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File Version Author Date Message
html 3e56058 Briana Mittleman 2018-07-17 Build site.
Rmd c287f4e Briana Mittleman 2018-07-17 call Brain peaks with Yangs script
html dc4a51a Briana Mittleman 2018-07-16 Build site.
Rmd 4fede84 Briana Mittleman 2018-07-16 add eval brain analysis

I downloaded the brain 3’ seq data from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM747470 and I want to use this analysis to see how similar their peaks are to ours eventhough the data is from different cell types.

First I will use the bedtools jaccard function to explore the overlaps. It will give me one stat that is the length(intersection)/length(union) - length(intersection). Here I can have file A brain peaks and file B be our peaks to see the similarity between the sets.

#!/bin/bash

#SBATCH --job-name=jaccard_brain
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=jacard_brain.out
#SBATCH --error=jacard_brain.err
#SBATCH --partition=broadwl
#SBATCH --mem=16G
#SBATCH --mail-type=END



module load Anaconda3
source activate three-prime-env 

bedtools jaccard -a /project2/gilad/briana/threeprimeseq/data/derti_brain/GSM747470_human_brain.sites.clustered.hg19.sort.bed -b /project2/gilad/briana/threeprimeseq/data/peaks/APApeaksYL.total.bed  > /project2/gilad/briana/threeprimeseq/data/derti_brain/total.jaccard.txt

Results: intersection union-intersection jaccard n_intersections 21371 25414133 0.00084091 21352

The brain set has 89110 peaks and our set has 288350. I will filter ours by score then see if the top 25% have a higher overlap percentage.

library(workflowr)
This is workflowr version 1.2.0
Run ?workflowr for help getting started
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(ggplot2)
library(reshape2)

Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':

    smiths
YL_peaks=read.table("../data/bedgraph_peaks/APApeaks.bed", col.names = c("chr", "start", "end", "count", "strand", "score")) %>% mutate(length=end-start)

I want the counts for the top 25% of the peaks.

quantile(YL_peaks$count)
          0%          25%          50%          75%         100% 
1.000000e+00 1.343902e+01 2.353933e+01 6.091061e+01 1.604636e+06

I will subset the peaks by having a count > 61.

awk '$4 >= 60 {print}' APApeaksYL.total.bed > APApeaksYL.top25.total.bed

I can rerun the jaccard with this and see if it changes, this new file has 72877 peaks.

Results:
intersection union-intersection jaccard n_intersections 13221 6452066 0.00204911 13207

The proportion of overlap increased. Next I can try to make plots where I seperate my peaks by if they have a corresponding one in the brain file then plot the scores. To do this I will first use bedtool intersect to get just my peaks that contain a peak in the brain file. I can then use dplyr to merge them.

Here A is my file and B is the brain file.

#!/bin/bash

#SBATCH --job-name=int_brain
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=int.brain.out
#SBATCH --error=int.brain.err
#SBATCH --partition=broadwl
#SBATCH --mem=16G
#SBATCH --mail-type=END


module load Anaconda3
source activate three-prime-env 

bedtools intersect -wa -a /project2/gilad/briana/threeprimeseq/data/peaks/APApeaksYL.total.bed  -b /project2/gilad/briana/threeprimeseq/data/derti_brain/GSM747470_human_brain.sites.clustered.hg19.sort.bed > /project2/gilad/briana/threeprimeseq/data/derti_brain/APApeaksYL.total.inbrain.bed

The resulting file has 21378 peaks.

YL_peaks_overlap=read.table("../data/APApeaksYL.total.inbrain.bed", col.names = c("chr", "start", "end", "count", "strand", "score")) %>% mutate(length=end-start) %>% mutate(in_brain="Y")

Now I need to join them.

YL_peaks_join=YL_peaks %>% full_join(YL_peaks_overlap, by = c("chr", "start", "end", "count", "strand", "score", "length"))

YL_peaks_join$in_brain[is.na(YL_peaks_join$in_brain)]="N"

YL_peaks_join_sel=YL_peaks_join %>% select(count, in_brain)

Plot these.

ggplot(YL_peaks_join_sel, aes(y=log10(count), x=in_brain, fill=in_brain)) + geom_boxplot() + labs(x="Peak called in brain dataset", y="log10 Score", title="Peak score distribution by inclusion in brain dataset")

Version Author Date
dc4a51a Briana Mittleman 2018-07-16 
ggplot(YL_peaks_join_sel, aes(x=log10(count), fill=in_brain), bins=50) + geom_density(position="identity", alpha=.5) + labs(x="log10 of Score", title="Distribution of log10 Scores in peaks included in brain dataset")

Version Author Date
dc4a51a Briana Mittleman 2018-07-16

It would be better if the background was just a random subset of the same number. There are 21378 included peaks so I should select a random 21378 to make a background distribution.

samp_YLpeaks= sample_n(YL_peaks, 21378)

ggplot() + geom_histogram(data=samp_YLpeaks, aes(log10(count)), bins=100) + geom_histogram(data=YL_peaks_overlap, aes(log10(count)),fill="Red", bins=100) + labs(x="Log10 of Score", title="Scores in Overlapping set compared to scores in random set")

Version Author Date
dc4a51a Briana Mittleman 2018-07-16

The next step is to download the Brain fastq data and call peaks using Yangs script. I used sra-tools to download SRR299106. Then I ran my snakemake pipeline on it.

I need to make the genome cov file then use Yangs script to call the peaks.

#!/bin/bash

#SBATCH --job-name=braingencovsplit
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=brain_gencovsplit.out
#SBATCH --error=brain_gencovaplit.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/derti_brain_raw/data/sort/derti_brain-sort.bam  -d -split > /project2/gilad/briana/derti_brain_raw/data/gencov/derti_brain.gencov.bed

Wrap Yangs script:

#!/bin/bash

#SBATCH --job-name=w_getpeakYLB
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=w_getpeakYLB.out
#SBATCH --error=w_getpeakYLB.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


for i in $(seq 1 22); do 
  sbatch callPeaksYL_derti.py $i
done


sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.1

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/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  ggplot2_3.0.0   tidyr_0.8.1    
[5] dplyr_0.7.6     workflowr_1.2.0

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.19     knitr_1.20       bindr_0.1.1      whisker_0.3-2   
 [5] magrittr_1.5     munsell_0.5.0    tidyselect_0.2.4 colorspace_1.3-2
 [9] R6_2.3.0         rlang_0.2.2      plyr_1.8.4       stringr_1.4.0   
[13] tools_3.5.1      grid_3.5.1       gtable_0.2.0     withr_2.1.2     
[17] git2r_0.24.0     htmltools_0.3.6  lazyeval_0.2.1   yaml_2.2.0      
[21] rprojroot_1.3-2  digest_0.6.17    assertthat_0.2.0 tibble_1.4.2    
[25] crayon_1.3.4     purrr_0.2.5      fs_1.2.6         glue_1.3.0      
[29] evaluate_0.13    rmarkdown_1.11   labeling_0.3     stringi_1.2.4   
[33] compiler_3.5.1   pillar_1.3.0     scales_1.0.0     backports_1.1.2 
[37] pkgconfig_2.0.2