Longer and Shorter UTR Simple Case

Last updated: 2020-04-13

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
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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
Rmd	c5fc23c	brimittleman	2020-04-13	add long short info

library(workflowr)

This is workflowr version 1.6.0
Run ?workflowr for help getting started

library(tidyverse)

── Attaching packages ────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

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library(ggpubr)

Loading required package: magrittr


Attaching package: 'magrittr'

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    extract

I will start by looking at the genes with 2 PAS in the orthologous 3’ UTRs. By understanding shortening and lengthening patterns, I may be able to learn more about mechanisms.

OverlapOrtho=read.table("../data/orthoUTR/FilteredPASOverlapOrthoUTR.text", header = T,stringsAsFactors = F)

Filter to genes with 2 PAS in the set.

TwoPAS=OverlapOrtho %>% group_by(gene) %>% summarise(nPAS=n()) %>% filter(nPAS==2)

OverlapOrtho2PAS= OverlapOrtho %>% filter(gene %in% TwoPAS$gene)

See if any of these are the only 2 PAS for the gene. That would be the most simple case. I can annotate these as long or short isoforms.

PASMeta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt",header = T) %>% group_by(gene) %>% mutate(nPAS=n()) %>% filter(nPAS==2)
ChimpUsage= PASMeta %>% ungroup() %>% select(PAS,Chimp) %>% rename(chimpusage=Chimp)

OverlapOrtho2PASoverall= OverlapOrtho2PAS %>% filter(gene %in% PASMeta$gene) %>% inner_join(ChimpUsage, by="PAS")

Warning: Column `PAS` joining character vector and factor, coercing into
character vector

OverlapOrtho2PASoverallPos = OverlapOrtho2PASoverall %>% filter(strand=="+") %>% group_by(gene) %>% arrange(startpas) %>% mutate(id = 1:n()) %>% mutate(Isofrom=ifelse(id==1, "Short", "Long"))%>% select(-id)

OverlapOrtho2PASoverallNeg = OverlapOrtho2PASoverall %>% filter(strand=="-") %>% group_by(gene) %>% arrange(startpas) %>% mutate(id = 1:n()) %>% mutate(Isofrom=ifelse(id==1, "Long", "Short")) %>% select(-id)

OverlapOrtho2PASoverallBoth=OverlapOrtho2PASoverallPos %>% bind_rows(OverlapOrtho2PASoverallNeg)

Look at the PAS that are differentially used:

DiffUsed=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt",header = T, stringsAsFactors = F) %>% filter(gene %in% OverlapOrtho2PASoverallBoth$gene) %>% select(chr, start, end, gene, deltaPAU, SigPAU2) %>% rename(chrpas=chr, startpas=start, endpas=end)

OverlapOrtho2PASoverallBothDiffUsed=OverlapOrtho2PASoverallBoth %>% inner_join(DiffUsed, by=c("chrpas", "startpas","endpas", "gene"))

OverlapOrtho2PASoverallBothDiffUsedSig= OverlapOrtho2PASoverallBothDiffUsed %>% filter(SigPAU2=="Yes")
nrow(OverlapOrtho2PASoverallBothDiffUsedSig)

[1] 36

nrow(OverlapOrtho2PASoverallBothDiffUsedSig %>% select(gene) %>% unique())

[1] 18

There are only 36 significant PAS in 18 genes.

Negative is

OverlapOrtho2PASoverallBothDiffUsedSig %>% select(PAS,gene, Isofrom, deltaPAU)

# A tibble: 36 x 4
# Groups:   gene [18]
   PAS         gene   Isofrom deltaPAU
   <chr>       <chr>  <chr>      <dbl>
 1 human138532 HIC1   Short     -0.305
 2 human138539 HIC1   Long       0.305
 3 chimp226080 MAN2B2 Short     -0.292
 4 human252668 MAN2B2 Long       0.292
 5 human145205 EIF1   Short      0.440
 6 human145206 EIF1   Long      -0.440
 7 human212400 BCAS4  Short      0.215
 8 human212411 BCAS4  Long      -0.215
 9 human134514 NOL3   Short      0.254
10 human134515 NOL3   Long      -0.254
# … with 26 more rows

Does long and short segregate with pos negative?

test= OverlapOrtho2PASoverallBothDiffUsedSig %>% select(PAS,gene, Isofrom, deltaPAU) 

ggplot(test, aes(x=Isofrom, y=deltaPAU)) + geom_boxplot() + stat_compare_means()

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] ggpubr_0.2      magrittr_1.5    forcats_0.3.0   stringr_1.3.1  
 [5] dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1     tidyr_0.8.3    
 [9] tibble_2.1.1    ggplot2_3.1.1   tidyverse_1.2.1 workflowr_1.6.0

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5 haven_1.1.2      lattice_0.20-38  colorspace_1.3-2
 [5] generics_0.0.2   htmltools_0.3.6  yaml_2.2.0       utf8_1.1.4      
 [9] rlang_0.4.0      later_0.7.5      pillar_1.3.1     glue_1.3.0      
[13] withr_2.1.2      modelr_0.1.2     readxl_1.1.0     plyr_1.8.4      
[17] munsell_0.5.0    gtable_0.2.0     cellranger_1.1.0 rvest_0.3.2     
[21] evaluate_0.12    labeling_0.3     knitr_1.20       httpuv_1.4.5    
[25] fansi_0.4.0      broom_0.5.1      Rcpp_1.0.2       promises_1.0.1  
[29] scales_1.0.0     backports_1.1.2  jsonlite_1.6     fs_1.3.1        
[33] hms_0.4.2        digest_0.6.18    stringi_1.2.4    grid_3.5.1      
[37] rprojroot_1.3-2  cli_1.1.0        tools_3.5.1      lazyeval_0.2.1  
[41] crayon_1.3.4     whisker_0.3-2    pkgconfig_2.0.2  xml2_1.2.0      
[45] lubridate_1.7.4  assertthat_0.2.0 rmarkdown_1.10   httr_1.3.1      
[49] rstudioapi_0.10  R6_2.3.0         nlme_3.1-137     git2r_0.26.1    
[53] compiler_3.5.1

Longer and Shorter UTR Simple Case

Briana Mittleman

4/13/2020