Last updated: 2020-05-20

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/DeandNumPAS.Rmd
    Modified:   analysis/DiffTop2SecondDom.Rmd
    Modified:   analysis/DirSelectionKhan.Rmd
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/PTM_analysis.Rmd
    Modified:   analysis/TotalDomStructure.Rmd
    Modified:   analysis/TotalVNuclearBothSpecies.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/changeMisprimcut.Rmd
    Modified:   analysis/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/dInforContent.Rmd
    Modified:   analysis/diffExpression.Rmd
    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/mRNADecay.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/phastCon.Rmd
    Modified:   analysis/pol2.Rmd
    Modified:   analysis/signalsites_doublefilter.Rmd
    Modified:   analysis/speciesSpecific.Rmd

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Rmd 66cadcb brimittleman 2020-05-20 biological insight? 
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() ──
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library(ggpubr)
Loading required package: magrittr

Attaching package: 'magrittr'
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    set_names
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    extract
library(cowplot)

Attaching package: 'cowplot'
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AU content in 3’ UTRs are usually related to stability through AU rich elements driving instability.

I will look at the correlation between AT content for the ortho UTRs seperated by if they are conserved at the APA level:

../data/orthoUTR/ChimpDistal3UTR.sort.bed ../data/orthoUTR/HumanDistal3UTR.sort.bed

Run bedtools nuc:

bedtools nuc -s -fi /project2/gilad/kenneth/References/human/genome/hg38.fa -bed ../data/orthoUTR/HumanDistal3UTR.sort.bed > ../data/orthoUTR/Human3UTR_nuc.txt


bedtools nuc -s -fi /project2/gilad/briana/genome_anotation_data/Chimp_genome/panTro6.fa -bed ../data/orthoUTR/ChimpDistal3UTR.sort.bed > ../data/orthoUTR/Chimp3UTR_nuc.txt

1_usercol 2_usercol 3_usercol 4_usercol 5_usercol 6_usercol 7_pct_at 8_pct_gc 9_num_A 10_num_C 11_num_G 12_num_T 13_num_N 14_num_oth 15_seq_len

names=c("chr", "start",'end', 'gene','score','strand','pct_at', 'pct_gc',   'num_A',    'num_C',    'num_G',    'num_T',    'num_N',    'num_oth',  'seq_len')
HumanUTRSeq=read.table("../data/orthoUTR/Human3UTR_nuc.txt", header = F,stringsAsFactors = F,col.names = names)%>% select(gene,pct_at, seq_len ) %>% rename(humanLen=seq_len, humanAT=pct_at)
ChimpUTRSeq=read.table("../data/orthoUTR/Chimp3UTR_nuc.txt", header = F,stringsAsFactors = F, col.names = names) %>% select(gene,pct_at, seq_len ) %>% rename(chimpLen=seq_len, chimpAT=pct_at)

BothUTR=HumanUTRSeq %>% inner_join(ChimpUTRSeq, by="gene")

plot human v chimp:

ggplot(BothUTR, aes(x=humanLen,y=chimpLen)) + geom_point() + stat_cor()

basically the same length

ggplot(BothUTR, aes(x=humanAT,y=chimpAT)) + geom_point() + stat_cor()

Join data about conservation:

Meta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F) 


Meta_genes= Meta %>% select(gene) %>% unique()

Meta_PAS=Meta %>%  select(PAS,gene)

dAPAGenes=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt", header = T, stringsAsFactors = F)
dAPAPAS=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(Meta, by=c("chr","start", "end","gene")) %>% select(PAS,gene,SigPAU2 ) 

dAPAPAS_genes= dAPAPAS %>% select(gene) %>% unique()

dAPATestedGenes= dAPAPAS  %>% select(gene) %>% unique() %>% mutate(dAPA=ifelse(gene %in% dAPAGenes$gene,"Yes", "No"))
dICdata= read.table("../data/IndInfoContent/SimpsonMedianSignificance.txt", header = T, stringsAsFactors = F)%>% select(sIC,gene)
dICdata_sig= dICdata %>% filter(sIC=="Yes")
dAPAandDic= dICdata %>% inner_join(dAPATestedGenes,by="gene") %>% mutate(Both=ifelse(sIC=="Yes" & dAPA=="Yes", "Yes","No"),OnlyIC=ifelse(sIC=="Yes" & dAPA=="No", "Yes","No"),OnlyAPA=ifelse(sIC=="No" & dAPA=="Yes", "Yes","No"))


OnlyAPAGenes= dAPAandDic %>% filter(OnlyAPA=="Yes") %>% select(gene) %>% mutate(set="Site")
IsoformGenes=  dAPAandDic %>% filter(OnlyIC=="Yes") %>% select(gene) %>% mutate(set="Isoform")
BothGenes=  dAPAandDic %>% filter(Both=="Yes") %>% select(gene) %>% mutate(set="Both")
NoneGenes=dAPAandDic %>% filter(dAPA=="No" & sIC=="No" ) %>% select(gene) %>% mutate(set="Conserved")


CharacterizeAllGenes= OnlyAPAGenes %>% bind_rows(IsoformGenes) %>% bind_rows(BothGenes)%>% bind_rows(BothGenes) %>% bind_rows(NoneGenes) %>% mutate(OverAllCons=ifelse(set=="Conserved", "Yes","No"))
CharacterizeAllGenesUTR= CharacterizeAllGenes %>% inner_join(BothUTR, by="gene")

lengthplot=ggplot(CharacterizeAllGenesUTR, aes(x=set, y= humanLen, fill=set))+ geom_boxplot() + stat_compare_means()+ scale_fill_brewer(palette = "RdYlBu")+ theme(legend.position = "none")+ labs(y="length orthologous 3' UTR", title="No difference in UTR length")

lengthplot

ggplot(CharacterizeAllGenesUTR, aes(x=OverAllCons, y= humanLen, fill=OverAllCons))+ geom_boxplot() + stat_compare_means()+ scale_fill_brewer(palette = "Set1")

No differences in UTR length:

AUProp=ggplot(CharacterizeAllGenesUTR, aes(x=set, y= humanAT, fill=set))+ geom_boxplot() + stat_compare_means()+ scale_fill_brewer(palette = "RdYlBu") + theme(legend.position = "none") + labs(y="%AU in orthologous 3' UTR", title="No difference in %AU")
AUProp

ggplot(CharacterizeAllGenesUTR, aes(x=OverAllCons, y= humanAT, fill=OverAllCons))+ geom_boxplot() + stat_compare_means()+ scale_fill_brewer(palette = "Set1") + labs(title="No differnece")

plot_grid(lengthplot, AUProp)


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] cowplot_0.9.4   ggpubr_0.2      magrittr_1.5    forcats_0.3.0  
 [5] stringr_1.3.1   dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1    
 [9] tidyr_0.8.3     tibble_2.1.1    ggplot2_3.1.1   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         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] workflowr_1.6.0    cellranger_1.1.0   rvest_0.3.2       
[22] evaluate_0.12      labeling_0.3       knitr_1.20        
[25] httpuv_1.4.5       broom_0.5.1        Rcpp_1.0.4.6      
[28] promises_1.0.1     scales_1.0.0       backports_1.1.2   
[31] jsonlite_1.6       fs_1.3.1           hms_0.4.2         
[34] 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       
[40] lazyeval_0.2.1     crayon_1.3.4       whisker_0.3-2     
[43] pkgconfig_2.0.2    xml2_1.2.0         lubridate_1.7.4   
[46] assertthat_0.2.0   rmarkdown_1.10     httr_1.3.1        
[49] rstudioapi_0.10    R6_2.3.0           nlme_3.1-137      
[52] git2r_0.26.1       compiler_3.5.1