5’ Spice Site strength

Last updated: 2020-02-22

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
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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	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 ──

✔ ggplot2 3.1.1       ✔ purrr   0.3.2  
✔ tibble  2.1.1       ✔ dplyr   0.8.0.1
✔ tidyr   0.8.3       ✔ stringr 1.3.1  
✔ readr   1.3.1       ✔ forcats 0.3.0

── Conflicts ─────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

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

5’ Spice Site strength

Briana Mittleman

2/21/2020