Last updated: 2019-05-22

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Rmd ee92964 brimittleman 2019-05-15 start ideas for inton analysis

I am interested in understanding where in the introns the nuclear peaks are. Are they closer to the three prime or five prime edge of the intron. This may help us understand if NMD is contributing to the loss of transcripts between the nuclear and total fraction.

I need to create an annotation with introns that do not overlap. For this I will use line up all of the exons for a gene then take the open spaces as introns.

library(tidyverse)
── Attaching packages ──────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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✔ tibble  2.1.1       ✔ dplyr   0.8.0.1
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✔ readr   1.3.1       ✔ forcats 0.3.0  
── Conflicts ─────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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library(workflowr)
This is workflowr version 1.3.0
Run ?workflowr for help getting started
library(cowplot)

Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':

    ggsave
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")

nucIntronicPeaksBed=nucIntronicPeaks %>% mutate(ID=paste(peak,gene,loc, sep=":")) %>% dplyr::select(chr, start, end, ID, avgUsage, strand)

write.table(nucIntronicPeaksBed, "../data/intron_analysis/NuclearIntronicPeaks.bed", col.names = F, row.names = F, quote = F,sep="\t")

I will need to assign each of these to an intron in the new annotation.

The genome annotation file, Transcript2GeneName.dms has the information i need. I will need to parse this file. I need all exons for a gene (longest transcript) The file has the exon starts and ends for each transcript.

I will remove the exon locations for full transcripts using bedtools subtract.

Create transcript file.I will select all of the transcripts in the dms file and merge by gene name. Then I can subtract the exons

python transcriptdm2bed.py

Sort the output, group by transcript and fix order of columns.

sort -k1,1 -k2,2n /project2/gilad/briana/genome_anotation_data/RefSeq_annotations/AllTranscriptsbyName.bed > /project2/gilad/briana/genome_anotation_data/RefSeq_annotations/AllTranscriptsbyName.sort.bed

sbatch grouptranscripts.py

python fixgroupedtranscript.py

I want to subract any exon or UTR seqeunce. I have an annotation bed file I will use:

exonandUTRs=read.table("../../genome_anotation_data/RefSeq_annotations/ncbiRefSeq_FormatedallAnnotation.sort.bed", col.names = c("CHR", "start", "end", "ID", "score", "strand"),stringsAsFactors = F)%>% separate(ID, into=c("loc", "gene"),sep=":") %>% filter(loc!="intron") %>% dplyr::select(-loc) %>% mutate(CHR=paste("chr", CHR, sep=""))


write.table(exonandUTRs, file="../data/intron_analysis/ExonandUTRloc.bed", quote=F, col.names = F, row.names = F, sep="\t")
sort -k1,1 -k2,2n ../data/intron_analysis/ExonandUTRloc.bed > ../data/intron_analysis/ExonandUTRloc.sort.bed
sbatch subtractExons.sh

sort:

sort -k1,1 -k2,2n /project2/gilad/briana/apaQTL/data/intron_analysis/transcriptsMinusExons.bed > /project2/gilad/briana/apaQTL/data/intron_analysis/transcriptsMinusExons.sort.bed

Next I will map the intronic peaks on these positions.

sbatch assignNucIntonpeak2intronlocs.sh

Plot percentage of intron where PAS is.

pas2intron=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(intronStart, intronEnd, gene, strand, PeakID, PASloc ,meanUsage) %>% mutate(intronLength=intronEnd-intronStart , distance2PAS= ifelse(strand=="+", PASloc-intronStart, intronEnd-PASloc), propIntron=distance2PAS/intronLength)
nuclearplot=ggplot(pas2intron, aes(x=propIntron)) + geom_histogram(bins=50, aes(y=..count../33345)) + labs(title="PAS position within intron \n for Nuclear PAS", y="Proportion of Intronic PAS", x="Proportion of Intronic Length")

Facet by usage 0-25, 25-50, 50-75, 75-1

pas2intron_usage=pas2intron %>% mutate(UsageCat=ifelse(meanUsage<=.5, "low", "high"))

ggplot(pas2intron_usage, aes(x=propIntron, fill=UsageCat)) + geom_histogram(bins=50, aes(y=..count../33345)) + labs(title="PAS position within intron", y="Proportion of Intronic PAS", x="Proportion of Intronic Length") + facet_grid(~UsageCat)

Version Author Date
d859f02 brimittleman 2019-05-21

Look at different intron lengths:

First i want to look at the distribution of intorn lengths:

ggplot(pas2intron_usage, aes(x=log10(intronLength))) + geom_density()

Version Author Date
d859f02 brimittleman 2019-05-21

I will look at above and below the mean intron length:

meanIntronlength=mean(pas2intron_usage$intronLength)

pas2intron_length=pas2intron %>% mutate(LengthCat=ifelse(intronLength<=meanIntronlength, "bottom", "top"))


ggplot(pas2intron_length, aes(x=propIntron, fill=LengthCat)) + geom_histogram(bins=50, aes(y=..count../33345)) + labs(title="PAS position within intron", y="Proportion of Intronic PAS", x="Proportion of Intronic Length") + facet_grid(~LengthCat)

ggplot(pas2intron_length, aes(x=distance2PAS, fill=LengthCat)) + geom_histogram(bins=50, aes(y=..count../33345)) + labs(title="PAS position within intron", y="Proportion of Intronic PAS", x="Proportion of Intronic Length") + facet_grid(~LengthCat)

Look at quartiles:

summary(pas2intron_usage$intronLength)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    106    3929    9220   22248   24094 1102540 
pas2intron_length2=pas2intron %>% mutate(LengthCat=ifelse(intronLength<=3929, "first", ifelse(intronLength>3929 &intronLength<=9220, "second", ifelse(intronLength>9220 &intronLength<=24094, "third", "fourth"))))
                                         
ggplot(pas2intron_length2, aes(x=propIntron, fill=LengthCat)) + geom_histogram(bins=50, aes(y=..count../33345)) + labs(title="PAS position within intron \n Nuclear intronic PAS", y="Proportion of Intronic PAS", x="Proportion of Intronic Length") + facet_grid(~LengthCat)+theme(axis.text.x = element_text(angle = 90, hjust = 1))

ggplot(pas2intron_length2, aes(x=log(distance2PAS)), by=LengthCat, col=LengthCat) + stat_ecdf(aes(col=LengthCat))

Look at distribution in total fraction:

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")

totIntronicPeaksBed=totIntronicPeaks %>% mutate(ID=paste(peak,gene,loc, sep=":")) %>% dplyr::select(chr, start, end, ID, avgUsage, strand)

write.table(totIntronicPeaksBed, "../data/intron_analysis/TotalIntronicPeaks.bed", col.names = F, row.names = F, quote = F,sep="\t")

map these to the intron file

sbatch assignTotIntronpeak2intronlocs.sh
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(intronStart, intronEnd, gene, strand, PeakID, PASloc ,meanUsage) %>% mutate(intronLength=intronEnd-intronStart , distance2PAS= ifelse(strand=="+", PASloc-intronStart, intronEnd-PASloc), propIntron=distance2PAS/intronLength)

nrow(pas2intronTot)
[1] 31954
totalplot=ggplot(pas2intronTot, aes(x=propIntron)) + geom_histogram(bins=50, aes(y=..count../31954)) + labs(title="PAS position within intron \nfor total PAS", y="Proportion of Intronic PAS", x="Proportion of Intronic Length")
plot_grid(totalplot, nuclearplot)

summary(pas2intronTot$intronLength)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    106    3785    8872   21032   22928 1102540 
pas2intron_totlength2=pas2intronTot %>% mutate(LengthCat=ifelse(intronLength<=3785, "first", ifelse(intronLength>3785 &intronLength<=8872, "second", ifelse(intronLength>8872 &intronLength<=22928, "third", "fourth"))))
                                         
ggplot(pas2intron_totlength2, aes(x=propIntron, fill=LengthCat)) + geom_histogram(bins=50, aes(y=..count../31954)) + labs(title="PAS position within intron \n Total intronic PAS", y="Proportion of Intronic PAS", x="Proportion of Intronic Length") + facet_grid(~LengthCat) + theme(axis.text.x = element_text(angle = 90, hjust = 1))


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:
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 [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] cowplot_0.9.4   workflowr_1.3.0 forcats_0.3.0   stringr_1.3.1  
 [5] dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1     tidyr_0.8.3    
 [9] tibble_2.1.1    ggplot2_3.1.1   tidyverse_1.2.1

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_0.23.0     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   reshape2_1.4.3   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] labeling_0.3     stringi_1.2.4    lazyeval_0.2.1   munsell_0.5.0   
[49] broom_0.5.1      crayon_1.3.4