cov.200bp.wind

Last updated: 2018-06-04

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I will use this analysis to bin the genome into 200bp windows and look at coverage for the 3’ seq libraries for each of these windows. I will use this data then in the leafcutter pipeline to look at differences between data from the total and nuclear fractions.

I performed a similar analysis for the net-seq data so some of the code will come from that. https://brimittleman.github.io/Net-seq/create_blacklist.html

The binned genome file is called: genome_200_wind_fix2.saf, it is in my genome annotation directory.

#!/bin/bash

#SBATCH --job-name=cov200
#SBATCH --time=8:00:00
#SBATCH --output=cov200.out
#SBATCH --error=cov200.err
#SBATCH --partition=broadwl
#SBATCH --mem=20G
#SBATCH --mail-type=END

module load Anaconda3  

source activate three-prime-env

#input is a bam 
sample=$1


describer=$(echo ${sample} | sed -e 's/.*\YL-SP-//' | sed -e "s/-sort.bam$//")



featureCounts -T 5 -a /project2/gilad/briana/genome_anotation_data/an.int.genome_200_strandspec.saf -F 'SAF' -o /project2/gilad/briana/threeprimeseq/data/cov_200/${describer}_FC200.cov.bed $1

I will need to create a wrapper to run this for all of the files.

#!/bin/bash

#SBATCH --job-name=w_cov200
#SBATCH --time=8:00:00
#SBATCH --output=w_cov200.out
#SBATCH --error=w_cov200.err
#SBATCH --partition=broadwl
#SBATCH --mem=8G
#SBATCH --mail-type=END


for i in $(ls /project2/gilad/briana/threeprimeseq/data/sort/*.bam); do
            sbatch cov200.sh $i 
        done

Current analysis is not stand specific. I need to make windows for the negative strand. To do this I need to copy the genome_200_wind_fix2.saf file but with geneIDs starting with the last number of the file and with a - for the strand. The last window number is 15685849. I will have to start from 15685850.

In general I will use awk to create the file. The last number is 31371698 because that is 2 * the number of bins in the genome. I w

#i will delete the top line at the end
seq 15685849 31371698 > neg.bin.num.txt

 cut -f1 neg.bin.num.txt | paste - genome_200_wind_fix2.saf | awk  '{ if (NR>1) print $1 "\t" $3 "\t" $4 "\t" $5 "\t" "-"}' >  genome_200_wind_fix2.negstrand.saf 

#cat files together  

cat genome_200_wind_fix2.saf  genome_200_wind_fix2.negstrand.saf  > genome_200_strandsspec_wind.saf
 

I can use this to get coverage in all of the windows with strand specificity. I will call this script ss_cov200.sh

#!/bin/bash

#SBATCH --job-name=sscov200
#SBATCH --time=8:00:00
#SBATCH --output=sscov200.out
#SBATCH --error=sscov200.err
#SBATCH --partition=broadwl
#SBATCH --mem=20G
#SBATCH --mail-type=END

module load Anaconda3  

source activate three-prime-env

#input is a bam 
sample=$1


describer=$(echo ${sample} | sed -e 's/.*\YL-SP-//' | sed -e "s/-sort.bam$//")



featureCounts -T 5 -s 1 -O --fraction -a /project2/gilad/briana/genome_anotation_data/genome_200_strandsspec_wind.saf -F 'SAF' -o /project2/gilad/briana/threeprimeseq/data/ss_cov200/${describer}_ssFC200.cov.bed $1

Try this with. /project2/gilad/briana/threeprimeseq/data/sort/YL-SP-18486-N_S10_R1_001-sort.bam

I will update my wrapper to use this script.

The current script does not allow reads that map to multiple bins. We expect then so I will update the featureCounts code to account for this.

-O allows multi mapping -fraction will put a fraction of the read in each bin

The next step is to add genes annotations to each bin. I will do this with bedtools closest on my window file.

gene file: /project2/gilad/briana/genome_anotation_data/gencode.v19.annotation.proteincodinggene.sort.bed

I want to keep the windows with gene and add the name of the gene they are in.

a= windows b= genes

force stranded= -s

I need to make the window file a sorted bed file. It should be the chr number without the ‘chr’ tag, start, end, bin number, “.”, strand.

awk '{if (NR>1) print $2 "\t" $3 "\t" $4 "\t" $1 "\t" "." "\t" $5}' genome_200_strandsspec_wind.saf  | sed 's/^chr//' | sort -k1,1 -k2,2n > genome_200_strandspec.bed

#!/bin/bash

#SBATCH --job-name=annotate_wind
#SBATCH --time=8:00:00
#SBATCH --output=an_wind.out
#SBATCH --error=an_wind.err
#SBATCH --partition=broadwl
#SBATCH --mem=30G
#SBATCH --mail-type=END

module load Anaconda3

source activate three-prime-env


bedtools closest -s -a genome_200_strandspec.bed -b gencode.v19.annotation.proteincodinggene.sort.bed > annotated.genome_200_strandspec.bed

Now i can use intersect to only keep the windows that interdect that protien coding genes.

#!/bin/bash

#SBATCH --job-name=int_wind
#SBATCH --time=8:00:00
#SBATCH --output=int_wind.out
#SBATCH --error=int_wind.err
#SBATCH --partition=broadwl
#SBATCH --mem=30G
#SBATCH --mail-type=END

module load Anaconda3

source activate three-prime-env


bedtools intersect -wa -sorted -s -a annotated.genome_200_strandspec.bed -b gencode.v19.annotation.proteincodinggene.sort.bed > annotated.int.genome_200_strandspec.bed

awk '{print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $10}'  annotated.int.genome_200_strandspec.bed >  an.int.genome_200_strandspec.bed

I went from 31590487 to 7371747 windows. I need to make this into a saf file and the name of the window will be the number.gene

awk '{print $4"."$7 "\t" $1 "\t" $2 "\t" $3 "\t" $6}'  an.int.genome_200_strandspec.bed > an.int.genome_200_strandspec.saf

#go into the file with vi and add header

Now I can change my feature counts script to use this file instead.

I need to get rid of the lines with 2 genes overlapping in the bin. I will do this by removing the lines with a :.

for i in $(ls *.bed); do
      cat $i | grep -v -e ";" > ../ss_cov200_no_overlap/$i
  done

The next step is to bind all of these files. This file will have all 6323877 windows as the rows and columns for each of the 32 files

less 18486-N_S10_R1_001_ssFC200.cov.bed  | cut -f1-6 > tmp 
for i in ./*cov.bed; do
echo "$i"
less ${i} | cut -f7 >col
paste tmp col> tmp2; mv tmp2 tmp; rm col; done

mv tmp ssFC200.cov.bed

This in now ready to move to R an work with it here.
rememeber!!! 223 order problem

library(workflowr)

Loading required package: rmarkdown

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

library(ggplot2)
library(dplyr)

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(edgeR)

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

Loading required package: limma

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

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

names=c("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")

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

Plot the density of the log of the counts.

cov_nums_only=cov_all[,7:38]
cov_nums_only_log=log10(cov_nums_only)
plotDensities(cov_nums_only_log,legend = "bottomright", main="bin log 10 counts")

Now I want to filter for bins that have 0 reads in >16 samples.

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

cov_all_filt=cov_all[keep.exprs,]
bin.genes=cov_all_filt[,1]

I will now look at the densities.

cov_all_filt_log=log10(cov_all_filt[,7:38] + 1) 

plotDensities(cov_all_filt_log,legend = "bottomright", main="Filtered bin log10 +1 counts")

I want to make boxplots for each of these lines. I should tidy the data with a column for total or nuclear.

sample=c("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")
fraction=c("N","T","N","T","N","T","N",'T',"N","T","N","T","N","T","N","T","N","T","N","T","N","T","N","N","T","T","N","T","N","T","N","T")

cov_all_filt_log_gen=cbind(bin.genes,cov_all_filt_log)

cov_all_tidy= cov_all_filt_log_gen%>% gather(sample, value, -bin.genes)


#add fraction column

cov_all_tidy_frac=cov_all_tidy %>% mutate(fraction=ifelse(grepl("T",sample), "total", "nuclear")) %>% mutate(line=substr(sample,3,7))

Make a heatmap:

bin_count=ggplot(cov_all_tidy_frac, aes(x = line, y=value,fill=fraction )) + geom_boxplot(position="dodge") + labs(y="log10 count + 1", title="Bins in nuclear fractions have larger counts " )
bin_count

#ggsave("../output/plots/bin_counts_by_line.png", bin_count)

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    reshape2_1.4.3  edgeR_3.20.9    limma_3.34.9   
[5] tidyr_0.7.2     dplyr_0.7.4     ggplot2_2.2.1   workflowr_1.0.1
[9] rmarkdown_1.8.5

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.15      compiler_3.4.2    pillar_1.1.0     
 [4] git2r_0.21.0      plyr_1.8.4        bindr_0.1        
 [7] R.methodsS3_1.7.1 R.utils_2.6.0     tools_3.4.2      
[10] digest_0.6.14     lattice_0.20-35   evaluate_0.10.1  
[13] tibble_1.4.2      gtable_0.2.0      pkgconfig_2.0.1  
[16] rlang_0.1.6       yaml_2.1.16       stringr_1.2.0    
[19] knitr_1.18        tidyselect_0.2.3  locfit_1.5-9.1   
[22] rprojroot_1.3-2   grid_3.4.2        glue_1.2.0       
[25] R6_2.2.2          purrr_0.2.4       magrittr_1.5     
[28] whisker_0.3-2     backports_1.1.2   scales_0.5.0     
[31] htmltools_0.3.6   assertthat_0.2.0  colorspace_1.3-2 
[34] labeling_0.3      stringi_1.1.6     lazyeval_0.2.1   
[37] munsell_0.4.3     R.oo_1.22.0      

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