Compare to annotated PAS

Last updated: 2020-03-23

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/NuclearSpecIncludeNotTested.Rmd
    Modified:   analysis/PASdescriptiveplots.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/TSS.Rmd
    Modified:   analysis/apaQTLoverlap.Rmd
    Modified:   analysis/apabyeQTLstatus.Rmd
    Modified:   analysis/decayAndStability.Rmd
    Modified:   analysis/miRNAdisrupt.Rmd
    Modified:   analysis/nascenttranscription.Rmd
    Modified:   analysis/nucSpecinEQTLs.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/pQTLexampleplot.Rmd
    Modified:   analysis/reads_graphs.Rmd
    Modified:   analysis/splicesitestrength.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/DistPAS2Sig.py
    Modified:   code/Script4NuclearQTLexamples.sh
    Modified:   code/Script4TotalQTLexamples.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/apaqtlfacetboxplots.R
    Modified:   code/environment.yaml
    Modified:   code/run_qtlFacetBoxplots.sh
    Deleted:    code/test.txt
    Deleted:    reads_graphs.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	b93e5d6	brimittleman	2020-03-23	fix sup figs for revision
html	e2df41e	brimittleman	2019-09-17	Build site.
Rmd	197e7c6	brimittleman	2019-09-17	fix misspell on figures
html	74d7b8d	brimittleman	2019-09-04	Build site.
Rmd	41d1961	brimittleman	2019-09-04	wflow_publish(c(“analysis/signalsiteanalysis.Rmd”, “analysis/corrbetweenind.Rmd”,
html	16e4212	brimittleman	2019-07-17	Build site.
Rmd	64bcc48	brimittleman	2019-07-17	fix plots meeting 7.15
html	d48ec93	brimittleman	2019-07-16	Build site.
Rmd	67481df	brimittleman	2019-07-16	frac spec anno and intron Pacbio
html	fb1fde6	brimittleman	2019-07-16	Build site.
Rmd	2572d13	brimittleman	2019-07-16	add compare annotated and additional coverage

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()

library(reshape2)


Attaching package: 'reshape2'

The following object is masked from 'package:tidyr':

    smiths

I will se the annotated PAS from the Tian lab database (http://exon.umdnj.edu/polya_db/v3/misc/download.php)

mkdir ../data/AnnotatedPAS/
  
#file =human.PAS.txt

I want to make this into a file I can overlap with my PAS. In order to know what resolution I should use for calling a PAS the same, I will look for the closest annotated PAS to each of my sites. To do this I will need to create a bed file with these.

python annotatedPAS2bed.py
sort -k1,1 -k2,2n ../data/AnnotatedPAS/human.PAS.bed > ../data/AnnotatedPAS/human.PAS.sort.bed
sort -k1,1 -k2,2n ../data/PAS/APAPAS_GeneLocAnno.5perc.bed > ../data/PAS/APAPAS_GeneLocAnno.5perc.sort.bed
sbatch closestannotated.sh

dist=read.table("../data/AnnotatedPAS/DistanceMyPAS2Anno.bed", col.names = c("chr", "start","end","myPAS", "score","strand","chr2", "start2", "end2", "anno", "score2", "strand2", "distance"),stringsAsFactors = F)

Plot the distance.

ggplot(dist,aes(x=distance))+ geom_histogram(bins=300) + xlim(-25, 25) + labs(y="Number of PAS", x="Distance between PAS and closest annotated")

Warning: Removed 17921 rows containing non-finite values (stat_bin).

Version	Author	Date
74d7b8d	brimittleman	2019-09-04
fb1fde6	brimittleman	2019-07-16

Looks like about 10 basepairs is ok resolution. I need to make sure these map 1 to 1 when you filter these.

PAS_withmatch=dist %>% filter(abs(distance)<=10) %>% select(myPAS,anno) %>% unique() %>% separate(myPAS, into=c("pasNum", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc"), sep="_")

ggplot(PAS_withmatch,aes(x=loc)) + geom_histogram(stat="count")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Version	Author	Date
74d7b8d	brimittleman	2019-09-04
fb1fde6	brimittleman	2019-07-16

I want to look at those I find that they do not.

allMyPAS=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.sort.bed",stringsAsFactors = F, col.names = c("chr","start","end", "PASID", "score","strand"))  %>% separate(PASID, into=c("pasNum", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc"), sep="_") %>% mutate(withAnno=ifelse(pasNum %in% PAS_withmatch$pasNum, "Yes","No"))

PASnoMatch=allMyPAS %>% anti_join(PAS_withmatch,by="pasNum")

ggplot(allMyPAS,aes(x=loc,fill=withAnno)) + geom_histogram(stat="count") + labs(title = "PAS by annotated PAS within 10bp") +scale_fill_brewer(palette = "Dark2")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Version	Author	Date
74d7b8d	brimittleman	2019-09-04
fb1fde6	brimittleman	2019-07-16

Try this as a scatter plot.

allMyPAS_group= allMyPAS %>% group_by(loc, withAnno) %>% summarise(nAnno=n()) %>% ungroup() %>% group_by(loc) %>% mutate(Loctot=sum(nAnno)) %>% ungroup() %>% filter(withAnno=="Yes") %>% mutate(PropAnno=nAnno/Loctot)


ggplot(allMyPAS_group, aes(x=Loctot,col=loc, y=nAnno )) + geom_point(size=3) + labs(x="Number of PAS", y="PAS in Database",color="Location", title="Number of Identified PAS in the database") + scale_color_discrete(labels=c("Coding", "5KB downstream", "Intronic", "3' UTR", "5' UTR"))

Look at total and nuclear seperatly.

python NuclearPAS_5per.bed.py


python TotalPAS_5perc.bed.py
sort -k1,1 -k2,2n ../data/PAS/APApeak_Peaks_GeneLocAnno.Nuclear.5perc.bed > ../data/PAS/APApeak_Peaks_GeneLocAnno.Nuclear.5perc.sort.bed

sort -k1,1 -k2,2n ../data/PAS/APApeak_Peaks_GeneLocAnno.Total.5perc.bed > ../data/PAS/APApeak_Peaks_GeneLocAnno.Total.5perc.sort.bed

Run the distance script with these.

sbatch closestannotated_byfrac.sh

Total

Totaldist=read.table("../data/AnnotatedPAS/Total_DistanceMyPAS2Anno.bed", col.names = c("chr", "start","end","myPAS", "score","strand","chr2", "start2", "end2", "anno", "score2", "strand2", "distance"),stringsAsFactors = F)

ggplot(Totaldist,aes(x=distance))+ geom_histogram(bins=300) + xlim(-25, 25)

Warning: Removed 11926 rows containing non-finite values (stat_bin).

Totaldist_withAnno=Totaldist %>% filter(abs(distance)<=10) %>% select(myPAS,anno) %>% unique() %>% separate(myPAS, into=c("pasNum", "geneID", "loc"), sep=":")

allTotalPAS=read.table("../data/PAS/APApeak_Peaks_GeneLocAnno.Total.5perc.sort.bed",stringsAsFactors = F, col.names = c("chr","start","end", "PASID", "score","strand"))  %>% separate(PASID, into=c("pasNum", "geneID", "loc"), sep=":") %>% mutate(withAnno=ifelse(pasNum %in% Totaldist_withAnno$pasNum, "Yes","No"))

ggplot(allTotalPAS,aes(x=loc,fill=withAnno)) + geom_histogram(stat="count") + labs(title = "TotalPAS by annotated PAS within 10bp") +scale_fill_brewer(palette = "Dark2")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Version	Author	Date
74d7b8d	brimittleman	2019-09-04
d48ec93	brimittleman	2019-07-16

Nuclear

Nucleardist=read.table("../data/AnnotatedPAS/Nuclear_DistanceMyPAS2Anno.bed", col.names = c("chr", "start","end","myPAS", "score","strand","chr2", "start2", "end2", "anno", "score2", "strand2", "distance"),stringsAsFactors = F)

ggplot(Nucleardist,aes(x=distance))+ geom_histogram(bins=300) + xlim(-25, 25)

Warning: Removed 17008 rows containing non-finite values (stat_bin).

Nucleardist_withAnno=Nucleardist %>% filter(abs(distance)<=10) %>% select(myPAS,anno) %>% unique() %>% separate(myPAS, into=c("pasNum", "geneID", "loc"), sep=":")

allNuclearPAS=read.table("../data/PAS/APApeak_Peaks_GeneLocAnno.Nuclear.5perc.sort.bed",stringsAsFactors = F, col.names = c("chr","start","end", "PASID", "score","strand"))  %>% separate(PASID, into=c("pasNum", "geneID", "loc"), sep=":") %>% mutate(withAnno=ifelse(pasNum %in% Nucleardist_withAnno$pasNum, "Yes","No"))

ggplot(allNuclearPAS,aes(x=loc,fill=withAnno)) + geom_histogram(stat="count") + labs(title = "Nuclear PAS by annotated PAS within 10bp") +scale_fill_brewer(palette = "Dark2")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Version	Author	Date
74d7b8d	brimittleman	2019-09-04
d48ec93	brimittleman	2019-07-16

Nuclear specific:

NuclearSpec=allNuclearPAS %>% anti_join(allTotalPAS,by = "pasNum")

ggplot(NuclearSpec,aes(x=loc,fill=withAnno)) + geom_histogram(stat="count") + labs(title = "Nuclear  Specific PAS by annotated PAS within 10bp") +scale_fill_brewer(palette = "Dark2")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Version	Author	Date
e2df41e	brimittleman	2019-09-17
74d7b8d	brimittleman	2019-09-04
d48ec93	brimittleman	2019-07-16

NuclearSpec_group= NuclearSpec %>% group_by(loc, withAnno) %>% summarise(nAnno=n()) %>% ungroup() %>% group_by(loc) %>% mutate(Loctot=sum(nAnno)) %>% ungroup() %>% filter(withAnno=="Yes") %>% mutate(PropAnno=nAnno/Loctot)


ggplot(NuclearSpec_group, aes(x=Loctot,col=loc, y=nAnno )) + geom_point(size=3) + labs(x="Number of PAS", y="PAS in Database",color="Location", title="Number of Nuclear Specific PAS in the database") + scale_color_discrete(labels=c("Coding", "5KB downstream", "Intronic", "3' UTR", "5' UTR"))

Version	Author	Date
e2df41e	brimittleman	2019-09-17
74d7b8d	brimittleman	2019-09-04
16e4212	brimittleman	2019-07-17

usedmoreNuc=read.table("../data/PAS/UsedMoreNuclearPAU2.bed")

Total Specific:

TotalSpec=allTotalPAS %>% anti_join(allNuclearPAS,by = "pasNum")

ggplot(TotalSpec,aes(x=loc,fill=withAnno)) + geom_histogram(stat="count") + labs(title = "Total  Specific PAS by annotated PAS within 10bp") +scale_fill_brewer(palette = "Dark2")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Used more in nuclear:

By usage

NuclearMeanUsage=read.table("../data/PAS/NuclearPASMeanUsage.txt",header = T, stringsAsFactors = F) %>% separate(ID, into=c("chr", "start", "end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PAS"),sep="_")

Warning: Expected 4 pieces. Additional pieces discarded in 3 rows [12630,
12631, 12632].

TotalMeanUsage=read.table("../data/PAS/TotalPASMeanUsage.txt",header = T, stringsAsFactors = F) %>% separate(ID, into=c("chr", "start", "end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PAS"),sep="_")

Warning: Expected 4 pieces. Additional pieces discarded in 3 rows [12630,
12631, 12632].

NuclearMeanUsage_25= NuclearMeanUsage %>% filter(meanUsage >.25)


allNuclearPAS_25= allNuclearPAS %>% mutate(PAS=paste("peak", pasNum,sep="")) %>% anti_join(NuclearMeanUsage_25, by="PAS")

ggplot(allNuclearPAS_25,aes(x=loc,fill=withAnno)) + geom_histogram(stat="count") + labs(title = "Nuclear PAS 25% by annotated PAS within 10bp") +scale_fill_brewer(palette = "Dark2")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Version	Author	Date
e2df41e	brimittleman	2019-09-17
74d7b8d	brimittleman	2019-09-04
16e4212	brimittleman	2019-07-17

NuclearMeanUsage_50= NuclearMeanUsage %>% filter(meanUsage >.5)


allNuclearPAS_50= allNuclearPAS %>% mutate(PAS=paste("peak", pasNum,sep="")) %>% anti_join(NuclearMeanUsage_50, by="PAS")

ggplot(allNuclearPAS_50,aes(x=loc,fill=withAnno)) + geom_histogram(stat="count") + labs(title = "Nuclear PAS 50% by annotated PAS within 10bp") +scale_fill_brewer(palette = "Dark2")

Warning: Ignoring unknown parameters: binwidth, bins, pad

NuclearMeanUsage_75= NuclearMeanUsage %>% filter(meanUsage >.75)


allNuclearPAS_75= allNuclearPAS %>% mutate(PAS=paste("peak", pasNum,sep="")) %>% anti_join(NuclearMeanUsage_75, by="PAS")

ggplot(allNuclearPAS_75,aes(x=loc,fill=withAnno)) + geom_histogram(stat="count") + labs(title = "Nuclear PAS 75% by annotated PAS within 10bp") +scale_fill_brewer(palette = "Dark2")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Proportion of previosly identified:

allNuclearPAS %>% group_by(withAnno) %>% summarise(All=n()) %>% ungroup() %>% mutate(Prop=All/sum(All))

# A tibble: 2 x 3
  withAnno   All  Prop
  <chr>    <int> <dbl>
1 No       17013 0.434
2 Yes      22151 0.566

allNuclearPAS_25 %>% group_by(withAnno) %>% summarise(TwentyFive=n()) %>% ungroup() %>% mutate(Prop=TwentyFive/sum(TwentyFive))

# A tibble: 2 x 3
  withAnno TwentyFive  Prop
  <chr>         <int> <dbl>
1 No            12950 0.580
2 Yes            9377 0.420

allNuclearPAS_50 %>% group_by(withAnno) %>% summarise(Fifty=n()) %>% ungroup() %>% mutate(Prop=Fifty/sum(Fifty))

# A tibble: 2 x 3
  withAnno Fifty  Prop
  <chr>    <int> <dbl>
1 No       15024 0.522
2 Yes      13785 0.478

allNuclearPAS_75 %>% group_by(withAnno) %>% summarise(Seventyfive=n()) %>% ungroup() %>% mutate(Prop=Seventyfive/sum(Seventyfive))

# A tibble: 2 x 3
  withAnno Seventyfive  Prop
  <chr>          <int> <dbl>
1 No             16100 0.484
2 Yes            17182 0.516

Do this for usage 1-100%

withAnno_nuc=function(fraction){
  NuclearMeanUsage_prop= NuclearMeanUsage %>% filter(meanUsage >= fraction)
  allNuclearPAS_prop= allNuclearPAS %>% mutate(PAS=paste("peak", pasNum,sep="")) %>% anti_join(NuclearMeanUsage_prop, by="PAS") %>% group_by(withAnno) %>% summarise(All=n()) %>% ungroup() %>% mutate(Prop=All/sum(All)) %>% filter(withAnno=="Yes")
  #print(paste(fraction,allNuclearPAS_prop$Prop))

return(allNuclearPAS_prop$Prop)
}

propYes=c()

cutoffs=seq(from=.1, to=1, by=.05)
for (val in cutoffs){
  newVal=withAnno_nuc(val)
  propYes=c(propYes,newVal )
}
nucCuttoff=cbind(cutoff=cutoffs,Nuclear=propYes)


withAnno_tot=function(fraction){
  TotalMeanUsage_prop=TotalMeanUsage %>% filter(meanUsage >= fraction)
  allTotalPAS_prop= allTotalPAS %>% mutate(PAS=paste("peak", pasNum,sep="")) %>% anti_join(TotalMeanUsage_prop, by="PAS") %>% group_by(withAnno) %>% summarise(All=n()) %>% ungroup() %>% mutate(Prop=All/sum(All)) %>% filter(withAnno=="Yes")
  #print(paste(fraction,allNuclearPAS_prop$Prop))
return(allTotalPAS_prop$Prop)
}

propYesTot=c()

cutoffs=seq(from=.1, to=1, by=.05)
for (val in cutoffs){
  newVal=withAnno_tot(val)
  propYesTot=c(propYesTot,newVal )
}
AllCuttoff=as.data.frame(cbind(cutoff=cutoffs,Nuclear=propYes, Total=propYesTot))

AllCuttoff_melt=melt(AllCuttoff,id.vars="cutoff", variable.name = "Fraction", value.name = "PropwithAnno")


ggplot(AllCuttoff_melt, aes(x=cutoff, col=Fraction, y= PropwithAnno)) + geom_line(size=2) + scale_color_brewer(palette = "Dark2") + labs(title="Cumulative proportion of PAS in annotation \n by usage filter", y="Proportion in Annotation", x="Usage Filter")

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] reshape2_1.4.3  forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1  
 [5] purrr_0.3.2     readr_1.3.1     tidyr_0.8.3     tibble_2.1.1   
 [9] ggplot2_3.1.1   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] xml2_1.2.0         lubridate_1.7.4    assertthat_0.2.0  
[49] rmarkdown_1.10     httr_1.3.1         rstudioapi_0.10   
[52] R6_2.3.0           nlme_3.1-137       git2r_0.26.1      
[55] compiler_3.5.1

Compare to annotated PAS

Briana Mittleman

7/16/2019

Total

Nuclear

By usage