Signal Site analysis

Last updated: 2019-09-17

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Knit directory: apaQTL/analysis/

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Rmd	197e7c6	brimittleman	2019-09-17	fix misspell on figures
html	3af0253	brimittleman	2019-09-17	Build site.
Rmd	f8cb7b8	brimittleman	2019-09-17	move inclusive, get numbers for paper
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Rmd	27b11e3	brimittleman	2019-04-23	start signal site analysis

library(seqLogo)

Loading required package: grid

library(tidyverse)

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library(RColorBrewer)
library(workflowr)

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library(reshape2)


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library(gplots)


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In this analysis I will plot the distribution of signal sites upstream of the PAS I have found.

First I use a python script to make a bed file with the 100 base pairs upsream of the PAS:

module load Anaconda3
source activate three-prime-env
mkdir ../data/SignalSiteFiles
python Upstream100Bases_general.py ../data/PAS/APAPAS_GeneLocAnno.5perc.bed ../data/SignalSiteFiles/APAPAS_100up.bed

Now I use bedtools nuc to get the sequence for each of these regions:

sbatch getSeq100up.sh

I can now run the DistPAS2Sig.py which will give me the location for the signal site for each PAS.I am running this with the 12 most common PAS signal sites.

sbatch run_distPAS2Sig.sh

Upload all of the results:

Loc_AATAAA= read.table("../data/SignalSiteFiles/Loc_AATAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AATAAA")
Loc_AAAAAG= read.table("../data/SignalSiteFiles/Loc_AAAAAG_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AAAAAG")
Loc_AATACA= read.table("../data/SignalSiteFiles/Loc_AATACA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AATACA")
Loc_AATAGA= read.table("../data/SignalSiteFiles/Loc_AATAGA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AATAGA")
Loc_AATATA= read.table("../data/SignalSiteFiles/Loc_AATATA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AATATA")
Loc_ACTAAA= read.table("../data/SignalSiteFiles/Loc_ACTAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="ACTAAA")
Loc_AGTAAA= read.table("../data/SignalSiteFiles/Loc_AGTAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AGTAAA")
Loc_ATTAAA= read.table("../data/SignalSiteFiles/Loc_ATTAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="ATTAAA")
Loc_CATAAA= read.table("../data/SignalSiteFiles/Loc_CATAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="CATAAA")
Loc_GATAAA= read.table("../data/SignalSiteFiles/Loc_GATAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="GATAAA")
Loc_TATAAA= read.table("../data/SignalSiteFiles/Loc_TATAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="TATAAA")
Loc_AAAAAA= read.table("../data/SignalSiteFiles/Loc_AAAAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AAAAAA")

Join these together:

AllsiteDF=as.data.frame(rbind(Loc_AATAAA,Loc_AAAAAG,Loc_AATACA,Loc_AATAGA,Loc_AATATA,Loc_ACTAAA,Loc_AGTAAA,Loc_ATTAAA, Loc_GATAAA,Loc_TATAAA,Loc_CATAAA, Loc_AAAAAA))

colourCount = length(unique(AllsiteDF$Site))
getPalette = colorRampPalette(brewer.pal(8, "Set1"))

AllsiteDF_sep = AllsiteDF %>% separate(PAS, int=c("GenePeak", "Location"), sep="_")
ggplot(AllsiteDF_sep, aes(x=Distance2PAS, by=Site, col=Site)) + stat_ecdf() + facet_wrap(~Location)

Version	Author	Date
74d7b8d	brimittleman	2019-09-04
96d85de	brimittleman	2019-07-07

Check to see if any PAS have more than one signal site detected:

AllsiteDFmultsites=AllsiteDF %>% group_by(PAS) %>% mutate(nSites=n()) %>% filter(nSites>1)

First take the perfect match within 50 bp then use the closest.

Write out the AllSite in order to use it in the chooseSignalSite.py script:

write.table(AllsiteDF, file="../data/SignalSiteFiles/AllSignalSite.txt", quote=F, col.names = F, row.names = F, sep="\t")

python chooseSignalSite.py ../data/SignalSiteFiles/AllSignalSite.txt ../data/SignalSiteFiles/AllSignalSite_1perPAS.txt

AllsiteDF_1per=read.table(file="../data/SignalSiteFiles/AllSignalSite_1perPAS.txt", col.names = colnames(AllsiteDF)) %>% mutate(NegCount=-1*as.integer(as.character(Distance2PAS)))

Plot

dist2signalsiteplot=ggplot(AllsiteDF_1per, aes(group=Site, x=NegCount, fill=Site)) + geom_histogram(position="stack",bins=50 ) + labs(x="Distance from PAS", y="N annotated Sites", title="Location of annotated signal sites")  +  scale_fill_manual(values = getPalette(colourCount))
dist2signalsiteplot

Version	Author	Date
74d7b8d	brimittleman	2019-09-04
96d85de	brimittleman	2019-07-07
26058a5	brimittleman	2019-04-24

ggsave(dist2signalsiteplot, file="../output/SignalSitePlot.png")

Saving 7 x 5 in image

Plot with proportion:

allPAS=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.bed", stringsAsFactors = F, col.names = c("chr","start","end","PAS","score","strand"))

AllsiteDF_1per_prop= AllsiteDF_1per %>% group_by(Site,NegCount) %>% summarise(CountperPos=n()) %>% mutate(TotCount=sum(CountperPos),prop=CountperPos/nrow(allPAS))

Plot with prop:

dist2signalsiteplotprop=ggplot(AllsiteDF_1per_prop, aes(group=Site, x=NegCount,y=prop, fill=Site)) + geom_histogram(position="stack",bins=50,stat="identity" ) + labs(x="Distance from PAS", y="Proportion of annotated Sites", title="Location of annotated signal sites",fill="Site Sequence") +  scale_fill_manual(values = getPalette(colourCount))+ theme(text = element_text(size=20, face="bold"),axis.text.x = element_text(size = 16),axis.text.y = element_text(size = 16), legend.position = "bottom",plot.title = element_text(size=22))+ guides(fill=guide_legend(nrow=3,byrow=TRUE))

Warning: Ignoring unknown parameters: binwidth, bins, pad

dist2signalsiteplotprop

Version	Author	Date
74d7b8d	brimittleman	2019-09-04
96d85de	brimittleman	2019-07-07

nrow(AllsiteDF_1per)/nrow(allPAS)

[1] 0.5718249

Seperate by location:

AllsiteDF_1per_sep= AllsiteDF_1per %>%separate(PAS, int=c("GenePeak", "Location"), sep="_")

dist2signalsiteplot_byloc=ggplot(AllsiteDF_1per_sep, aes(group=Site, x=NegCount, fill=Site)) + geom_histogram(position="stack",bins=50 ) + labs(x="Distance from PAS", y="N annotated Sites", title="Location of annotated signal sites") + facet_wrap(~Location)+  scale_fill_manual(values = getPalette(colourCount))

dist2signalsiteplot_byloc

Version	Author	Date
74d7b8d	brimittleman	2019-09-04
96d85de	brimittleman	2019-07-07
9c1dd66	brimittleman	2019-06-13
4eb21ab	brimittleman	2019-04-26

ggsave(dist2signalsiteplot_byloc, file="../output/SignalSitePlotbyLoc.png")

Saving 7 x 5 in image

Proportion:

allPAS_byloc=allPAS %>% separate(PAS,into=c("peakid", "loc"),sep="_") %>% group_by(loc) %>% summarise(nLoc=n())

allPAS_byloc_new=as.data.frame(allPAS_byloc$nLoc %>% t())
colnames(allPAS_byloc_new) = allPAS_byloc$loc



AllsiteDF_1per_sep_INTRON=AllsiteDF_1per_sep %>% filter(Location=="intron") %>%  group_by(Site,NegCount) %>% summarise(CountperPos=n()) %>% mutate(TotCount=sum(CountperPos),prop=CountperPos/(allPAS_byloc_new$intron)) %>% mutate(Cononical=ifelse(Site=="AATAAA", "AATAAA", ifelse(Site=="ATTAAA", "AATTAA", "Other")))

ggplot(AllsiteDF_1per_sep_INTRON, aes(group=Cononical, x=NegCount,y=prop, fill=Cononical)) + geom_histogram(position="stack",bins=5, stat="identity" ) + labs(x="Distance from PAS", y="Proportion of annotated Sites", title="Location of annotated signal sites \nfor Intronic PAS", caption = "Other: AAAAAA, AAAAAG, AATACA, AATAGA,AATATA, ACTAAA, AGTAAA,CATAAA, GATAAA,TATAAA") +  scale_fill_manual(values = getPalette(colourCount))+  theme(text = element_text(size=20, face="bold"),axis.text.x = element_text(size = 16), legend.position = "bottom", axis.text.y = element_text(size = 16),plot.title = element_text(size=22),plot.caption = element_text(hjust = 0,size=10))

Warning: Ignoring unknown parameters: binwidth, bins, pad

Version	Author	Date
4933ed7	brimittleman	2019-09-11
74d7b8d	brimittleman	2019-09-04
9c38a22	brimittleman	2019-07-31
96d85de	brimittleman	2019-07-07
9c1dd66	brimittleman	2019-06-13
ef7ddfe	brimittleman	2019-04-27
4eb21ab	brimittleman	2019-04-26

AllsiteDF_1per_sep_UTR=AllsiteDF_1per_sep %>% filter(Location=="utr3") %>%  group_by(Site,NegCount) %>% summarise(CountperPos=n()) %>% mutate(TotCount=sum(CountperPos),prop=CountperPos/(allPAS_byloc_new$intron)) %>% mutate(Cononical=ifelse(Site=="AATAAA", "AATAAA", ifelse(Site=="ATTAAA", "AATTAA", "Other")))

ggplot(AllsiteDF_1per_sep_UTR, aes(group=Cononical, x=NegCount,y=prop, fill=Cononical)) + geom_histogram(position="stack", stat="identity" ) + labs(x="Distance from PAS", y="Proportion of annotated Sites", title="Location of annotated signal sites \nin UTR",caption = "Other: AAAAAA, AAAAAG, AATACA, AATAGA,AATATA, ACTAAA, AGTAAA,CATAAA, GATAAA,TATAAA") +  scale_fill_manual(values = getPalette(colourCount)) + theme(text = element_text(size=20, face="bold"),axis.text.x = element_text(size = 16), legend.position = "bottom", axis.text.y = element_text(size = 16),plot.title = element_text(size=22),plot.caption = element_text(hjust = 0,size=10))+ guides(fill=guide_legend(nrow=1,byrow=TRUE))

Warning: Ignoring unknown parameters: binwidth, bins, pad

Version	Author	Date
4933ed7	brimittleman	2019-09-11
74d7b8d	brimittleman	2019-09-04
9c38a22	brimittleman	2019-07-31
96d85de	brimittleman	2019-07-07

Propwith=c(nrow(AllsiteDF_1per_sep %>% filter(Location=="intron"))/allPAS_byloc_new$intron,nrow(AllsiteDF_1per_sep %>% filter(Location=="utr3"))/allPAS_byloc_new$utr3,nrow(AllsiteDF_1per_sep %>% filter(Location=="utr5"))/allPAS_byloc_new$utr5,nrow(AllsiteDF_1per_sep %>% filter(Location=="cds"))/allPAS_byloc_new$cds,nrow(AllsiteDF_1per_sep %>% filter(Location=="end"))/allPAS_byloc_new$end)
Locations=c("intron", "utr3", "utr5", "Coding", "Downstream")
propdf=as.data.frame(cbind(Location=Locations,Proportion=Propwith))
propdf$Proportion=as.numeric(as.character(propdf$Proportion))

all=ggplot(propdf,aes(x=Location, y=Proportion, fill=Location)) + geom_bar(stat="identity") + theme(axis.text.x = element_text(angle = 90, hjust = 1))
all

Version	Author	Date
4933ed7	brimittleman	2019-09-11
9c1dd66	brimittleman	2019-06-13
ef7ddfe	brimittleman	2019-04-27
4eb21ab	brimittleman	2019-04-26

AllsiteDF_1per_sep_noncon=AllsiteDF_1per_sep %>% filter(Site != "AATAAA")

Propwithnotcon=c(nrow(AllsiteDF_1per_sep_noncon %>% filter(Location=="intron"))/allPAS_byloc_new$intron,nrow(AllsiteDF_1per_sep_noncon %>% filter(Location=="utr3"))/allPAS_byloc_new$utr3,nrow(AllsiteDF_1per_sep_noncon %>% filter(Location=="utr5"))/allPAS_byloc_new$utr5,nrow(AllsiteDF_1per_sep_noncon %>% filter(Location=="cds"))/allPAS_byloc_new$cds,nrow(AllsiteDF_1per_sep_noncon %>% filter(Location=="end"))/allPAS_byloc_new$end)
Locations=c("intron", "utr3", "utr5", "Coding", "Downstream")
propdf_noncon=as.data.frame(cbind(Location=Locations,Proportion=Propwithnotcon))
propdf_noncon$Proportion=as.numeric(as.character(propdf_noncon$Proportion))

non=ggplot(propdf_noncon,aes(x=Location, y=Proportion, fill=Location)) + geom_bar(stat="identity")+theme(axis.text.x = element_text(angle = 90, hjust = 1))
non

Version	Author	Date
74d7b8d	brimittleman	2019-09-04
96d85de	brimittleman	2019-07-07
9c1dd66	brimittleman	2019-06-13
ef7ddfe	brimittleman	2019-04-27
4eb21ab	brimittleman	2019-04-26

AllsiteDF_1per_sep_con=AllsiteDF_1per_sep %>% filter(Site == "AATAAA")

Propwithcon=c(nrow(AllsiteDF_1per_sep_con %>% filter(Location=="intron"))/allPAS_byloc_new$intron,nrow(AllsiteDF_1per_sep_con %>% filter(Location=="utr3"))/allPAS_byloc_new$utr3,nrow(AllsiteDF_1per_sep_con %>% filter(Location=="utr5"))/allPAS_byloc_new$utr5,nrow(AllsiteDF_1per_sep_con %>% filter(Location=="cds"))/allPAS_byloc_new$cds,nrow(AllsiteDF_1per_sep_con %>% filter(Location=="end"))/allPAS_byloc_new$end)
Locations=c("intron", "utr3", "utr5", "Coding", "Downstream")
propdf_con=as.data.frame(cbind(Location=Locations,Proportion=Propwithcon))
propdf_con$Proportion=as.numeric(as.character(propdf_con$Proportion))

con=ggplot(propdf_con,aes(x=Location, y=Proportion, fill=Location)) + geom_bar(stat="identity")+ theme(axis.text.x = element_text(angle = 90, hjust = 1))
con

Version	Author	Date
74d7b8d	brimittleman	2019-09-04
96d85de	brimittleman	2019-07-07
9c1dd66	brimittleman	2019-06-13
ef7ddfe	brimittleman	2019-04-27
4eb21ab	brimittleman	2019-04-26

plot_grid(all, con, non, labels=c("All PAS", "Cononical PAS", "Non-conical PAS"))

Version	Author	Date
74d7b8d	brimittleman	2019-09-04
96d85de	brimittleman	2019-07-07

Strong evidence PAS

For future analysis I want to have a set of PAS with evidence for a signal site. I want those signal sites upstream 10-50 basepairs.

AllsiteDF_1per_use= AllsiteDF_1per %>% filter(Distance2PAS>10, Distance2PAS<50) %>% separate(PAS,into=c("peakid", "loc"),sep="_") %>% separate(peakid,into=c("Peaknum", "gene"),sep=":") %>% mutate(PAS=paste("peak", Peaknum, sep="")) %>% dplyr::rename("UpstreamDist"=NegCount) %>%  select(PAS, gene, loc, Site, UpstreamDist)

ggplot(AllsiteDF_1per_use, aes(x=loc)) + geom_histogram(stat="count")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Version	Author	Date
74d7b8d	brimittleman	2019-09-04
96d85de	brimittleman	2019-07-07

Write these out for

write.table(AllsiteDF_1per_use, file="../data/PAS/PASwSignalSite.txt", col.names = T, row.names = F, quote = F, sep="\t")

Plot color cononical vs non cononical:

AllsiteDF_1per_prop_col= AllsiteDF_1per_prop %>% mutate(Cononical=ifelse(Site=="AATAAA", "AATAAA", ifelse(Site=="ATTAAA", "AATTAA", "Other")))

dist2signalsiteplotprop=ggplot(AllsiteDF_1per_prop_col, aes(group=Cononical, x=NegCount,y=prop, fill=Cononical)) + geom_histogram(position="stack",bins=50,stat="identity" ) + labs(x="Distance from PAS", y="Proportion of annotated Sites", title="Location of annotated signal sites",fill=" ", caption = "Other: AAAAAA, AAAAAG, AATACA, AATAGA,AATATA, ACTAAA, AGTAAA,CATAAA, GATAAA,TATAAA") +   theme(text = element_text(size=20, face="bold"),axis.text.x = element_text(size = 16), legend.position = "bottom", axis.text.y = element_text(size = 16),plot.title = element_text(size=22),plot.caption = element_text(hjust = 0,size=10))+ guides(fill=guide_legend(nrow=1,byrow=TRUE))  +  scale_fill_manual(values = getPalette(colourCount))

Warning: Ignoring unknown parameters: binwidth, bins, pad

dist2signalsiteplotprop

Version	Author	Date
74d7b8d	brimittleman	2019-09-04
9c38a22	brimittleman	2019-07-31
1a928ed	brimittleman	2019-07-17
16e4212	brimittleman	2019-07-17

#scale_fill_discrete(name="Site", labels=c("AATAAA","ATTAAA", "AAAAAA, AAAAAG, AATACA, AATAGA, AATATA, ACTAAA, AGTAAA,CATAAA, GATAAA,TATAAA")

Enrichment in intron

N intronic that have a PAS:

propwSS_intron=AllsiteDF_1per_use %>% filter(loc=="intron") %>% nrow()

#nuclear PAS
intronPAS=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.bed", col.names = c("chr","start","end", "id", "score", "strand")) %>% separate(id, into=c("pas", "loc"), sep="_") %>% filter(loc=="intron") %>% nrow()


withSS_intron=propwSS_intron/intronPAS
withSS_intron

[1] 0.2477136

intronannotation=read.table("/project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_intron.sort.bed", col.names = c("chr", "start", "end", "loc", "gene", "score", "strand"))%>% mutate(name=paste(gene, loc, strand, sep="_")) %>% select(chr, start, end, name, score, strand)



write.table(intronannotation, "/project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_intronNamed.sort.bed", col.names = F, row.names = F,quote = F, sep="\t")

Compare this to a background:

I need 40 basepair regions in introns.

bedtools makewindows -i src -b /project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_intronNamed.sort.bed -w 40 > /project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_intron.sort_randomIntervals.bed

Make this into a bed file (with strand):

python fix_randomIntron.py

I need to get the sequences for these with bedtools nuc.

bedtools nuc -s -seq -fi /project2/gilad/briana/genome_anotation_data/genome/Homo_sapiens.GRCh37.75.dna_sm.all.fa -bed /project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_intron.sort_randomIntervals.fixed.bed > /project2/gilad/briana/apaQTL/data/SignalSiteFiles/ncbiRefSeq_intron.sort_randomIntervalsSeq.bed

sbatch run_dist2sig_randomintron.sh

Upload all of the results:

Loc_AATAAA_randomIntron= read.table("../data/SignalSiteFiles/Loc_AATAAA_Distance2end_randomIntron.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AATAAA")
Loc_AAAAAG_randomIntron= read.table("../data/SignalSiteFiles/Loc_AAAAAG_Distance2end_randomIntron.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AAAAAG")
Loc_AATACA_randomIntron= read.table("../data/SignalSiteFiles/Loc_AATACA_Distance2end_randomIntron.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AATACA")
Loc_AATAGA_randomIntron= read.table("../data/SignalSiteFiles/Loc_AATAGA_Distance2end_randomIntron.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AATAGA")
Loc_AATATA_randomIntron= read.table("../data/SignalSiteFiles/Loc_AATATA_Distance2end_randomIntron.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AATATA")
Loc_ACTAAA_randomIntron= read.table("../data/SignalSiteFiles/Loc_ACTAAA_Distance2end_randomIntron.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="ACTAAA")
Loc_AGTAAA_randomIntron= read.table("../data/SignalSiteFiles/Loc_AGTAAA_Distance2end_randomIntron.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AGTAAA")
Loc_ATTAAA_randomIntron= read.table("../data/SignalSiteFiles/Loc_ATTAAA_Distance2end_randomIntron.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="ATTAAA")
Loc_CATAAA_randomIntron= read.table("../data/SignalSiteFiles/Loc_CATAAA_Distance2end_randomIntron.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="CATAAA")
Loc_GATAAA_randomIntron= read.table("../data/SignalSiteFiles/Loc_GATAAA_Distance2end_randomIntron.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="GATAAA")
Loc_TATAAA_randomIntron= read.table("../data/SignalSiteFiles/Loc_TATAAA_Distance2end_randomIntron.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="TATAAA")
Loc_AAAAAA_randomIntron= read.table("../data/SignalSiteFiles/Loc_AAAAAA_Distance2end_randomIntron.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AAAAAA")

Join these together:

AllsiteDF_randomIntron=as.data.frame(rbind(Loc_AATAAA_randomIntron,Loc_AAAAAG_randomIntron,Loc_AATACA_randomIntron,Loc_AATAGA_randomIntron,Loc_AATATA_randomIntron,Loc_ACTAAA_randomIntron,Loc_AGTAAA_randomIntron,Loc_ATTAAA_randomIntron, Loc_GATAAA_randomIntron,Loc_TATAAA_randomIntron,Loc_CATAAA_randomIntron, Loc_AAAAAA_randomIntron))

Number of tested sites:

withSS_random=nrow(AllsiteDF_randomIntron)
possiblereg=84432042

propwithRandom=withSS_random/possiblereg
propwithRandom

[1] 0.002426792

Difference in prop test:

prop.test(x=c(withSS_random,propwSS_intron), n=c(possiblereg,intronPAS),alternative = "less" )


    2-sample test for equality of proportions with continuity
    correction

data:  c(withSS_random, propwSS_intron) out of c(possiblereg, intronPAS)
X-squared = 313010, df = 1, p-value < 2.2e-16
alternative hypothesis: less
95 percent confidence interval:
 -1.0000000 -0.2389699
sample estimates:
     prop 1      prop 2 
0.002426792 0.247713593

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] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] cowplot_0.9.4      gplots_3.0.1       reshape2_1.4.3    
 [4] workflowr_1.4.0    RColorBrewer_1.1-2 forcats_0.3.0     
 [7] stringr_1.3.1      dplyr_0.8.0.1      purrr_0.3.2       
[10] readr_1.3.1        tidyr_0.8.3        tibble_2.1.1      
[13] ggplot2_3.1.1      tidyverse_1.2.1    seqLogo_1.48.0    

loaded via a namespace (and not attached):
 [1] gtools_3.8.1       tidyselect_0.2.5   haven_1.1.2       
 [4] lattice_0.20-38    colorspace_1.3-2   generics_0.0.2    
 [7] htmltools_0.3.6    stats4_3.5.1       yaml_2.2.0        
[10] rlang_0.4.0        pillar_1.3.1       glue_1.3.0        
[13] withr_2.1.2        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        caTools_1.17.1.1  
[22] evaluate_0.12      labeling_0.3       knitr_1.20        
[25] highr_0.7          broom_0.5.1        Rcpp_1.0.2        
[28] KernSmooth_2.23-15 backports_1.1.2    scales_1.0.0      
[31] gdata_2.18.0       jsonlite_1.6       fs_1.3.1          
[34] hms_0.4.2          digest_0.6.18      stringi_1.2.4     
[37] rprojroot_1.3-2    bitops_1.0-6       cli_1.1.0         
[40] tools_3.5.1        magrittr_1.5       lazyeval_0.2.1    
[43] crayon_1.3.4       whisker_0.3-2      pkgconfig_2.0.2   
[46] xml2_1.2.0         lubridate_1.7.4    assertthat_0.2.0  
[49] rmarkdown_1.10     httr_1.3.1         rstudioapi_0.10   
[52] R6_2.3.0           nlme_3.1-137       git2r_0.25.2      
[55] compiler_3.5.1

Signal Site analysis

Briana Mittleman

4/23/2019

Strong evidence PAS

Plot color cononical vs non cononical: