Last updated: 2020-05-14
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Knit directory: Comparative_APA/analysis/
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Unstaged changes:
Modified: analysis/DICNotDEDP.Rmd
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Modified: analysis/speciesSpecific.Rmd
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I want to get the sequence between the dominant PAS when they are different. I can test for different destabilizing motifs in these regions. I will start with the .4 cutoff.
library(workflowr)
This is workflowr version 1.6.0
Run ?workflowr for help getting started
library(tidyverse)
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library(Biostrings)
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mkdir ../data/DistTwoDom
HumanRes=read.table("../data/DomDefGreaterX/Human_AllGenes_DiffTop.txt", col.names = c("Human_PAS", "gene","Human_DiffDom"),stringsAsFactors = F)
ChimpRes=read.table("../data/DomDefGreaterX/Chimp_AllGenes_DiffTop.txt", col.names = c("Chimp_PAS", "gene","Chimp_DiffDom"),stringsAsFactors = F)
BothRes=HumanRes %>% inner_join(ChimpRes,by="gene")
BothRes_40=BothRes %>% filter(Chimp_DiffDom >=0.4 | Human_DiffDom>=0.4) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=40)
BothRes_40_diff= BothRes_40 %>% filter(Set=="Different")
I need the meta data for the PAS:
metaPAS=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt",stringsAsFactors = F, header = T) %>% mutate(midpoint=start+100)
metaPAS_sm= metaPAS %>% select(PAS, gene, midpoint)
metaPAS_bed= metaPAS %>% select(chr, gene, strandFix) %>% unique()
BothRes_40_diff_sm= BothRes_40_diff %>% select(gene, Chimp_PAS, Human_PAS) %>% gather("species", "PAS", -gene) %>% inner_join(metaPAS_sm, by=c("gene", "PAS"))
Spread this back out so i have both midpoints
BothRes_40_diff_spread=BothRes_40_diff_sm %>% mutate(extra="PAS") %>% spread(extra,midpoint) %>% group_by(gene) %>% summarise(minPAS=min(PAS), maxPAS=max(PAS)) %>% inner_join(metaPAS_bed, by="gene") %>%mutate(score=0) %>% select(chr, minPAS, maxPAS, gene, score, strandFix ) %>% arrange(chr, minPAS)
write.table(BothRes_40_diff_spread, "../data/DistTwoDom/SeqBetweenDom_4.bed", quote = F, col.names = F, row.names = F, sep="\t")
bedtools nuc -s -seq -fi /project2/gilad/kenneth/References/human/genome/hg38.fa -bed ../data/DistTwoDom/SeqBetweenDom_4.bed > ../data/DistTwoDom/SeqBetweenDom_4_sort_nuc.bed
Look at results:
SeqBetween=read.table("../data/DistTwoDom/SeqBetweenDom_4_sort_nuc.bed", col.names = c(colnames(BothRes_40_diff_spread),"AT", "GC", "A", "C", "G", "T","N", "other", "len", "seq" ))
summary(SeqBetween$len)
Min. 1st Qu. Median Mean 3rd Qu. Max.
187 1526 5088 16477 15893 222368
plot(sort(SeqBetween$len))
Version | Author | Date |
---|---|---|
1bf3145 | brimittleman | 2020-05-12 |
ggplot(SeqBetween, aes(x=len))+geom_density()
Version | Author | Date |
---|---|---|
1bf3145 | brimittleman | 2020-05-12 |
Compare those that are de and those that are not:
nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F)
DEgenes=read.table("../data/DiffExpression/DE_genes.txt", header = F,col.names = c("Gene_stable_ID"),stringsAsFactors = F) %>% inner_join(nameID, by="Gene_stable_ID") %>% dplyr::select(Gene.name) %>% dplyr::rename("gene"=Gene.name)
DEgenestested=read.table("../data/DiffExpression/DE_Testedgenes.txt", header = F,col.names = c("Gene_stable_ID"),stringsAsFactors = F) %>% inner_join(nameID, by="Gene_stable_ID") %>% dplyr::select(Gene.name) %>% dplyr::rename("gene"=Gene.name) %>% mutate(DE=ifelse(gene %in% DEgenes$gene, "Yes", "No"))
SeqBetweenFix= SeqBetween %>% mutate(seqUp=toupper(seq)) %>% inner_join(DEgenestested, by="gene")
Warning: Column `gene` joining factor and character vector, coercing into
character vector
ggplot(SeqBetweenFix, aes(x=log10(len), fill=DE))+geom_density(alpha=.4)
Version | Author | Date |
---|---|---|
1bf3145 | brimittleman | 2020-05-12 |
Seq_de= SeqBetweenFix %>% filter(DE=="Yes")
Seq_node= SeqBetweenFix %>% filter(DE=="No")
wilcox.test(Seq_de$len,Seq_node$len )
Wilcoxon rank sum test with continuity correction
data: Seq_de$len and Seq_node$len
W = 3154, p-value = 0.5876
alternative hypothesis: true location shift is not equal to 0
atplot=ggplot(SeqBetweenFix, aes(x=DE, y=AT, fill=DE)) +geom_boxplot()+geom_jitter()+stat_compare_means() +scale_fill_brewer(palette = "Set1")+ theme(legend.position = "none")
atplot
Version | Author | Date |
---|---|---|
1bf3145 | brimittleman | 2020-05-12 |
GCplot=ggplot(SeqBetweenFix, aes(x=DE, y=GC,fill=DE)) +geom_boxplot() +geom_jitter()+stat_compare_means()+scale_fill_brewer(palette = "Set1") + theme(legend.position = "none")
GCplot
Version | Author | Date |
---|---|---|
1bf3145 | brimittleman | 2020-05-12 |
No difference in length, AT, or GC proportion in these.
plot_grid(GCplot, atplot)
Version | Author | Date |
---|---|---|
1bf3145 | brimittleman | 2020-05-12 |
Expand length analysis to all 9 cutoffs:
BothRes_10=BothRes %>% filter(Chimp_DiffDom >=0.1 | Human_DiffDom>=0.1) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=10)
BothRes_20=BothRes %>% filter(Chimp_DiffDom >=0.2 | Human_DiffDom>=0.2) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=20)
BothRes_30=BothRes %>% filter(Chimp_DiffDom >=0.3 | Human_DiffDom>=0.3) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=30)
BothRes_40=BothRes %>% filter(Chimp_DiffDom >=0.4 | Human_DiffDom>=0.4) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=40)
BothRes_50=BothRes %>% filter(Chimp_DiffDom >=0.5 | Human_DiffDom>=0.5) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=50)
BothRes_60=BothRes %>% filter(Chimp_DiffDom >=0.6 | Human_DiffDom>=0.6) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=60)
BothRes_70=BothRes %>% filter(Chimp_DiffDom >=0.7 | Human_DiffDom>=0.7) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=70)
BothRes_80=BothRes %>% filter(Chimp_DiffDom >=0.8 | Human_DiffDom>=0.8) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=80)
BothRes_90=BothRes %>% filter(Chimp_DiffDom >=0.9 | Human_DiffDom>=0.9) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=90)
BothResAll=BothRes_10 %>% bind_rows(BothRes_20) %>% bind_rows(BothRes_30) %>% bind_rows(BothRes_40) %>% bind_rows(BothRes_50) %>% bind_rows(BothRes_60) %>% bind_rows(BothRes_70) %>% bind_rows(BothRes_80) %>% bind_rows(BothRes_90)
BothResallDiff= BothResAll %>%
filter(Set=="Different") %>%
select(gene, Chimp_PAS, Human_PAS, cut) %>%
gather("species", "PAS", -gene, -cut) %>%
inner_join(metaPAS_sm, by=c("gene", "PAS")) %>%
mutate(extra="PAS") %>%
spread(extra,midpoint) %>%
group_by(cut, gene) %>%
summarise(minPAS=min(PAS), maxPAS=max(PAS)) %>%
mutate(length= maxPAS-minPAS)
BothResallDiff$cut=factor(BothResallDiff$cut)
ggplot(BothResallDiff, aes(x=cut, y=length,fill=cut)) +geom_boxplot() + theme(legend.position = "none")+ scale_fill_brewer(palette = "Set1")
ggplot(BothResallDiff, aes(x=cut, y=log10(length),fill=cut)) +geom_boxplot() + theme(legend.position = "none")+ scale_fill_brewer(palette = "Set1")
Number of genes in each:
BothResallDiff %>% group_by(cut) %>% summarise(n())
# A tibble: 9 x 2
cut `n()`
<fct> <int>
1 10 1256
2 20 605
3 30 310
4 40 182
5 50 118
6 60 75
7 70 39
8 80 27
9 90 12
I can use homer to look for enrichment in the -rna mode:
findMotifsGenome.pl bedfile genome.fa output dir -rna -seqlogo -h -len 8
no background at first
mkdir ../data/DistTwoDom/FindMotif
mkdir ../data/DistTwoDom/mRNAMotif
cut -f 4 ../data/DistTwoDom/SeqBetweenDom_4.bed > ../data/DistTwoDom/SeqBetweenDom_4_genes.txt
sbatch DiffDom_RNAmotif_4.sh
Try running with DE v no DE as background:
Seq_de= SeqBetweenFix %>% filter(DE=="Yes")
Seq_node= SeqBetweenFix %>% filter(DE=="No")
Seq_de_bed= Seq_de %>% select(chr, minPAS, maxPAS, gene, score, strandFix)
write.table(Seq_de_bed, "../data/DistTwoDom/SeqBetweenDom_4_withDE.bed", col.names = F, quote = F, row.names = F, sep="\t")
Seq_node_bed= Seq_node %>% select(chr, minPAS, maxPAS, gene, score, strandFix)
write.table(Seq_node_bed, "../data/DistTwoDom/SeqBetweenDom_4_withNODE.bed", col.names = F, quote = F, row.names = F, sep="\t")
mkdir ../data/DistTwoDom/DE_8
mkdir ../data/DistTwoDom/DE_10
mkdir ../data/DistTwoDom/DE_12
sbatch DiffDom_RNAmotif_4_splitDE.sh
try homer on all pas: rna sites 6 bp
mkdir ../data/PAS_doubleFilter/FindMotif/
mkdir ../data/PAS_doubleFilter/FindMotif_chimp/
sbatch RNAmotif_PAS.sh
sbatch RNAmotif_PAS_chimp.sh
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] cowplot_0.9.4 ggpubr_0.2 magrittr_1.5
[4] Biostrings_2.50.1 XVector_0.22.0 IRanges_2.16.0
[7] S4Vectors_0.20.1 BiocGenerics_0.28.0 forcats_0.3.0
[10] stringr_1.3.1 dplyr_0.8.0.1 purrr_0.3.2
[13] readr_1.3.1 tidyr_0.8.3 tibble_2.1.1
[16] ggplot2_3.1.1 tidyverse_1.2.1 workflowr_1.6.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.4.6 lubridate_1.7.4 lattice_0.20-38
[4] utf8_1.1.4 assertthat_0.2.0 rprojroot_1.3-2
[7] digest_0.6.18 R6_2.3.0 cellranger_1.1.0
[10] plyr_1.8.4 backports_1.1.2 evaluate_0.12
[13] httr_1.3.1 pillar_1.3.1 zlibbioc_1.28.0
[16] rlang_0.4.0 lazyeval_0.2.1 readxl_1.1.0
[19] rstudioapi_0.10 whisker_0.3-2 rmarkdown_1.10
[22] labeling_0.3 munsell_0.5.0 broom_0.5.1
[25] compiler_3.5.1 httpuv_1.4.5 modelr_0.1.2
[28] pkgconfig_2.0.2 htmltools_0.3.6 tidyselect_0.2.5
[31] fansi_0.4.0 crayon_1.3.4 withr_2.1.2
[34] later_0.7.5 grid_3.5.1 nlme_3.1-137
[37] jsonlite_1.6 gtable_0.2.0 git2r_0.26.1
[40] scales_1.0.0 cli_1.1.0 stringi_1.2.4
[43] fs_1.3.1 promises_1.0.1 xml2_1.2.0
[46] generics_0.0.2 RColorBrewer_1.1-2 tools_3.5.1
[49] glue_1.3.0 hms_0.4.2 yaml_2.2.0
[52] colorspace_1.3-2 rvest_0.3.2 knitr_1.20
[55] haven_1.1.2