Last updated: 2019-06-20
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Knit directory: apaQTL/analysis/
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Unstaged changes:
Modified: analysis/Readdistagainstfeatures.Rmd
Modified: analysis/molQTL.Rmd
Modified: analysis/overlapapaqtlsandeqtls.Rmd
Modified: analysis/signalsiteanalysis.Rmd
Modified: code/BothFracDTPlotGeneRegions.sh
Modified: code/Snakefile
Deleted: code/Upstream10Bases_general.py
Modified: code/apaQTLCorrectPvalMakeQQ.R
Modified: code/apaQTL_Nominal.sh
Modified: code/apaQTL_permuted.sh
Modified: code/apaQTLsnake.err
Modified: code/bam2bw.sh
Modified: code/bed2saf.py
Modified: code/cluster.json
Modified: code/clusterfiltPAS.json
Modified: code/config.yaml
Modified: code/environment.yaml
Modified: code/makePheno.py
Deleted: code/test.txt
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File | Version | Author | Date | Message |
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Rmd | 5a6fd30 | brimittleman | 2019-06-20 | add fc code |
html | b9b92aa | brimittleman | 2019-06-13 | Build site. |
Rmd | cf1e13e | brimittleman | 2019-06-13 | fix bug |
html | 38a75ec | brimittleman | 2019-06-04 | Build site. |
Rmd | dd9ab8e | brimittleman | 2019-06-04 | add nascent plots for first intron and apa only plot in chrom hmm |
html | a71262e | brimittleman | 2019-05-29 | Build site. |
Rmd | 6432c1b | brimittleman | 2019-05-29 | inititate nascent analysis |
library(workflowr)
This is workflowr version 1.3.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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library(reshape2)
Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':
smiths
We recently recieved nascent RNA seq from the Staley and Li lab collaboration. I want to explore this data in relation to my PAS.
Data: /project2/yangili1/yangili/RNAseqmap/nascentRNAseq/1*.sorted.bam
mkdir ../data/NascentRNA
First I will merge all of the individuals.
sbatch mergeBamNascent.sh
sbatch bam2bw.sh ../data/NascentRNA/NascentRNAMerged.sort.bam ../data/NascentRNA/NascentRNAMerged.sort.bw
First just look at the data along gene bodies:
(chromosomes have chr)
sbatch NascentRNAdtPlot.sh
I want to look at these at my PAS. First I will need to add CHR to my pas in /project2/gilad/briana/apaQTL/data/PAS/
sed -e 's/^/chr/' ../data/PAS/APAPAS_GeneLocAnno.5perc.bed > ../data/PAS/APAPAS_GeneLocAnno.5perc_withCHR.bed
sbatch NascnetRNAdtPlotPAS.sh
Look at the PAS used more in total and nuclear:
sbatch NascentRNAdtPlotTotPAS.sh
sbatch NascentRNAdtPlotNucPAS.sh
Intronic PAS
pasIntron=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc_withCHR.bed",col.names = c("Chr", "start", "end", "PeakID", "score", "strand")) %>% separate(PeakID, into=c("peaknum", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc"), sep="_") %>% filter(loc=="intron") %>% mutate(peakIDuse=paste(peaknum,gene, sep=":")) %>% select(Chr, start,end, peaknum, score, strand)
write.table(pasIntron, "../data/PAS/APAPAS_GeneLocAnno.5perc_withCHR_Intronic.bed", col.names = F, row.names = F, quote = F, sep="\t")
pasUTR=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc_withCHR.bed",col.names = c("Chr", "start", "end", "PeakID", "score", "strand")) %>% separate(PeakID, into=c("peaknum", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc"), sep="_") %>% filter(loc=="utr3") %>% mutate(peakIDuse=paste(peaknum,gene, sep=":"))%>% select(Chr, start,end, peakIDuse, score, strand)
write.table(pasUTR, "../data/PAS/APAPAS_GeneLocAnno.5perc_withCHR_3UTR.bed", col.names = F, row.names = F, quote = F, sep="\t")
sbatch NascentRNAdtPlot3UTRPAS.sh
sbatch NascentRNAdtPlotintronicPAS.sh
For the intronic PAS I want to seperate by those in the first intron and not.
allPAS=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc_withCHR.bed", col.names = c("chr", "start", "end", "peak", "score", "strand")) %>% separate(peak, into=c("peakNumber", "geneloc"), sep=":") %>% mutate(PeakNum=paste("peak", peakNumber, sep=""))
peakIntron=read.table(file="../data/intron_analysis/PeakIdwithIntronID.txt", header = T) %>% separate(PeakID, into=c("PeakNum", "gene", "loc"),sep=":") %>% select(PeakNum, Intornid)
Join these to write seperate bed files:
allPASwIntron=allPAS %>% inner_join(peakIntron, by="PeakNum")
FirstIntron= allPASwIntron %>% filter(Intornid==1 ) %>% select(-PeakNum,-Intornid)
write.table(FirstIntron, "../data/PAS/APAPAS_GeneLocAnno.5perc_withCHR_FirstIntron.bed",col.names = F, row.names = F, quote = F, sep="\t")
NotfirstIntron= allPASwIntron %>% filter(Intornid!=1 ) %>% select(-PeakNum,-Intornid)
write.table(NotfirstIntron, "../data/PAS/APAPAS_GeneLocAnno.5perc_withCHR_ExcludeFirstIntron.bed",col.names = F, row.names = F, quote = F, sep="\t")
sbatch NascentRNAdtPlotFirstintronicPAS.sh
sbatch NascentRNAdtPlotExcludeFirstintronicPAS.sh
I also want to quantify nascent seq at each gene for comparing to other data types. I will use feature counts.
I need to add the chr to /project2/gilad/briana/genome_anotation_data/refseq.ProteinCoding.noCHR.SAF
genes=read.table("/project2/gilad/briana/genome_anotation_data/refseq.ProteinCoding.noCHR.SAF", header = T, stringsAsFactors = F) %>% mutate(Chr=paste("chr", Chr, sep=""))
write.table(genes,"/project2/gilad/briana/genome_anotation_data/refseq.ProteinCoding.SAF", col.names = T, row.names = F, quote = F, sep="\t")
sbatch FC_nascentseq.sh
sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] reshape2_1.4.3 forcats_0.3.0 stringr_1.3.1 dplyr_0.8.0.1
[5] purrr_0.3.2 readr_1.3.1 tidyr_0.8.3 tibble_2.1.1
[9] ggplot2_3.1.1 tidyverse_1.2.1 workflowr_1.3.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.0 cellranger_1.1.0 pillar_1.3.1 compiler_3.5.1
[5] git2r_0.25.2 plyr_1.8.4 tools_3.5.1 digest_0.6.18
[9] lubridate_1.7.4 jsonlite_1.6 evaluate_0.12 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_0.10 yaml_2.2.0 haven_1.1.2
[21] withr_2.1.2 xml2_1.2.0 httr_1.3.1 knitr_1.20
[25] hms_0.4.2 generics_0.0.2 fs_1.2.6 rprojroot_1.3-2
[29] grid_3.5.1 tidyselect_0.2.5 glue_1.3.0 R6_2.3.0
[33] readxl_1.1.0 rmarkdown_1.10 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_0.5.1 crayon_1.3.4