Last updated: 2020-02-12

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Unstaged changes:
    Modified:   analysis/ExploreNpas.Rmd
    Modified:   analysis/NuclearSpecIncludeNotTested.Rmd
    Modified:   analysis/PASdescriptiveplots.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/TSS.Rmd
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    Modified:   analysis/nucSpecinEQTLs.Rmd
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    Modified:   analysis/propeQTLs_explained.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/DistPAS2Sig.py
    Modified:   code/Script4NuclearQTLexamples.sh
    Modified:   code/Script4TotalQTLexamples.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/environment.yaml
    Modified:   code/run_qtlFacetBoxplots.sh
    Deleted:    code/test.txt
    Deleted:    reads_graphs.Rmd

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File Version Author Date Message
Rmd b8d5787 brimittleman 2020-02-12 add tissue var and conservation
html 5d29368 brimittleman 2020-01-31 Build site.
Rmd 3a8156f brimittleman 2020-01-31 wflow_publish(c(“analysis/index.Rmd”, “analysis/ConservationPAS.Rmd”))

In this analysis I want to study the conservation of the PAS. I will compare PAS with QTLs and those without. I will use PhyloP score. PhyloP scores for 100 vertibrates are available on the genome browser.

library(workflowr)
This is workflowr version 1.6.0
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library(tidyverse)
── Attaching packages ─────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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library(ggpubr)
Loading required package: magrittr

Attaching package: 'magrittr'
The following object is masked from 'package:purrr':

    set_names
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    extract
mkdir ../data/phylop/

PhyloP: Column #1 contains a one-based position coordinate. Column #2 contains a score showing the posterior probability that the phylogenetic hidden Markov model (HMM) of phastCons is in its most conserved state at that base position. I will use pybigwig to extract the regions I care about.

I will look at the 200 basepairs around each PAS.

pas=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.sort.bed",col.names = c("chr","start","end","name","score","strand")) %>% mutate(newStart=end-100,newEnd=end+100) %>% select(chr,newStart,newEnd, name)

write.table(pas,"../data/phylop/PAS_regions.txt",col.names = F,row.names = F,quote = F,sep="\t")
python extractPACmeanPhyloP.py

Add information about qtl or not:

nucQTL=read.table("../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.txt",header = T) 
phylores=read.table("../data/phylop/PAS_phyloP.txt", col.names = c("chr","start","end","name", "phyloP"), stringsAsFactors = F) %>% drop_na() %>% separate(name,into=c("pasnum","geneid"), sep=":") %>% mutate(PAS=paste("peak",pasnum,sep="")) %>% mutate(HasQTL=ifelse(PAS %in% nucQTL$Peak, "Yes","No"))

41,810 - 41649

lost 161 to NAs

Plot:

ggplot(phylores,aes(x=phyloP, by=HasQTL, fill=HasQTL)) +geom_density(alpha=.5)

Version Author Date
5d29368 brimittleman 2020-01-31 
ggplot(phylores,aes(y=phyloP, x=HasQTL,fill=HasQTL)) + geom_boxplot() + stat_compare_means()+ scale_fill_brewer(palette = "Dark2", name="Signficant")

Version Author Date
5d29368 brimittleman 2020-01-31

With a QTL is lower score. Look at enrichment for negative

x=nrow(phylores %>% filter(HasQTL=="Yes", phyloP<0))
m= nrow(phylores %>% filter(phyloP<0))
n=nrow(phylores %>% filter(phyloP>=0))
k=nrow(phylores %>% filter(HasQTL=="Yes"))


#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))
[1] 140
#actual:
x
[1] 196
#pval
phyper(x,m,n,k,lower.tail=F)
[1] 8.44718e-08

Enriched for regions expected to be rapidly evolving.

Try with smaller regions:

pas100=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.sort.bed",col.names = c("chr","start","end","name","score","strand")) %>% mutate(newStart=end-50,newEnd=end+50) %>% select(chr,newStart,newEnd, name)

write.table(pas,"../data/phylop/PAS100_regions.txt",col.names = F,row.names = F,quote = F,sep="\t")
python extactPAS100meanphyloP.py
phylores100=read.table("../data/phylop/PAS100_phyloP.txt", col.names = c("chr","start","end","name", "phyloP"), stringsAsFactors = F) %>% drop_na() %>% separate(name,into=c("pasnum","geneid"), sep=":") %>% mutate(PAS=paste("peak",pasnum,sep="")) %>% mutate(HasQTL=ifelse(PAS %in% nucQTL$Peak, "Yes","No"))
ggplot(phylores100,aes(x=phyloP, by=HasQTL, fill=HasQTL)) +geom_density(alpha=.5)

Version Author Date
5d29368 brimittleman 2020-01-31 
ggplot(phylores100,aes(y=phyloP, x=HasQTL,fill=HasQTL)) + geom_boxplot() + stat_compare_means()+ scale_fill_brewer(palette = "Dark2", name="Signficant")

Version Author Date
5d29368 brimittleman 2020-01-31 
x=nrow(phylores100 %>% filter(HasQTL=="Yes", phyloP<0))
m= nrow(phylores100 %>% filter(phyloP<0))
n=nrow(phylores100 %>% filter(phyloP>=0))
k=nrow(phylores100 %>% filter(HasQTL=="Yes"))


#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))
[1] 140
#actual:
x
[1] 196
#pval
phyper(x,m,n,k,lower.tail=F)
[1] 8.44718e-08

Exact same results.

Conservation of regions

I will look at the region 50bp upstream of PAS. The upstream 50bp would contain the signal site. I will compare the regions 50upstream to 50 upstream of that.

I can look at genes by number of PAS.

Create the bed regions

upstream: + strand :subtract 50 from start

  • strand :add 50 to end

region higher:

  • strand:
  • strand:
pas50up=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.sort.bed",col.names = c("chr","start","end","name","score","strand")) %>% mutate(newStart=ifelse(strand=="+", start - 50, start), newEnd=ifelse(strand=="+", end, end +50)) %>% select(chr, newStart,newEnd, name, score, strand)


write.table(pas50up,"../data/phylop/PAS_50upregions.bed",col.names = F,row.names = F,quote = F,sep="\t")

pas100up=pas50up %>% mutate(start2=ifelse(strand=="+", newStart-50, newEnd), end2=ifelse(strand=="+", newStart, newEnd + 50)) %>% select(chr, start2,end2, name, score, strand)
write.table(pas100up,"../data/phylop/PAS_extra50upregions.bed",col.names = F,row.names = F,quote = F,sep="\t")
python extractPhylop50up.py
python extractPhylopextra50.py
Phylo50up=read.table("../data/phylop/PAS_50upregions_phylop.txt",stringsAsFactors = F, col.names = c("chr", "start","end", "PAS","PAS_Phylop")) %>% select(PAS, PAS_Phylop)

PhyloControl=read.table("../data/phylop/PAS_extra50upregions_phylop.txt",stringsAsFactors = F, col.names = c("chr", "start","end", "PAS","Control_Phylop")) %>% select(PAS, Control_Phylop)

BothPhylop=Phylo50up %>% inner_join(PhyloControl,by="PAS") %>%
  separate(PAS, into = c("num","geneloc"), sep=":") %>%
  separate(geneloc,into=c("gene",'loc'),sep="_") %>% 
  select(-num, -loc) %>% 
  group_by(gene) %>% 
  mutate(nPAS=n()) %>% 
  ungroup() %>% 
  gather("Set", "Phylop", -gene, -nPAS)


BothPhylop$nPAS=as.factor(BothPhylop$nPAS)
ggplot(BothPhylop, aes(x=nPAS, y=Phylop, by=Set, fill=Set)) + geom_boxplot() + labs(x="Number of PAS", y="Phylop score for PAS", title="Region at PAS conservation vs region upstream") + scale_fill_brewer(palette = "Dark2")
Warning: Removed 233 rows containing non-finite values (stat_boxplot).

Do this where I take the mean per gene.

BothPhylopMean=Phylo50up %>% inner_join(PhyloControl,by="PAS") %>%
  separate(PAS, into = c("num","geneloc"), sep=":") %>%
  separate(geneloc,into=c("gene",'loc'),sep="_") %>% 
  select(-num, -loc) %>% 
  gather("Set", "Phylop", -gene) %>% 
  group_by(gene, Set) %>% 
  summarise(meanPhylop=mean(Phylop),nPAS=n())

BothPhylopMean$nPAS=as.factor(BothPhylopMean$nPAS)
ggplot(BothPhylopMean, aes(x=nPAS, y=meanPhylop, by=Set, fill=Set)) + geom_boxplot() + labs(x="Number of PAS", y="Mean Phylop score for PAS in Gene", title="Region at PAS conservation vs region upstream") + scale_fill_brewer(palette = "Dark2")
Warning: Removed 194 rows containing non-finite values (stat_boxplot).


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   
 [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