Last updated: 2019-09-06
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Knit directory: apaQTL/analysis/
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Unstaged changes:
Modified: analysis/Readdistagainstfeatures.Rmd
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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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── Conflicts ───────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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library(BSgenome)
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library(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
#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