Last updated: 2019-09-06

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
    Modified:   code/SnakefilefiltPAS
    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
    Modified:   code/mergeAllBam.sh
    Modified:   code/mergeByFracBam.sh
    Modified:   code/mergePeaks.sh
    Modified:   code/peakFC.sh
    Modified:   code/snakemake.batch
    Modified:   code/snakemakePAS.batch
    Modified:   code/snakemakefiltPAS.batch
    Modified:   code/submit-snakemake.sh
    Modified:   code/submit-snakemakePAS.sh
    Modified:   code/submit-snakemakefiltPAS.sh
    Deleted:    code/test.txt
    Modified:   data/MetaDataSequencing.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
html 023fd9f brimittleman 2019-06-13 Build site.
Rmd 02db3a7 brimittleman 2019-06-13 fixbug
html e783f5c brimittleman 2019-06-12 Build site.
Rmd 1f203f7 brimittleman 2019-06-12 add examples
html 5d71c2e brimittleman 2019-06-10 Build site.
Rmd c5fe1c2 brimittleman 2019-06-10 add motif disruption

In this analysis I will identify apaQTL that modify signal sites for the associated PAS. To do this I will look at the sequences 5bp up and downtream of each QTL snp and look for evidence of AATAAA disruption.

library(tidyverse)
── Attaching packages ────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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── Conflicts ───────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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library(BSgenome)
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Attaching package: 'IRanges'
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    collapse, desc, slice
The following object is masked from 'package:purrr':

    reduce
Loading required package: GenomeInfoDb
Loading required package: GenomicRanges
Loading required package: Biostrings
Loading required package: XVector

Attaching package: 'XVector'
The following object is masked from 'package:purrr':

    compact

Attaching package: 'Biostrings'
The following object is masked from 'package:base':

    strsplit
Loading required package: rtracklayer
library(workflowr)
This is workflowr version 1.4.0
Run ?workflowr for help getting started
library(reshape2)

Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':

    smiths

Get bedfiles for qtls with the strand:

python QTL2bed_withstrand.py Total 
python QTL2bed_withstrand.py Nuclear

Make bedfile with 5 bases upstream and downstream of snp. Names is gene:peak:loc and the score is the distance between PAS and the snp

totQTLbed=read.table("../data/apaQTLs/Total_apaQTLs4pc_5fdr.WITHSTRAND.bed", header = T, stringsAsFactors = F) %>%  mutate(start=as.integer(SNPstart)-4, end=as.integer(SNPend)+6,snpChrint=as.integer(SNPchr) ) %>% select(SNPchr, start, end, name, score, strand)

nucQTLbed=read.table("../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.WITHSTRAND.bed", header = T, stringsAsFactors = F) %>%  mutate(start=as.integer(SNPstart)-4, end=as.integer(SNPend)+6,snpChrint=as.integer(SNPchr) ) %>% select(SNPchr, start, end, name, score, strand)

Write these files so I can run bedtools nuc on them.

mkdir ../data/motifdistrupt
write.table(totQTLbed, file="../data/motifdistrupt/TotQTLregion.bed", col.names = F, row.names = F, quote = F, sep="\t")
write.table(nucQTLbed, file="../data/motifdistrupt/NucQTLregion.bed", col.names = F, row.names = F, quote = F, sep="\t")
sbatch qtlRegionseq.sh

Evaluate results:

totSeq=read.table("../data/motifdistrupt/TotQTLregionSequences.bed", header = F, stringsAsFactors = F, col.names =c("chr","start", "end", "name", "Dist", "strand", "pctAT", "pctGC", "numA", "numC", "numG", "numT", "numN", "numoth", "length", "seq") )

First plot the distance:

ggplot(totSeq,aes(x=Dist)) + geom_histogram(bins=100)

Version Author Date
023fd9f brimittleman 2019-06-13
e783f5c brimittleman 2019-06-12
nucSeq=read.table("../data/motifdistrupt/NucQTLregionSequences.bed", header = F, stringsAsFactors = F, col.names =c("chr","start", "end", "name", "Dist", "strand", "pctAT", "pctGC", "numA", "numC", "numG", "numT", "numN", "numoth", "length", "seq") )

First plot the distance:

ggplot(nucSeq,aes(x=Dist)) + geom_histogram(bins=100)

Version Author Date
023fd9f brimittleman 2019-06-13
e783f5c brimittleman 2019-06-12

Try with getting the sequences with bedtools getfasta (This reverse compliments the negative strand)

sbatch getQTLfastq.sh

extract the sequences from these to match with the nuc file above. This is important because this uses the reverse compliment. The snp is the 6th letter.
(fraction is Tot /Nuc)

python extractseqfromqtlfastq.py Tot
python extractseqfromqtlfastq.py Nuc

not needed

#Totsequp=read.table("../data/motifdistrupt/TotQTLregionSequenceOnly.txt", header = F, stringsAsFactors = F, col.names = "CorrectSeq")
#TotSeqComp=as.data.frame(cbind(totSeq,Totsequp)) %>% mutate(sig=ifelse(grepl("AATAAA",CorrectSeq),1, 0))
#TotSeqCompSig=TotSeqComp %>% filter(sig==1)
#TotSeqCompSig
#Nucsequp=read.table("../data/motifdistrupt/NucQTLregionSequenceOnly.txt", header = F, stringsAsFactors = F, col.names = "CorrectSeq")
#NucSeqComp=as.data.frame(cbind(nucSeq,Nucsequp)) %>% mutate(sig=ifelse(grepl("AATAAA",CorrectSeq),1, 0))
#NucSeqCompSig=NucSeqComp %>% filter(sig==1)
#NucSeqCompSig

These are pretty far from the peak and probably not the mechanism for these.

I can look at this another way by subsetting to those close to the peak.

#TotSeqComp_Close=TotSeqComp %>% filter(abs(Dist)<200) %>% select(name,Dist,CorrectSeq,sig)
#NucSeqComp_Close=NucSeqComp %>% filter(abs(Dist)<200) %>% select(name,Dist,CorrectSeq,sig)

Look at examples:

Nuclear:

Disrupt: - ZNF418 rs75991626 T C (break signal site for peak68038), also associated with increased usage of the downstream UTR pas.

  • LRRK1:peak47802:utr3 rs15342 T-C disrupt signal site for peak47802

  • KLF2:peak64650:utr3 rs11086029 T- A disrupt signal site for peak64650, increased usage of upstream pas still in UTR

  • ANKRD44:peak77452:intron rs715185 T-C disrupt signal site for ANKRD44

Creating site: - LOC102725022- peak43230 rs4566122 G->A creates a signal site for PAS

Total:

Disrupt: - ZNF418 rs75991626 T C (break signal site for peak68038), also associated with increased usage of the downstream UTR pas.

  • ANKRD44:peak77452:intron rs715185 T-C disrupt signal site for ANKRD44

  • KLF2:peak64650:utr3 rs11086029 T- A disrupt signal site for peak64650, increased usage of upstream pas still in UTR


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] stats4    parallel  stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] reshape2_1.4.3       workflowr_1.4.0      BSgenome_1.50.0     
 [4] rtracklayer_1.42.0   Biostrings_2.50.1    XVector_0.22.0      
 [7] GenomicRanges_1.34.0 GenomeInfoDb_1.18.1  IRanges_2.16.0      
[10] S4Vectors_0.20.1     BiocGenerics_0.28.0  forcats_0.3.0       
[13] stringr_1.3.1        dplyr_0.8.0.1        purrr_0.3.2         
[16] readr_1.3.1          tidyr_0.8.3          tibble_2.1.1        
[19] ggplot2_3.1.1        tidyverse_1.2.1     

loaded via a namespace (and not attached):
 [1] Biobase_2.42.0              httr_1.3.1                 
 [3] jsonlite_1.6                modelr_0.1.2               
 [5] assertthat_0.2.0            highr_0.7                  
 [7] GenomeInfoDbData_1.2.0      cellranger_1.1.0           
 [9] Rsamtools_1.34.0            yaml_2.2.0                 
[11] pillar_1.3.1                backports_1.1.2            
[13] lattice_0.20-38             glue_1.3.0                 
[15] digest_0.6.18               rvest_0.3.2                
[17] colorspace_1.3-2            htmltools_0.3.6            
[19] Matrix_1.2-15               plyr_1.8.4                 
[21] XML_3.98-1.16               pkgconfig_2.0.2            
[23] broom_0.5.1                 haven_1.1.2                
[25] zlibbioc_1.28.0             scales_1.0.0               
[27] whisker_0.3-2               BiocParallel_1.16.0        
[29] git2r_0.25.2                generics_0.0.2             
[31] withr_2.1.2                 SummarizedExperiment_1.12.0
[33] lazyeval_0.2.1              cli_1.1.0                  
[35] magrittr_1.5                crayon_1.3.4               
[37] readxl_1.1.0                evaluate_0.12              
[39] fs_1.3.1                    nlme_3.1-137               
[41] xml2_1.2.0                  tools_3.5.1                
[43] hms_0.4.2                   matrixStats_0.54.0         
[45] munsell_0.5.0               DelayedArray_0.8.0         
[47] compiler_3.5.1              rlang_0.4.0                
[49] grid_3.5.1                  RCurl_1.95-4.11            
[51] rstudioapi_0.10             labeling_0.3               
[53] bitops_1.0-6                rmarkdown_1.10             
[55] gtable_0.2.0                R6_2.3.0                   
[57] GenomicAlignments_1.18.0    lubridate_1.7.4            
[59] knitr_1.20                  rprojroot_1.3-2            
[61] stringi_1.2.4               Rcpp_1.0.0                 
[63] tidyselect_0.2.5