Last updated: 2019-09-04
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Knit directory: apaQTL/analysis/
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I am interested in finding examples of intronic PAS that show RNAseq signatures upstream of the PAS but not downstream. To do this I will create a ratio of reads upstream/reads downstream standardized by the length of the region (up/downstream).
To do this I can use the work I did previously. Here I assigned each intronic PAS to an intron. I will do this analysis with the total fraction because I will be looking at steady state RNA seq.
library(workflowr)
This is workflowr version 1.4.0
Run ?workflowr for help getting started
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()
mkdir ../data/intronRNAratio
totIntronicPeaks=read.table("../data/peaks_5perc/APApeak_Peaks_GeneLocAnno.Total.5perc.fc", stringsAsFactors = F, header = F,col.names = c("chr", "start", "end", "gene", "loc", "strand", "peak", "avgUsage")) %>% filter(loc=="intron")
pas2intronTot=read.table("../data/intron_analysis/TotalIntronPeaksontoIntrons.bed",col.names = c("intronCHR", "intronStart", "intronEnd", "gene", "score", "strand", "peakCHR", "peakStart", "peakEnd", "PeakID", "meanUsage", "peakStrand")) %>% mutate(PASloc=ifelse(strand=="+", peakEnd, peakStart)) %>% dplyr::select(intronCHR,intronStart, intronEnd, gene, strand, PeakID, PASloc ,meanUsage)
write.table(pas2intronTot, "../data/intronRNAratio/TotalIntronicPAS2Intron.txt", quote = F, row.names = F, col.names = F, sep="\t")
Make upstream and downstream PAS saf files using python.
python getIntronUpstreamPAS.py
python getIntronDownstreamPAS.py
These make Bed and SAF files. I will use the SAF files for feature counts with all of the RNA seq. These files are in /project2/yangili1/LCL/RNAseqGeuvadisBams/*.final.bam
sbatch FC_intornUpandDownsteamPAS.sh
Downstream Results:
downstream=read.table("../data/intronRNAratio/DownstreamIntron.fc", header = T,stringsAsFactors = F)
downstreamMean=rowSums(downstream[,7:ncol(downstream)])
downstreanMeanDF=as.data.frame(cbind(downstream[,1:6], downstreamMean)) %>% mutate(DownstreamMean_st=downstreamMean/Length) %>% select(Geneid,DownstreamMean_st )
Upstream Results:
upstream=read.table("../data/intronRNAratio/UpstreamIntron.fc", header = T,stringsAsFactors = F)
upstreamMean=rowSums(upstream[,7:ncol(upstream)])
upstreamMeanDF=as.data.frame(cbind(upstream[,1:6], upstreamMean)) %>% mutate(UpstreamMean_st=upstreamMean/Length) %>% select(Geneid,UpstreamMean_st )
Join Results:
I will use upstream - downstream
pas2intronTot_peaks=pas2intronTot %>% separate(PeakID, into=c("PAS", "gene", "loc"), sep=":") %>% select(PAS)
UpandDown=upstreamMeanDF %>% inner_join(downstreanMeanDF, by="Geneid") %>% mutate(UpMinusDown=UpstreamMean_st-DownstreamMean_st) %>% arrange(desc(UpMinusDown)) %>% separate(Geneid, sep=":", into=c("PAS", "gene", "loc", "PASloc", "Usage")) %>% semi_join(pas2intronTot_peaks, by="PAS")
summary(UpandDown$UpMinusDown)
Min. 1st Qu. Median Mean 3rd Qu. Max.
-1246.7926 -0.0324 0.0000 0.3251 0.2171 541.0908
I want to know how many are positive:
MoreUp=UpandDown %>% filter(UpMinusDown>0)
summary(MoreUp$UpMinusDown)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.0352 0.2268 2.7767 1.1271 541.0908
nrow(MoreUp)
[1] 4131
4131 examples where there are more reads upstream in the intron than downstream.
To look for examples:
head(MoreUp)
PAS gene loc PASloc Usage UpstreamMean_st
1 peak5519 HFM1 intron 91852934 0.889259259259259 541.0908
2 peak31203 NAP1L1 intron 76443346 0.906666666666667 473.3895
3 peak87311 BHLHE40 intron 5023296 0.866111111111111 321.8041
4 peak50349 ATXN2L intron 28844314 0.0661111111111111 217.0654
5 peak7377 NOTCH2NL intron 145277488 0.715925925925926 186.1226
6 peak60248 MYL12A intron 3248635 0.862777777777778 175.4362
DownstreamMean_st UpMinusDown
1 0.000000 541.0908
2 99.452381 373.9371
3 6.033469 315.7707
4 14.939394 202.1260
5 12.959547 173.1631
6 9.109116 166.3271
write.table(MoreUp, file="../data/intronRNAratio/TotalPAS_MoreUpstreamRNAreads.txt", col.names = T, row.names = F, quote = F, sep="\t" )
We also have nacent RNA seq. I will do this with nuclear intronic PAS.
nucIntronicPeaks=read.table("../data/peaks_5perc/APApeak_Peaks_GeneLocAnno.Nuclear.5perc.fc", stringsAsFactors = F, header = F,col.names = c("chr", "start", "end", "gene", "loc", "strand", "peak", "avgUsage")) %>% filter(loc=="intron")
pas2intronNuc=read.table("../data/intron_analysis/IntronPeaksontoIntrons.bed",col.names = c("intronCHR", "intronStart", "intronEnd", "gene", "score", "strand", "peakCHR", "peakStart", "peakEnd", "PeakID", "meanUsage", "peakStrand")) %>% mutate(PASloc=ifelse(strand=="+", peakEnd, peakStart)) %>% dplyr::select(intronCHR,intronStart, intronEnd, gene, strand, PeakID, PASloc ,meanUsage)
write.table(pas2intronNuc, "../data/intronRNAratio/NuclearIntronicPAS2Intron.txt", quote = F, row.names = F, col.names = F, sep="\t")
Make upstream and downstream PAS saf files using python.
python getUpstreamIntronNuclear.py
python getDownstreamIntronNuclear.py
These make Bed and SAF files. I will use the SAF files for feature counts with all of the nacent RNA seq.
sbatch FC_NucintornUpandDown.sh
Downstream Results:
downstreamNuc=read.table("../data/intronRNAratio/NuclearDownstreamIntron.fc", col.names = c("Geneid","Chr","Start", "End", "Strand", "Length", "Downstream"), header = T,stringsAsFactors = F)%>% mutate(Downstream_st=Downstream/Length) %>% select(Geneid,Downstream_st )
upstreamNuc=read.table("../data/intronRNAratio/NuclearUpstreamIntron.fc", col.names = c("Geneid","Chr","Start", "End", "Strand", "Length", "Upstream"), header = T,stringsAsFactors = F)%>% mutate(Upstream_st=Upstream/Length) %>% select(Geneid,Upstream_st )
pas2intronNucPAS=pas2intronNuc %>% separate(PeakID, into=c("PAS", "gene", "loc"), sep=":") %>% select(PAS)
UpandDown_nuc=upstreamNuc %>% inner_join(downstreamNuc, by="Geneid") %>% mutate(UpMinusDown=Upstream_st-Downstream_st) %>% arrange(desc(UpMinusDown)) %>% separate(Geneid, sep=":", into=c("PAS", "gene", "loc", "PASloc", "Usage")) %>% semi_join(pas2intronNucPAS, by="PAS")
summary(UpandDown_nuc$UpMinusDown)
Min. 1st Qu. Median Mean 3rd Qu. Max.
-269.33846 -0.06775 0.01552 0.08418 0.36174 57.52867
MoreUpNuc=UpandDown_nuc %>% filter(UpMinusDown>0)
summary(MoreUpNuc$UpMinusDown)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00001 0.08524 0.29229 0.66527 0.74025 57.52867
nrow(MoreUpNuc)
[1] 7625
head(MoreUpNuc)
PAS gene loc PASloc Usage Upstream_st
1 peak18198 BTAF1 intron 93719017 0.468148148148148 68.64486
2 peak83249 MIR155HG intron 26939933 0.0524074074074074 44.98583
3 peak86087 DDX17 intron 38887949 0.0955555555555556 89.22321
4 peak9172 SLAMF1 intron 160597898 0.123518518518519 17.37310
5 peak94269 RSRC1 intron 157921145 0.0548148148148148 18.57143
6 peak111306 IRF4 intron 403950 0.0694444444444444 15.43238
Downstream_st UpMinusDown
1 11.116190 57.52867
2 0.000000 44.98583
3 57.814883 31.40833
4 0.000000 17.37310
5 2.275192 16.29624
6 0.000000 15.43238
write.table(MoreUpNuc, file="../data/intronRNAratio/NuclearPAS_MoreUpstreamNascentreads.txt", col.names = T, row.names = F, quote = F, sep="/t" )
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 workflowr_1.4.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.0 cellranger_1.1.0 plyr_1.8.4 compiler_3.5.1
[5] pillar_1.3.1 git2r_0.25.2 highr_0.7 tools_3.5.1
[9] digest_0.6.18 lubridate_1.7.4 jsonlite_1.6 evaluate_0.12
[13] nlme_3.1-137 gtable_0.2.0 lattice_0.20-38 pkgconfig_2.0.2
[17] rlang_0.4.0 cli_1.1.0 rstudioapi_0.10 yaml_2.2.0
[21] haven_1.1.2 withr_2.1.2 xml2_1.2.0 httr_1.3.1
[25] knitr_1.20 hms_0.4.2 generics_0.0.2 fs_1.3.1
[29] rprojroot_1.3-2 grid_3.5.1 tidyselect_0.2.5 glue_1.3.0
[33] R6_2.3.0 readxl_1.1.0 rmarkdown_1.10 modelr_0.1.2
[37] magrittr_1.5 whisker_0.3-2 backports_1.1.2 scales_1.0.0
[41] htmltools_0.3.6 rvest_0.3.2 assertthat_0.2.0 colorspace_1.3-2
[45] stringi_1.2.4 lazyeval_0.2.1 munsell_0.5.0 broom_0.5.1
[49] crayon_1.3.4