Last updated: 2020-01-09

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/Nuclear_HvC.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/multiMap.Rmd

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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 07697b2 brimittleman 2020-01-09 add distance distribution
html 6d9e369 brimittleman 2020-01-08 Build site.
Rmd 203eae5 brimittleman 2020-01-08 first steps for intron loc analysis 
library(workflowr)
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The goal of this analysis is to look at the distribution of intronic location for the shared and not shared dominant PAS. This will help me know if the pattern is due to annotation or not. In this analysis I will use the nuclear results.

The first step is assigning each PAS to the intron it comes from.

HumanIntronicChimpUTR=read.table("../data/DominantPAS/Nuclear_HumanIntronicChimpUTR.txt",header = T, stringsAsFactors = F)%>% dplyr::select(gene, HumanPAS, HumanMean)
SameDomIntron=read.table("../data/DominantPAS/SameDominantIntronic.txt",header = T, stringsAsFactors = F)%>% dplyr::select(gene, HumanPAS,HumanMean)
HumanPAS= read.table("../data/Pheno_5perc/Human_Pheno_5perc.txt", header = T,stringsAsFactors = F) %>% dplyr::select(chr, start, end, gene, strand, PAS) %>% rename("HumanPAS"=PAS)

Subset this file by those in the set and select it as a bed file for overlap with intron file. I will use the human mean as the score for now.

HumanPAS_samedom=HumanPAS %>% inner_join(SameDomIntron, by="HumanPAS") %>% mutate(GenePAS=paste(gene.x, HumanPAS, sep="_")) %>% dplyr::select(chr, start, end, GenePAS, HumanMean,strand)

HumanPAS_diffDom=HumanPAS %>% inner_join(HumanIntronicChimpUTR, by="HumanPAS") %>% mutate(GenePAS=paste(gene.x, HumanPAS, sep="_")) %>% dplyr::select(chr, start, end, GenePAS, HumanMean,strand)

I can write these out as bed files.

write.table(HumanPAS_samedom, "../data/DominantPAS/SameDominantPAS_intronic.bed", quote = F, row.names = F, col.names = F,sep = "\t")
write.table(HumanPAS_diffDom, "../data/DominantPAS/DifferentDominantPAS_intronic.bed", quote = F, row.names = F, col.names = F,sep = "\t")

I can use bedtools intersect to find the intron these are in.

bedtools intersect -s -sorted -loj -a (PAS file) -b intron file > output

sbatch FindIntronForDomPAS.sh

There are places where multiple transcripts with the intron included. this means the same PAS shows up multiple times ( i can look for uniq intron locations to take care of this)

SameDomIntron=read.table("../data/DominantPAS/SameDominantPAS_intronic_mapped2Intron.txt", stringsAsFactors = F, col.names = c("PASchr", "PASstart", "PASend", "PASname", "PASusage", "PASstrand", "Intronchr", "IntronStart", "IntronEnd", "IntronName", "IntronScore", "IntronStrand"))
#group by intronname and keep top intron

SameDomIntronOne=SameDomIntron %>% group_by(PASname) %>% slice(1) %>% ungroup()



DiffDomIntron=read.table("../data/DominantPAS/DifferentDominantPAS_intronic_mapped2Intron.txt", stringsAsFactors = F, col.names = c("PASchr", "PASstart", "PASend", "PASname", "PASusage", "PASstrand", "Intronchr", "IntronStart", "IntronEnd", "IntronName", "IntronScore", "IntronStrand"))
DiffDomIntronOne=DiffDomIntron %>% group_by(PASname) %>% slice(1) %>% ungroup()

Now I need to find the midpoint for the PAS and get the percent distance to the start of the intron. I will use the intron strand because this is the genomic strand.

SameDomIntronOne_dist=SameDomIntronOne %>% mutate(centerPAS=PASstart +100, intronLength=IntronEnd-IntronStart, distance2PAS=ifelse(IntronStrand=="+", centerPAS -IntronStart, IntronEnd-centerPAS),propIntron=distance2PAS/intronLength)

DiffDomIntronOne_dist=DiffDomIntronOne %>% mutate(centerPAS=PASstart +100, intronLength=IntronEnd-IntronStart, distance2PAS=ifelse(IntronStrand=="+", centerPAS -IntronStart, IntronEnd-centerPAS),propIntron=distance2PAS/intronLength)

Plot both distributions, do percentage and absolute distance

ggplot(SameDomIntronOne_dist, aes(x=distance2PAS)) + geom_histogram(bins=100)  + labs(x="Distance from intron start to PAS", title="Same Dominant Intron, absolute distance")

ggplot(SameDomIntronOne_dist, aes(x=propIntron)) + geom_histogram(bins=100) +labs(x="Proportion of intron", title="Same Dominant Intron, proportion of intron")

SameDomIntronOne_dist_filt= SameDomIntronOne_dist %>% filter(distance2PAS<=100000)

ggplot(SameDomIntronOne_dist_filt, aes(x=distance2PAS)) + geom_histogram(bins=100)   +labs(x="Distance from intron start to PAS", title="Same Dominant Intron, absolute distance (less than 100kb)")

ggplot(DiffDomIntronOne_dist, aes(x=distance2PAS)) + geom_histogram(bins=100)+ labs(x="Distance from intron start to PAS", title="Human Dominant Intronic, Chimp Dominant 3' UTR, absolute distance")

ggplot(DiffDomIntronOne_dist, aes(x=propIntron)) + geom_histogram(bins=100) +labs(x="Proportion of intron", title="Human Dominant Intronic, Chimp Dominant 3' UTR, Proportion of intron")

DiffDomIntronOne_dist_filt= DiffDomIntronOne_dist %>% filter(distance2PAS<=100000)

ggplot(DiffDomIntronOne_dist_filt, aes(x=distance2PAS)) + geom_histogram(bins=100) +  labs(x="Distance from intron start to PAS", title="Human Dominant Intronic, Chimp Dominant 3' UTR \nabsolute distance (less than 100kb)")

Distributions look pretty similar.


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:
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[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 workflowr_1.5.0

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     tools_3.5.1     
 [9] digest_0.6.18    lubridate_1.7.4  jsonlite_1.6     evaluate_0.12   
[13] nlme_3.1-137     gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2 
[17] rlang_0.4.0      cli_1.1.0        rstudioapi_0.10  yaml_2.2.0      
[21] haven_1.1.2      withr_2.1.2      xml2_1.2.0       httr_1.3.1      
[25] knitr_1.20       hms_0.4.2        generics_0.0.2   fs_1.3.1        
[29] rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5 glue_1.3.0      
[33] R6_2.3.0         readxl_1.1.0     rmarkdown_1.10   modelr_0.1.2    
[37] magrittr_1.5     whisker_0.3-2    backports_1.1.2  scales_1.0.0    
[41] promises_1.0.1   htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0
[45] colorspace_1.3-2 httpuv_1.4.5     labeling_0.3     stringi_1.2.4   
[49] lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1      crayon_1.3.4