Last updated: 2020-02-16

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Unstaged changes:
    Modified:   analysis/ExploreNpas.Rmd
    Modified:   analysis/NuclearSpecIncludeNotTested.Rmd
    Modified:   analysis/PASdescriptiveplots.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/TSS.Rmd
    Modified:   analysis/decayAndStability.Rmd
    Modified:   analysis/miRNAdisrupt.Rmd
    Modified:   analysis/nascenttranscription.Rmd
    Modified:   analysis/nucSpecinEQTLs.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/pQTLexampleplot.Rmd
    Modified:   analysis/propeQTLs_explained.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/DistPAS2Sig.py
    Modified:   code/Script4NuclearQTLexamples.sh
    Modified:   code/Script4TotalQTLexamples.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/environment.yaml
    Modified:   code/run_qtlFacetBoxplots.sh
    Deleted:    code/test.txt
    Deleted:    reads_graphs.Rmd

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
Rmd c6fde88 brimittleman 2020-02-16 add res 
library(tidyverse)
── Attaching packages ──────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.1       ✔ 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()

The locuszoom package is in:
/scratch/midway2/brimittleman/locuszoom

Run example in /example. Use /scratch/midway2/brimittleman/locuszoom/bin/locuszoom –metal Kathiresan_2009_HDL.txt –refgene FADS1 –pop AFR –build hg19 –source 1000G_March2012

Modules to load:

module load R
module load plink
module load htslib

File formats needed:

MarkerName P-value Weight

This will be the snp and the nominal pvalues.

–refsnp –flank 500kb –metal

title=“My region” geneFontSize=1.1

I can do one then loop it.

mkdir ../data/LZ_both/ 
mkdir ../data/LZ_both/NuclearAssoc
mkdir ../data/LZ_both/RNAAssoc

/scratch/midway2/brimittleman/locuszoom/

I need a file with all of the apaQTL associations for the PAS of interest. Instead of genes of interest I can work with PAS of interest.

NuclearQTLs=read.table("../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.txt", head=T) %>% select(Peak) %>% unique()

write.table(NuclearQTLs,"../data/apaQTLs/NuclearQTL_PAS.txt", col.names = F, row.names = F, quote = F)

Select these from /project2/gilad/briana/apaQTL/data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm_AllChrom.txt

I will have to do this seperatly in case there are snp overlaps.

Make the file rsid and pval for each.

Write a python script that will take in the PAS and write out the file. I need to do this by gene. I will do the genes with the apaQTL genes.

NucQ=read.table("../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.txt", head=T) %>% select(Peak, sid, Gene)  

write.table(NucQ,"../data/apaQTLs/NuclearQTLs_PeakSNP.txt", quote=F, row.names = F, col.names = F)
 sbatch ExtractPASforLZ_run.sh 
 #rna file
 # ../data/molQTLs/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.AllNomRes.GeneName.txt
 #filter this
 sbatch extractGeneLZfiles.sh

Test one with RNA. rs7076314

Do this in ../data/LZ_both/ /scratch/midway2/brimittleman/locuszoom/bin/locuszoom –metal RNA_Res.txt –refsnp rs7076314 –flank 100kb title=“rs7076314” geneFontSize=1.1 –pop AFR –build hg19 –source 1000G_March2012

I need a file with the snp and PAS.

I can plot nuclear PAS wit hthe –bed-tracks 

NuclearPAS=read.table("../data/PAS/APApeak_Peaks_GeneLocAnno.Nuclear.5perc.sort.bed",col.names = c("chr","start", "end", "name", "score", "strand")) %>% mutate(Col="PAS", score2=".") %>% select(chr, start,end, Col, score2, strand)

write.table(NuclearPAS, "../data/LZ_both/NuclearPAS.bed", col.names = F, row.names = F, quote=F, sep="\t")

If I want to add GWAS, I can add –gwas-cat whole-cat_significant-only

Test

/scratch/midway2/brimittleman/locuszoom/bin/locuszoom --metal  RNAAssoc/ABTB1RNA_Res.txt --refsnp rs60645978 --flank 100kb title="rs60645978" geneFontSize=1.1 --pop AFR --build hg19 --source 1000G_March2012 --bed-tracks NuclearPAS.bed  theme="publication"

#peak91979
/scratch/midway2/brimittleman/locuszoom/bin/locuszoom --metal  NuclearAssoc/peak91979_NuclearResults4LZ.txt --refsnp rs60645978 --flank 100kb title="rs60645978" geneFontSize=1.1 --pop AFR --build hg19 --source 1000G_March2012 --bed-tracks NuclearPAS.bed  theme="publication"

Wrap all run in the dir i want the data to come out

cd ../data/LZ_both/RNAAssoc
sbatch CreateRNALZnucAPAqtls.sh

cd ../data/LZ_both/NuclearAssoc
sbatch CreateAPALZnucAPAqtls.sh

All of the plots moved to

/project2/gilad/briana/apaQTL/data/LZ_both/Both_Plots

They are named by the region.

I can look at these.

QTLs=read.table("../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.txt", head=T)

By eye 41 colocalize:

rs74064859 rs7523082 rs72786277 rs12468045 rs7587301 rs1859925 rs2067987 rs4974091 rs142895723 rs6785658 rs3181141 rs13327891 rs62291862 rs114653275 rs568902 rs2287371 rs113268660 rs73527330 rs145898840 rs12339205 rs73251478 rs7071284 rs17112791 rs7897454 rs41291460 rs41291462 rs3740400 rs7904973 (c10orf88) rs10840038 rs10891659 rs6650507 rs1956367 rs448113 rs10149617 rs4902346 rs75664871 rs113727694 rs115018779 rs78139339 rs6085202 rs2184159

of 524 succesful

(41/524)


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] forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1   purrr_0.3.2    
[5] readr_1.3.1     tidyr_0.8.3     tibble_2.1.1    ggplot2_3.1.1  
[9] tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.2       cellranger_1.1.0 plyr_1.8.4       compiler_3.5.1  
 [5] pillar_1.3.1     later_0.7.5      git2r_0.26.1     workflowr_1.6.0 
 [9] tools_3.5.1      digest_0.6.18    lubridate_1.7.4  jsonlite_1.6    
[13] evaluate_0.12    nlme_3.1-137     gtable_0.2.0     lattice_0.20-38 
[17] pkgconfig_2.0.2  rlang_0.4.0      cli_1.1.0        rstudioapi_0.10 
[21] yaml_2.2.0       haven_1.1.2      withr_2.1.2      xml2_1.2.0      
[25] httr_1.3.1       knitr_1.20       hms_0.4.2        generics_0.0.2  
[29] fs_1.3.1         rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5
[33] glue_1.3.0       R6_2.3.0         readxl_1.1.0     rmarkdown_1.10  
[37] modelr_0.1.2     magrittr_1.5     whisker_0.3-2    backports_1.1.2 
[41] scales_1.0.0     promises_1.0.1   htmltools_0.3.6  rvest_0.3.2     
[45] assertthat_0.2.0 colorspace_1.3-2 httpuv_1.4.5     stringi_1.2.4   
[49] lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1      crayon_1.3.4