   analysis/NuclearSpecIncludeNotTested.Rmd
    Modified:   analysis/PASdescriptiveplots.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
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    Modified:   code/DistPAS2Sig.py
    Modified:   code/apaQTLsnake.err
    Deleted:    code/test.txt
    Deleted:    reads_graphs.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 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
html	9808654	brimittleman	2019-11-14	Build site.
Rmd	79d65a0	brimittleman	2019-11-14	add averages
html	42f352c	brimittleman	2019-09-07	Build site.
Rmd	c5c6546	brimittleman	2019-09-07	add enrichment analysis

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()

In this analysis I will look at the enrichment of 3’ UTR pas over all PAS. I need to get the size of all UTR regions and the size of all regions. I can do this by merging the annotations.

I can use bedtools merge on

ncbiRefSeq_FormatedallAnnotation.sort.bed and the UTR 3 regions of this.

annotation=read.table("/project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_FormatedallAnnotation.sort.bed", col.names = c("chr", "start", "end", "id", "score", "strand")) %>% separate(id,into=c("loc", "gene"),sep=":")

utr3=annotation %>% filter(loc=="utr3") 

write.table(utr3, "/project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_UTR3.bed", col.names = F, row.names = F,quote = F, sep="\t")

sort:

sort -k1,1 -k2,2n /project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefseq_3UTR.dms > /project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefseq_3UTR.sort.bed

sed 's/^chr//' /project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefseq_3UTR.sort.bed > /project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefseq_3UTR.sort.noCHR.bed
sbatch mergeAnnotations.sh

chroms=as.character(seq(1,22))

utrMerged=read.table("/project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_Merged_UTR3.sort.bed",col.names = c("chr", "start", "end")) %>% mutate(length=end-start) %>% filter(chr %in% chroms)
allMerged=read.table("/project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_Merged.FormatedallAnnotation.sort.bed" , col.names = c("chr", "start", "end")) %>% mutate(length=end-start) %>% filter(chr %in% chroms)
totalLength=sum(allMerged$length)
utrLength=sum(utrMerged$length)

N pas in UTR3

utr3pas=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.bed", col.names = c("chr", "start", "end", "id","score", "strand")) %>% separate(id, into = c("pasnum", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc"),sep="_") %>% filter(loc=="utr3")
allPAS=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.bed")

Values:

pas_length=nrow(allPAS)/totalLength
utr_length=nrow(utr3pas)/utrLength 


enrichment=utr_length/pas_length
enrichment

[1] 19.43483

This is a 19.43 enrichment.

Average usage of these across.

UsageNuclear=read.table("../data/PAS/NuclearPASMeanUsage.txt", stringsAsFactors = F,header = T) %>% filter(meanUsage>.05) %>% separate(ID,into=c("chr", "start","end", "PASID"), sep=":") %>% separate(PASID, into=c("gene", "loc","strand","PAS"),sep="_")

Warning: Expected 4 pieces. Additional pieces discarded in 3 rows [5127,
5128, 5129].

utrpasNum= utr3pas %>% mutate(PAS=paste("peak", pasnum, sep="")) %>% select(PAS)
UtrWusage=UsageNuclear %>% filter(PAS %in% utrpasNum$PAS)

mean(UtrWusage$meanUsage)

[1] 0.4623877

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_1.0.2       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_1.5.0 
 [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

3’ UTR PAS enrichment

Briana Mittleman

9/7/2019