Last updated: 2020-02-22
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Knit directory: Comparative_APA/analysis/
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Unstaged changes:
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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_10perc_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] 8146
From 11060 to 8146.
I will only look at the 8146 intronic PAS that map intersect 3 basepairs of the 5’ splice site to an ortho exon.
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: 171 x 2
PAS n
<chr> <int>
1 chimp10327 2
2 chimp108153 2
3 chimp130492 2
4 chimp130494 2
5 chimp132166 2
6 chimp13702 2
7 chimp13703 2
8 chimp13858 2
9 chimp147711 2
10 chimp151277 2
# … with 161 more rows
PAS_5SS_both_filt %>% group_by(PAS) %>% summarise(n=n()) %>% filter(n>1) %>% nrow()
[1] 171
171 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] 13
nrow(unliftedSS)
[1] 18
BADSS= as.data.frame(cbind(PAS=c(liftedSS_wrongsize$PAS,unliftedSS$PAS)))
Remove the 31 that to not lift or lift to the wrong size.
ChimpSS=liftedSS %>% filter(legnth==9) %>% select(-legnth)
nrow(ChimpSS)
[1] 8298
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] 8298
I will look at 8298 PAS.
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")
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)
cor.test(BothSSandReswUsage$ChimpScore,BothSSandReswUsage$HumanScore )
Pearson's product-moment correlation
data: BothSSandReswUsage$ChimpScore and BothSSandReswUsage$HumanScore
t = 446.07, df = 8688, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.9779605 0.9797205
sample estimates:
cor
0.9788586
ggplot(BothSSandReswUsage,aes(x=ChimpUsage, y=HumanUsage)) + geom_point() + geom_density2d(col="green") + geom_smooth(method="lm",col="orange") + annotate("text", label="Pearsons Correlation= 0.58", x=.65,y=.8)
cor.test(BothSSandReswUsage$ChimpUsage,BothSSandReswUsage$HumanUsage )
Pearson's product-moment correlation
data: BothSSandReswUsage$ChimpUsage and BothSSandReswUsage$HumanUsage
t = 65.703, df = 8688, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.5619293 0.5900259
sample estimates:
cor
0.5761478
How many have different score:
BothSSandReswUsage_diff= BothSSandReswUsage %>% filter(ChimpScore!=HumanScore)
nrow(BothSSandReswUsage_diff)
[1] 229
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() + geom_smooth(method="lm")
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
-10.6097 -1.5064 -0.4609 0.6594 12.9320
Coefficients:
Estimate Std. Error t value
(Intercept) 0.7341 0.1951 3.762
BothSSandReswUsage_diff_score$DiffUsage 1.4361 3.1798 0.452
Pr(>|t|)
(Intercept) 0.000215 ***
BothSSandReswUsage_diff_score$DiffUsage 0.651963
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.94 on 227 degrees of freedom
Multiple R-squared: 0.0008978, Adjusted R-squared: -0.003504
F-statistic: 0.204 on 1 and 227 DF, p-value: 0.652
No correlation
Are any of these the differentially used PAS.
Meta=read.table("../data/PAS_doubleFilter/PAS_10perc_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 )
Join:
BothSSandReswUsage_diff_score_iso=BothSSandReswUsage_diff_score %>% inner_join(DiffIsoRes, by="PAS")
BothSSandReswUsage_diff_score_iso %>% filter(SigPAU2=="Yes") %>% select(PAS,HumanScore, HumanUsage, ChimpScore,ChimpUsage)
PAS HumanScore HumanUsage ChimpScore ChimpUsage
1 human34001 9.44 0.11000000 8.27 0.000000000
2 human43606 7.21 0.06833333 7.94 0.115000000
3 human43627 7.21 0.12333333 7.94 0.023333333
4 human70865 10.90 0.07833333 -1.48 0.042500000
5 human86581 6.49 0.08500000 7.31 0.049166667
6 human95658 7.40 0.12666667 7.96 0.028333333
7 human100575 9.46 0.08250000 11.08 0.036666667
8 human114714 8.95 0.06750000 10.03 0.040000000
9 human201213 7.07 0.20583333 -0.68 0.104166667
10 human182838 10.47 0.09250000 8.40 0.166666667
11 human183710 6.62 0.07750000 -1.56 0.055000000
12 human183711 6.62 0.06083333 -1.56 0.047500000
13 human183867 9.46 0.00500000 9.79 0.229166667
14 human213120 7.64 0.07583333 7.23 0.042500000
15 human238691 7.63 0.01666667 3.78 0.243333333
16 human303540 9.72 0.06416667 11.00 0.013333333
17 human324822 9.13 0.58083333 1.37 0.379166667
18 human324822 9.13 0.58083333 1.37 0.379166667
19 human324822 9.13 0.58083333 1.37 0.379166667
20 human324822 9.13 0.58083333 1.37 0.379166667
21 human338065 11.81 0.09916667 11.45 0.009166667
nrow(BothSSandReswUsage_diff_score_iso %>% filter(SigPAU2=="Yes"))
[1] 21
Examples conforming to expectation :
human100575 human86581 human114714 human182838 human238691 human303540
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 colorspace_1.3-2
[5] generics_0.0.2 htmltools_0.3.6 yaml_2.2.0 utf8_1.1.4
[9] rlang_0.4.0 later_0.7.5 pillar_1.3.1 glue_1.3.0
[13] withr_2.1.2 modelr_0.1.2 readxl_1.1.0 plyr_1.8.4
[17] munsell_0.5.0 gtable_0.2.0 cellranger_1.1.0 rvest_0.3.2
[21] evaluate_0.12 labeling_0.3 knitr_1.20 httpuv_1.4.5
[25] fansi_0.4.0 broom_0.5.1 Rcpp_1.0.2 promises_1.0.1
[29] scales_1.0.0 backports_1.1.2 jsonlite_1.6 fs_1.3.1
[33] hms_0.4.2 digest_0.6.18 stringi_1.2.4 grid_3.5.1
[37] rprojroot_1.3-2 cli_1.1.0 tools_3.5.1 magrittr_1.5
[41] lazyeval_0.2.1 crayon_1.3.4 whisker_0.3-2 pkgconfig_2.0.2
[45] MASS_7.3-51.1 xml2_1.2.0 lubridate_1.7.4 assertthat_0.2.0
[49] rmarkdown_1.10 httr_1.3.1 rstudioapi_0.10 R6_2.3.0
[53] nlme_3.1-137 git2r_0.26.1 compiler_3.5.1