Last updated: 2019-02-28

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Knit directory: threeprimeseq/analysis/

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Unstaged changes:
    Modified:   analysis/28ind.peak.explore.Rmd
    Modified:   analysis/CompareLianoglouData.Rmd
    Modified:   analysis/NewPeakPostMP.Rmd
    Modified:   analysis/apaQTLoverlapGWAS.Rmd
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    Modified:   analysis/flash2mash.Rmd
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    Modified:   analysis/overlapMolQTL.opposite.Rmd
    Modified:   analysis/overlap_qtls.Rmd
    Modified:   analysis/peakOverlap_oppstrand.Rmd
    Modified:   analysis/peakQCPPlots.Rmd
    Modified:   analysis/peakQCplotsSTARprocessing.Rmd
    Modified:   analysis/pheno.leaf.comb.Rmd
    Modified:   analysis/pipeline_55Ind.Rmd
    Modified:   analysis/swarmPlots_QTLs.Rmd
    Modified:   analysis/test.max2.Rmd
    Modified:   analysis/test.smash.Rmd
    Modified:   analysis/understandPeaks.Rmd
    Modified:   analysis/unexplainedeQTL_analysis.Rmd
    Modified:   code/Snakefile

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

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File Version Author Date Message
Rmd ea34b3c Briana Mittleman 2019-02-28 add nuclear spec peaks 
library(tidyverse)
── Attaching packages ──────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.0.0     ✔ purrr   0.2.5
✔ tibble  1.4.2     ✔ dplyr   0.7.6
✔ tidyr   0.8.1     ✔ stringr 1.4.0
✔ readr   1.1.1     ✔ forcats 0.3.0
Warning: package 'stringr' was built under R version 3.5.2
── Conflicts ─────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(workflowr)
This is workflowr version 1.2.0
Run ?workflowr for help getting started

merge net seq data:

/project2/gilad/briana/Net-seq/Net-seq3/data/sort/*-sort.bam

/project2/gilad/briana/Net-seq/Net-seq3/data/sort/mergeAndMakeBWnet3.sh

#!/bin/bash

#SBATCH --job-name=mergeAndMakeBWnet3
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=mergeAndMakeBWnet3.out
#SBATCH --error=mergeAndMakeBWnet3.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

samtools merge /project2/gilad/briana/Net-seq/Net-seq3/data/sort/Merged.Net3.bam /project2/gilad/briana/Net-seq/Net-seq3/data/sort/*-sort.bam  
samtools sort /project2/gilad/briana/Net-seq/Net-seq3/data/sort/Merged.Net3.bam > /project2/gilad/briana/Net-seq/Net-seq3/data/sort/Merged.Net3.sort.bam
samtools index /project2/gilad/briana/Net-seq/Net-seq3/data/sort/Merged.Net3.sort.bam

bamCoverage -b  /project2/gilad/briana/Net-seq/Net-seq3/data/sort/Merged.Net3.sort.bam -o  /project2/gilad/briana/Net-seq/Net-seq3/data/sort/Merged.Net3.sort.bw

Look 3kb on each side

NetseqDTPlotmyPeaks_noMPFilt.sh

#!/bin/bash

#SBATCH --job-name=NetseqDTPlotmyPeaks_noMPFilt
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=NetseqDTPlotmyPeaks_noMPFilt.out
#SBATCH --error=NetseqDTPlotmyPeaks_noMPFilt.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


computeMatrix reference-point -S /project2/gilad/briana/Net-seq/Net-seq3/data/sort/Merged.Net3.sort.bw -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand.bed -b 3000 -a 3000  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_myPeaksNompfilt.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_myPeaksNompfilt.gz --refPointLabel "Called PAS" --plotTitle "Combined NetSeq at All Called PAS" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_myPeaksNompfilt.png

Do this for intronic peaks

NetseqDTPlotmyIntronPeaks_noMPFilt.sh

#!/bin/bash

#SBATCH --job-name=NetseqDTPlotmyIntronPeaks_noMPFilt
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=NetseqDTPlotmyIntronPeaks_noMPFilt.out
#SBATCH --error=NetseqDTPlotmyIntronPeaks_noMPFilt.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


computeMatrix reference-point -S /project2/gilad/briana/Net-seq/Net-seq3/data/sort/Merged.Net3.sort.bw -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_INTRON.bed -b 3000 -a 3000  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_myIntronPeaksNompfilt.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_myIntronPeaksNompfilt.gz --refPointLabel "Called PAS" --plotTitle "Combined NetSeq at Intronic Called PAS" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_myIntronPeaksNompfilt.png

Top used intronic in nuclear
NetseqDTPlotmyToptIntronPeaks_noMPFilt.sh

#!/bin/bash

#SBATCH --job-name=NetseqDTPlotmyToptIntronPeaks_noMPFilt
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=NetseqDTPlotmyTopIntronPeaks_noMPFilt.out
#SBATCH --error=NetseqDTPlotmyTopIntronPeaks_noMPFilt.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


computeMatrix reference-point -S /project2/gilad/briana/Net-seq/Net-seq3/data/sort/Merged.Net3.sort.bw -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_INTRON_top200Usage.Nuclear.sort.bed -b 3000 -a 3000  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_myTopIntronPeaksNompfilt.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_myTopIntronPeaksNompfilt.gz --refPointLabel "Called PAS" --plotTitle "Combined NetSeq at Top Nuclear Intronic Called PAS" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_myTotIntronPeaksNompfilt.png

/project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_INTRON_top200Usage_Total.sort.bed NetseqDTPlotmyTopTotIntronPeaks_noMPFilt.sh

#!/bin/bash

#SBATCH --job-name=NetseqDTPlotmyTopTotIntronPeaks_noMPFilt
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=NetseqDTPlotmyTopTotIntronPeaks_noMPFilt.out
#SBATCH --error=NetseqDTPlotmyTopTotIntronTotPeaks_noMPFilt.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


computeMatrix reference-point -S /project2/gilad/briana/Net-seq/Net-seq3/data/sort/Merged.Net3.sort.bw -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_INTRON_top200Usage_Total.sort.bed -b 3000 -a 3000  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_myTopTotIntronPeaksNompfilt.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_myTopTotIntronPeaksNompfilt.gz --refPointLabel "Called PAS" --plotTitle "Combined NetSeq at Top Total Intronic Called PAS" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_myTopTotIntronPeaksNompfilt.png

UTR peaks

NetseqDTPlotmyUTRPeaks_noMPFilt.sh

#!/bin/bash

#SBATCH --job-name=NetseqDTPlotmyUTRPeaks_noMPFilt
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=NetseqDTPlotmyUTRPeaks_noMPFilt.out
#SBATCH --error=NetseqDTPlotmyUTRPeaks_noMPFilt.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


computeMatrix reference-point -S /project2/gilad/briana/Net-seq/Net-seq3/data/sort/Merged.Net3.sort.bw -R /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc_3UTR/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed_5perc_FixedStrand_3UTR.bed -b 3000 -a 3000  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_myUTRPeaksNompfilt.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_myUTRPeaksNompfilt.gz --refPointLabel "Called PAS" --plotTitle "Combined NetSeq at 3' UTR Called PAS" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_myUTRPeaksNompfilt.png

I want to look at the peaks used more often in the nucelear fraction and see if we have enrichment at these.

NucPeaks=read.table("../data/diff_iso_GeneLocAnno/SigPeaksHigherInNuc.txt", header=T, stringsAsFactors = F) %>% separate(intron, into=c("Chr2", "Start", "End", "gene"), sep=":")
NucPeaks$Start=as.numeric(NucPeaks$Start)
NucPeaks$End=as.numeric(NucPeaks$End)

Now I want to pull in the corrected strand peaks.

DTPeaks=read.table("../data/peaks4DT/APAPeaks_5percCov_fixedStrand.bed",header = F, col.names = c("Chr", "Start", "End","Name", "Score", "Strand" )) %>% mutate(Chr2=paste("chr", Chr, sep=""))

DTPeaks_filt=DTPeaks %>% semi_join(NucPeaks, by=c("Chr2", "Start", "End")) %>% select(-Chr2)


#write it  
write.table(DTPeaks_filt, "../data/peaks4DT/APAPeaks_5percCov_fixedStrand_SigUsageNuc.bed", col.names = F, row.names = F, quote=F, sep="\t")

NetseqDTPlot_NucDiffUsedPeaks_noMPFilt.sh

#!/bin/bash

#SBATCH --job-name=NetseqDTPlot_NucDiffUsedPeaks_noMPFilt
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=NetseqDTPlot_NucDiffUsedPeaks_noMPFilt.out
#SBATCH --error=NetseqDTPlot_NucDiffUsedPeaks_noMPFilt.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


computeMatrix reference-point -S /project2/gilad/briana/Net-seq/Net-seq3/data/sort/Merged.Net3.sort.bw -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_SigUsageNuc.bed -b 3000 -a 3000  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_NucDiffUsedPeaksNompfilt.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_NucDiffUsedPeaksNompfilt.gz --refPointLabel "Called PAS" --plotTitle "Combined NetSeq at Nuclear Specific PAS" --heatmapHeight 7 --colorMap YlGnBu  --averageTypeSummaryPlot "mean" -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_NucDiffUsedPeaksNompfilt.png

There are a few peaks driving the signal. I can split these up into 5 peak files

DTPeaks_filt_1= DTPeaks_filt %>% slice(1:150)
DTPeaks_filt_2= DTPeaks_filt %>% slice(151:300)
DTPeaks_filt_3= DTPeaks_filt %>% slice(301:450)
DTPeaks_filt_4= DTPeaks_filt %>% slice(451:600)
DTPeaks_filt_5= DTPeaks_filt %>% slice(601:762)

write.table(DTPeaks_filt_1, "../data/peaks4DT/APAPeaks_5percCov_fixedStrand_SigUsageNuc_1.bed", col.names = F, row.names = F, quote=F, sep="\t")
write.table(DTPeaks_filt_2, "../data/peaks4DT/APAPeaks_5percCov_fixedStrand_SigUsageNuc_2.bed", col.names = F, row.names = F, quote=F, sep="\t")
write.table(DTPeaks_filt_3, "../data/peaks4DT/APAPeaks_5percCov_fixedStrand_SigUsageNuc_3.bed", col.names = F, row.names = F, quote=F, sep="\t")
write.table(DTPeaks_filt_4, "../data/peaks4DT/APAPeaks_5percCov_fixedStrand_SigUsageNuc_4.bed", col.names = F, row.names = F, quote=F, sep="\t")
write.table(DTPeaks_filt_5, "../data/peaks4DT/APAPeaks_5percCov_fixedStrand_SigUsageNuc_5.bed", col.names = F, row.names = F, quote=F, sep="\t")

NetseqDTPlot_NucDiffUsedPeaks_noMPFilt_5plots.sh

#!/bin/bash

#SBATCH --job-name=NetseqDTPlot_NucDiffUsedPeaks_noMPFilt_5plots
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=NetseqDTPlot_NucDiffUsedPeaks_noMPFilt_5plots.out
#SBATCH --error=NetseqDTPlot_NucDiffUsedPeaks_noMPFilt_5plots.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

#1
computeMatrix reference-point -S /project2/gilad/briana/Net-seq/Net-seq3/data/sort/Merged.Net3.sort.bw -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_SigUsageNuc_1.bed  -b 3000 -a 3000  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_NucDiffUsedPeaksNompfilt1.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_NucDiffUsedPeaksNompfilt1.gz --refPointLabel "Called PAS" --plotTitle "Combined NetSeq at Nuclear Specific PAS" --heatmapHeight 7 --colorMap YlGnBu  --averageTypeSummaryPlot "mean" -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_NucDiffUsedPeaksNompfilt1.png

#2
computeMatrix reference-point -S /project2/gilad/briana/Net-seq/Net-seq3/data/sort/Merged.Net3.sort.bw -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_SigUsageNuc_2.bed  -b 3000 -a 3000  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_NucDiffUsedPeaksNompfilt2.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_NucDiffUsedPeaksNompfilt2.gz --refPointLabel "Called PAS" --plotTitle "Combined NetSeq at Nuclear Specific PAS" --heatmapHeight 7 --colorMap YlGnBu  --averageTypeSummaryPlot "mean" -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_NucDiffUsedPeaksNompfilt2.png

#3

computeMatrix reference-point -S /project2/gilad/briana/Net-seq/Net-seq3/data/sort/Merged.Net3.sort.bw -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_SigUsageNuc_3.bed  -b 3000 -a 3000  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_NucDiffUsedPeaksNompfilt3.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_NucDiffUsedPeaksNompfilt3.gz --refPointLabel "Called PAS" --plotTitle "Combined NetSeq at Nuclear Specific PAS" --heatmapHeight 7 --colorMap YlGnBu  --averageTypeSummaryPlot "mean" -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_NucDiffUsedPeaksNompfilt3.png


#4
computeMatrix reference-point -S /project2/gilad/briana/Net-seq/Net-seq3/data/sort/Merged.Net3.sort.bw -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_SigUsageNuc_4.bed  -b 3000 -a 3000  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_NucDiffUsedPeaksNompfilt4.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_NucDiffUsedPeaksNompfilt4.gz --refPointLabel "Called PAS" --plotTitle "Combined NetSeq at Nuclear Specific PAS" --heatmapHeight 7 --colorMap YlGnBu  --averageTypeSummaryPlot "mean" -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_NucDiffUsedPeaksNompfilt4.png


#5

computeMatrix reference-point -S /project2/gilad/briana/Net-seq/Net-seq3/data/sort/Merged.Net3.sort.bw -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_SigUsageNuc_5.bed  -b 3000 -a 3000  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_NucDiffUsedPeaksNompfilt5.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_NucDiffUsedPeaksNompfilt5.gz --refPointLabel "Called PAS" --plotTitle "Combined NetSeq at Nuclear Specific PAS" --heatmapHeight 7 --colorMap YlGnBu  --averageTypeSummaryPlot "mean" -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Netseq_NucDiffUsedPeaksNompfilt5.png
Data are a bit too noisy to say anything here.


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  workflowr_1.2.0 forcats_0.3.0   stringr_1.4.0  
 [5] dplyr_0.7.6     purrr_0.2.5     readr_1.1.1     tidyr_0.8.1    
 [9] tibble_1.4.2    ggplot2_3.0.0   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.19     cellranger_1.1.0 plyr_1.8.4       compiler_3.5.1  
 [5] pillar_1.3.0     git2r_0.24.0     bindr_0.1.1      tools_3.5.1     
 [9] digest_0.6.17    lubridate_1.7.4  jsonlite_1.6     evaluate_0.13   
[13] nlme_3.1-137     gtable_0.2.0     lattice_0.20-35  pkgconfig_2.0.2 
[17] rlang_0.2.2      cli_1.0.1        rstudioapi_0.9.0 yaml_2.2.0      
[21] haven_1.1.2      withr_2.1.2      xml2_1.2.0       httr_1.3.1      
[25] knitr_1.20       hms_0.4.2        fs_1.2.6         rprojroot_1.3-2 
[29] grid_3.5.1       tidyselect_0.2.4 glue_1.3.0       R6_2.3.0        
[33] readxl_1.1.0     rmarkdown_1.11   modelr_0.1.2     magrittr_1.5    
[37] whisker_0.3-2    backports_1.1.2  scales_1.0.0     htmltools_0.3.6 
[41] rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2 stringi_1.2.4   
[45] lazyeval_0.2.1   munsell_0.5.0    broom_0.5.0      crayon_1.3.4