Reads mapping to 3’ UTR

Last updated: 2019-05-06

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Knit directory: apaQTL/analysis/

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File	Version	Author	Date	Message
Rmd	6826c3d	brimittleman	2019-05-06	add graphs
html	9062ebd	brimittleman	2019-05-03	Build site.
Rmd	0e5af55	brimittleman	2019-05-03	add utr coverage

library(workflowr)

This is workflowr version 1.3.0
Run ?workflowr for help getting started

library(reshape2)
library(tidyverse)

── Attaching packages ──────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

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library(cowplot)


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    ggsave

To help asses quality I want to look at the number and percent of reads mapping to the 3’ UTR. We expect this to be where most reads fall and this should be reasonanably similar between libraries. I will do this for the new set and the old set to see the difference between the old batch 4 and new batch 4.

mkdir ../data/Reads2UTR

mkdir ../data/Reads2UTR/Total
mkdir ../data/Reads2UTR/Nuclear

The 3’ UTR annotations are in /project2/gilad/briana/genome_anotation_data/RefSeq_annotations and were downloaded using the ucsc table browser. I will convert the 3’ UTR annotation to an SAF in order to run feature counts. The summary of the feature counts information will provide me the information I need.

python utrdms2saf.py

Run feature counts:

sbatch FC_UTR.sh

Fix the headers on all of these files:

python fixFChead_summary.py ../data/Reads2UTR/Nuclear_olddata_UTR.fc.summary ../data/Reads2UTR/Nuclear_olddata_UTR.fc.fixed.summary

python fixFChead_summary.py ../data/Reads2UTR/Nuclear_newdata_UTR.fc.summary ../data/Reads2UTR/Nuclear_newdata_UTR.fc.fixed.summary

python fixFChead_summary.py ../data/Reads2UTR/Total_olddata_UTR.fc.summary ../data/Reads2UTR/Total_olddata_UTR.fc.fixed.summary

python fixFChead_summary.py ../data/Reads2UTR/Total_newdata_UTR.fc.summary ../data/Reads2UTR/Total_newdata_UTR.fc.fixed.summary

Process old data to keep only information about the 15 ind in batch 4.

batch4=read.table("../data/MetaDataSequencing.txt", header=T,stringsAsFactors = F)%>% filter(fraction=="total") %>%  select(line, batch, Mapped_noMP) %>% filter(batch == 4) 

colnames(batch4)=c("Individual", "batch", "MappedReads")

AllInd=read.table("../data/MetaDataSequencing.txt", header=T,stringsAsFactors = F)%>% filter(fraction=="total") %>%  select(line, batch, Mapped_noMP)
colnames(AllInd)=c("Individual", "batch", "MappedReads")

Total:

totalOld=read.table("../data/Reads2UTR/Total_olddata_UTR.fc.fixed.summary", header = T) %>% filter(Status=="Assigned")

totalOld_melt=melt(totalOld, id.vars = "Status", variable.name = "Ind", value.name = "nReads") %>% separate(Ind,into=c("indiv", "fraction"), sep="_") %>%mutate(Individual=paste("NA",substring(indiv,2, 6),sep="")) %>% inner_join(batch4, by="Individual" )%>% mutate(Individual=paste(Individual, "Old", sep="_")) %>% mutate(PropUTR=nReads/MappedReads) %>% select(fraction, Individual, PropUTR,nReads)

totalNew=read.table("../data/Reads2UTR/Total_newdata_UTR.fc.fixed.summary", header = T) %>% filter(Status=="Assigned")

totalNew_melt=melt(totalNew, id.vars = "Status", variable.name = "Ind", value.name = "nReads") %>% separate(Ind,into=c("Individual", "fraction"), sep="_") %>%mutate(Individual=paste("NA",substring(Individual,2, 6),sep="")) %>% inner_join(AllInd, by="Individual" ) %>% mutate(PropUTR=nReads/MappedReads) %>% select(fraction, Individual, PropUTR,nReads)

Join these to plot togetehr

totalboth=rbind(totalOld_melt,totalNew_melt) %>% mutate(batch=ifelse(grepl("Old",Individual), "Old", "New"))

ggplot(totalboth, aes(x=Individual, fill=batch, y=PropUTR)) + geom_bar(stat="identity") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + labs(title="Proportion of reads mapping to 3' UTR Total")

ggplot(totalboth, aes(x=Individual, fill=batch, y=nReads)) + geom_bar(stat="identity") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + labs(title="Number of reads mapping to 3' UTR Total")

Nuclear

nuclearOld=read.table("../data/Reads2UTR/Nuclear_olddata_UTR.fc.fixed.summary", header = T) %>% filter(Status=="Assigned")

nuclearOld_melt=melt(nuclearOld, id.vars = "Status", variable.name = "Ind", value.name = "nReads") %>% separate(Ind,into=c("indiv", "fraction"), sep="_") %>%mutate(Individual=paste("NA",substring(indiv,2, 6),sep="")) %>% inner_join(batch4, by="Individual" )%>% mutate(Individual=paste(Individual, "Old", sep="_")) %>% mutate(PropUTR=nReads/MappedReads) %>% select(fraction, Individual, PropUTR,nReads)

nuclearNew=read.table("../data/Reads2UTR/Nuclear_newdata_UTR.fc.fixed.summary", header = T) %>% filter(Status=="Assigned")

nuclearNew_melt=melt(nuclearNew, id.vars = "Status", variable.name = "Ind", value.name = "nReads")%>% separate(Ind,into=c("Individual", "fraction"), sep="_") %>%mutate(Individual=paste("NA",substring(Individual,2, 6),sep="")) %>% inner_join(AllInd, by="Individual" ) %>% mutate(PropUTR=nReads/MappedReads) %>% select(fraction, Individual, PropUTR,nReads)

Join these to plot togetehr

nuclearboth=rbind(nuclearOld_melt,nuclearNew_melt) %>% mutate(batch=ifelse(grepl("Old",Individual), "Old", "New"))

ggplot(nuclearboth, aes(x=Individual, fill=batch, y=PropUTR)) + geom_bar(stat="identity") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + labs(title="Proportion of reads mapping to 3' UTR Nuclear")

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:
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 [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 reshape2_1.4.3  workflowr_1.3.0

loaded via a namespace (and not attached):
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 [9] lubridate_1.7.4  jsonlite_1.6     evaluate_0.12    nlme_3.1-137    
[13] gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2  rlang_0.3.1     
[17] cli_1.0.1        rstudioapi_0.10  yaml_2.2.0       haven_1.1.2     
[21] withr_2.1.2      xml2_1.2.0       httr_1.3.1       knitr_1.20      
[25] hms_0.4.2        generics_0.0.2   fs_1.2.6         rprojroot_1.3-2 
[29] grid_3.5.1       tidyselect_0.2.5 glue_1.3.0       R6_2.3.0        
[33] readxl_1.1.0     rmarkdown_1.10   modelr_0.1.2     magrittr_1.5    
[37] whisker_0.3-2    backports_1.1.2  scales_1.0.0     htmltools_0.3.6 
[41] rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2 labeling_0.3    
[45] stringi_1.2.4    lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1     
[49] crayon_1.3.4

Reads mapping to 3’ UTR

Briana Mittleman

5/3/2019

Total:

Nuclear