Last updated: 2018-06-11

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    File Version Author Date Message
    Rmd 03c7b14 Briana Mittleman 2018-06-11 start A analysis

The goal of this analysis is to start to understand the sequence composition of the three prime seq reads. This may help me detect misspriming at AAAAA rich regions rather than true site usage. The genomic sequence does not carry the polyadenylation signal, this means reads mapping to a genomic AAAAAA region may be false positives. Gruber et al. removed reads that consisted of more than 80% AAAA.

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")

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

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