5’ Splice site strength

Last updated: 2020-02-20

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Knit directory: apaQTL/analysis/

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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/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

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File	Version	Author	Date	Message
Rmd	d2056c1	brimittleman	2020-02-20	add lcls, add coloc package, add 5’ss by decile
html	38b532c	brimittleman	2020-02-18	Build site.
Rmd	fe3acb2	brimittleman	2020-02-18	add res
html	0fde09f	brimittleman	2020-02-17	Build site.
Rmd	f4a296f	brimittleman	2020-02-17	add initial res for splice site

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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✔ readr   1.3.1       ✔ forcats 0.3.0

── Conflicts ───────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
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library(ggpubr)

Loading required package: magrittr


Attaching package: 'magrittr'

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

    set_names

The following object is masked from 'package:tidyr':

    extract

I will assess the 5’ splice site strength with maxentscore to see if this can tell us anything interesting about intronic polyadenylation.

http://hollywood.mit.edu/burgelab/maxent/Xmaxentscan_scoreseq.html

How to use MaxEntScan::score5ss

Each sequence must be 9 bases long. [3 bases in exon][6 bases in intron] Input sequences as a FastA file with one sequence per line (no linebreaks). Non-ACGT sequences will not be processed.

Example Fasta File

> dummy1
cagGTAAGT
> dummy2 
gagGTAAGT
> dummy3 
taaATAAGT

I assigned PAS to introns. in https://brimittleman.github.io/apaQTL/nucintronicanalysis.html

pas2intron=read.table("../data/intron_analysis/IntronPeaksontoIntrons.bed",col.names = c("intronCHR", "intronStart", "intronEnd", "gene", "score", "strand", "peakCHR", "peakStart", "peakEnd", "PeakID", "meanUsage", "peakStrand")) 

#%>% mutate(PASloc=ifelse(strand=="+", peakEnd, peakStart)) %>% dplyr::select(intronStart, intronEnd, gene, strand, PeakID, PASloc ,meanUsage) %>% mutate(intronLength=intronEnd-intronStart , distance2PAS= ifelse(strand=="+", PASloc-intronStart, intronEnd-PASloc), propIntron=distance2PAS/intronLength)

I need a file with the PAS and the 5’ splice site. For negative strand the 5’ is the end and postitive strand PAS it is the start.

postive: start= start-3 end= start + 6

negative: start= end -6 end= end + 3

mkdir ../data/splicesite

PAS_5SS_pos= pas2intron %>% filter(strand=="+") %>% mutate(start=intronStart-3, end= intronStart +6) %>% select(intronCHR, start,end, PeakID,meanUsage, strand)
PAS_5SS_neg=pas2intron %>% filter(strand=="-") %>% mutate(start=intronEnd-6, end= intronEnd +3) %>% select(intronCHR, start,end, PeakID,meanUsage, strand)
PAS_5SS_both= PAS_5SS_neg %>% bind_rows(PAS_5SS_pos)



write.table(PAS_5SS_pos, "../data/splicesite/TestPosSS.bed", col.names = F, row.names = F, quote=F, sep="\t")

write.table(PAS_5SS_neg, "../data/splicesite/TestNegSS.bed", col.names = F, row.names = F, quote=F, sep="\t")


write.table(PAS_5SS_both, "../data/splicesite/AllPASSS.bed", col.names = F, row.names = F, quote=F, sep="\t")

Merge and sort these to get the nucleotides:

sort -k1,1 -k2,2n ../data/splicesite/AllPASSS.bed > ../data/splicesite/AllPASSS.sort.bed


#cut chr  

sed 's/^chr//' ../data/splicesite/AllPASSS.sort.bed >  ../data/splicesite/AllPASSS.sort.noChr.bed


#bedtools nuc

bedtools nuc -fi /project2/gilad/briana/genome_anotation_data/genome/Homo_sapiens.GRCh37.75.dna_sm.all.fa -bed ../data/splicesite/AllPASSS.sort.noChr.bed -seq -s > ../data/splicesite/AllPASSS.sort.Nuc.txt

This works and it flips the strand. the first 3 bases are the exon and the next 6 are the intron.

I need to turn this into a FA file. with the first 3 lower case and second 6 upper like the example. I can do this in python.

For each PAS i will have the name then the bases in the next

python splicesite2fasta.py

Score online with site and use Maximum Entropy Model.

splice result to keep every other line. Then I can join the reults with the initial bed.

python parseSSres.py

res=read.table("../data/splicesite/MaxIntResParsed.txt", col.names=c("splicesite", "maxentscore"), header=F, stringsAsFactors = F)

bothSS=read.table("../data/splicesite/AllPASSS.sort.noChr.bed", header = F, col.names = c("chr", 'start','end','PAS', "NuclearUsage", 'strand'))


bothandres=bothSS %>% bind_cols(res)

Plot usage and score:

cor.test(bothandres$NuclearUsage, bothandres$maxentscore)


    Pearson's product-moment correlation

data:  bothandres$NuclearUsage and bothandres$maxentscore
t = -3.547, df = 12534, p-value = 0.0003911
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.04914437 -0.01416833
sample estimates:
        cor 
-0.03166605

ggplot(bothandres, aes(x=maxentscore, y=NuclearUsage)) + geom_point() + geom_density2d(col="red")

Version	Author	Date
0fde09f	brimittleman	2020-02-17

Filter usage higher (25%) and score above 0

bothandres_filt= bothandres %>% filter(NuclearUsage>0.25, maxentscore>0)

ggplot(bothandres_filt, aes(x=maxentscore, y=NuclearUsage)) + geom_point() + geom_density2d(col="red") + geom_smooth(method="lm")

Version	Author	Date
0fde09f	brimittleman	2020-02-17

Does not look like there is a relationship here.

Expectation is a stronger 5’ SS means lower intronic usage. I will compare top 10% usage and bottom 10% usage

quantile(bothandres$NuclearUsage,probs=c(.1,.9))

       10%        90% 
0.05653846 0.37990385

bothandres_topbottom = bothandres %>% filter(NuclearUsage<= 0.056 | NuclearUsage >=0.38) %>% mutate(Usage=ifelse(NuclearUsage <=.15, "Low","High"))

ggplot(bothandres_topbottom,aes(x=Usage, y=maxentscore))+ geom_boxplot()

Version	Author	Date
38b532c	brimittleman	2020-02-18

bothandres_top=bothandres_topbottom %>% filter(Usage=="High")
bothandres_bottom=bothandres_topbottom %>% filter(Usage=="Low")

#x to the left of y  
wilcox.test(bothandres_top$maxentscore, bothandres_bottom$maxentscore, alternative="less")


    Wilcoxon rank sum test with continuity correction

data:  bothandres_top$maxentscore and bothandres_bottom$maxentscore
W = 681970, p-value = 0.00136
alternative hypothesis: true location shift is less than 0

top used have lower scores. This is in line with expectation.

Compare to a random set of splice sites. Select 12536

chroms=c("chr1", 'chr2', 'chr3', 'chr4', 'chr5', 'chr6', 'chr7', 'chr8', 'chr9', 'chr10', 'chr11', 'chr12', 'chr13', 'chr14', 'chr15', 'chr16', 'chr17', 'chr18', 'chr19', 'chr20', 'chr21', 'chr22')
allIntron=read.table("/project2/gilad/briana/apaQTL/data/intron_analysis/transcriptsMinusExons.sort.bed", col.names = c("chr","start","end", 'gene', 'score','strand'),header = T, stringsAsFactors = F) %>% filter(chr %in% chroms)

#sampleIntron= allIntron %>% sample_n(12536, replace = F)

Get the 5’ splice site for these:

#randPAS_5SS_pos= sampleIntron %>% filter(strand=="+") %>% mutate(newStart=start-3, newEnd= start +6) %>% select(chr, newStart,newEnd, gene,score, strand)



#randPAS_5SS_neg=sampleIntron %>% filter(strand=="-") %>% mutate(newStart=end-6, newEnd= end +3) %>% select(chr, newStart,newEnd, gene,score, strand)


#randPAS_both= randPAS_5SS_pos %>% bind_rows(randPAS_5SS_neg)


#write.table(randPAS_both,"../data/splicesite/RandomIntronSS.bed",sep="\t", col.names = F, row.names = F, quote = F)

sort -k1,1 -k2,2n ../data/splicesite/RandomIntronSS.bed | sed 's/^chr//' > ../data/splicesite/RandomIntronSS_noChr.bed



#bedtools nuc

bedtools nuc -fi /project2/gilad/briana/genome_anotation_data/genome/Homo_sapiens.GRCh37.75.dna_sm.all.fa -bed ../data/splicesite/RandomIntronSS_noChr.bed -seq -s > ../data/splicesite/RandomIntronSS_noChr.Nuc.bed


python Randomsplicesite2fasta.py

python parseRanodmSSres.py

Eval:

RandomSites=read.table("../data/splicesite/RandomIntronSS.bed",col.names = c('chr','start','end','name','score','strand'))
RandomRes= read.table("../data/splicesite/RandomSSMaxentParsed.txt", col.names = c("splicesite", "maxentscore_cont"), stringsAsFactors = F, header = F)

RandomSitewRes=RandomSites %>% bind_cols(RandomRes)

Compare these to the actual:

RealandCont=as.data.frame(cbind(Control=RandomSitewRes$maxentscore_cont, PAS=bothandres$maxentscore))
RealandContG=RealandCont %>%  gather("set", "score")

ggplot(RealandContG, aes(x=set, y = score,fill=set)) + geom_boxplot() + stat_compare_means()

Version	Author	Date
38b532c	brimittleman	2020-02-18

summary(RandomSitewRes$maxentscore_cont)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-45.450   7.070   8.620   7.477   9.800  11.810

summary(bothandres$maxentscore)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-47.350   7.298   8.760   6.872   9.990  11.810

wilcox.test(RandomSitewRes$maxentscore_cont,bothandres$maxentscore, alternative = "less")


    Wilcoxon rank sum test with continuity correction

data:  RandomSitewRes$maxentscore_cont and bothandres$maxentscore
W = 74942000, p-value = 1.133e-10
alternative hypothesis: true location shift is less than 0

Test if any of the QTLs fall in 5’ splice sites. For this I will look at the 5’ site for every intron:

allIntron_sspos= allIntron %>% filter(strand=="+") %>% mutate(newStart=start-3, newEnd= start +6) %>% select(chr, newStart,newEnd, gene,score, strand)
allIntron_ssneg= allIntron  %>% filter(strand=="-") %>% mutate(newStart=end-6, newEnd= end +3) %>% select(chr, newStart,newEnd, gene,score, strand)

AllIntron_both=allIntron_ssneg %>% bind_rows(allIntron_sspos)

write.table(AllIntron_both, "../data/splicesite/AllIntron5primeSS.bed", col.names = F, row.names = F, quote = F, sep="\t")

sort and intersect with qtl snps.


sort -k1,1 -k2,2n ../data/splicesite/AllIntron5primeSS.bed| sed 's/^chr//' > ../data/splicesite/AllIntron5primeSS_sort.bed

sort -k1,1 -k2,2n ../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.WITHSTRAND.bed | sed '1d' | head -n -1 > ../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.WITHSTRAND.sort.bed

bedtools intersect -wo -a ../data/splicesite/AllIntron5primeSS_sort.bed -b ../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.WITHSTRAND.sort.bed -s > ../data/splicesite/QTLin5SS.txt

1 example.

15 31229459 31229468 FAN1 . + 15 31229462 31229463 FAN1:peak42822:utr3 83 + 1

Intron by presence of intronic

Seperate the introns by splice site strength. First run the nuc on all of them

#../data/splicesite/AllIntron5primeSS_sort.bed



#bedtools nuc

bedtools nuc -fi /project2/gilad/briana/genome_anotation_data/genome/Homo_sapiens.GRCh37.75.dna_sm.all.fa -bed ../data/splicesite/AllIntron5primeSS_sort.bed -seq -s > ../data/splicesite/AllIntron5primeSS_sort_Nuc.bed


python Allsplicesite2fasta.py

python parseALLSSres.py

IntronSites=read.table("../data/splicesite/AllIntron5primeSS_sort.bed", col.names =c('chr','start','end','name','score','strand'))
IntronRes=read.table("../data/splicesite/AllIntron_Parsed.txt", col.names=c("splicesite", "maxentscore") )

bothandres_g= bothandres %>% mutate(intron=paste(chr,start,end,sep=":")) %>% group_by(intron) %>% summarise(nPAS=n())

IntronSiteswRes=IntronSites %>% bind_cols(IntronRes) %>% mutate(intron=paste(chr,start,end,sep=":")) %>% full_join(bothandres_g,by="intron") %>% replace_na(list(nPAS = 0))

Now I need to decide the cutoffs:

ggplot(IntronSiteswRes, aes(x=maxentscore)) + geom_density()

summary(IntronSiteswRes$maxentscore)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-48.470   7.130   8.680   7.514   9.800  11.810

quantile(IntronSiteswRes$maxentscore, seq(0,1, by=.1))

    0%    10%    20%    30%    40%    50%    60%    70%    80%    90% 
-48.47   4.83   6.64   7.54   8.23   8.68   9.10   9.60  10.07  10.51 
  100% 
 11.81

IntronSiteswRes_dec= IntronSiteswRes %>% mutate(decile_rank = ntile(IntronSiteswRes$maxentscore,10))

IntronSiteswRes_decG= IntronSiteswRes_dec%>% group_by(decile_rank) %>% summarise(PAS=sum(nPAS))

ggplot(IntronSiteswRes_decG, aes(x=decile_rank, y=PAS)) +geom_bar(stat="identity") + labs(x="5' Splice Site strength Decile", y= "Number of Intronic PAS", title="Number of intronic PAS by 5' Splice site strength")

hypergeometric significance

x=2116
m=IntronSiteswRes_dec %>% filter(decile_rank == 1) %>% nrow()
n=IntronSiteswRes_dec %>% filter(decile_rank != 1) %>% nrow()
k=sum(IntronSiteswRes_dec$nPAS)
  

#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))

[1] 1400

[1] 2116

phyper(x, m, n, k,lower.tail=F)

[1] 8.065256e-87

This means there is significant enrichment for PAS in introns with the weakest 5’ splice sites.

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  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     highr_0.7       
[25] broom_0.5.1      Rcpp_1.0.2       promises_1.0.1   scales_1.0.0    
[29] backports_1.1.2  jsonlite_1.6     fs_1.3.1         hms_0.4.2       
[33] digest_0.6.18    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   crayon_1.3.4    
[41] whisker_0.3-2    pkgconfig_2.0.2  MASS_7.3-51.1    xml2_1.2.0      
[45] lubridate_1.7.4  assertthat_0.2.0 rmarkdown_1.10   httr_1.3.1      
[49] rstudioapi_0.10  R6_2.3.0         nlme_3.1-137     git2r_0.26.1    
[53] compiler_3.5.1

5’ Splice site strength

Briana Mittleman

2/17/2020

Intron by presence of intronic