Last updated: 2019-04-24
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Knit directory: apaQTL/analysis/
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File | Version | Author | Date | Message |
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html | ccebe33 | brimittleman | 2019-04-24 | Build site. |
Rmd | 4febc15 | brimittleman | 2019-04-24 | return to original SAF |
html | 627fc45 | brimittleman | 2019-04-24 | Build site. |
html | dd07ef7 | brimittleman | 2019-04-24 | Build site. |
Rmd | 6dc25d8 | brimittleman | 2019-04-24 | update after SAF bug |
html | 26058a5 | brimittleman | 2019-04-24 | Build site. |
Rmd | 76900e4 | brimittleman | 2019-04-24 | add plots after parsing for 1 site per |
html | 12d1cb0 | brimittleman | 2019-04-23 | Build site. |
Rmd | e985ecd | brimittleman | 2019-04-23 | add initial plot |
html | 214c05c | brimittleman | 2019-04-23 | Build site. |
Rmd | 27b11e3 | brimittleman | 2019-04-23 | start signal site analysis |
library(tidyverse)
── Attaching packages ───────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.0 ✔ purrr 0.3.2
✔ tibble 2.1.1 ✔ dplyr 0.8.0.1
✔ tidyr 0.8.3 ✔ stringr 1.3.1
✔ readr 1.3.1 ✔ forcats 0.3.0
── Conflicts ──────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
library(workflowr)
This is workflowr version 1.3.0
Run ?workflowr for help getting started
library(reshape2)
Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':
smiths
In this analysis I will plot the distribution of signal sites upstream of the PAS I have found.
First I use a python script to make a bed file with the 100 base pairs upsream of the PAS:
module load Anaconda3
source activate three-prime-env
mkdir ../data/SignalSiteFiles
python Upstream100Bases_general.py ../data/PAS/APAPAS_GeneLocAnno.5perc.bed ../data/SignalSiteFiles/APAPAS_100up.bed
Now I use bedtools nuc to get the sequence for each of these regions:
sbatch getSeq100up.sh
I can now run the DistPAS2Sig.py which will give me the location for the signal site for each PAS.I am running this with the 12 most common PAS signal sites.
sbatch run_distPAS2Sig.sh
Upload all of the results:
Loc_AATAAA= read.table("../data/SignalSiteFiles/Loc_AATAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AATAAA")
Loc_AAAAAG= read.table("../data/SignalSiteFiles/Loc_AAAAAG_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AAAAAG")
Loc_AATACA= read.table("../data/SignalSiteFiles/Loc_AATACA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AATACA")
Loc_AATAGA= read.table("../data/SignalSiteFiles/Loc_AATAGA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AATAGA")
Loc_AATATA= read.table("../data/SignalSiteFiles/Loc_AATATA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AATATA")
Loc_ACTAAA= read.table("../data/SignalSiteFiles/Loc_ACTAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="ACTAAA")
Loc_AGTAAA= read.table("../data/SignalSiteFiles/Loc_AGTAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AGTAAA")
Loc_ATTAAA= read.table("../data/SignalSiteFiles/Loc_ATTAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="ATTAAA")
Loc_CATAAA= read.table("../data/SignalSiteFiles/Loc_CATAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="CATAAA")
Loc_GATAAA= read.table("../data/SignalSiteFiles/Loc_GATAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="GATAAA")
Loc_TATAAA= read.table("../data/SignalSiteFiles/Loc_TATAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="TATAAA")
Loc_AAAAAA= read.table("../data/SignalSiteFiles/Loc_AAAAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AAAAAA")
Join these together:
AllsiteDF=as.data.frame(rbind(Loc_AATAAA,Loc_AAAAAG,Loc_AATACA,Loc_AATAGA,Loc_AATATA,Loc_ACTAAA,Loc_AGTAAA,Loc_ATTAAA, Loc_GATAAA,Loc_TATAAA,Loc_CATAAA, Loc_AAAAAA))
AllsiteDF_sep = AllsiteDF %>% separate(PAS, int=c("GenePeak", "Location"), sep="_")
ggplot(AllsiteDF_sep, aes(x=Distance2PAS, by=Site, col=Site)) + stat_ecdf() + facet_wrap(~Location)
Check to see if any PAS have more than one signal site detected:
AllsiteDFmultsites=AllsiteDF %>% group_by(PAS) %>% mutate(nSites=n()) %>% filter(nSites>1)
First take the perfect match within 50 bp then use the closest.
Write out the AllSite in order to use it in the chooseSignalSite.py script:
write.table(AllsiteDF, file="../data/SignalSiteFiles/AllSignalSite.txt", quote=F, col.names = F, row.names = F, sep="\t")
python chooseSignalSite.py ../data/SignalSiteFiles/AllSignalSite.txt ../data/SignalSiteFiles/AllSignalSite_1perPAS.txt
AllsiteDF_1per=read.table(file="../data/SignalSiteFiles/AllSignalSite_1perPAS.txt", col.names = colnames(AllsiteDF)) %>% mutate(NegCount=-1*as.integer(as.character(Distance2PAS)))
Plot
dist2signalsiteplot=ggplot(AllsiteDF_1per, aes(group=Site, x=NegCount, fill=Site)) + geom_histogram(position="stack",bins=50 ) + labs(x="Distance from PAS", y="N annotated Sites", title="Location of annotated signal sites")
dist2signalsiteplot
Version | Author | Date |
---|---|---|
dd07ef7 | brimittleman | 2019-04-24 |
ggsave(dist2signalsiteplot, file="../output/SignalSitePlot.png")
Saving 7 x 5 in image
Seperate by location:
AllsiteDF_1per_sep= AllsiteDF_1per %>%separate(PAS, int=c("GenePeak", "Location"), sep="_")
dist2signalsiteplot_byloc=ggplot(AllsiteDF_1per_sep, aes(group=Site, x=NegCount, fill=Site)) + geom_histogram(position="stack",bins=50 ) + labs(x="Distance from PAS", y="N annotated Sites", title="Location of annotated signal sites") + facet_wrap(~Location)
dist2signalsiteplot_byloc
Version | Author | Date |
---|---|---|
dd07ef7 | brimittleman | 2019-04-24 |
ggsave(dist2signalsiteplot_byloc, file="../output/SignalSitePlotbyLoc.png")
Saving 7 x 5 in image
.
Next plot: look at presence of signal site compared to PAS usage
I need to look at the mean usage and fraction it by if the peak has a signal site.
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 workflowr_1.3.0 forcats_0.3.0 stringr_1.3.1
[5] dplyr_0.8.0.1 purrr_0.3.2 readr_1.3.1 tidyr_0.8.3
[9] tibble_2.1.1 ggplot2_3.1.0 tidyverse_1.2.1
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_0.23.0 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 labeling_0.3
[45] stringi_1.2.4 lazyeval_0.2.1 munsell_0.5.0 broom_0.5.1
[49] crayon_1.3.4