Last updated: 2020-04-21

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Knit directory: Comparative_APA/analysis/

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Rmd 0f53338 brimittleman 2020-04-21 add de and diff dom mech 
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http://arescore.dkfz.de/arescore.pl

I want to test if there are genes where destabilization of the mRNA could connect the different dominant PAS and DE. I will test the genes with 2 UTR different dominant PAS that are DE.

I will see if these have higher destabilizing scores than a random set,

Pull out the ortho UTRs for these too:

OrthoUTR=read.table("../data/orthoUTR/HumanDistal3UTR.sort.bed",stringsAsFactors = F,header = F, col.names = c("chr", "utrstart","utrend", "gene", "score", "strand")) %>% select(gene, utrstart, utrend)
Genes2Use=read.table("../data/DomDefGreaterX/DE_Diffdom4_UTRboth.txt", header = T, stringsAsFactors = F) %>% inner_join(OrthoUTR, by="gene")

Great all of these genes have an ortho UTR.

I need a fasta format for the site. I will use bedtools nuc on these regions. hile I am doing this i can get the seq for all of the ortho utrs:

mkdir ../data/AREelements

bedtools nuc -s -seq -bed ../data/orthoUTR/HumanDistal3UTR.sort.bed -fi /project2/gilad/kenneth/References/human/genome/hg38.fa > ../data/orthoUTR/HumanDistal3UTR.sort_withSeq.bed

I can write a python script that will write a fasta style doc when given a list of genes.

genelist= Genes2Use %>% select(gene)
write.table(genelist,"../data/AREelements/DiffDomDE_UTR.txt", col.names = F, row.names = F, quote = F)
python UTR2FASTA.py ../data/AREelements/DiffDomDE_UTR.txt ../data/AREelements/DiffDomDE_UTR_fasta.txt
Res=read.table("../data/AREelements/DiffDomDE_UTR_ARE_res.txt", sep="\t", header = T)

Res %>% select(Name, Score) %>% arrange(desc(Score)) %>% head()
      Name Score
1   NDUFA5  24.4
2  BLOC1S6  15.6
3     DDX6  11.1
4 ITPRIPL2   6.9
5    YWHAQ   6.2
6    CISD2   5.2

Next I will run this for all genes:

OrthoUTRGene=OrthoUTR %>% select(gene)
write.table(OrthoUTRGene,"../data/AREelements/AllGenesOrthoUTR.txt", col.names = F, row.names = F, quote = F)
python UTR2FASTA.py ../data/AREelements/AllGenesOrthoUTR.txt ../data/AREelements/AllGenesOrthoUTR_fasta.txt

Site cannot handle full set.
I will do 5 random sets:

sample1= sample_n(OrthoUTRGene, 28)
#write.table(sample1,"../data/AREelements/Sample1Genes.txt", col.names = F, row.names = F, quote = F)
sample2= sample_n(OrthoUTRGene, 28)
#write.table(sample2,"../data/AREelements/Sample2Genes.txt", col.names = F, row.names = F, quote = F)
sample3= sample_n(OrthoUTRGene, 28)
#write.table(sample3,"../data/AREelements/Sample3Genes.txt", col.names = F, row.names = F, quote = F)
sample4= sample_n(OrthoUTRGene, 28)
#write.table(sample4,"../data/AREelements/Sample4Genes.txt", col.names = F, row.names = F, quote = F)
sample5= sample_n(OrthoUTRGene, 28)

#write.table(sample5,"../data/AREelements/Sample5Genes.txt", col.names = F, row.names = F, quote = F)
python UTR2FASTA.py ../data/AREelements/Sample1Genes.txt ../data/AREelements/Sample1Genes.fasta.txt
python UTR2FASTA.py ../data/AREelements/Sample2Genes.txt ../data/AREelements/Sample2Genes.fasta.txt
python UTR2FASTA.py ../data/AREelements/Sample3Genes.txt ../data/AREelements/Sample3Genes.fasta.txt
python UTR2FASTA.py ../data/AREelements/Sample4Genes.txt ../data/AREelements/Sample4Genes.fasta.txt
python UTR2FASTA.py ../data/AREelements/Sample5Genes.txt ../data/AREelements/Sample5Genes.fasta.txt
Actual=read.table("../data/AREelements/DiffDomDE_UTR_ARE_res.txt", sep="\t", header = T) %>% select(Score) %>% mutate(set="Actual")
Res1=read.table("../data/AREelements/Sample1_res.txt", sep="\t", header = T) %>% select(Score) %>% mutate(set="Sample1")
Res2=read.table("../data/AREelements/Sample2_res.txt", sep="\t", header = T)%>% select(Score) %>% mutate(set="Sample2")
Res3=read.table("../data/AREelements/Sample3_res.txt", sep="\t", header = T)%>% select(Score) %>% mutate(set="Sample3")
Res4=read.table("../data/AREelements/Sample4_res.txt", sep="\t", header = T)%>% select(Score) %>% mutate(set="Sample4")
Res5=read.table("../data/AREelements/Sample5_res.txt", sep="\t", header = T)%>% select(Score) %>% mutate(set="Sample5")

AllSample=Res1 %>% bind_rows(Actual)%>% bind_rows(Res2)%>% bind_rows(Res3)%>% bind_rows(Res4)%>% bind_rows(Res5)
ggplot(AllSample,aes(y=Score, x=set,fill=set)) + geom_boxplot() + geom_jitter(alpha=.3)+ scale_fill_brewer(palette = "Dark2") + stat_compare_means() + theme(legend.position = "none") + labs(x="Sample", y="ARE destabilization score", title="ARE scores for UTR:UTR Dominance changes and DE")

This is probably not driving the trend but may explain some examples.


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    tidyverse_1.2.1
 [9] ggpubr_0.2      magrittr_1.5    ggplot2_3.1.1   workflowr_1.6.0

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5   haven_1.1.2        lattice_0.20-38   
 [4] colorspace_1.3-2   generics_0.0.2     htmltools_0.3.6   
 [7] yaml_2.2.0         rlang_0.4.0        later_0.7.5       
[10] pillar_1.3.1       glue_1.3.0         withr_2.1.2       
[13] RColorBrewer_1.1-2 modelr_0.1.2       readxl_1.1.0      
[16] plyr_1.8.4         munsell_0.5.0      gtable_0.2.0      
[19] cellranger_1.1.0   rvest_0.3.2        evaluate_0.12     
[22] labeling_0.3       knitr_1.20         httpuv_1.4.5      
[25] broom_0.5.1        Rcpp_1.0.2         promises_1.0.1    
[28] scales_1.0.0       backports_1.1.2    jsonlite_1.6      
[31] fs_1.3.1           hms_0.4.2          digest_0.6.18     
[34] stringi_1.2.4      grid_3.5.1         rprojroot_1.3-2   
[37] cli_1.1.0          tools_3.5.1        lazyeval_0.2.1    
[40] crayon_1.3.4       whisker_0.3-2      pkgconfig_2.0.2   
[43] xml2_1.2.0         lubridate_1.7.4    assertthat_0.2.0  
[46] rmarkdown_1.10     httr_1.3.1         rstudioapi_0.10   
[49] R6_2.3.0           nlme_3.1-137       git2r_0.26.1      
[52] compiler_3.5.1