Last updated: 2020-04-06

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
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    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/MMExpreiment.Rmd
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    Modified:   analysis/TotalVNuclearBothSpecies.Rmd
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    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/establishCutoffs.Rmd
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    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/speciesSpecific.Rmd

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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.

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Rmd 92f06e4 brimittleman 2020-04-06 update anno
html 5e2ad6d brimittleman 2020-02-22 Build site.
Rmd 7e2fb38 brimittleman 2020-02-22 add ss res
html 5bcde2f brimittleman 2020-02-21 Build site.
Rmd 2e8ed44 brimittleman 2020-02-21 add Splice site strength 
library(workflowr)
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library(tidyverse)
── Attaching packages ─────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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There is a hypothesis that increased 5’ splice site strength is assocaited with decreased usage of intronic PAS. This is relate to competition and binding of the U1 snurp. I will ask if there are differences in 5’ splcie sites for humans and chimp.

Need to be careful about orthology here. To be conservative. I will only look at regions that map downstream of an ortho exon.

First step is to map each intronic PAS to a human intron annotation.

I created a transcript minus exon file for my previos project. I will lift this file over andcheck it. I can remake it

mkdir ../data/SpliceSite  

liftOver /project2/gilad/briana/apaQTL/data/intron_analysis/transcriptsMinusExons.sort.bed  ../data/liftover_files/hg19ToHg38.over.chain.gz ../data/SpliceSite/transcriptMinusexon_hg38.bed ../data/SpliceSite/UnliftedIntron.bed

These look really good, they line up well.

Pull out intronic PAS

PAS_metaIntron=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F) %>% filter(loc=="intron")
PAS=read.table("../data/PAS_doubleFilter/PAS_doublefilter_either_HumanCoordHummanUsage.bed", col.names = c("chr", "start", "end", "PAS", "score", "strand"),stringsAsFactors = F) %>% semi_join(PAS_metaIntron, by="PAS")

write.table(PAS, "../data/SpliceSite/IntronicPAS_humanCoord.bed", col.names = F, row.names = F, quote = F, sep="\t")
sbatch assignPeak2Intronicregion.sh

Get the 5’ splice sites for all of these.

(lose ~800)

PAS2Intron=read.table("../data/SpliceSite/IntronincPAS2Introns_humanCoord.bed",col.names = c("IntronChr", "IntronStart", "IntronEnd", "Gene", "Score", "Strand", "PASChr", "PASStart","PASEnd", "PAS", "humanUsage", "passtrand"),stringsAsFactors = F)

Lost= PAS %>% anti_join(PAS2Intron, by="PAS")

write.table(Lost, "../data/SpliceSite/LostinIntersect.bed", col.names = F, row.names = F, quote =F, sep = "\t")

Lose some with multiple isoforms. Downstream of a gene may be an intron in one. It is probably not possible to get perfect annotation.

PAS2Intron_pos= PAS2Intron %>% filter(Strand=="+") %>% mutate(start=IntronStart-3, end= IntronStart +6) %>% select(IntronChr, start,end, PAS,humanUsage, Strand)
PAS2Intron_neg=PAS2Intron %>% filter(Strand=="-") %>% mutate(start=IntronEnd-6, end= IntronEnd +3) %>% select(IntronChr, start,end, PAS,humanUsage, Strand)
PAS_5SS_both= PAS2Intron_neg %>% bind_rows(PAS2Intron_pos)

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

Sort and assign to ortho exon. I need a small amount of overlap with the human ortho exon file. This comes from Kenneth’s work. /project2/gilad/kenneth/OrthoExonPartialMapping/human.noM.gtf

Ortho exon needs to be converted to bed to intersect.

sort -k1,1 -k2,2n ../data/SpliceSite/IntronicPAS_SS_humanCoord.bed > ../data/SpliceSite/IntronicPAS_SS_humanCoord.sort.bed 


bedtools intersect -a ../data/SpliceSite/IntronicPAS_SS_humanCoord.sort.bed  -b /project2/gilad/kenneth/OrthoExonPartialMapping/human.noM.gtf -s -wao > ../data/SpliceSite/IntronicPAS_SS_intersectOrthoExon.txt

#looking for 3 base overlap with splice sites  

IntersectRes=read.table("../data/SpliceSite/IntronicPAS_SS_intersectOrthoExon.txt",stringsAsFactors = F,sep="\t", col.names = c("chr",'ssstart','ssend','PAS', 'humanusage','passtrand', 'file', 'loc','exonchr', 'enonstart','exonend', 'score', 'strand', 'score2', 'geneinfo', 'overlap')) %>% filter(overlap==3)


IntersectRes_group= IntersectRes %>% group_by(PAS) %>% summarise(nExon=n())
nrow(IntersectRes_group)
[1] 11381

Filter :

PAS_5SS_both_filt= PAS_5SS_both %>% semi_join(IntersectRes_group, by="PAS") 

PAS_5SS_both_filt %>% group_by(PAS) %>% summarise(n=n()) %>% filter(n>1)
# A tibble: 228 x 2
   PAS             n
   <chr>       <int>
 1 chimp10327      2
 2 chimp108153     2
 3 chimp12642      2
 4 chimp130492     2
 5 chimp130494     2
 6 chimp132166     2
 7 chimp13702      2
 8 chimp13703      2
 9 chimp13858      2
10 chimp147711     2
# … with 218 more rows
PAS_5SS_both_filt %>% group_by(PAS) %>% summarise(n=n()) %>% filter(n>1) %>% nrow()
[1] 228

228 map to 2 introns. Count each site for now.

Write these out to sort and liftover.

write.table(PAS_5SS_both_filt, "../data/SpliceSite/IntronicPAS_SS_humanCoord_filterOotho.bed", col.names = F, row.names = F, quote = F, sep="\t")
sort -k1,1 -k2,2n ../data/SpliceSite/IntronicPAS_SS_humanCoord_filterOotho.bed > ../data/SpliceSite/IntronicPAS_SS_humanCoord_filterOotho.sort.bed


liftOver ../data/SpliceSite/IntronicPAS_SS_humanCoord_filterOotho.sort.bed ../data/chainFiles/hg38ToPanTro6.over.chain ../data/SpliceSite/IntronicPAS_SS_ChimpCoord_filterOotho.sort.bed ../data/SpliceSite/ChimpCoordUnliftedSS.txt

Remove unlifted from human

unliftedSS=read.table("../data/SpliceSite/ChimpCoordUnliftedSS.txt",col.names = c("chr", 'start','end','PAS', 'humanscore', 'strand'), stringsAsFactors = F)
#check still 9 bases
liftedSS=read.table("../data/SpliceSite/IntronicPAS_SS_ChimpCoord_filterOotho.sort.bed",col.names = c("chr", 'start','end','PAS', 'humanscore', 'strand'), stringsAsFactors = F) %>% mutate(legnth=end-start) 

liftedSS_wrongsize= liftedSS %>%  filter(legnth!=9)


nrow(liftedSS_wrongsize)
[1] 14
nrow(unliftedSS)
[1] 22
BADSS= as.data.frame(cbind(PAS=c(liftedSS_wrongsize$PAS,unliftedSS$PAS)))

Remove the 36 that to not lift or lift to the wrong size.

ChimpSS=liftedSS %>% filter(legnth==9) %>% select(-legnth)
nrow(ChimpSS)
[1] 11590
HumanSS=PAS_5SS_both_filt %>% anti_join(BADSS, by="PAS")
Warning: Column `PAS` joining character vector and factor, coercing into
character vector
nrow(HumanSS)
[1] 11590

Next step is to use bedtools nuc to get the strand specific basepairs.

write.table(ChimpSS, "../data/SpliceSite/Chimp_SS.bed", col.names = F, row.names = F, quote = F, sep="\t")

write.table(HumanSS, "../data/SpliceSite/Human_SS.bed", col.names = F, row.names = F, quote = F, sep="\t")
sort -k1,1 -k2,2n ../data/SpliceSite/Chimp_SS.bed > ../data/SpliceSite/Chimp_SS_sort.bed

sort -k1,1 -k2,2n ../data/SpliceSite/Human_SS.bed > ../data/SpliceSite/Human_SS.sort.bed

#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

bedtools nuc -fi /project2/gilad/briana/genome_anotation_data/Chimp_genome/panTro6.fa -bed ../data/SpliceSite/Chimp_SS_sort.bed -seq -s > ../data/SpliceSite/Chimp_SS_sort.Nuc.txt

bedtools nuc -fi /project2/gilad/kenneth/References/human/genome/hg38.fa -bed ../data/SpliceSite/Human_SS.sort.bed -seq -s > ../data/SpliceSite/Human_SS.sort.Nuc.txt

#parse
python spliceSite2Fasta.py ../data/SpliceSite/Chimp_SS_sort.Nuc.txt ../data/SpliceSite/Chimp_SS_sort.Nuc.fasta

python spliceSite2Fasta.py ../data/SpliceSite/Human_SS.sort.Nuc.txt ../data/SpliceSite/Human_SS_sort.Nuc.fasta

#run ss maxent  
cd /MaxEntCode/fordownload
perl score5.pl ../../../data/SpliceSite/Chimp_SS_sort.Nuc.fasta >  ../../../data/SpliceSite/Chimp_SS_sort.Nuc.MaxentScores.txt
perl score5.pl  ../../../data/SpliceSite/Human_SS_sort.Nuc.fasta >  ../../../data/SpliceSite/Human_SS_sort.Nuc.MaxentScore.txt
ChimpSS=read.table("../data/SpliceSite/Chimp_SS_sort.bed", col.names = c("chr",'start','end','PAS', 'HumanUsage', 'strand'), stringsAsFactors = F) %>% select(PAS,HumanUsage)
ChimpRES=read.table("../data/SpliceSite/Chimp_SS_sort.Nuc.MaxentScores.txt", col.names =c("Chimpseq", "ChimpScore"))
ChimpSSandRes=ChimpSS %>% bind_cols(ChimpRES)

HumanSS=read.table("../data/SpliceSite/Human_SS.sort.bed", col.names = c("chr",'start','end','PAS', 'HumanUsage', 'strand'), stringsAsFactors = F)%>% select(PAS,HumanUsage)
HumanRES=read.table("../data/SpliceSite/Human_SS_sort.Nuc.MaxentScore.txt", col.names = c("Humanseq", "HumanScore")) 
HumanSSandRes=HumanSS %>% bind_cols(HumanRES)


BothSSandRes= ChimpSSandRes %>% inner_join(HumanSSandRes, by=c('PAS','HumanUsage'))

Add mean chimp

ChimpPASUsage=read.table("../data/PAS_doubleFilter/PAS_doublefilter_either_ChimpCoordChimpUsage.sort.bed",col.names = c('chr','start','end',"PAS", 'ChimpUsage','strand' ),stringsAsFactors = F) %>% select(PAS, ChimpUsage)

BothSSandReswUsage=BothSSandRes %>% inner_join(ChimpPASUsage,by='PAS')
ggplot(BothSSandReswUsage, aes(x=HumanScore, y=HumanUsage)) +geom_point(col="blue", alpha=.3) + geom_point(data=BothSSandReswUsage, aes(x=ChimpScore, y=ChimpUsage), alpha=.3,col="orange")

Version Author Date
5e2ad6d brimittleman 2020-02-22 
ggplot(BothSSandReswUsage,aes(x=ChimpScore, y=HumanScore)) + geom_point() + geom_density2d(col="green") + geom_smooth(method="lm",col="orange") + annotate("text",label="Pearsons Correlation = .98", x=-10, y=10) + labs(title="5' Splice Site Strength for Intronic PAS")+ theme(text= element_text(size=16))

Version Author Date
5e2ad6d brimittleman 2020-02-22 
cor.test(BothSSandReswUsage$ChimpScore,BothSSandReswUsage$HumanScore)

    Pearson's product-moment correlation

data:  BothSSandReswUsage$ChimpScore and BothSSandReswUsage$HumanScore
t = 489.38, df = 12120, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.9747456 0.9764612
sample estimates:
      cor 
0.9756183

Plot this by if the PAS is signfiicantly different

PASMeta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F) %>% dplyr::select(PAS, chr, start,end, gene, loc)

DiffUsagePAS=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(PASMeta, by=c("gene","chr", "start", "end"))  %>% select(PAS, SigPAU2)
BothSSandReswUsage_withSig=BothSSandReswUsage %>% inner_join(DiffUsagePAS, by="PAS")
ggplot(BothSSandReswUsage_withSig,aes(x=ChimpScore, y=HumanScore, col=SigPAU2)) + geom_point(alpha=.5) + annotate("text",label="Pearsons Correlation = .98", x=-10, y=10) + labs(title="5' Splice Site Strength for Intronic PAS",col="Differentially \nused PAS")+ theme(text= element_text(size=16)) + scale_color_brewer(palette = "Dark2")

cor.test(BothSSandReswUsage$ChimpScore,BothSSandReswUsage$HumanScore)

    Pearson's product-moment correlation

data:  BothSSandReswUsage$ChimpScore and BothSSandReswUsage$HumanScore
t = 489.38, df = 12120, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.9747456 0.9764612
sample estimates:
      cor 
0.9756183 
ggplot(BothSSandReswUsage_withSig,aes(x=ChimpUsage, y=HumanUsage,col=SigPAU2)) + geom_point(alpha=.5)  + geom_smooth(method="lm",col="orange") + annotate("text", label="Pearsons Correlation= 0.54", x=.65,y=.8) + theme(text= element_text(size=16)) + scale_color_brewer(palette = "Dark2") + labs(title="Usage of PAS by species",col="Differentially \nused PAS")

Version Author Date
5e2ad6d brimittleman 2020-02-22 
cor.test(BothSSandReswUsage$ChimpUsage,BothSSandReswUsage$HumanUsage )

    Pearson's product-moment correlation

data:  BothSSandReswUsage$ChimpUsage and BothSSandReswUsage$HumanUsage
t = 70.368, df = 12120, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.5258034 0.5510827
sample estimates:
      cor 
0.5385643

How many have different score:

BothSSandReswUsage_diff= BothSSandReswUsage_withSig %>% filter(ChimpScore!=HumanScore)

nrow(BothSSandReswUsage_diff)
[1] 220

229/8298 PAS have different scores in human and chimp

I expect higher scores to have lower usage

Plot difference in score and diff in usage

BothSSandReswUsage_diff_score= BothSSandReswUsage_diff %>% mutate(DiffScore=HumanScore-ChimpScore, DiffUsage=HumanUsage-ChimpUsage)

ggplot(BothSSandReswUsage_diff_score, aes(x=DiffScore, y=DiffUsage)) + geom_point(aes(col=SigPAU2)) + geom_smooth(method="lm") +theme(text= element_text(size=16)) + scale_color_brewer(palette = "Dark2") + labs(title="Relationship between differene in \nusage and difference in 5' SS score",col="Differentially \nused PAS") 

summary(lm(BothSSandReswUsage_diff_score$DiffScore ~ BothSSandReswUsage_diff_score$DiffUsage))

Call:
lm(formula = BothSSandReswUsage_diff_score$DiffScore ~ BothSSandReswUsage_diff_score$DiffUsage)

Residuals:
     Min       1Q   Median       3Q      Max 
-10.6012  -1.5470  -0.5127   0.5640  12.8121 

Coefficients:
                                        Estimate Std. Error t value
(Intercept)                               0.7741     0.2104   3.680
BothSSandReswUsage_diff_score$DiffUsage   0.7280     2.8263   0.258
                                        Pr(>|t|)    
(Intercept)                             0.000294 ***
BothSSandReswUsage_diff_score$DiffUsage 0.796965    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2.907 on 218 degrees of freedom
Multiple R-squared:  0.0003043, Adjusted R-squared:  -0.004281 
F-statistic: 0.06635 on 1 and 218 DF,  p-value: 0.797

No correlation

Are any of these the differentially used PAS.

Meta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F) %>% select(PAS,chr, loc, start, end)

DiffIsoRes=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T,stringsAsFactors = F) %>% inner_join(Meta, by=c("chr", 'start','end')) %>% select(PAS,SigPAU2 )

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_1.6.0

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5   haven_1.1.2        lattice_0.20-38   
 [4] colorspace_1.3-2   generics_0.0.2     htmltools_0.3.6   
 [7] yaml_2.2.0         utf8_1.1.4         rlang_0.4.0       
[10] later_0.7.5        pillar_1.3.1       glue_1.3.0        
[13] withr_2.1.2        RColorBrewer_1.1-2 modelr_0.1.2      
[16] readxl_1.1.0       plyr_1.8.4         munsell_0.5.0     
[19] gtable_0.2.0       cellranger_1.1.0   rvest_0.3.2       
[22] evaluate_0.12      labeling_0.3       knitr_1.20        
[25] httpuv_1.4.5       fansi_0.4.0        broom_0.5.1       
[28] Rcpp_1.0.2         promises_1.0.1     scales_1.0.0      
[31] backports_1.1.2    jsonlite_1.6       fs_1.3.1          
[34] hms_0.4.2          digest_0.6.18      stringi_1.2.4     
[37] grid_3.5.1         rprojroot_1.3-2    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] MASS_7.3-51.1      xml2_1.2.0         lubridate_1.7.4   
[49] assertthat_0.2.0   rmarkdown_1.10     httr_1.3.1        
[52] rstudioapi_0.10    R6_2.3.0           nlme_3.1-137      
[55] git2r_0.26.1       compiler_3.5.1