Last updated: 2019-08-27

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/NuclearSpecAPAqtl.Rmd
    Modified:   analysis/PAS_graphs.Rmd
    Modified:   analysis/PrematureTermQTL.Rmd
    Modified:   analysis/compareAnnotatedpas.Rmd
    Modified:   analysis/nucSpecinEQTLs.Rmd
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    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
    Modified:   code/apaQTLCorrectPvalMakeQQ.R
    Modified:   code/apaQTL_Nominal.sh
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    Modified:   code/bed2saf.py
    Modified:   code/cluster.json
    Modified:   code/clusterfiltPAS.json
    Modified:   code/config.yaml
    Modified:   code/environment.yaml
    Modified:   code/makePheno.py
    Modified:   code/mergeAllBam.sh
    Modified:   code/mergeByFracBam.sh
    Modified:   code/mergePeaks.sh
    Modified:   code/peakFC.sh
    Modified:   code/snakemake.batch
    Modified:   code/snakemakePAS.batch
    Modified:   code/snakemakefiltPAS.batch
    Modified:   code/submit-snakemake.sh
    Modified:   code/submit-snakemakePAS.sh
    Modified:   code/submit-snakemakefiltPAS.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 21b9ef6 brimittleman 2019-08-27 add 7As
html 4cd5f28 brimittleman 2019-08-26 Build site.
Rmd 5289aa1 brimittleman 2019-08-26 add extra MP qc 
library(tidyverse)
── Attaching packages ───────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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── Conflicts ──────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
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library(cowplot)

Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':

    ggsave

After looking at PAC bio long read seq for 5 of the lines we are concered 3’ seq is not base pair specific. This means we are removing misprimmed reads but we may still have PAS with evidence for misprimming. In order to account for this in my analysis I will flag these PAS and rerun the QTL analysis.

I will look for a stretch of 6 As in the 20bp downstream of the PAS. I will look at the 10 basepairs downstream of ../data/PAS/APAPAS_GeneLocAnno.5perc.sort.bed. These are corrected for strand and have the specific base for the PAS.

I will make a new bed file with the 20 basepiars downstream.

mkdir ../data/PAS_postFlag/
python fiftyBPupstreamPAS.py
python tenBPupstreamPAS.py
python fifteenBPupstreamPAS.py

sort -k1,1n -k2,2 ../data/PAS_postFlag/APAPAS_GeneLocAnno.5perc.10down.bed > ../data/PAS_postFlag/APAPAS_GeneLocAnno.5perc.10down.sort.bed

sort -k1,1n -k2,2 ../data/PAS_postFlag/APAPAS_GeneLocAnno.5perc.50down.bed > ../data/PAS_postFlag/APAPAS_GeneLocAnno.5perc.50down.sort.bed

sort -k1,1n -k2,2 ../data/PAS_postFlag/APAPAS_GeneLocAnno.5perc.15down.bed > ../data/PAS_postFlag/APAPAS_GeneLocAnno.5perc.15down.sort.bed

Use bedtools nuc to get the nucleotide for these positions.

bedtools nuc -s -seq -fi /project2/gilad/briana/genome_anotation_data/genome/Homo_sapiens.GRCh37.75.dna_sm.all.fa -bed ../data/PAS_postFlag/APAPAS_GeneLocAnno.5perc.50down.sort.bed > ../data/PAS_postFlag/APAPAS_GeneLocAnno.5perc.50downNUC.txt

bedtools nuc -s -seq -fi /project2/gilad/briana/genome_anotation_data/genome/Homo_sapiens.GRCh37.75.dna_sm.all.fa -bed ../data/PAS_postFlag/APAPAS_GeneLocAnno.5perc.10down.sort.bed > ../data/PAS_postFlag/APAPAS_GeneLocAnno.5perc.10downNUC.txt

bedtools nuc -s -seq -fi /project2/gilad/briana/genome_anotation_data/genome/Homo_sapiens.GRCh37.75.dna_sm.all.fa -bed ../data/PAS_postFlag/APAPAS_GeneLocAnno.5perc.15down.sort.bed > ../data/PAS_postFlag/APAPAS_GeneLocAnno.5perc.15downNUC.txt

python filterMPPAS.py
python filterMPPAS_50.py
python filterMPPAS_15.py

PAS: 43963

6 A’s in 50 upstream: 37682 6 A’s in 50 upstream or 70% As: 37671

6 A’s in 40 upstream: 38031 6 A’s in 40 upstream or 70% As: 38005

6 A’s in 30 upstream: 38403 6 A’s in 30 upstream or 70% As: 38313

6 A’s in 20 upstream: 38863 6 A’s in 20 upstream or 70% As: 38280 39991

6 A’s in 10 upstream: 42802 6 A’s in 10 upstream or 70% As: 42356

graph results:

flagstats=read.csv("../data/PAS_postFlag/potentialMPPAS.csv", head=T) %>% mutate(PropPAS=FilteredPAS/All_PAS)
ggplot(flagstats, aes(x=N_basepair, y=PropPAS, group=Filter, fill=Filter)) + geom_bar(stat="identity", position="dodge") + labs(x="Number of Basepairs downstream of PAS", y="Proportion of PAS passing filter", title="Proportions of PAS passing various cutoffs")  + 
  geom_text(aes(label = round(flagstats$PropPAS,digits=2), hjust = 1.5), position = position_dodge(width = 10), 
            angle = 90)

Version Author Date
4cd5f28 brimittleman 2019-08-26 
ggplot(flagstats, aes(x=N_basepair, y=FilteredPAS, group=Filter, fill=Filter)) + geom_bar(stat="identity", position="dodge")

Version Author Date
4cd5f28 brimittleman 2019-08-26

I want to see if any of the filtered out PAS are nuclear apaQTLs. I will need to 10,50,15. I can do this with filter join.

bednames=c("chr", "start", "end", "name", "score","strand")
PAS=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.sort.bed", col.names = bednames, stringsAsFactors = F)
PAS_50=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.sort.50noMP.bed", col.names = bednames, stringsAsFactors = F) 
PAS_15=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.sort.15noMP.bed", col.names = bednames, stringsAsFactors = F)
PAS_10=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.sort.10noMP.bed", col.names = bednames, stringsAsFactors = F)
Removed50=PAS %>% anti_join(PAS_50, by=c("chr", "start", "end", "name", "score","strand")) %>% separate(name,into=c("PAS","gene"), sep=":") %>% mutate(Peak=paste("peak", PAS,sep=""))
Removed15=PAS %>% anti_join(PAS_15, by=c("chr", "start", "end", "name", "score","strand"))%>% separate(name,into=c("PAS","gene"), sep=":") %>% mutate(Peak=paste("peak", PAS,sep=""))
Removed10=PAS %>% anti_join(PAS_10, by=c("chr", "start", "end", "name", "score","strand"))%>% separate(name,into=c("PAS","gene"), sep=":") %>% mutate(Peak=paste("peak", PAS,sep=""))

Nuclear QTLs:

QTLPAS=read.table("../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.txt",stringsAsFactors = F, header = T)
Removed50_qtl=Removed50 %>% inner_join(QTLPAS, by="Peak") 
Removed15_qtl=Removed15 %>% inner_join(QTLPAS, by="Peak")
Removed10_qtl=Removed10 %>% inner_join(QTLPAS, by="Peak")
propofremoved=c(nrow(Removed50_qtl)/nrow(Removed50),nrow(Removed15_qtl)/nrow(Removed15),nrow(Removed10_qtl)/nrow(Removed10))
propofQTL=c(nrow(Removed50_qtl)/nrow(QTLPAS),nrow(Removed15_qtl)/nrow(QTLPAS),nrow(Removed10_qtl)/nrow(QTLPAS))
cutoffs=c("50 basepairs", "15 basepairs", "10 basepairs")

propqithQTLdf=as.data.frame(cbind(cutoffs,propofremoved,propofQTL ))

propqithQTLdf$propofremoved= as.numeric(as.character(propqithQTLdf$propofremoved))
propqithQTLdf$propofQTL= as.numeric(as.character(propqithQTLdf$propofQTL))

nrow(QTLPAS)/nrow(PAS)
[1] 0.01753747
removedPlot=ggplot(propqithQTLdf, aes(x=cutoffs, y=propofremoved )) + geom_bar(stat="identity", fill="darkorchid4") + labs(title="Proportion of Removed PAS with a Nuclear QTL", y="Proportion of removed", x="Filter")
removedQTLPlot=ggplot(propqithQTLdf, aes(x=cutoffs, y=propofQTL )) + geom_bar(stat="identity", fill="blue4") + labs(title="Proportion of Nuclear QTL in a removed PAS", y="Proportion of QTLs",x="Filter")
plot_grid(removedPlot,removedQTLPlot)

Version Author Date
4cd5f28 brimittleman 2019-08-26

Total QTLs:

TotalQTLPAS=read.table("../data/apaQTLs/Total_apaQTLs4pc_5fdr.txt",stringsAsFactors = F, header = T)
Removed50_TOTALqtl=Removed50 %>% inner_join(TotalQTLPAS, by="Peak") 
Removed15_TOTALqtl=Removed15 %>% inner_join(TotalQTLPAS, by="Peak")
Removed10_TOTALqtl=Removed10 %>% inner_join(TotalQTLPAS, by="Peak")
propofremovedT=c(nrow(Removed50_TOTALqtl)/nrow(Removed50),nrow(Removed15_TOTALqtl)/nrow(Removed15),nrow(Removed10_TOTALqtl)/nrow(Removed10))
propofQTLT=c(nrow(Removed50_TOTALqtl)/nrow(TotalQTLPAS),nrow(Removed15_TOTALqtl)/nrow(TotalQTLPAS),nrow(Removed10_TOTALqtl)/nrow(TotalQTLPAS))


propqithQTLdfT=as.data.frame(cbind(cutoffs,propofremovedT,propofQTLT ))

propqithQTLdfT$propofremovedT= as.numeric(as.character(propqithQTLdfT$propofremovedT))
propqithQTLdfT$propofQTLT= as.numeric(as.character(propqithQTLdfT$propofQTLT))
removedPlotT=ggplot(propqithQTLdfT, aes(x=cutoffs, y=propofremovedT )) + geom_bar(stat="identity", fill="darkorchid4") + labs(title="Proportion of Removed PAS with a Total QTL", y="Proportion of removed", x="Filter")
removedQTLPlotT=ggplot(propqithQTLdfT, aes(x=cutoffs, y=propofQTLT )) + geom_bar(stat="identity", fill="blue4") + labs(title="Proportion of Total QTL in a removed PAS", y="Proportion of QTLs",x="Filter")
plot_grid(removedPlotT,removedQTLPlotT)

Version Author Date
4cd5f28 brimittleman 2019-08-26

Try with 7As in a row.

python filterMPPAS_15_7As.py
PAS_15_7=read.table("../data/PAS/APAPAS_GeneLocAnno.15noMP_7As.bed", col.names = bednames, stringsAsFactors = F)

nrow(PAS_15_7)
[1] 40859
nrow(PAS_15)
[1] 39991
Removed15_7=PAS %>% anti_join(PAS_15_7, by=c("chr", "start", "end", "name", "score","strand"))%>% separate(name,into=c("PAS","gene"), sep=":") %>% mutate(Peak=paste("peak", PAS,sep=""))
Removed15_7_qtl=Removed15_7 %>% inner_join(QTLPAS, by="Peak")
nrow(Removed15_7_qtl)/nrow(QTLPAS)
[1] 0.156939

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] cowplot_0.9.4   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

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0       cellranger_1.1.0 plyr_1.8.4       compiler_3.5.1  
 [5] pillar_1.3.1     git2r_0.25.2     highr_0.7        workflowr_1.4.0 
 [9] tools_3.5.1      digest_0.6.18    lubridate_1.7.4  jsonlite_1.6    
[13] evaluate_0.12    nlme_3.1-137     gtable_0.2.0     lattice_0.20-38 
[17] pkgconfig_2.0.2  rlang_0.4.0      cli_1.1.0        rstudioapi_0.10 
[21] yaml_2.2.0       haven_1.1.2      withr_2.1.2      xml2_1.2.0      
[25] httr_1.3.1       knitr_1.20       hms_0.4.2        generics_0.0.2  
[29] fs_1.3.1         rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5
[33] glue_1.3.0       R6_2.3.0         readxl_1.1.0     rmarkdown_1.10  
[37] modelr_0.1.2     magrittr_1.5     whisker_0.3-2    backports_1.1.2 
[41] scales_1.0.0     htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0
[45] colorspace_1.3-2 labeling_0.3     stringi_1.2.4    lazyeval_0.2.1  
[49] munsell_0.5.0    broom_0.5.1      crayon_1.3.4