Last updated: 2018-07-03

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    Rmd 1e2ff4c Briana Mittleman 2018-07-02 evaluate bedgraph regions

Create Bedgraph

I will call peaks de novo in the combined total and nuclear fraction 3’ Seq. The data is reletevely clean so I will start with regions that have continuous coverage. I will first create a bedgraph.

#!/bin/bash

#SBATCH --job-name=Tbedgraph
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=Tbedgraph.out
#SBATCH --error=Tbedgraph.err
#SBATCH --partition=broadwl
#SBATCH --mem=40G
#SBATCH --mail-type=END


module load Anaconda3
source activate three-prime-env 

samtools sort -o /project2/gilad/briana/threeprimeseq/data/macs2/TotalBamFiles.sort.bam /project2/gilad/briana/threeprimeseq/data/macs2/TotalBamFiles.bam

bedtools genomecov -ibam /project2/gilad/briana/threeprimeseq/data/macs2/TotalBamFiles.sort.bam -bga > /project2/gilad/briana/threeprimeseq/data/bedgraph/TotalBamFiles.bedgraph

Next I will create the file without the 0 places in the genome. I will be able to use this for the bedtools merge function.

awk '{if ($4 != 0) print}' TotalBamFiles.bedgraph >TotalBamFiles_no0.bedgraph

I can merge the regions with consequtive reads using the bedtools merge function.

  • -i input bed

  • -c colomn to act on

  • -o collapse, print deliminated list of the counts from -c call

  • -delim “,”

This is the mergeBedgraph.sh script. It takes in the no 0 begraph filename without the path.

#!/bin/bash

#SBATCH --job-name=merge
#SBATCH --account=pi-yangili1
#SBATCH --time=8:00:00
#SBATCH --output=merge.out
#SBATCH --error=merge.err
#SBATCH --partition=broadwl
#SBATCH --mem=16G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env 

bedgraph=$1
describer=$(echo ${bedgraph} | sed -e "s/.bedgraph$//")

bedtools merge -c 4,4,4 -o count,mean,collapse -delim "," -i /project2/gilad/briana/threeprimeseq/data/bedgraph/$1 > /project2/gilad/briana/threeprimeseq/data/bedgraph/${describer}.peaks.bed

Run this first on the total bedgraph, TotalBamFiles_no0.bedgraph. The file has chromosome, start, end, number of regions, mean, and a string of the values.

This is not exaclty what I want. I need to go back and do genome cov not collapsing with bedgraph.

To evaluate this I will bring the file into R and plot some statistics about it.

#!/bin/bash

#SBATCH --job-name=Tgencov
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=Tgencov.out
#SBATCH --error=Tgencov.err
#SBATCH --partition=broadwl
#SBATCH --mem=40G
#SBATCH --mail-type=END


module load Anaconda3
source activate three-prime-env 


bedtools genomecov -ibam /project2/gilad/briana/threeprimeseq/data/macs2/TotalBamFiles.sort.bam -d > /project2/gilad/briana/threeprimeseq/data/bedgraph/TotalBamFiles.genomecov.bed

I will now remove the bases with 0 coverage.

awk '{if ($3 != 0) print}' TotalBamFiles.genomecov.bed > TotalBamFiles.genomecov.no0.bed 

awk '{print $1 "\t" $2 "\t"  $2 "\t" $3}' TotalBamFiles.genomecov.no0.bed > TotalBamFiles.genomecov.no0.fixed.bed

I will now merge the genomecov_no0 file with mergeGencov.sh

#!/bin/bash

#SBATCH --job-name=mergegc
#SBATCH --account=pi-yangili1
#SBATCH --time=8:00:00
#SBATCH --output=mergegc.out
#SBATCH --error=mergegc.err
#SBATCH --partition=broadwl
#SBATCH --mem=16G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env 

gencov=$1
describer=$(echo ${gencov} | sed -e "s/.genomecov.no0.fixed.bed$//")

bedtools merge -c 4,4,4 -o count,mean,collapse -delim "," -i /project2/gilad/briana/threeprimeseq/data/bedgraph/$1 > /project2/gilad/briana/threeprimeseq/data/bedgraph/${describer}.gencovpeaks.bed

This method gives us 811,637 regions.

Evaluate regions

Bedgraph results

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(ggplot2)
library(workflowr)
Loading required package: rmarkdown
This is workflowr version 1.0.1
Run ?workflowr for help getting started
library(tidyr)

First I will look at the bedgraph file. This is not as imformative becuase it combined regions with the same counts.

total_bedgraph=read.table("../data/bedgraph_peaks/TotalBamFiles_no0.peaks.bed",col.names = c("chr", "start", "end", "regions", "mean", "counts"))

Plot the mean:

plot(sort(log10(total_bedgraph$mean), decreasing=T), xlab="Region", ylab="log10 of bedgraph region bin", main="Distribution of log10 region means from bedgraph")

I want to look at the distribution of how many bases are included in the regions.

Tregion_bases=total_bedgraph %>% mutate(bases=end-start) %>% select(bases)
Warning: package 'bindrcpp' was built under R version 3.4.4
plot(sort(log10(Tregion_bases$bases), decreasing = T), xlab="Region", ylab="log10 of region size", main="Distribution of bases in regions- log10")

Given the reads are abotu 60bp this is probably pretty good.

GenomeCov results

I am only going to look at the number of bases in region and mean coverage columns here because the file is really big.

total_gencov=read.table("../data/bedgraph_peaks/TotalBamFiles.gencovpeaks_noregstring.bed",col.names = c("chr", "start", "end", "regions", "mean"))

Plot the mean:

plot(sort(log10(total_gencov$mean), decreasing=T), xlab="Region", ylab="log10 of mean bin count", main="Distribution of log10 region means")

plot(sort(log10(total_gencov$regions), decreasing = T), xlab="Region", ylab="log10 of region size", main="Distribution of bases in regions- log10")

Plot number of bases against the mean:

ggplot(total_gencov, aes(y=log10(regions), x=log10(mean))) +
         geom_point(na.rm = TRUE, size = 0.1) +
         geom_density2d(na.rm = TRUE, size = 1, colour = 'red') +
         ylab('Log10 Region size') +
         xlab('Log10 Mean region coverage') + 
        ggtitle("Region size vs Region Coverage: Combined Total Libraries")

Troubleshooting

Account for split reads

In the previous analysis I did not account for split reads in the genome coveragre step. This may explain some of the long regions that are an effect of splicing. This script is

#!/bin/bash

#SBATCH --job-name=Tgencovsplit
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=Tgencovsplit.out
#SBATCH --error=Tgencovaplit.err
#SBATCH --partition=broadwl
#SBATCH --mem=40G
#SBATCH --mail-type=END


module load Anaconda3
source activate three-prime-env 


bedtools genomecov -ibam /project2/gilad/briana/threeprimeseq/data/macs2/TotalBamFiles.sort.bam -d -split > /project2/gilad/briana/threeprimeseq/data/bedgraph/TotalBamFiles.split.genomecov.bed

Investigate long regions

Some of the regions are long and probably represent 2 or more sites. This is evident in highly expressed genes such as actB. I will look at some of the long regions and make histograms with the strings of coverage in the region.

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  tidyr_0.7.2     workflowr_1.0.1 rmarkdown_1.8.5
[5] ggplot2_2.2.1   dplyr_0.7.5    

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   MASS_7.3-48      
[31] assertthat_0.2.0  colorspace_1.3-2  labeling_0.3     
[34] stringi_1.2.2     lazyeval_0.2.1    munsell_0.4.3    
[37] R.oo_1.22.0

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