Last updated: 2019-03-13

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Knit directory: threeprimeseq/analysis/

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    Ignored:    .Rproj.user/
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Unstaged changes:
    Modified:   analysis/28ind.peak.explore.Rmd
    Modified:   analysis/CompareLianoglouData.Rmd
    Modified:   analysis/NewPeakPostMP.Rmd
    Modified:   analysis/NuclearSpecQTL.Rmd
    Modified:   analysis/PeakToXper.Rmd
    Modified:   analysis/apaQTLoverlapGWAS.Rmd
    Modified:   analysis/characterize_apaQTLs.Rmd
    Modified:   analysis/cleanupdtseq.internalpriming.Rmd
    Modified:   analysis/coloc_apaQTLs_protQTLs.Rmd
    Modified:   analysis/dif.iso.usage.leafcutter.Rmd
    Modified:   analysis/diff_iso_pipeline.Rmd
    Modified:   analysis/explainpQTLs.Rmd
    Modified:   analysis/explore.filters.Rmd
    Modified:   analysis/fixBWChromNames.Rmd
    Modified:   analysis/flash2mash.Rmd
    Modified:   analysis/initialPacBioQuant.Rmd
    Modified:   analysis/mispriming_approach.Rmd
    Modified:   analysis/overlapMolQTL.Rmd
    Modified:   analysis/overlapMolQTL.opposite.Rmd
    Modified:   analysis/overlap_qtls.Rmd
    Modified:   analysis/peakOverlap_oppstrand.Rmd
    Modified:   analysis/peakQCPPlots.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.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File Version Author Date Message
html 416ce21 Briana Mittleman 2019-02-28 Build site.
Rmd ea34b3c Briana Mittleman 2019-02-28 add nuclear spec peaks 
library(tidyverse)
── Attaching packages ───────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.0       ✔ purrr   0.3.1  
✔ tibble  2.0.1       ✔ dplyr   0.8.0.1
✔ tidyr   0.8.3       ✔ stringr 1.4.0  
✔ readr   1.3.1       ✔ forcats 0.4.0  
Warning: package 'tibble' was built under R version 3.5.2
Warning: package 'tidyr' was built under R version 3.5.2
Warning: package 'purrr' was built under R version 3.5.2
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── 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] workflowr_1.2.0 forcats_0.4.0   stringr_1.4.0   dplyr_0.8.0.1  
 [5] purrr_0.3.1     readr_1.3.1     tidyr_0.8.3     tibble_2.0.1   
 [9] ggplot2_3.1.0   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0       cellranger_1.1.0 plyr_1.8.4       pillar_1.3.1    
 [5] compiler_3.5.1   git2r_0.24.0     tools_3.5.1      digest_0.6.18   
 [9] lubridate_1.7.4  jsonlite_1.6     evaluate_0.13    nlme_3.1-137    
[13] gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2  rlang_0.3.1     
[17] cli_1.0.1        rstudioapi_0.9.0 yaml_2.2.0       haven_2.1.0     
[21] xfun_0.5         withr_2.1.2      xml2_1.2.0       httr_1.4.0      
[25] knitr_1.21       hms_0.4.2        generics_0.0.2   fs_1.2.6        
[29] rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5 glue_1.3.0      
[33] R6_2.4.0         readxl_1.3.0     rmarkdown_1.11   modelr_0.1.4    
[37] magrittr_1.5     whisker_0.3-2    backports_1.1.3  scales_1.0.0    
[41] htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0 colorspace_1.4-0
[45] stringi_1.3.1    lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1     
[49] crayon_1.3.4