Last updated: 2020-04-06
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Knit directory: Comparative_APA/analysis/
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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() ──
✖ dplyr::filter() masks stats::filter()
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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