“A” content in each mapped read

Percent nucleotide in bins

One method is to measure the number of AAAAAs in my bins with bedtools nuc. I willl need a fasta file and a bed file with the bin.

library(workflowr)

Loading required package: rmarkdown

This is workflowr version 1.0.1
Run ?workflowr for help getting started

library(ggplot2)
library(dplyr)

Warning: package 'dplyr' was built under R version 3.4.4

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(tidyr)
library(reshape2)

Warning: package 'reshape2' was built under R version 3.4.3

Attaching package: 'reshape2'

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

    smiths

#!/bin/bash

#SBATCH --job-name=nuc.bin
#SBATCH --time=8:00:00
#SBATCH --output=nuc.bin.out
#SBATCH --error=nuc.bin.err
#SBATCH --partition=broadwl
#SBATCH --mem=20G
#SBATCH --mail-type=END

module load Anaconda3  

source activate three-prime-env


bedtools nuc -s -fi /project2/gilad/briana/genome_anotation_data/genome/Homo_sapiens.GRCh37.75.dna_sm.all.fa -bed /project2/gilad/briana/genome_anotation_data/an.int.genome_200_strandspec.bed > /project2/gilad/briana/threeprimeseq/data/bin200.nuccov.bed  

I can now pull this file into R.

bin_nuccov=read.table("../data/bin200.nuccov.bed")
names(bin_nuccov)=c("chr", "start", "end", "bin", "score", "strand", "gene", "pct_at", "pct_gc", "numA", "numC", "numG", "numT", "numN", "numOther", "seqlen")

perc_A_bin=bin_nuccov %>% select("chr", "start", "end","bin", "strand", "gene", "numA") %>% mutate(percA=numA/200)

Warning: package 'bindrcpp' was built under R version 3.4.4

ggplot(perc_A_bin, aes(percA)) + geom_histogram(bins=30) + labs(title="Percent of As in the bins")

I will apply the same filter as I did in the cov.200bp.wind file. I will keep bins with greater than 0 reads in half of the libraries.

cov_all=read.table("../data/ssFC200.cov.bed", header = T, stringsAsFactors = FALSE)
#remember name switch!
names=c("Geneid","Chr", "Start", "End", "Strand", "Length", "N_18486","T_18486","N_18497","T_18497","N_18500","T_18500","N_18505",'T_18505',"N_18508","T_18508","N_18853","T_18853","N_18870","T_18870","N_19128","T_19128","N_19141","T_19141","N_19193","T_19193","N_19209","T_19209","N_19223","N_19225","T_19225","T_19223","N_19238","T_19238","N_19239","T_19239","N_19257","T_19257")

colnames(cov_all)= names

cov_nums_only=cov_all[,7:38]

keep.exprs=rowSums(cov_nums_only>0) >= 16

cov_all_filt=cov_all[keep.exprs,] 

cov_all_filt_bins= cov_all_filt %>% separate(col=Geneid, into=c("bin","gene"), sep=".E") %>% select(bin)

cov_all_filt_bins$bin=as.integer(cov_all_filt_bins$bin)

I will intersect the percA file with the bins in the filtered file.

perc_A_bin_filt= perc_A_bin %>% semi_join(cov_all_filt_bins, by="bin")

I can no plot the distribution of percA in this.

ggplot(perc_A_bin_filt, aes(percA)) + geom_histogram(bins = 30) + labs(title="Percent of As in the bins after filtering")

I will compare this distribution to those for other nucleotides. (C)

perc_C_bin=bin_nuccov %>% select("chr", "start", "end","bin", "strand", "gene", "numC") %>% mutate(percC=numC/200)

ggplot(perc_C_bin, aes(percC)) + geom_histogram(bins=30) + labs(title="Percent of Cs in the bins")

perc_C_bin_filt= perc_C_bin %>% semi_join(cov_all_filt_bins, by="bin")
ggplot(perc_C_bin_filt, aes(percC)) + geom_histogram(bins = 30) + labs(title="Percent of Cs in the bins after filtering")

’ For G

perc_G_bin=bin_nuccov %>% select("chr", "start", "end","bin", "strand", "gene", "numG") %>% mutate(percG=numG/200)

ggplot(perc_G_bin, aes(percG)) + geom_histogram(bins=30) + labs(title="Percent of Gs in the bins")

perc_G_bin_filt= perc_G_bin %>% semi_join(cov_all_filt_bins, by="bin")
ggplot(perc_G_bin_filt, aes(percG)) + geom_histogram(bins = 30) + labs(title="Percent of Gs in the bins after filtering")

for T

perc_T_bin=bin_nuccov %>% select("chr", "start", "end","bin", "strand", "gene", "numT") %>% mutate(percT=numT/200)

ggplot(perc_T_bin, aes(percT)) + geom_histogram(bins=30) + labs(title="Percent of Ts in the bins")

perc_T_bin_filt= perc_T_bin %>% semi_join(cov_all_filt_bins, by="bin")
ggplot(perc_T_bin_filt, aes(percT)) + geom_histogram(bins = 30) + labs(title="Percent of Ts in the bins after filtering")

Now I will join all of the percent usage of each nucleotide in the filtered bins so I can plot them on one plot.

percNuc= perc_A_bin_filt %>% left_join(perc_T_bin_filt, by=c("chr", "start", "end", "bin", "strand", "gene")) %>% left_join(perc_G_bin_filt, by=c("chr", "start", "end", "bin", "strand", "gene")) %>% left_join(perc_C_bin_filt, by=c("chr", "start", "end", "bin", "strand", "gene")) %>% select("bin", "percA", "percT", "percG", "percC")

percNuc_melt=melt(percNuc, id.vars = "bin")

ggplot(percNuc_melt, aes(value)) + geom_histogram(bins = 30) + facet_wrap(~variable) + labs(title="Percent each nucleotide in filtered bins")

Strech of Nucleotides

Next check is if the bins have 5 As in a row. I can do this using bedtools nuc as well.
###Five A’s

#!/bin/bash

#SBATCH --job-name=nucA
#SBATCH --time=8:00:00
#SBATCH --output=nucA.out
#SBATCH --error=nucA.err
#SBATCH --partition=broadwl
#SBATCH --mem=20G
#SBATCH --mail-type=END

module load Anaconda3  

source activate three-prime-env


bedtools nuc -s -fi /project2/gilad/briana/genome_anotation_data/genome/Homo_sapiens.GRCh37.75.dna_sm.all.fa -bed /project2/gilad/briana/genome_anotation_data/an.int.genome_200_strandspec.bed -pattern "AAAAA" > /project2/gilad/briana/threeprimeseq/data/bin200.5.A.nuccov.bed  

bin_Anuccov=read.table("../data/bin200.Anuccov.bed")
names(bin_Anuccov)=c("chr", "start", "end", "bin", "score", "strand", "gene", "pct_at", "pct_gc", "numA", "numC", "numG", "numT", "numN", "numOther", "seqlen", "fiveA")

hist(bin_Anuccov$fiveA)

I will filter this the same way I filtered the other file.

 bin_Anuccov_filt = bin_Anuccov %>% semi_join(cov_all_filt_bins, by="bin") %>% select( bin,  gene,  fiveA)

hist(bin_Anuccov_filt$fiveA)

summary(bin_Anuccov_filt$fiveA)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.4404  0.0000 13.0000 

Count the number of bins with each value for number of 5 AAAAA

countA_regions= bin_Anuccov_filt %>% group_by(fiveA) %>% count(fiveA)

#!/bin/bash

#SBATCH --job-name=nucT
#SBATCH --time=8:00:00
#SBATCH --output=nucT.out
#SBATCH --error=nucT.err
#SBATCH --partition=broadwl
#SBATCH --mem=20G
#SBATCH --mail-type=END

module load Anaconda3  

source activate three-prime-env


bedtools nuc -s -fi /project2/gilad/briana/genome_anotation_data/genome/Homo_sapiens.GRCh37.75.dna_sm.all.fa -bed /project2/gilad/briana/genome_anotation_data/an.int.genome_200_strandspec.bed -pattern "TTTTT" > /project2/gilad/briana/threeprimeseq/data/bin200.5.T.nuccov.bed  

bin_Tnuccov=read.table("../data/bin200.5.T.nuccov.bed")
names(bin_Tnuccov)=c("chr", "start", "end", "bin", "score", "strand", "gene", "pct_at", "pct_gc", "numA", "numC", "numG", "numT", "numN", "numOther", "seqlen", "fiveT")

summary(bin_Tnuccov$fiveT)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.4191  0.0000 14.0000 

bin_Tnuccov_filt = bin_Tnuccov %>% semi_join(cov_all_filt_bins, by="bin") %>% select( bin,  gene,  fiveT)

hist(bin_Tnuccov_filt$fiveT)

summary(bin_Tnuccov_filt$fiveT)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.5163  1.0000  9.0000 

countT_regions= bin_Tnuccov_filt %>% group_by(fiveT) %>% count(fiveT)

Session information

sessionInfo()

R version 3.4.2 (2017-09-28)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] bindrcpp_0.2.2  reshape2_1.4.3  tidyr_0.7.2     dplyr_0.7.5    
[5] ggplot2_2.2.1   workflowr_1.0.1 rmarkdown_1.8.5

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.17      compiler_3.4.2    pillar_1.1.0     
 [4] git2r_0.21.0      plyr_1.8.4        bindr_0.1.1      
 [7] R.methodsS3_1.7.1 R.utils_2.6.0     tools_3.4.2      
[10] digest_0.6.15     evaluate_0.10.1   tibble_1.4.2     
[13] gtable_0.2.0      pkgconfig_2.0.1   rlang_0.2.1      
[16] yaml_2.1.19       stringr_1.3.1     knitr_1.18       
[19] rprojroot_1.3-2   grid_3.4.2        tidyselect_0.2.4 
[22] glue_1.2.0        R6_2.2.2          purrr_0.2.5      
[25] magrittr_1.5      whisker_0.3-2     backports_1.1.2  
[28] scales_0.5.0      htmltools_0.3.6   assertthat_0.2.0 
[31] colorspace_1.3-2  labeling_0.3      stringi_1.2.2    
[34] lazyeval_0.2.1    munsell_0.4.3     R.oo_1.22.0