Last updated: 2020-02-03

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Unstaged changes:
    Modified:   analysis/LDregress.Rmd
    Modified:   analysis/NuclearSpecIncludeNotTested.Rmd
    Modified:   analysis/PASdescriptiveplots.Rmd
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    Modified:   analysis/nucSpecinEQTLs.Rmd
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    Modified:   code/DistPAS2Sig.py
    Modified:   code/apaQTLsnake.err
    Deleted:    code/test.txt
    Deleted:    reads_graphs.Rmd

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File Version Author Date Message
Rmd 63c435a brimittleman 2020-02-03 add mult TSS results
html 723946b brimittleman 2020-02-03 Build site.
Rmd d0f4f33 brimittleman 2020-02-03 add TSS analysis 
library(tidyverse)
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library(workflowr)
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library(ggpubr)
Loading required package: magrittr

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    extract

I will download encode cage data for TSS evidence in LCLs (GM12878). This cell line is a hapmap female of european ancestry. I will download the bed TSS file for HG19 for the nuclear fraction. I will also download the cytosolic fraction. This way I can ask if some of the isoforms in the nuclear specific overlap.

mkdir ../data/TSS

Nuclear file ENCFF358CEV.bed.gz There are 12344 peaks reported. Cytosolic file ENCFF140PCA.bed.gz There are 10991 peaks reported I will unzip and remove the chr.

gunzip ../data/TSS/ENCFF358CEV.bed.gz
sed 's/^chr//' ../data/TSS/ENCFF358CEV.bed >  ../data/TSS/ENCFF358CEV_Nuclear_noChr.bed
sort -k1,1  -k2,2n ../data/TSS/ENCFF358CEV_Nuclear_noChr.bed  > ../data/TSS/ENCFF358CEV_Nuclear_noChr.sort.bed


gunzip ../data/TSS/ENCFF140PCA.bed.gz
sed 's/^chr//' ../data/TSS/ENCFF140PCA.bed >  ../data/TSS/ENCFF140PCA_Cyto_noChr.bed
sort -k1,1 -k2,2n ../data/TSS/ENCFF140PCA_Cyto_noChr.bed > ../data/TSS/ENCFF140PCA_Cyto_noChr.sort.bed

I will look for nuclear specific TSS using bedtools.

-v in a not in b

bedtools intersect -v -a ../data/TSS/ENCFF358CEV_Nuclear_noChr.sort.bed -b  ../data/TSS/ENCFF140PCA_Cyto_noChr.sort.bed -s -sorted > ../data/TSS/CageSeq_NuclearSpecific.bed

There are 8049 TSS peaks in this set.

cut -f 1-6 ../data/TSS/ENCFF358CEV_Nuclear_noChr.sort.bed  > ../data/TSS/ENCFF358CEV_Nuclear_noChr.sort.small.bed


cut -f 1-6 ../data/TSS/CageSeq_NuclearSpecific.bed > ../data/TSS/CageSeq_NuclearSpecific.small.bed


cut -f 1-6 ../data/TSS/ENCFF140PCA_Cyto_noChr.sort.bed  > ../data/TSS/ENCFF140PCA_Cyto_noChr.sort.small.bed

I need to map these to genes. I will take the longest then extend the start 1000bp to account for changes

genes=read.table("../../genome_anotation_data/RefSeq_annotations/Hg19_refseq_genes.txt",header = T,stringsAsFactors = F) %>%
  mutate(Genelength=txEnd-txStart) %>% 
  group_by(name2) %>% 
  arrange(desc(Genelength)) %>% 
  dplyr::slice(1) %>% 
  mutate(newStart=ifelse(strand=="+", txStart-1000, txStart), newEnd=ifelse(strand=="+",txEnd, txEnd+1000 )) %>% 
  select(chrom,newStart, newEnd, name2,Genelength, strand)

write.table(genes,"../data/TSS/longest_transcript_refseqGene_exd1000.bed", sep="\t", col.names=F, row.names=F, quote=F)

Use bedtools closest to map all of the TSS to genes:

sed 's/^chr//' ../data/TSS/longest_transcript_refseqGene_exd1000.bed | sort -k1,1 -k2,2n > ../data/TSS/longest_transcript_refseqGene_exd1000.noChr.bed

bedtools closest -a ../data/TSS/ENCFF358CEV_Nuclear_noChr.sort.small.bed -b ../data/TSS/longest_transcript_refseqGene_exd1000.noChr.bed -s  > ../data/TSS/ENCFF358CEV_Nuclear_noChr.sort.small_withGene.txt

bedtools closest -a ../data/TSS/CageSeq_NuclearSpecific.small.bed -b ../data/TSS/longest_transcript_refseqGene_exd1000.noChr.bed -s  > ../data/TSS/CageSeq_NuclearSpecific.small_withGene.txt

bedtools closest -a ../data/TSS/ENCFF140PCA_Cyto_noChr.sort.small.bed -b ../data/TSS/longest_transcript_refseqGene_exd1000.noChr.bed  -s  > ../data/TSS/ENCFF140PCA_Cyto_noChr.sort.small_withGene.txt

Nuclear TSS

Colapse to get the number of TSS

TSS=read.table("../data/TSS/ENCFF358CEV_Nuclear_noChr.sort.small_withGene.txt", col.names = c('tsschr','tssstart','tssend','tssName','tssscore','tssstrand', 'genechr','genestart','geneend','gene','length','strand'), stringsAsFactors = F) %>% 
  group_by(gene) %>% 
  summarise(nTSS=n())
PAS=read.table("../data/PAS/APApeak_Peaks_GeneLocAnno.Nuclear.5perc.sort.bed",col.names = c("chr","start","end","name","score","strand")) %>% separate(name,into=c("pas", 'gene','loc'), sep=":") %>% group_by(gene) %>% summarise(nPAS=n())

Join, look only at genes with at least one TSS and PAS in the gene.

TSSandPAS=TSS %>% inner_join(PAS, by="gene")

Looknig at 9896 genes.

ggplot(TSSandPAS,aes(x=nTSS,y=nPAS)) + geom_point()

Correlation:

cor.test(TSSandPAS$nTSS, TSSandPAS$nPAS)

    Pearson's product-moment correlation

data:  TSSandPAS$nTSS and TSSandPAS$nPAS
t = 5.9971, df = 9894, p-value = 2.079e-09
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.04052755 0.07979047
sample estimates:
       cor 
0.06018229

Low but significant correlation.

TSSandPAS_qual=TSSandPAS %>% 
  mutate(multTSS=ifelse(nTSS>1, "Yes","No"), multPAS=ifelse(nPAS >1, "Yes","No"))

are genes with mure than 1 PAS enriched for genes with more than 1 TSS.

x=nrow(TSSandPAS_qual %>% filter(multTSS=="Yes", multPAS=="Yes"))
m= nrow(TSSandPAS_qual %>% filter(multTSS=="Yes"))
n= nrow(TSSandPAS_qual %>% filter(multTSS!="Yes"))
k=nrow(TSSandPAS_qual %>% filter(multPAS=="Yes"))


#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))
[1] 2695
#actual:
x
[1] 2788
#pval
phyper(x,m,n,k,lower.tail=F)
[1] 7.974885e-06

Significant enrichment here.

Look at if the gene has a QTL, is this associated with TSS number.

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"))
TSSandQTL=QTLTested_genes %>% inner_join(TSS,by="gene")

8468 genes
Plot:

ggplot(TSSandQTL,aes(x=QTL, y=nTSS))+ geom_boxplot() + stat_compare_means()

TSSandQTL_filt= TSSandQTL %>% filter(nTSS<=5)

ggplot(TSSandQTL_filt,aes(x=QTL, y=nTSS))+ geom_boxplot() + stat_compare_means()

Small difference but not a strong relationship.

Cytoplasm TSS

Do these analysis with the cytoplasm TSS.

TSS_cyt=read.table("../data/TSS/ENCFF140PCA_Cyto_noChr.sort.small_withGene.txt", col.names = c('tsschr','tssstart','tssend','tssName','tssscore','tssstrand', 'genechr','genestart','geneend','gene','length','strand'), stringsAsFactors = F) %>% 
  group_by(gene) %>% 
  summarise(nTSS=n())

Join, look only at genes with at least one TSS and PAS in the gene.

TSScytandPAS=TSS_cyt %>% inner_join(PAS, by="gene")

Looknig at 8241 genes.

ggplot(TSScytandPAS,aes(x=nTSS,y=nPAS)) + geom_point()

Correlation:

cor.test(TSScytandPAS$nTSS, TSScytandPAS$nPAS)

    Pearson's product-moment correlation

data:  TSScytandPAS$nTSS and TSScytandPAS$nPAS
t = 2.0793, df = 8239, p-value = 0.03762
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.001311819 0.044470876
sample estimates:
       cor 
0.02290202

Low but significant correlation.

TSScytandPAS_qual=TSScytandPAS %>% 
  mutate(multTSS=ifelse(nTSS>1, "Yes","No"), multPAS=ifelse(nPAS >1, "Yes","No"))

are genes with mure than 1 PAS enriched for genes with more than 1 TSS.

x=nrow(TSScytandPAS_qual %>% filter(multTSS=="Yes", multPAS=="Yes"))
m= nrow(TSScytandPAS_qual %>% filter(multTSS=="Yes"))
n= nrow(TSScytandPAS_qual %>% filter(multTSS!="Yes"))
k=nrow(TSScytandPAS_qual %>% filter(multPAS=="Yes"))


#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))
[1] 2097
#actual:
x
[1] 2161
#pval
phyper(x,m,n,k,lower.tail=F)
[1] 0.0005400501

Enrichment is a bit lower but still significant.

Nuclear specific TSS

TSS_nucSpec=read.table("../data/TSS/CageSeq_NuclearSpecific.small_withGene.txt", col.names = c('tsschr','tssstart','tssend','tssName','tssscore','tssstrand', 'genechr','genestart','geneend','gene','length','strand'), stringsAsFactors = F) %>% 
  group_by(gene) %>% 
  summarise(nTSS=n())

Join, look only at genes with at least one TSS and PAS in the gene. Full join here starting with the PAS

TSS_nucSpecandPAS=PAS %>% full_join(TSS_nucSpec, by="gene") %>% replace_na(list(nTSS=0, nPAS=0)) %>% mutate(SpecTSS=ifelse(nTSS>0, "Yes","No"))

Are genes with a nuclear specific tss more likely to have more than one PAS

ggplot(TSS_nucSpecandPAS,aes(x=SpecTSS,y=nPAS)) + geom_boxplot() + stat_compare_means()

No significant difference.


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    workflowr_1.5.0 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  colorspace_1.3-2
 [5] generics_0.0.2   htmltools_0.3.6  yaml_2.2.0       rlang_0.4.0     
 [9] later_0.7.5      pillar_1.3.1     glue_1.3.0       withr_2.1.2     
[13] modelr_0.1.2     readxl_1.1.0     plyr_1.8.4       munsell_0.5.0   
[17] gtable_0.2.0     cellranger_1.1.0 rvest_0.3.2      evaluate_0.12   
[21] labeling_0.3     knitr_1.20       httpuv_1.4.5     broom_0.5.1     
[25] Rcpp_1.0.2       promises_1.0.1   scales_1.0.0     backports_1.1.2 
[29] jsonlite_1.6     fs_1.3.1         hms_0.4.2        digest_0.6.18   
[33] stringi_1.2.4    grid_3.5.1       rprojroot_1.3-2  cli_1.1.0       
[37] tools_3.5.1      lazyeval_0.2.1   crayon_1.3.4     whisker_0.3-2   
[41] pkgconfig_2.0.2  xml2_1.2.0       lubridate_1.7.4  assertthat_0.2.0
[45] rmarkdown_1.10   httr_1.3.1       rstudioapi_0.10  R6_2.3.0        
[49] nlme_3.1-137     git2r_0.26.1     compiler_3.5.1