Last updated: 2019-06-13
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Knit directory: apaQTL/analysis/
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Unstaged changes:
Modified: analysis/Readdistagainstfeatures.Rmd
Modified: analysis/index.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
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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
Deleted: code/test.txt
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Rmd | 02db3a7 | brimittleman | 2019-06-13 | fixbug |
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Rmd | 1f203f7 | brimittleman | 2019-06-12 | add examples |
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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)
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✔ tidyr 0.8.3 ✔ stringr 1.3.1
✔ readr 1.3.1 ✔ forcats 0.3.0
── Conflicts ────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
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library(BSgenome)
Loading required package: BiocGenerics
Loading required package: parallel
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clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
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is.unsorted, lapply, lengths, Map, mapply, match, mget, order,
paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind,
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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'
The following object is masked from 'package:tidyr':
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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)
Version | Author | Date |
---|---|---|
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 |
---|---|---|
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
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 15 101610289 101610300 LRRK1:peak47802:utr3 67 + 0.818182
2 19 16438656 16438667 KLF2:peak64649:utr3 454 + 0.909091
3 19 16438656 16438667 KLF2:peak64650:utr3 63 + 0.909091
4 19 43978507 43978518 PHLDB3:peak66481:utr3 -746 - 1.000000
5 2 131132114 131132125 PTPN18:peak74525:utr3 -776 + 0.818182
6 2 197855147 197855158 ANKRD44:peak77452:intron 20 - 1.000000
pctGC numA numC numG numT numN numoth length seq CorrectSeq
1 0.181818 8 1 1 1 0 0 11 AAAATAAACAG AAAATAAACAG
2 0.090909 8 1 0 2 0 0 11 AAAATAAAACT AAAATAAAACT
3 0.090909 8 1 0 2 0 0 11 AAAATAAAACT AAAATAAAACT
4 0.000000 1 0 0 10 0 0 11 ttttatttttt AAAAAATAAAA
5 0.181818 8 0 2 1 0 0 11 AGAAATAAAAG AGAAATAAAAG
6 0.000000 5 0 0 6 0 0 11 TTTATTTAAAA TTTTAAATAAA
sig
1 1
2 1
3 1
4 1
5 1
6 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 15 101610289 101610300 LRRK1:peak47802:utr3 67 + 0.818182
2 15 101610289 101610300 LRRK1:peak47806:utr3 -2713 + 0.818182
3 19 16438656 16438667 KLF2:peak64649:utr3 454 + 0.909091
4 19 16438656 16438667 KLF2:peak64650:utr3 63 + 0.909091
5 19 43978507 43978518 PHLDB3:peak66481:utr3 -746 - 1.000000
6 19 58433644 58433655 ZNF418:peak68038:utr3 18 - 0.818182
7 2 33775278 33775289 RASGRP3:peak69728:utr3 -12759 + 1.000000
8 4 84367754 84367765 MRPS18C:peak99427:utr3 -14730 + 0.727273
9 6 167409236 167409247 MIR3939:peak119106:end 1476 - 1.000000
10 6 167409236 167409247 MIR3939:peak119107:end -1662 - 1.000000
pctGC numA numC numG numT numN numoth length seq CorrectSeq
1 0.181818 8 1 1 1 0 0 11 AAAATAAACAG AAAATAAACAG
2 0.181818 8 1 1 1 0 0 11 AAAATAAACAG AAAATAAACAG
3 0.090909 8 1 0 2 0 0 11 AAAATAAAACT AAAATAAAACT
4 0.090909 8 1 0 2 0 0 11 AAAATAAAACT AAAATAAAACT
5 0.000000 1 0 0 10 0 0 11 ttttatttttt AAAAAATAAAA
6 0.181818 3 2 0 6 0 0 11 cttttattaac GTTAATAAAAG
7 0.000000 8 0 0 3 0 0 11 ataataaataa ATAATAAATAA
8 0.272727 7 2 1 1 0 0 11 agccAATAAAA AGCCAATAAAA
9 0.000000 2 0 0 9 0 0 11 tattttttatt AATAAAAAATA
10 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
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
Make boxplots for these:
I wrote the code for these in the example plots analysis
Fraction=$1 gene=$2 chrom=$3 snp=$4 peakID=$5
sbatch run_qtlFacetBoxplots.sh Nuclear ZNF418 19 rs75991626 peak68038
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