Last updated: 2019-06-20

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Knit directory: apaQTL/analysis/

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Unstaged changes:
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    Modified:   code/makePheno.py
    Deleted:    code/test.txt

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File Version Author Date Message
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