Last updated: 2020-02-10

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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
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/pQTLexampleplot.Rmd
    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/run_qtlFacetBoxplots.sh
    Deleted:    code/test.txt
    Deleted:    reads_graphs.Rmd

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File Version Author Date Message
Rmd 5fbd229 brimittleman 2020-02-10 add results for exosome
html 02266a3 brimittleman 2020-02-10 Build site.
Rmd e792ad9 brimittleman 2020-02-10 add miRNA binding sites
html 34ce932 brimittleman 2020-01-30 Build site.
Rmd 97dec83 brimittleman 2020-01-30 add decay and stability, nmd analysis 
library(workflowr)
This is workflowr version 1.5.0
Run ?workflowr for help getting started
require(data.table)
Loading required package: data.table
library(tidyverse)
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── Conflicts ──────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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In this analysis I want to look as both decay and stability elements. I can see if there are overlaps with apaQTLs of the differentially used between total and nuclear.

NMD

Colombo et al. looked at transcriptome wide identification of NMD-targeted transcripts, in https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5238794/pdf/189.pdf.

Supplemental table 2 has a list of differentailly expressed genes. They say top 1000 are the most significant. This analysis is in hela cells. The meta_meta column has the pvalues used for the final analysis. It is from a combind score from the SMGs and UPF1 data.

Nguyen et al. - Similar analysis in LCLs but from individuals with intelectual disability. The knock down experiment is in hela cells. table 1 has similar studies that used micro arrays

I will use the Colombo set because it is the most recent and comprehensive. This study used RNA seq rather than arrays.

mkdir ../data/NMD

Saved the supplementary table there.

Pull in the apaQTL genes, NMD genes, and Total/nuclear genes.

I will need all of those tested in each set to do the overlap.

NMD=read.table("../data/NMD/NMD_res_Colomboetal.txt",stringsAsFactors = F, header = T) 
NMD_sig= NMD %>% dplyr::slice(1:1000)
apaTested=read.table("../data/apaQTLs/TestedNuclearapaQTLGenes.txt",col.names = c('gene'),stringsAsFactors = F)
apaSig=read.table("../data/apaQTLs/NuclearapaQTLGenes.txt", col.names = c("gene"),stringsAsFactors = F) 

totalTest=read.table("../data/apaQTLs/TestedTotalapaQTLGenes.txt",col.names = c('gene'),stringsAsFactors = F)
totalSig=read.table("../data/apaQTLs/TotalapaQTLGenes.txt", col.names = c("gene"),stringsAsFactors = F) 
#chr10:27035787:27035907:ABI1  
TvNTested=read.table("../data/DiffIso/EffectSizes.txt", header = T,stringsAsFactors = F) %>% separate(intron, into = c("chr", "start","end", "gene"),sep=":")

TvNsig=read.table("../data/highdiffsiggenes.txt",col.names = "gene", stringsAsFactors = F)

Overlap:

Nuclear QTL set

x=length(intersect(apaSig$gene, NMD_sig$gene_name))
m=nrow(NMD_sig) 
n=nrow(NMD)-1000
k=nrow(apaSig)
  

#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))  
[1] 17
#actual:
x
[1] 38
#pval
phyper(x, m, n, k,lower.tail=F)
[1] 5.109008e-06
apaQTL_intron= read.table("../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.txt", header=T,stringsAsFactors = F) %>% filter(Loc=="intron")
apaQTL_utr= read.table("../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.txt", header=T,stringsAsFactors = F) %>% filter(Loc=="utr3")
x=length(intersect(apaQTL_intron$Gene, NMD_sig$gene_name))
m=nrow(NMD_sig) 
n=nrow(NMD)-1000
k=nrow(apaQTL_intron)
  

#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))  
[1] 5
#actual:
x
[1] 13
#pval
phyper(x, m, n, k,lower.tail=F)
[1] 0.001403853
x=length(intersect(apaQTL_utr$Gene, NMD_sig$gene_name))
m=nrow(NMD_sig) 
n=nrow(NMD)-1000
k=nrow(apaQTL_utr)
  

#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))  
[1] 9
#actual:
x
[1] 19
#pval
phyper(x, m, n, k,lower.tail=F)
[1] 0.001398305

Total qtls:

x=length(intersect(totalSig$gene, NMD_sig$gene_name))
m=nrow(NMD_sig) 
n=nrow(NMD)-1000
k=nrow(totalSig)
  
#length(intersect(apaSig$gene, NMD$gene_name))


#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))  
[1] 10
#actual:
x
[1] 23
#pval
phyper(x, m, n, k,lower.tail=F)
[1] 0.0001202593

Nuclear apaQTLs are more enriched for NMD

TVN set

x=length(intersect(TvNsig$gene, NMD_sig$gene_name))
m=nrow(NMD_sig) 
n=nrow(NMD)-1000
k=length(TvNsig$gene)
  
  #length(intersect(TvNsig$gene, NMD$gene_name))


#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))  
[1] 39
#actual:
x
[1] 64
#pval
phyper(x, m, n, k,lower.tail=F)
[1] 4.589876e-05

Look at the expression independent ones:

expInd=read.table("../data/ExpressionIndependentapaQTLs.txt", header = T, stringsAsFactors = F) %>% dplyr::select(Gene) %>% unique()

x=length(intersect(expInd$Gene, NMD_sig$gene_name))
m=nrow(NMD_sig) 
n=nrow(NMD)-1000
k=length(expInd$Gene)
  


#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))  
[1] 2
#actual:
x
[1] 0
#pval
phyper(x, m, n, k,lower.tail=F)
[1] 0.5093779

No overlap here

Stability

From the ARED come back to this.

miRNAs

http://www.targetscan.org/cgi-bin/targetscan/data_download.vert72.cgi

mkdir ../data/miRNAbinding

I downloaded all of the targets and the bedfile with targets. The Targets_CS_pctiles.hg19.consFam.consSite.bed files has the conserved family miRNA and conserved binding sites. This is the most conservative analysis

PAS=read.table("../data/PAS/APApeak_Peaks_GeneLocAnno.Nuclear.5perc.sort.bed", col.names = c("chr",'start','end','id','score','strand')) %>% separate(id, into=c("num",'gene','loc'),sep=":")
miRNAtargets=read.table("../data/miRNAbinding/Targets_CS_pctiles.hg19.consFam.consSite.bed", stringsAsFactors = F,col.names = c("chr", "start", "end", "ID", "score", "strand", "thikStart", "thinkEnd", "RGB", "blockcount", "blocksize", "blockstart")) %>% separate(ID, into= c("gene", "miRNA"), sep=":") %>% filter(gene %in% PAS$gene)


PASGene=PAS %>% select(gene) %>% unique()

I will group by gene and look how many miRNA binding sites.

miRNAtargetsbygene=miRNAtargets %>% group_by(gene) %>% summarise(nMi=n()) %>% full_join(PASGene, by="gene") %>%  mutate(nMi = ifelse(is.na(nMi), 0, nMi), withSite=ifelse(nMi >0, "Yes", "No"),SigAPA=ifelse(gene %in% apaSig$gene,"Yes","No"), TvN=ifelse(gene %in% TvNsig$gene, "Yes","No"))

Are genes with QTLs more likely to have conserved miRNA sites.

x=miRNAtargetsbygene %>% filter(withSite=='Yes', SigAPA=='Yes') %>% nrow()
m=miRNAtargetsbygene %>% filter(withSite=='Yes') %>% nrow()
n=miRNAtargetsbygene %>% filter(withSite=='No') %>% nrow()
k=miRNAtargetsbygene %>% filter(SigAPA=='Yes') %>% nrow()
  

#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))  
[1] 292
#actual:
x
[1] 356
#pval
phyper(x, m, n, k,lower.tail=F)
[1] 5.117716e-08

This is significant. We can say that genes with a QTL are enriched for genes with conserved miRNA target sites.

We expect to this to be stronger with looking at apaQTLs affecting UTR isoforms. The mechanism could be that one isoform is degraded by miRNAs and we only see the other isoform. This is more likely to be a utr mechanism.

miRNAtargetsbyloc=miRNAtargets %>% group_by(gene) %>% summarise(nMi=n()) %>% full_join(PASGene, by="gene") %>%  mutate(nMi = ifelse(is.na(nMi), 0, nMi), withSite=ifelse(nMi >0, "Yes", "No"),Intronic=ifelse(gene %in% apaQTL_intron$Gene, "Yes","No"),UTR=ifelse(gene %in% apaQTL_utr$Gene, "Yes","No"))
x=miRNAtargetsbyloc %>% filter(withSite=='Yes', Intronic=='Yes') %>% nrow()
m=miRNAtargetsbyloc %>% filter(withSite=='Yes') %>% nrow()
n=miRNAtargetsbyloc %>% filter(withSite=='No') %>% nrow()
k=miRNAtargetsbyloc %>% filter(Intronic=='Yes') %>% nrow()
  

#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))  
[1] 87
#actual:
x
[1] 113
#pval
phyper(x, m, n, k,lower.tail=F)
[1] 3.180578e-05
x=miRNAtargetsbyloc %>% filter(withSite=='Yes', UTR=='Yes') %>% nrow()
m=miRNAtargetsbyloc %>% filter(withSite=='Yes') %>% nrow()
n=miRNAtargetsbyloc %>% filter(withSite=='No') %>% nrow()
k=miRNAtargetsbyloc %>% filter(UTR=='Yes') %>% nrow()
  

#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))  
[1] 127
#actual:
x
[1] 150
#pval
phyper(x, m, n, k,lower.tail=F)
[1] 0.001544478

We cannot differentiate between location here either. This is most likely due to the ratios. We are not sure which isoform is actually the one with the mechanism of action and which is reactionary.

Do this with total v nuclear as well.

x=miRNAtargetsbygene %>% filter(withSite=='Yes', TvN=='Yes') %>% nrow()
m=miRNAtargetsbygene %>% filter(withSite=='Yes') %>% nrow()
n=miRNAtargetsbygene %>% filter(withSite=='No') %>% nrow()
k=miRNAtargetsbygene %>% filter(TvN=='Yes') %>% nrow()
  

#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))  
[1] 640
#actual:
x
[1] 762
#pval
phyper(x, m, n, k,lower.tail=F)
[1] 1.092968e-12

This is significant as well. miRNAs may be part of the reason we do not detect as many total transcripts as total transcripts.

Exosome machinery

Knockdown of exosome activity in hela cells. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6499326/

MTR4 and RRP40 knockdown in HeLa cells.

From supplement: Genes with RPM>1 and the ratio of MTR4/control or RRP40/control >1.5 are considered to be significantly accumulated.

I will take the mean for each replicate and compare it to the mean control first tiwht MTR4 then RRP40.

mkdir ../data/exosome
Fandata=read.table("../data/exosome/Fanaldata.csv",header=T,stringsAsFactors = F,sep=",") %>% mutate(meanMt4=rowMeans(select(., contains(".mtr4.KD")), na.rm = TRUE),meanCont=rowMeans(select(., contains("luciferase")), na.rm = TRUE), meanrrp40=rowMeans(select(., contains("rrp40")), na.rm = TRUE) ) %>% select(-contains(".KD"), -Gene, -Type) %>% mutate(MtRatio=meanMt4/meanCont, rrp4Ratio=meanrrp40/meanCont)
    

MT4Up= Fandata %>% filter(meanMt4 > 1, MtRatio > 1.5)
rrp4Up= Fandata %>% filter(meanrrp40 > 1, rrp4Ratio > 1.5)


Fandata_wsig= Fandata %>% mutate(SigMT=ifelse(Name %in% MT4Up$Name, "Yes","No"), Sigrrp4=ifelse(Name %in% rrp4Up$Name, "Yes","No"))

Look in TVN set

MTR4

x=length(intersect(TvNsig$gene, MT4Up$Name))
m=nrow(MT4Up) 
n=Fandata_wsig %>% filter(SigMT=="No") %>% nrow()
k=length(TvNsig$gene)
  

#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))  
[1] 69
#actual:
x
[1] 69
#pval
phyper(x, m, n, k,lower.tail=F)
[1] 1

rrp4

x=length(intersect(TvNsig$gene, rrp4Up$Name))
m=nrow(rrp4Up) 
n=Fandata_wsig %>% filter(Sigrrp4=="No") %>% nrow()
k=length(TvNsig$gene)
  

#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))  
[1] 138
#actual:
x
[1] 138
#pval
phyper(x, m, n, k,lower.tail=F)
[1] 1

No enrichment for these.


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    
 [4] purrr_0.3.2       readr_1.3.1       tidyr_0.8.3      
 [7] tibble_2.1.1      ggplot2_3.1.1     tidyverse_1.2.1  
[10] data.table_1.12.0 workflowr_1.5.0  

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.2       cellranger_1.1.0 plyr_1.8.4       compiler_3.5.1  
 [5] pillar_1.3.1     later_0.7.5      git2r_0.26.1     tools_3.5.1     
 [9] digest_0.6.18    lubridate_1.7.4  jsonlite_1.6     evaluate_0.12   
[13] nlme_3.1-137     gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2 
[17] rlang_0.4.0      cli_1.1.0        rstudioapi_0.10  yaml_2.2.0      
[21] haven_1.1.2      withr_2.1.2      xml2_1.2.0       httr_1.3.1      
[25] knitr_1.20       hms_0.4.2        generics_0.0.2   fs_1.3.1        
[29] rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5 glue_1.3.0      
[33] R6_2.3.0         readxl_1.1.0     rmarkdown_1.10   modelr_0.1.2    
[37] magrittr_1.5     whisker_0.3-2    backports_1.1.2  scales_1.0.0    
[41] promises_1.0.1   htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0
[45] colorspace_1.3-2 httpuv_1.4.5     stringi_1.2.4    lazyeval_0.2.1  
[49] munsell_0.5.0    broom_0.5.1      crayon_1.3.4