Last updated: 2020-03-27

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Knit directory: Comparative_APA/analysis/

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File Version Author Date Message
Rmd f92b89c brimittleman 2020-03-26 add ortho exon and new mm 
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 am still concerned about annotation. I want to see if using the ortho exon file as an annotation tool would be more appropriate. I want to look at it and see if there is a way to combine exons by gene and see if the spaces inbetween could be introns.

First look at the human one.

HumanOrtho=read.table("/project2/gilad/kenneth/OrthoExonPartialMapping/human.noM.gtf", sep="\t")

Parse this into a bed file with a python script.

mkdir ../data/OrthoExonBed
python SAF2Bed.py /project2/gilad/kenneth/OrthoExonPartialMapping/human.noM.gtf ../data/OrthoExonBed/human.noM.bed

sort -k1,1 -k2,2n ../data/OrthoExonBed/human.noM.bed > ../data/OrthoExonBed/human.noM.sort.bed

I will check if some of the problem genes, I found in my pipeline are in this.

ChimpMM=read.table("../data/multimap/Chimp_Uniq_multimapPAS.txt", stringsAsFactors = F, header = T)
HumanMM=read.table("../data/multimap/Human_Uniq_multimapPAS.txt", stringsAsFactors = F, header = T)
BothMM=read.table("../data/multimap/Both_multimapPAS.txt",stringsAsFactors = F, header = T)


AllMM=ChimpMM %>% bind_rows(HumanMM) %>% bind_rows(BothMM)

I will overlap this with the ortho exon file. I will look at those that overlap and those that do not. I need a bed file

AllMM_bed=AllMM %>% mutate(Name=paste(gene, PAS,loc, MultiMap, sep=":")) %>% select(chr, start,end, Name, Human, strandFix)

write.table(AllMM_bed,"../data/multimap/allMM.bed",col.names = F, quote = F, row.names = F, sep="\t")
sort -k1,1 -k2,2n ../data/multimap/allMM.bed > ../data/multimap/allMM.sort.bed

Overlap.

sbatch overlapMMandOrthoexon.sh

Results:

InOrtho=read.table("../data/OrthoExonBed/allMMinOrtho.bed", col.names = c("chr", "start", "end", "name", "score", "strand")) %>% group_by(name) %>% summarize(n=n())%>% separate(name, into=c("gene", "PAS","loc", "MM"),sep=":") %>% mutate(OE="In")
NotInOrtho=read.table("../data/OrthoExonBed/allMM_NOT_inOrtho.bed", col.names = c("chr", "start", "end", "name", "score", "strand")) %>% group_by(name) %>% summarize(n=n()) %>% separate(name, into=c("gene", "PAS","loc", "MM"),sep=":")  %>% mutate(OE="Out")

AllOrthores=InOrtho %>% bind_rows(NotInOrtho)

ggplot(AllOrthores, aes(x=OE, by=loc,fill=loc)) +geom_bar(stat="count",position = "dodge") + labs(x="Is PAS overlapping ortho exon", title="PAS impacted by multimapping and ortho exon",y="Number of PAS")+ scale_fill_brewer(palette = "Dark2")

Proportion:

AllOrthores %>% group_by(OE) %>% summarise(n=n())
# A tibble: 2 x 2
  OE        n
  <chr> <int>
1 In      831
2 Out     578
#look only at utr  

AllOrthores %>% filter(loc=="utr3") %>% group_by(OE) %>% summarise(n=n())
# A tibble: 2 x 2
  OE        n
  <chr> <int>
1 In      492
2 Out     120

Look at the UTR sequences that are in:

AllOrthores %>% filter(OE=="In", loc=="utr3")
# A tibble: 492 x 6
   gene    PAS         loc   MM        n OE   
   <chr>   <chr>       <chr> <chr> <int> <chr>
 1 AARS2   human285010 utr3  Human     2 In   
 2 ABCB10  human30678  utr3  Both      2 In   
 3 ABHD17A human153108 utr3  Both      8 In   
 4 ABR     chimp125493 utr3  Human    10 In   
 5 ACAD8   chimp63259  utr3  Chimp     6 In   
 6 ACAD8   human66039  utr3  Chimp     6 In   
 7 ACTB    human299134 utr3  Both      7 In   
 8 ADAM9   chimp303728 utr3  Human     8 In   
 9 ADAM9   chimp303729 utr3  Human     8 In   
10 ADH5    human247010 utr3  Chimp     2 In   
# … with 482 more rows

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