miRNA analysis
Briana Mittleman
4/13/2020
Last updated: 2020-04-13
Checks: 7 0
Knit directory: Comparative_APA/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 6997920 | brimittleman | 2020-04-13 | initial miRNA |
In this analysis I will ask about conservation and number of conserved miRNA sites.
I have the miRNA target info from TargetScanHuman. I downloaded the predicted targets for conserved targets from conserved miRNA families.
I will look from the human perspective and ask if genes with conserved vs non concerved sites have different numbers of miRNA binding sites. I will standaradize this by length of 3’ UTR. This means I can only look at those with orthologous utrs.
library(workflowr)This is workflowr version 1.6.0
Run ?workflowr for help getting startedlibrary(ggpubr)Loading required package: ggplot2Loading required package: magrittrlibrary(tidyverse)── Attaching packages ───────────────────────────────────────────────────────── tidyverse 1.2.1 ──✔ tibble 2.1.1 ✔ purrr 0.3.2
✔ tidyr 0.8.3 ✔ dplyr 0.8.0.1
✔ readr 1.3.1 ✔ stringr 1.3.1
✔ tibble 2.1.1 ✔ forcats 0.3.0 ── Conflicts ──────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ tidyr::extract() masks magrittr::extract()
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✖ purrr::set_names() masks magrittr::set_names()OrthoUTR=read.table("../data/orthoUTR/HumanDistal3UTR.sort.bed", col.names = c("chr",'start','end','gene','score','strand'),stringsAsFactors = F) %>% mutate(length=end-start) %>% select(gene, length)miRNADB=read.table("../data/miRNA/Conserved_Family_Info.txt", header= T, stringsAsFactors = F,sep="\t")
miRNADBgenes= miRNADB %>% group_by(Gene.Symbol) %>% summarise(nSites=n()) %>% rename(gene= Gene.Symbol) %>% inner_join(OrthoUTR, by="gene") %>% mutate(density=nSites/length)Look at my set and overlap these.
DiffIsoGene=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt", header = T,stringsAsFactors = F)
DiffIso=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T,stringsAsFactors = F) %>% select(gene) %>% unique() %>% mutate(Conserved=ifelse(gene %in% DiffIsoGene$gene, "No", "Yes")) %>% inner_join(miRNADBgenes,by="gene")Plot this:
ggplot(DiffIso, aes(x=Conserved, y=log10(density),fill=Conserved)) +geom_boxplot() + stat_compare_means() +labs(title="Number of annotated miRNA sites\nConserved = no differnetially used PAS") + theme(legend.position = "none")+ scale_fill_brewer(palette = "Dark2")
DiffIso %>% group_by(Conserved) %>% summarise(mean(density))# A tibble: 2 x 2
Conserved `mean(density)`
<chr> <dbl>
1 No 0.595
2 Yes 0.526Look specifically at genes with differentially used 3’ UTR PAS:
PASINFO=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt",stringsAsFactors = F, header = T) %>% select(PAS, chr, start, end, loc)
DiffIsoUTR=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T,stringsAsFactors = F) %>% inner_join(PASINFO,by=c("chr", "start", "end")) %>% filter(SigPAU2=="Yes", loc=='utr3') %>% select(gene) %>% unique()
DiffIsoUTRall=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T,stringsAsFactors = F) %>% select(gene) %>% unique() %>% mutate(Conserved=ifelse(gene %in% DiffIsoUTR$gene, "No", "Yes")) %>% inner_join(miRNADBgenes,by="gene")
DiffIsoIntron=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T,stringsAsFactors = F) %>% inner_join(PASINFO,by=c("chr", "start", "end")) %>% filter(SigPAU2=="Yes", loc=='intron') %>% select(gene) %>% unique()
DiffIsoIntronall=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T,stringsAsFactors = F) %>% select(gene) %>% unique() %>% mutate(Conserved=ifelse(gene %in% DiffIsoIntron$gene, "No", "Yes")) %>% inner_join(miRNADBgenes,by="gene")ggplot(DiffIsoUTRall, aes(x=Conserved, y=log10(density),fill=Conserved)) +geom_boxplot() + stat_compare_means() +labs(title="Number of annotated miRNA sites\nConserved = no differnetially used 3' UTR PAS") + theme(legend.position = "none")+ scale_fill_brewer(palette = "Dark2")
ggplot(DiffIsoIntronall, aes(x=Conserved, y=log10(density),fill=Conserved)) +geom_boxplot() + stat_compare_means() +labs(title="Number of annotated miRNA sites\nConserved = no differnetially used Intronic PAS") + theme(legend.position = "none")+ scale_fill_brewer(palette = "Dark2")
Can I look at conserved another way. Same dominant:
SameDom=read.table("../data/DominantPAS_DF/Nuclear_SameDom.txt",header = T, stringsAsFactors = F)
Allgenes=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T,stringsAsFactors = F) %>% select(gene) %>% unique() %>% mutate(ConservedDom=ifelse(gene %in% SameDom$gene, "Yes","No")) %>% inner_join(miRNADBgenes,by="gene")
ggplot(Allgenes, aes(x=ConservedDom, y=log10(density),fill=ConservedDom)) +geom_boxplot() + stat_compare_means() +labs(title="Number of annotated miRNA sites by Conservation of Dominant PAS", x="Same Dominant PAS") + theme(legend.position = "none")+ scale_fill_brewer(palette = "Dark2")
Allgenes %>% group_by(ConservedDom) %>% summarise(mean(density))# A tibble: 2 x 2
ConservedDom `mean(density)`
<chr> <dbl>
1 No 0.618
2 Yes 0.471Different dominant PAS more dense with miRNA binding sites
Differentially used conditioned on dominant:
Look at the differentially used PAS when the differentially used PAS is the dominant PAS.
Usage=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt",stringsAsFactors = F, header = T)
ChimpDom= Usage %>%
group_by(gene) %>%
top_n(1,Chimp) %>%
select(PAS,gene)
HumanDom= Usage %>%
group_by(gene) %>%
top_n(1,Human) %>%
select(PAS,gene)
DomEither= ChimpDom %>% bind_rows(HumanDom) %>% unique()DiffIsoDom=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T,stringsAsFactors = F) %>% inner_join(PASINFO,by=c("chr", "start", "end")) %>% mutate(Dom=ifelse(PAS %in% DomEither$PAS, "Yes", "No"))%>% filter(Dom =="Yes",SigPAU2=="Yes") %>% select(gene) %>% unique()
AllgenesDom=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T,stringsAsFactors = F) %>% select(gene) %>% unique() %>% mutate(DiffUsed=ifelse(gene %in% DiffIsoDom$gene, "Yes","No")) %>% inner_join(miRNADBgenes,by="gene")
ggplot(AllgenesDom, aes(x=DiffUsed, y=log10(density),fill=DiffUsed)) +geom_boxplot() + stat_compare_means() +labs(title="Number of annotated miRNA sites by Conservation Gene with Diff used dominant PAS", x="Same Dominant PAS") + theme(legend.position = "none")+ scale_fill_brewer(palette = "Dark2")
AllgenesDom %>% group_by(DiffUsed) %>% summarise(n(), mean(density))# A tibble: 2 x 3
DiffUsed `n()` `mean(density)`
<chr> <int> <dbl>
1 No 6761 0.501
2 Yes 1230 0.590When the gene has a differentially used pas that is dominant we see the effect.
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 tidyverse_1.2.1
[9] ggpubr_0.2 magrittr_1.5 ggplot2_3.1.1 workflowr_1.6.0
loaded via a namespace (and not attached):
[1] tidyselect_0.2.5 haven_1.1.2 lattice_0.20-38
[4] colorspace_1.3-2 generics_0.0.2 htmltools_0.3.6
[7] yaml_2.2.0 utf8_1.1.4 rlang_0.4.0
[10] later_0.7.5 pillar_1.3.1 glue_1.3.0
[13] withr_2.1.2 RColorBrewer_1.1-2 modelr_0.1.2
[16] readxl_1.1.0 plyr_1.8.4 munsell_0.5.0
[19] gtable_0.2.0 cellranger_1.1.0 rvest_0.3.2
[22] evaluate_0.12 labeling_0.3 knitr_1.20
[25] httpuv_1.4.5 fansi_0.4.0 broom_0.5.1
[28] Rcpp_1.0.2 promises_1.0.1 scales_1.0.0
[31] backports_1.1.2 jsonlite_1.6 fs_1.3.1
[34] hms_0.4.2 digest_0.6.18 stringi_1.2.4
[37] grid_3.5.1 rprojroot_1.3-2 cli_1.1.0
[40] tools_3.5.1 lazyeval_0.2.1 crayon_1.3.4
[43] whisker_0.3-2 pkgconfig_2.0.2 xml2_1.2.0
[46] lubridate_1.7.4 assertthat_0.2.0 rmarkdown_1.10
[49] httr_1.3.1 rstudioapi_0.10 R6_2.3.0
[52] nlme_3.1-137 git2r_0.26.1 compiler_3.5.1