Motif Distruption

Last updated: 2019-06-12

Checks: 6 0

Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/DiffIsoAnalysis.Rmd
    Modified:   analysis/PASusageQC.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/choosePCs.Rmd
    Modified:   analysis/corrbetweenind.Rmd
    Modified:   analysis/mapapaQTL.Rmd
    Modified:   analysis/nascenttranscription.Rmd
    Modified:   analysis/nucintronicanalysis.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/rna_netseq_h3k12ac.Rmd
    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
    Deleted:    code/Upstream10Bases_general.py
    Modified:   code/apaQTLCorrectPvalMakeQQ.R
    Modified:   code/apaQTL_permuted.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/bam2bw.sh
    Modified:   code/bed2saf.py
    Modified:   code/cluster.json
    Modified:   code/config.yaml
    Modified:   code/environment.yaml
    Deleted:    code/test.txt

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	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 ──

✔ 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()

library(BSgenome)

Loading required package: BiocGenerics

Loading required package: parallel


Attaching package: 'BiocGenerics'

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    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

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Loading required package: rtracklayer

library(workflowr)

This is workflowr version 1.3.0
Run ?workflowr for help getting started

library(reshape2)


Attaching package: 'reshape2'

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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)

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)

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

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

  chr     start       end                     name Dist strand    pctAT
1  19  16438656  16438667      KLF2:peak64649:utr3  454      + 0.909091
2  19  16438656  16438667      KLF2:peak64650:utr3   63      + 0.909091
3  19  58433644  58433655    ZNF418:peak68038:utr3   18      - 0.818182
4   2 197855147 197855158 ANKRD44:peak77452:intron   20      - 1.000000
5   7 107562562 107562573       DLD:peak124968:end   61      + 0.727273
     pctGC numA numC numG numT numN numoth length         seq  CorrectSeq
1 0.090909    8    1    0    2    0      0     11 AAAATAAAACT AAAATAAAACT
2 0.090909    8    1    0    2    0      0     11 AAAATAAAACT AAAATAAAACT
3 0.181818    3    2    0    6    0      0     11 cttttattaac GTTAATAAAAG
4 0.000000    5    0    0    6    0      0     11 TTTATTTAAAA TTTTAAATAAA
5 0.272727    6    3    0    2    0      0     11 ctcaataaaca CTCAATAAACA
  sig
1   1
2   1
3   1
4   1
5   1

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

   chr     start       end                     name   Dist strand    pctAT
1   11 121500616 121500627     SORL1:peak26194:utr3     64      + 0.818182
2   12  54712768  54712779   COPZ1:peak30032:intron  -7514      + 0.909091
3   15 101610289 101610300     LRRK1:peak47802:utr3     67      + 0.818182
4   15 101610289 101610300     LRRK1:peak47806:utr3  -2713      + 0.818182
5   19  16438656  16438667      KLF2:peak64649:utr3    454      + 0.909091
6   19  16438656  16438667      KLF2:peak64650:utr3     63      + 0.909091
7   19  58433644  58433655    ZNF418:peak68038:utr3     18      - 0.818182
8    2 197855147 197855158 ANKRD44:peak77452:intron     20      - 1.000000
9    4  84367754  84367765   MRPS18C:peak99426:utr3 -14538      + 0.727273
10   4  84367754  84367765   MRPS18C:peak99427:utr3 -14730      + 0.727273
11   6 167409236 167409247   MIR3939:peak119106:end   1476      - 1.000000
      pctGC numA numC numG numT numN numoth length         seq  CorrectSeq
1  0.181818    7    2    0    2    0      0     11 TAATAAAAACC TAATAAAAACC
2  0.090909    6    1    0    4    0      0     11 catttaataaa CATTTAATAAA
3  0.181818    8    1    1    1    0      0     11 AAAATAAACAG AAAATAAACAG
4  0.181818    8    1    1    1    0      0     11 AAAATAAACAG AAAATAAACAG
5  0.090909    8    1    0    2    0      0     11 AAAATAAAACT AAAATAAAACT
6  0.090909    8    1    0    2    0      0     11 AAAATAAAACT AAAATAAAACT
7  0.181818    3    2    0    6    0      0     11 cttttattaac GTTAATAAAAG
8  0.000000    5    0    0    6    0      0     11 TTTATTTAAAA TTTTAAATAAA
9  0.272727    7    2    1    1    0      0     11 agccAATAAAA AGCCAATAAAA
10 0.272727    7    2    1    1    0      0     11 agccAATAAAA AGCCAATAAAA
11 0.000000    2    0    0    9    0      0     11 tattttttatt AATAAAAAATA
   sig
1    1
2    1
3    1
4    1
5    1
6    1
7    1
8    1
9    1
10   1
11   1

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.

SORL1:peak26194:utr3 rs75085036 A-T disrupt signal site for 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
DLD:peak124968:end rs144143960 A-G disrupt site for peak124968

This is not the best way to look at this. It may be a snp in LD. Also this is the distance to the peak not the PAS.

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_1.3.0      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            GenomeInfoDbData_1.2.0     
 [7] cellranger_1.1.0            Rsamtools_1.34.0           
 [9] yaml_2.2.0                  pillar_1.3.1               
[11] backports_1.1.2             lattice_0.20-38            
[13] glue_1.3.0                  digest_0.6.18              
[15] rvest_0.3.2                 colorspace_1.3-2           
[17] htmltools_0.3.6             Matrix_1.2-15              
[19] plyr_1.8.4                  XML_3.98-1.16              
[21] pkgconfig_2.0.2             broom_0.5.1                
[23] haven_1.1.2                 zlibbioc_1.28.0            
[25] scales_1.0.0                whisker_0.3-2              
[27] BiocParallel_1.16.0         git2r_0.25.2               
[29] generics_0.0.2              withr_2.1.2                
[31] SummarizedExperiment_1.12.0 lazyeval_0.2.1             
[33] cli_1.0.1                   magrittr_1.5               
[35] crayon_1.3.4                readxl_1.1.0               
[37] evaluate_0.12               fs_1.2.6                   
[39] nlme_3.1-137                xml2_1.2.0                 
[41] tools_3.5.1                 hms_0.4.2                  
[43] matrixStats_0.54.0          munsell_0.5.0              
[45] DelayedArray_0.8.0          compiler_3.5.1             
[47] rlang_0.3.1                 grid_3.5.1                 
[49] RCurl_1.95-4.11             rstudioapi_0.10            
[51] labeling_0.3                bitops_1.0-6               
[53] rmarkdown_1.10              gtable_0.2.0               
[55] R6_2.3.0                    GenomicAlignments_1.18.0   
[57] lubridate_1.7.4             knitr_1.20                 
[59] rprojroot_1.3-2             stringi_1.2.4              
[61] Rcpp_1.0.0                  tidyselect_0.2.5

Motif Distruption

Briana Mittleman

6/10/2019