Last updated: 2020-02-11

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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
    Modified:   analysis/decayAndStability.Rmd
    Modified:   analysis/miRNAdisrupt.Rmd
    Modified:   analysis/nascenttranscription.Rmd
    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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Rmd 13c1e8f brimittleman 2020-02-11 add examples for binding disruption
html 1c3d3e7 brimittleman 2020-02-10 Build site.
Rmd 9ac4e5f brimittleman 2020-02-10 add upf1
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Rmd fda9908 brimittleman 2020-02-02 add RBP res 
library(workflowr)
This is workflowr version 1.5.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ───────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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✔ readr   1.3.1       ✔ forcats 0.3.0  
── Conflicts ──────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

I will use eClip data from encode to study RNA binding. I will use the results from K562 cells. They do not have data for LCLs.

https://www.encodeproject.org/search/?type=Experiment&status=released&assay_title=eCLIP&biosample_ontology.term_name=K562&assay_title=eCLIP

Downloading the bed files for 25 different proteins.

mkdir ../data/eCLip/

I will search in all of the gene UTRs for each of these. I will see if there is more likely to be an overlap in genes with APA.

I need to cut the CHR from each file.

for i in $(ls ../data/eCLip/*.bed)
do
name=$(echo ${i} | cut -f 4 -d '/' | cut -f 1 -d '.')
sed 's/^chr//' $i >  ../data/eCLip/${name}.noCHR.bed
done

Run overlap for all othese with bedtools. I will make a bedfile with the longest UTR annoation for each gene.

UTR=read.table("../../genome_anotation_data/RefSeq_annotations/ncbiRefSeq_UTR3.sort.bed",col.names = c('chr','start','end','utr','gene', 'score','strand'),stringsAsFactors = F) %>% 
  mutate(UTRlength=end-start) %>% 
  group_by(gene)%>% 
  arrange(desc(UTRlength)) %>% 
  filter(row_number() == 1L) %>% 
  select(chr, start,end, gene, score, strand)

write.table(UTR, "../data/eCLip/UTRregions.bed", row.names = F, col.names = F, quote = F,sep="\t")
sort -k1,1 -k2,2n ../data/eCLip/UTRregions.bed > ../data/eCLip/UTRregions.sort.bed

Merge the regions with the name being the name of the RNA.

cat ../data/eCLip/*.noCHR.bed > ../data/eCLip/ALLRBP.noCHR.bed
sort -k1,1 -k2,2n ../data/eCLip/ALLRBP.noCHR.bed | cut -f 1-6 > ../data/eCLip/ALLRBP.noCHR.sort.bed
cat: ../data/eCLip/ALLRBP.noCHR.bed: input file is output file

Now I can map.Print the distinct RBP in each UTR.

bedtools map -a ../data/eCLip/UTRregions.sort.bed -b ../data/eCLip/ALLRBP.noCHR.sort.bed -c 4 -o distinct -s > ../data/eCLip/AllUTRsMappedallRBP.txt

I will also run this all seperatly for downstream analysis:

sbatch MapAllRBP.sh

I will compare genes with and without QTLs.

QTL_genes=read.table("../data/apaQTLs/NuclearapaQTLGenes.txt",col.names = "gene",stringsAsFactors = F)
QTLTested_genes=read.table("../data/apaQTLs/TestedNuclearapaQTLGenes.txt",col.names = "gene",stringsAsFactors = F) %>% mutate(QTL=ifelse(gene %in% QTL_genes$gene, "Yes","No"))
PHF6=read.table("../data/eCLip/UTRregions_ENCFF016IHL_PHF6.txt",header=F, col.names = c('chr','start','end','gene','score','strand','RBP'),stringsAsFactors = F) %>% inner_join(QTLTested_genes, by='gene') %>% mutate(PHF6=ifelse(RBP=="PHF6_K562_rep01", "Yes","No"))
x=nrow(PHF6 %>% filter(PHF6=="Yes", QTL=="Yes"))
m= nrow(PHF6 %>% filter(PHF6=="Yes"))
n=nrow(PHF6 %>% filter(PHF6!="Yes"))
k=nrow(PHF6 %>% filter(QTL=="Yes"))


#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))
[1] 77
#actual:
x
[1] 77
#pval
phyper(x,m,n,k,lower.tail=F)
[1] 0.8531311

Test for any RBP:

All=read.table("../data/eCLip/AllUTRsMappedallRBP.txt",header=F, col.names = c('chr','start','end','gene','score','strand','RBP'),stringsAsFactors = F)  %>% 
  inner_join(QTLTested_genes, by='gene') %>% 
  mutate(HasRBP=ifelse(RBP!=".", "Yes","No"))
x=nrow(All %>% filter(HasRBP=="Yes", QTL=="Yes"))
m= nrow(All %>% filter(HasRBP=="Yes"))
n=nrow(All %>% filter(HasRBP!="Yes"))
k=nrow(All %>% filter(QTL=="Yes"))


#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))
[1] 423
#actual:
x
[1] 444
#pval
phyper(x,m,n,k,lower.tail=F)
[1] 0.0195104

This means genes with QTLs are enriched for genes with an identified RBP in the its UTR.

Let’s look at this by the location of QTL.

QTL_intron=read.table("../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.bed",stringsAsFactors = F,header = T) %>% 
  separate(name, into=c("gene", "PAS","loc"),sep=":") %>% 
  filter(loc=="intron")
QTL_UTR=read.table("../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.bed",stringsAsFactors = F,header = T) %>% 
  separate(name, into=c("gene", "PAS","loc"),sep=":") %>% 
  filter(loc=="utr3")


QTLTested_intron=read.table("../data/apaQTLs/TestedNuclearapaQTLGenes.txt",col.names = "gene",stringsAsFactors = F) %>% mutate(QTL=ifelse(gene %in% QTL_intron$gene, "Yes","No"))


QTLTested_utr=read.table("../data/apaQTLs/TestedNuclearapaQTLGenes.txt",col.names = "gene",stringsAsFactors = F) %>% mutate(QTL=ifelse(gene %in% QTL_UTR$gene, "Yes","No"))
All_intron=read.table("../data/eCLip/AllUTRsMappedallRBP.txt",header=F, col.names = c('chr','start','end','gene','score','strand','RBP'),stringsAsFactors = F)  %>% 
  inner_join(QTLTested_intron, by='gene') %>% 
  mutate(HasRBP=ifelse(RBP!=".", "Yes","No"))

x=nrow(All_intron %>% filter(HasRBP=="Yes", QTL=="Yes"))
m= nrow(All_intron %>% filter(HasRBP=="Yes"))
n=nrow(All_intron %>% filter(HasRBP!="Yes"))
k=nrow(All_intron %>% filter(QTL=="Yes"))


#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))
[1] 120
#actual:
x
[1] 120
#pval
phyper(x,m,n,k,lower.tail=F)
[1] 0.7092743
All_UTR=read.table("../data/eCLip/AllUTRsMappedallRBP.txt",header=F, col.names = c('chr','start','end','gene','score','strand','RBP'),stringsAsFactors = F)  %>% 
  inner_join(QTLTested_utr, by='gene') %>% 
  mutate(HasRBP=ifelse(RBP!=".", "Yes","No"))

x=nrow(All_UTR %>% filter(HasRBP=="Yes", QTL=="Yes"))
m= nrow(All_UTR %>% filter(HasRBP=="Yes"))
n=nrow(All_UTR %>% filter(HasRBP!="Yes"))
k=nrow(All_UTR %>% filter(QTL=="Yes"))


#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))
[1] 191
#actual:
x
[1] 205
#pval
phyper(x,m,n,k,lower.tail=F)
[1] 0.01927862

This is only significant for genes with QTL’s associated with the 3’ UTR.

Try to find which RBP is driving the assocations.

I want a function that will go through each of the RBPs and test for this enrichment association.

for i in $(ls ../data/eCLip/ENCFF*.small.noCHR.bed)
do
name=$(echo ${i} | cut -f 4 -d '/' | cut -f 1 -d '.')
echo $name >> ../data/eCLip/RBPtested.txt
done
RBP_names=read.table("../data/eCLip/RBPtested.txt", col.names = "RBP",stringsAsFactors = F)
expected=c()
actual=c()
pval=c()
for (RBP in RBP_names$RBP){
RBPfile=read.table(paste("../data/eCLip/UTRregions_", RBP,".txt", sep=""),header=F, col.names = c('chr','start','end','gene','score','strand','RBP'), stringsAsFactors = F) %>%
  inner_join(QTLTested_genes, by='gene') %>% 
  mutate(HasRBP=ifelse(RBP!=".", "Yes","No"))
x=nrow(RBPfile %>% filter(HasRBP=="Yes", QTL=="Yes"))
m= nrow(RBPfile %>% filter(HasRBP=="Yes"))
n=nrow(RBPfile %>% filter(HasRBP!="Yes"))
k=nrow(RBPfile %>% filter(QTL=="Yes"))
expected=c(expected, which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k))))
actual=c(actual,x)
pval=c(pval,phyper(x,m,n,k,lower.tail=F))
}
RBP_names_res= as.data.frame(cbind(RBP=RBP_names$RBP, expected,actual,pval)) %>% separate(RBP, into=c("exp", "protein"),sep="_")

RBP_names_res$pval=as.numeric(as.character(RBP_names_res$pval))

ggplot(RBP_names_res, aes(x=protein, y=-log10(pval),fill=protein))+geom_bar(stat="identity") +theme(legend.position = "none", axis.text.x = element_text(angle = 90)) +labs(title="Enrichment for nuclear apaQTL genes with RBP in UTR",y="-log10(Enrichment pval)") + geom_hline(yintercept = 2)

Version Author Date
1c3d3e7 brimittleman 2020-02-10
bd6d99c brimittleman 2020-02-02

Do this for UTR QTL

expectedUTR=c()
actualUTR=c()
pvalUTR=c()
for (RBP in RBP_names$RBP){
RBPfile=read.table(paste("../data/eCLip/UTRregions_", RBP,".txt", sep=""),header=F, col.names = c('chr','start','end','gene','score','strand','RBP'), stringsAsFactors = F) %>%
  inner_join(QTLTested_utr, by='gene') %>% 
  mutate(HasRBP=ifelse(RBP!=".", "Yes","No"))
x=nrow(RBPfile %>% filter(HasRBP=="Yes", QTL=="Yes"))
m= nrow(RBPfile %>% filter(HasRBP=="Yes"))
n=nrow(RBPfile %>% filter(HasRBP!="Yes"))
k=nrow(RBPfile %>% filter(QTL=="Yes"))
expectedUTR=c(expectedUTR, which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k))))
actualUTR=c(actualUTR,x)
pvalUTR=c(pvalUTR,phyper(x,m,n,k,lower.tail=F))
}
RBP_names_resUTR= as.data.frame(cbind(RBP=RBP_names$RBP, expectedUTR,actualUTR,pvalUTR)) %>% separate(RBP, into=c("exp", "protein"),sep="_")

RBP_names_resUTR$pvalUTR=as.numeric(as.character(RBP_names_resUTR$pvalUTR))

ggplot(RBP_names_resUTR, aes(x=protein, y=-log10(pvalUTR),fill=protein))+geom_bar(stat="identity") +theme(legend.position = "none", axis.text.x = element_text(angle = 90)) +labs(title="Enrichment for 3' UTR nuclear apaQTL genes with RBP in UTR",y="-log10(Enrichment pval)") + geom_hline(yintercept = 2)

Version Author Date
1c3d3e7 brimittleman 2020-02-10
bd6d99c brimittleman 2020-02-02

Intronic:

expectedIntron=c()
actualIntron=c()
pvalIntron=c()
for (RBP in RBP_names$RBP){
RBPfile=read.table(paste("../data/eCLip/UTRregions_", RBP,".txt", sep=""),header=F, col.names = c('chr','start','end','gene','score','strand','RBP'), stringsAsFactors = F) %>%
  inner_join(QTLTested_intron, by='gene') %>% 
  mutate(HasRBP=ifelse(RBP!=".", "Yes","No"))
x=nrow(RBPfile %>% filter(HasRBP=="Yes", QTL=="Yes"))
m= nrow(RBPfile %>% filter(HasRBP=="Yes"))
n=nrow(RBPfile %>% filter(HasRBP!="Yes"))
k=nrow(RBPfile %>% filter(QTL=="Yes"))
expectedIntron=c(expectedIntron, which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k))))
actualIntron=c(actualIntron,x)
pvalIntron=c(pvalIntron,phyper(x,m,n,k,lower.tail=F))
}
RBP_names_resIntron= as.data.frame(cbind(RBP=RBP_names$RBP, expectedIntron,actualIntron,pvalIntron)) %>% separate(RBP, into=c("exp", "protein"),sep="_")

RBP_names_resIntron$pvalIntron=as.numeric(as.character(RBP_names_resIntron$pvalIntron))

ggplot(RBP_names_resIntron, aes(x=protein, y=-log10(pvalIntron),fill=protein))+geom_bar(stat="identity") +theme(legend.position = "none", axis.text.x = element_text(angle = 90)) +labs(title="Enrichment for Intronic nuclear apaQTL genes with RBP in UTR",y="-log10(Enrichment pval)") + geom_hline(yintercept = 2)

Version Author Date
1c3d3e7 brimittleman 2020-02-10
bd6d99c brimittleman 2020-02-02

Looks like the protiens driving this are SAFB and FUS.

FUS: Associated both with RNA splicing and nuclear export.

SAFB: cotranscriptional.

Compare to total apaQTLs:

TotalQTL_genes=read.table("../data/apaQTLs/TotalapaQTLGenes.txt",col.names = "gene",stringsAsFactors = F)
TotalQTLTested_genes=read.table("../data/apaQTLs/TestedTotalapaQTLGenes.txt",col.names = "gene",stringsAsFactors = F) %>% mutate(QTL=ifelse(gene %in% QTL_genes$gene, "Yes","No"))
expectedT=c()
actualT=c()
pvalT=c()
for (RBP in RBP_names$RBP){
RBPfile=read.table(paste("../data/eCLip/UTRregions_", RBP,".txt", sep=""),header=F, col.names = c('chr','start','end','gene','score','strand','RBP'), stringsAsFactors = F) %>%
  inner_join(TotalQTLTested_genes, by='gene') %>% 
  mutate(HasRBP=ifelse(RBP!=".", "Yes","No"))
x=nrow(RBPfile %>% filter(HasRBP=="Yes", QTL=="Yes"))
m= nrow(RBPfile %>% filter(HasRBP=="Yes"))
n=nrow(RBPfile %>% filter(HasRBP!="Yes"))
k=nrow(RBPfile %>% filter(QTL=="Yes"))
expectedT=c(expectedT, which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k))))
actualT=c(actualT,x)
pvalT=c(pvalT,phyper(x,m,n,k,lower.tail=F))
}
RBP_names_resT= as.data.frame(cbind(RBP=RBP_names$RBP, expectedT,actualT,pvalT)) %>% separate(RBP, into=c("exp", "protein"),sep="_")

RBP_names_resT$pvalT=as.numeric(as.character(RBP_names_resT$pvalT))

ggplot(RBP_names_resT, aes(x=protein, y=-log10(pvalT),fill=protein))+geom_bar(stat="identity") +theme(legend.position = "none", axis.text.x = element_text(angle = 90)) +labs(title="Enrichment for total apaQTL genes with RBP in UTR",y="-log10(Enrichment pval)") + geom_hline(yintercept = 2)

Version Author Date
1c3d3e7 brimittleman 2020-02-10
bd6d99c brimittleman 2020-02-02

Total fraction GRWD1 and HNRNPC pop up too

GRWD1: encoded protein may play a critical role in ribosome biogenesis and may also play a role in histone methylation through interactions

HNRNPC: The hnRNPs are RNA binding proteins and they complex with heterogeneous nuclear RNA (hnRNA). These proteins are associated with pre-mRNAs in the nucleus and appear to influence pre-mRNA processing and other aspects of mRNA metabolism and transport

Linked to it:
-CPEB1 - shuttles between nucleus and the cytoplasm (Bava, F.A. et al. (2013) CPEB1 coordinates alternative 3-UTR formation with translational regulation. Nature 495, 121–125) consensus sequence 5’-UUUUUAU-3’

Will most likely have to look for binding motfis for this one.

binding disruption

Binding site disruption

Use the binding sites above

sbatch RBPdisrupt.sh 
NucRes=read.table("../data/eCLip/NuclearQTLoverlap_RBPbinding.txt",col.names = c("snpchr",'snpstart','snpend', 'qtl', 'dist', 'strand', 'chr','start','end', 'rbp','score', 'strndrbp'),stringsAsFactors = F)
NucRes %>% select(qtl) %>% unique() %>% nrow()
[1] 37
TotRes=read.table("../data/eCLip/TotalQTLoverlap_RBPbinding.txt",col.names = c("snpchr",'snpstart','snpend', 'qtl', 'dist', 'strand', 'chr','start','end', 'rbp','score', 'strndrbp'),stringsAsFactors = F)
TotRes %>% select(qtl) %>% unique() %>% nrow()
[1] 26

37 nuclear and 26 total qtls overlap eclip binding.

head(NucRes)
  snpchr snpstart   snpend                   qtl   dist strand chr
1      1  2125171  2125172   FAAP20:peak318:utr3   1830      -   1
2      1  2125171  2125172   FAAP20:peak318:utr3   1830      -   1
3      1  2125171  2125172   FAAP20:peak318:utr3   1830      -   1
4      1  6272492  6272493   RNF207:peak457:utr3  -8763      +   1
5      1  6272492  6272493   RNF207:peak457:utr3  -8763      +   1
6      1 29479003 29479004 SRSF4:peak2215:intron -14895      -   1
     start      end               rbp score strndrbp
1  2125091  2125187    FUS_K562_rep01   200        -
2  2125111  2125192 ZNF800_K562_rep01   200        -
3  2125158  2125213  ABCF1_K562_rep01   200        -
4  6272477  6272514    AQR_K562_rep01   200        +
5  6272478  6272505  GRWD1_K562_rep01   200        +
6 29479001 29479040   UPF1_K562_rep02   200        -

SRSF4 intronic QTL in a UPF1 binding site. UPF1 associated with NMD (https://www.sciencedirect.com/science/article/pii/S2211124718305837). only a qtl in the nuclear fraction.

PRR13 2 UTR variants, in UPF1 binding. total and nuclear QTL. alternative C allele associated with decreased usage of downstream isoform.


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    ggplot2_3.1.1  
 [9] tidyverse_1.2.1 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     labeling_0.3     stringi_1.2.4   
[49] lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1      crayon_1.3.4