Last updated: 2019-06-10
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Knit directory: apaQTL/analysis/
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Rmd | c753b24 | brimittleman | 2019-06-10 | add nuc res |
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Rmd | 3e5cc8e | brimittleman | 2019-06-07 | code up to FC |
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 1.3.0
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 instead of a ratio to deal with 0s
UpandDown=upstreamMeanDF %>% inner_join(downstreanMeanDF, by="Geneid") %>% mutate(ratio=UpstreamMean_st-DownstreamMean_st) %>% arrange(desc(ratio)) %>% separate(Geneid, sep=":", into=c("PAS", "gene", "loc", "PASloc", "Usage"))
summary(UpandDown$ratio)
Min. 1st Qu. Median Mean 3rd Qu. Max.
-26617.062 -0.103 0.000 -2.511 0.236 5380.027
I want to know how many are positive:
MoreUp=UpandDown %>% filter(ratio>0)
summary(MoreUp$ratio)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 0.064 0.346 7.631 2.044 5380.027
nrow(MoreUp)
[1] 13903
13903 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 peak113671 RPS18 intron 33240934 0.110925925925926 5404.273
2 peak56811 RPL27 intron 41152263 0.796296296296296 3739.972
3 peak78512 RPL37A intron 217365085 0.407407407407407 3284.307
4 peak70756 RPS27A intron 55460229 0.537037037037037 3233.041
5 peak113669 RPS18 intron 33240357 0.537222222222222 2942.865
6 peak20394 RPLP2 intron 810791 0.648148148148148 1683.759
DownstreamMean_st ratio
1 24.246212 5380.027
2 7.555006 3732.417
3 15.007150 3269.300
4 15.651852 3217.389
5 369.229167 2573.636
6 25.031017 1658.728
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(pas2intronTot, "../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 )
UpandDown_nuc=upstreamNuc %>% inner_join(downstreamNuc, by="Geneid") %>% mutate(ratio=Upstream_st-Downstream_st) %>% arrange(desc(ratio)) %>% separate(Geneid, sep=":", into=c("PAS", "gene", "loc", "PASloc", "Usage"))
summary(UpandDown_nuc$ratio)
Min. 1st Qu. Median Mean 3rd Qu. Max.
-1120.4860 -0.1540 0.0000 -0.1154 0.3679 57.5287
MoreUpNuc=UpandDown_nuc %>% filter(ratio>0)
summary(MoreUpNuc$ratio)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00001 0.13175 0.39811 0.86737 0.96318 57.52867
nrow(MoreUpNuc)
[1] 14913
head(MoreUpNuc)
PAS gene loc PASloc Usage Upstream_st
1 peak18198 BTAF1 intron 93719017 0.836666666666667 68.64486
2 peak113043 HLA-A intron 29911562 0.296296296296296 56.75309
3 peak32403 HSP90B1 intron 104335869 0.302037037037037 40.16410
4 peak86087 DDX17 intron 38887949 0.781851851851852 89.22321
5 peak69710 RASGRP3 intron 33741824 0.32462962962963 38.86842
6 peak92554 PARP14 intron 122414653 0.087037037037037 32.15172
Downstream_st ratio
1 11.116190 57.52867
2 23.910979 32.84211
3 8.178899 31.98520
4 57.814883 31.40833
5 7.689828 31.17859
6 3.907366 28.24436
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_1.3.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.0 cellranger_1.1.0 pillar_1.3.1 compiler_3.5.1
[5] git2r_0.25.2 plyr_1.8.4 tools_3.5.1 digest_0.6.18
[9] lubridate_1.7.4 jsonlite_1.6 evaluate_0.12 nlme_3.1-137
[13] gtable_0.2.0 lattice_0.20-38 pkgconfig_2.0.2 rlang_0.3.1
[17] cli_1.0.1 rstudioapi_0.10 yaml_2.2.0 haven_1.1.2
[21] withr_2.1.2 xml2_1.2.0 httr_1.3.1 knitr_1.20
[25] hms_0.4.2 generics_0.0.2 fs_1.2.6 rprojroot_1.3-2
[29] grid_3.5.1 tidyselect_0.2.5 glue_1.3.0 R6_2.3.0
[33] readxl_1.1.0 rmarkdown_1.10 modelr_0.1.2 magrittr_1.5
[37] whisker_0.3-2 backports_1.1.2 scales_1.0.0 htmltools_0.3.6
[41] rvest_0.3.2 assertthat_0.2.0 colorspace_1.3-2 stringi_1.2.4
[45] lazyeval_0.2.1 munsell_0.5.0 broom_0.5.1 crayon_1.3.4