5’ Spice Site strength

Last updated: 2020-04-10

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/TotalVNuclearBothSpecies.Rmd
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    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/pol2.Rmd
    Modified:   analysis/speciesSpecific.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	4feb869	brimittleman	2020-04-10	remove 18499
html	2242e4a	brimittleman	2020-04-06	Build site.
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)

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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── Conflicts ────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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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] 10692

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: 212 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 chimp143287     2
# … with 202 more rows

PAS_5SS_both_filt %>% group_by(PAS) %>% summarise(n=n()) %>% filter(n>1) %>% nrow()

[1] 212

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] 15

nrow(unliftedSS)

[1] 20

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] 10885

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] 10885

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
2242e4a	brimittleman	2020-04-06
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
2242e4a	brimittleman	2020-04-06
5e2ad6d	brimittleman	2020-02-22

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


    Pearson's product-moment correlation

data:  BothSSandReswUsage$ChimpScore and BothSSandReswUsage$HumanScore
t = 473.41, df = 11379, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.9746377 0.9764137
sample estimates:
      cor 
0.9755416

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")

Version	Author	Date
2242e4a	brimittleman	2020-04-06

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


    Pearson's product-moment correlation

data:  BothSSandReswUsage$ChimpScore and BothSSandReswUsage$HumanScore
t = 473.41, df = 11379, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.9746377 0.9764137
sample estimates:
      cor 
0.9755416

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
2242e4a	brimittleman	2020-04-06
5e2ad6d	brimittleman	2020-02-22

cor.test(BothSSandReswUsage$ChimpUsage,BothSSandReswUsage$HumanUsage )


    Pearson's product-moment correlation

data:  BothSSandReswUsage$ChimpUsage and BothSSandReswUsage$HumanUsage
t = 72.57, df = 11379, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.5497949 0.5749167
sample estimates:
      cor 
0.5624856

How many have different score:

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

nrow(BothSSandReswUsage_diff)

[1] 289

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")

Version	Author	Date
2242e4a	brimittleman	2020-04-06

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 
-10.6957  -1.6455  -0.5823   0.5475  12.7219 

Coefficients:
                                        Estimate Std. Error t value
(Intercept)                               0.8902     0.1991   4.471
BothSSandReswUsage_diff_score$DiffUsage   0.6274     3.1032   0.202
                                        Pr(>|t|)    
(Intercept)                             1.12e-05 ***
BothSSandReswUsage_diff_score$DiffUsage     0.84    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 3.103 on 287 degrees of freedom
Multiple R-squared:  0.0001424, Adjusted R-squared:  -0.003341 
F-statistic: 0.04088 on 1 and 287 DF,  p-value: 0.8399

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

5’ Spice Site strength

Briana Mittleman

2/21/2020