Last updated: 2020-05-14

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/DICNotDEDP.Rmd
    Modified:   analysis/DeandNumPAS.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/establishCutoffs.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/mRNADecay.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/pol2.Rmd
    Modified:   analysis/signalsites_doublefilter.Rmd
    Modified:   analysis/speciesSpecific.Rmd

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Rmd 3c19233 brimittleman 2020-05-14 add which PAS and seq between
html 84c8d10 brimittleman 2020-05-13 Build site.
Rmd c9bca57 brimittleman 2020-05-13 add homer
html 1bf3145 brimittleman 2020-05-12 Build site.
Rmd b98f844 brimittleman 2020-05-12 add seq between analysis

I want to get the sequence between the dominant PAS when they are different. I can test for different destabilizing motifs in these regions. I will start with the .4 cutoff.

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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✔ tidyr   0.8.3       ✔ stringr 1.3.1  
✔ readr   1.3.1       ✔ forcats 0.3.0  
── Conflicts ──────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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library(Biostrings)
Loading required package: BiocGenerics
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library(ggpubr)
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mkdir ../data/DistTwoDom
HumanRes=read.table("../data/DomDefGreaterX/Human_AllGenes_DiffTop.txt", col.names = c("Human_PAS", "gene","Human_DiffDom"),stringsAsFactors = F)

ChimpRes=read.table("../data/DomDefGreaterX/Chimp_AllGenes_DiffTop.txt", col.names = c("Chimp_PAS", "gene","Chimp_DiffDom"),stringsAsFactors = F)

BothRes=HumanRes %>% inner_join(ChimpRes,by="gene")

BothRes_40=BothRes %>% filter(Chimp_DiffDom >=0.4 | Human_DiffDom>=0.4) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=40)


BothRes_40_diff= BothRes_40 %>% filter(Set=="Different")

I need the meta data for the PAS:

metaPAS=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt",stringsAsFactors = F, header = T) %>% mutate(midpoint=start+100)
metaPAS_sm= metaPAS %>% select(PAS, gene, midpoint)

metaPAS_bed= metaPAS %>% select(chr, gene, strandFix) %>% unique()

BothRes_40_diff_sm= BothRes_40_diff %>% select(gene, Chimp_PAS, Human_PAS) %>% gather("species", "PAS", -gene) %>% inner_join(metaPAS_sm, by=c("gene", "PAS"))

Spread this back out so i have both midpoints

BothRes_40_diff_spread=BothRes_40_diff_sm %>% mutate(extra="PAS") %>%  spread(extra,midpoint) %>% group_by(gene) %>% summarise(minPAS=min(PAS), maxPAS=max(PAS)) %>% inner_join(metaPAS_bed, by="gene") %>%mutate(score=0) %>% select(chr, minPAS, maxPAS, gene, score, strandFix ) %>% arrange(chr, minPAS)

write.table(BothRes_40_diff_spread, "../data/DistTwoDom/SeqBetweenDom_4.bed", quote = F, col.names = F, row.names = F, sep="\t")
bedtools nuc -s -seq -fi /project2/gilad/kenneth/References/human/genome/hg38.fa -bed ../data/DistTwoDom/SeqBetweenDom_4.bed > ../data/DistTwoDom/SeqBetweenDom_4_sort_nuc.bed

Look at results:

SeqBetween=read.table("../data/DistTwoDom/SeqBetweenDom_4_sort_nuc.bed", col.names = c(colnames(BothRes_40_diff_spread),"AT", "GC", "A", "C", "G", "T","N", "other", "len", "seq" ))

summary(SeqBetween$len)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    187    1526    5088   16477   15893  222368 
plot(sort(SeqBetween$len))

Version Author Date
1bf3145 brimittleman 2020-05-12 
ggplot(SeqBetween, aes(x=len))+geom_density()

Version Author Date
1bf3145 brimittleman 2020-05-12

Compare those that are de and those that are not:

nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F)
DEgenes=read.table("../data/DiffExpression/DE_genes.txt", header = F,col.names = c("Gene_stable_ID"),stringsAsFactors = F) %>% inner_join(nameID, by="Gene_stable_ID") %>% dplyr::select(Gene.name) %>% dplyr::rename("gene"=Gene.name)
DEgenestested=read.table("../data/DiffExpression/DE_Testedgenes.txt", header = F,col.names = c("Gene_stable_ID"),stringsAsFactors = F) %>% inner_join(nameID, by="Gene_stable_ID") %>% dplyr::select(Gene.name) %>% dplyr::rename("gene"=Gene.name) %>% mutate(DE=ifelse(gene %in% DEgenes$gene, "Yes", "No"))
SeqBetweenFix= SeqBetween %>% mutate(seqUp=toupper(seq))  %>% inner_join(DEgenestested, by="gene")
Warning: Column `gene` joining factor and character vector, coercing into
character vector
ggplot(SeqBetweenFix, aes(x=log10(len), fill=DE))+geom_density(alpha=.4)

Version Author Date
1bf3145 brimittleman 2020-05-12 
Seq_de= SeqBetweenFix %>% filter(DE=="Yes")
Seq_node= SeqBetweenFix %>% filter(DE=="No")

wilcox.test(Seq_de$len,Seq_node$len )

    Wilcoxon rank sum test with continuity correction

data:  Seq_de$len and Seq_node$len
W = 3154, p-value = 0.5876
alternative hypothesis: true location shift is not equal to 0
atplot=ggplot(SeqBetweenFix, aes(x=DE, y=AT, fill=DE)) +geom_boxplot()+geom_jitter()+stat_compare_means() +scale_fill_brewer(palette = "Set1")+ theme(legend.position = "none")
atplot

Version Author Date
1bf3145 brimittleman 2020-05-12 
GCplot=ggplot(SeqBetweenFix, aes(x=DE, y=GC,fill=DE)) +geom_boxplot() +geom_jitter()+stat_compare_means()+scale_fill_brewer(palette = "Set1") + theme(legend.position = "none")

GCplot

Version Author Date
1bf3145 brimittleman 2020-05-12

No difference in length, AT, or GC proportion in these.

plot_grid(GCplot, atplot)

Version Author Date
1bf3145 brimittleman 2020-05-12

Expand length analysis to all 9 cutoffs:

BothRes_10=BothRes %>% filter(Chimp_DiffDom >=0.1 | Human_DiffDom>=0.1) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=10) 
BothRes_20=BothRes %>% filter(Chimp_DiffDom >=0.2 | Human_DiffDom>=0.2) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=20)
BothRes_30=BothRes %>% filter(Chimp_DiffDom >=0.3 | Human_DiffDom>=0.3) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=30)
BothRes_40=BothRes %>% filter(Chimp_DiffDom >=0.4 | Human_DiffDom>=0.4) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=40)
BothRes_50=BothRes %>% filter(Chimp_DiffDom >=0.5 | Human_DiffDom>=0.5) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=50)
BothRes_60=BothRes %>% filter(Chimp_DiffDom >=0.6 | Human_DiffDom>=0.6) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=60)
BothRes_70=BothRes %>% filter(Chimp_DiffDom >=0.7 | Human_DiffDom>=0.7) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=70)
BothRes_80=BothRes %>% filter(Chimp_DiffDom >=0.8 | Human_DiffDom>=0.8) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=80)
BothRes_90=BothRes %>% filter(Chimp_DiffDom >=0.9 | Human_DiffDom>=0.9) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=90)

BothResAll=BothRes_10 %>% bind_rows(BothRes_20) %>% bind_rows(BothRes_30) %>% bind_rows(BothRes_40) %>% bind_rows(BothRes_50) %>% bind_rows(BothRes_60) %>% bind_rows(BothRes_70) %>% bind_rows(BothRes_80) %>% bind_rows(BothRes_90)
BothResallDiff= BothResAll %>%
  filter(Set=="Different") %>% 
  select(gene, Chimp_PAS, Human_PAS, cut) %>% 
  gather("species", "PAS", -gene, -cut) %>% 
  inner_join(metaPAS_sm, by=c("gene", "PAS")) %>% 
  mutate(extra="PAS") %>% 
  spread(extra,midpoint) %>% 
  group_by(cut, gene) %>% 
  summarise(minPAS=min(PAS), maxPAS=max(PAS)) %>% 
  mutate(length= maxPAS-minPAS)
  
BothResallDiff$cut=factor(BothResallDiff$cut)
ggplot(BothResallDiff, aes(x=cut, y=length,fill=cut)) +geom_boxplot() + theme(legend.position = "none")+ scale_fill_brewer(palette = "Set1")  

ggplot(BothResallDiff, aes(x=cut, y=log10(length),fill=cut)) +geom_boxplot() + theme(legend.position = "none")+ scale_fill_brewer(palette = "Set1")

Number of genes in each:

BothResallDiff %>% group_by(cut) %>% summarise(n())
# A tibble: 9 x 2
  cut   `n()`
  <fct> <int>
1 10     1256
2 20      605
3 30      310
4 40      182
5 50      118
6 60       75
7 70       39
8 80       27
9 90       12

I can use homer to look for enrichment in the -rna mode:

findMotifsGenome.pl bedfile genome.fa output dir -rna -seqlogo -h -len 8

no background at first


mkdir ../data/DistTwoDom/FindMotif
mkdir ../data/DistTwoDom/mRNAMotif
cut -f 4 ../data/DistTwoDom/SeqBetweenDom_4.bed > ../data/DistTwoDom/SeqBetweenDom_4_genes.txt

sbatch DiffDom_RNAmotif_4.sh

Try running with DE v no DE as background:

Seq_de= SeqBetweenFix %>% filter(DE=="Yes")
Seq_node= SeqBetweenFix %>% filter(DE=="No")

Seq_de_bed= Seq_de %>% select(chr, minPAS, maxPAS, gene, score, strandFix)
write.table(Seq_de_bed, "../data/DistTwoDom/SeqBetweenDom_4_withDE.bed", col.names = F, quote = F, row.names = F, sep="\t")

Seq_node_bed= Seq_node %>% select(chr, minPAS, maxPAS, gene, score, strandFix)
write.table(Seq_node_bed, "../data/DistTwoDom/SeqBetweenDom_4_withNODE.bed", col.names = F, quote = F, row.names = F, sep="\t")
mkdir ../data/DistTwoDom/DE_8

mkdir ../data/DistTwoDom/DE_10

mkdir ../data/DistTwoDom/DE_12


sbatch DiffDom_RNAmotif_4_splitDE.sh

try homer on all pas: rna sites 6 bp

mkdir ../data/PAS_doubleFilter/FindMotif/

mkdir ../data/PAS_doubleFilter/FindMotif_chimp/
sbatch RNAmotif_PAS.sh

sbatch RNAmotif_PAS_chimp.sh


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] stats4    parallel  stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] cowplot_0.9.4       ggpubr_0.2          magrittr_1.5       
 [4] Biostrings_2.50.1   XVector_0.22.0      IRanges_2.16.0     
 [7] S4Vectors_0.20.1    BiocGenerics_0.28.0 forcats_0.3.0      
[10] stringr_1.3.1       dplyr_0.8.0.1       purrr_0.3.2        
[13] readr_1.3.1         tidyr_0.8.3         tibble_2.1.1       
[16] ggplot2_3.1.1       tidyverse_1.2.1     workflowr_1.6.0    

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.4.6       lubridate_1.7.4    lattice_0.20-38   
 [4] utf8_1.1.4         assertthat_0.2.0   rprojroot_1.3-2   
 [7] digest_0.6.18      R6_2.3.0           cellranger_1.1.0  
[10] plyr_1.8.4         backports_1.1.2    evaluate_0.12     
[13] httr_1.3.1         pillar_1.3.1       zlibbioc_1.28.0   
[16] rlang_0.4.0        lazyeval_0.2.1     readxl_1.1.0      
[19] rstudioapi_0.10    whisker_0.3-2      rmarkdown_1.10    
[22] labeling_0.3       munsell_0.5.0      broom_0.5.1       
[25] compiler_3.5.1     httpuv_1.4.5       modelr_0.1.2      
[28] pkgconfig_2.0.2    htmltools_0.3.6    tidyselect_0.2.5  
[31] fansi_0.4.0        crayon_1.3.4       withr_2.1.2       
[34] later_0.7.5        grid_3.5.1         nlme_3.1-137      
[37] jsonlite_1.6       gtable_0.2.0       git2r_0.26.1      
[40] scales_1.0.0       cli_1.1.0          stringi_1.2.4     
[43] fs_1.3.1           promises_1.0.1     xml2_1.2.0        
[46] generics_0.0.2     RColorBrewer_1.1-2 tools_3.5.1       
[49] glue_1.3.0         hms_0.4.2          yaml_2.2.0        
[52] colorspace_1.3-2   rvest_0.3.2        knitr_1.20        
[55] haven_1.1.2