Compare to annotated PAS

Last updated: 2019-07-16

Checks: 6 1

Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/DiffIsoAnalysis.Rmd
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    Deleted:    code/test.txt

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
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 1.4.0
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()

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

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)

Warning: Removed 20495 rows containing non-finite values (stat_bin).

Version	Author	Date
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
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
fb1fde6	brimittleman	2019-07-16

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 13578 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

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 19400 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

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

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

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_1.4.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0         RColorBrewer_1.1-2 cellranger_1.1.0  
 [4] plyr_1.8.4         compiler_3.5.1     pillar_1.3.1      
 [7] git2r_0.25.2       highr_0.7          tools_3.5.1       
[10] digest_0.6.18      lubridate_1.7.4    jsonlite_1.6      
[13] evaluate_0.12      nlme_3.1-137       gtable_0.2.0      
[16] lattice_0.20-38    pkgconfig_2.0.2    rlang_0.4.0       
[19] cli_1.1.0          rstudioapi_0.10    yaml_2.2.0        
[22] haven_1.1.2        withr_2.1.2        xml2_1.2.0        
[25] httr_1.3.1         knitr_1.20         hms_0.4.2         
[28] generics_0.0.2     fs_1.3.1           rprojroot_1.3-2   
[31] grid_3.5.1         tidyselect_0.2.5   glue_1.3.0        
[34] R6_2.3.0           readxl_1.1.0       rmarkdown_1.10    
[37] modelr_0.1.2       magrittr_1.5       whisker_0.3-2     
[40] backports_1.1.2    scales_1.0.0       htmltools_0.3.6   
[43] rvest_0.3.2        assertthat_0.2.0   colorspace_1.3-2  
[46] labeling_0.3       stringi_1.2.4      lazyeval_0.2.1    
[49] munsell_0.5.0      broom_0.5.1        crayon_1.3.4

Compare to annotated PAS

Briana Mittleman

7/16/2019

Total

Nuclear