Liftover PAS
Briana Mittleman
10/1/2019
Last updated: 2020-04-10
Checks: 7 0
Knit directory: Comparative_APA/analysis/
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Unstaged changes:
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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 623663b | brimittleman | 2020-01-25 | Build site. |
| Rmd | e26cfd1 | brimittleman | 2020-01-25 | fix plot for CM |
| html | aee923e | brimittleman | 2019-12-17 | Build site. |
| Rmd | 83fb66e | brimittleman | 2019-12-17 | update liftover PAS |
| html | 3da520f | brimittleman | 2019-10-04 | Build site. |
| Rmd | 00c308a | brimittleman | 2019-10-04 | fix overlap |
| html | e0ac227 | brimittleman | 2019-10-03 | Build site. |
| Rmd | e3f0cdf | brimittleman | 2019-10-03 | add annotation analysis |
| html | 5fbf02b | brimittleman | 2019-10-02 | Build site. |
| Rmd | 1c6c5e2 | brimittleman | 2019-10-02 | add results for full set |
| html | 8dd5eec | brimittleman | 2019-10-02 | Build site. |
| Rmd | 558afe6 | brimittleman | 2019-10-02 | add liftover res |
| html | b5edd8e | brimittleman | 2019-10-01 | Build site. |
| Rmd | b5a2151 | brimittleman | 2019-10-01 | add analysis for liftover |
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()Next step will be to prepare these for liftover. I will get the PAS as the furthest downstream base. I will liftover the regions 100bp upstream and 100bp downstream of the PAS.
As of now the PAS are still on the opposite strand.
Pos strand: pas is the end - start_new= end-100 - end_new=end + 100
neg strand: pas is the start - start_new= start-100 - end_new= start + 100
human: /project2/gilad/briana/Comparative_APA/Human/data/cleanPeaks/human_APApeaks.ALLChrom.Filtered.Named.Cleaned.bed chimp: /project2/gilad/briana/Comparative_APA/Chimp/data/cleanPeaks/chimp_APApeaks.ALLChrom.Filtered.Named.Cleaned.bed
Output:
mkdir ../data/cleanPeaks_byspecies/
python preparePAS4lift.py ../Human/data/cleanPeaks/human_APApeaks.ALLChrom.Filtered.Named.Cleaned.bed ../data/cleanPeaks_byspecies/human_APApeaks.ALLChrom.Filtered.Named.Cleaned_100bpreg.bed
python preparePAS4lift.py ../Chimp/data/cleanPeaks/chimp_APApeaks.ALLChrom.Filtered.Named.Cleaned.bed ../data/cleanPeaks_byspecies/chimp_APApeaks.ALLChrom.Filtered.Named.Cleaned_100bpreg.bed
Chain files from: http://hgdownload.soe.ucsc.edu/downloads.html#chimp dowload to ../data/chainFiles/
Liftover pipeline:
start with human- lift to chimp and back start with chimp- lift to human and back
mkdir ../data/primaryLift
mkdir ../data/reverseLift
sbatch primaryLift.sh
sbatch reverseLift.shResults from primary lift:
unliftedH=read.table("../data/primaryLift/human_APApeaks_primarylift2Chimp_UNLIFTED.bed",stringsAsFactors = F) %>% nrow()
unliftedC=read.table("../data/primaryLift/chimp_APApeaks_primarylift2Human_UNLIFTED.bed",stringsAsFactors = F) %>% nrow()
liftedH=read.table("../data/primaryLift/human_APApeaks_primarylift2Chimp.bed",stringsAsFactors = F) %>% nrow()
liftedC=read.table("../data/primaryLift/chimp_APApeaks_primarylift2Human.bed",stringsAsFactors = F) %>% nrow()
primaryUnC=c("Chimp","Unlifted", unliftedC)
primaryUnH=c("Human","Unlifted", unliftedH)
primaryLH=c("Human","Lifted", liftedH)
primaryLC=c("Chimp","Lifted", liftedC)
header=c("species", "liftStat", "PAS")
primaryDF= as.data.frame(rbind(primaryLH,primaryLC, primaryUnH,primaryUnC))
colnames(primaryDF)=header
primaryDF$PAS=as.numeric(as.character(primaryDF$PAS))
primaryDF= primaryDF %>% group_by(species) %>% mutate(nPAS=sum(PAS)) %>% ungroup() %>% mutate(proportion=PAS/nPAS)ggplot(primaryDF,aes(x=species, y=PAS, fill=liftStat)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Primary Liftover Results")
ggplot(primaryDF,aes(x=species, y=proportion, fill=liftStat)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Primary Liftover Results")
Reverse lift:
re_unliftedH=read.table("../data/reverseLift/human_APApeaks_primarylift2Human_rev2Human_UNLIFTED.bed",stringsAsFactors = F) %>% nrow()
re_unliftedC=read.table("../data/reverseLift/chimp_APApeaks_primarylift2Human_rev2Chimp_UNLIFTED.bed",stringsAsFactors = F) %>% nrow()
re_liftedH=read.table("../data/reverseLift/human_APApeaks_primarylift2Chimp_rev2Human.bed",stringsAsFactors = F) %>% nrow()
re_liftedC=read.table("../data/reverseLift/chimp_APApeaks_primarylift2Human_rev2Chimp.bed",stringsAsFactors = F) %>% nrow()
re_UnC=c("Chimp","Unlifted", re_unliftedC)
re_UnH=c("Human","Unlifted", re_unliftedH)
re_LH=c("Human","Lifted", re_liftedH)
re_LC=c("Chimp","Lifted", re_liftedC)
header=c("species", "liftStat", "PAS")
re_DF= as.data.frame(rbind(re_LH,re_LC, re_UnH,re_UnC))
colnames(re_DF)=header
re_DF$PAS=as.numeric(as.character(re_DF$PAS))
re_DF= re_DF %>% group_by(species) %>% mutate(nPAS=sum(PAS)) %>% ungroup() %>% mutate(proportion=PAS/nPAS)ggplot(re_DF,aes(x=species, y=PAS, fill=liftStat)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Reverse Liftover Results")
ggplot(re_DF,aes(x=species, y=proportion, fill=liftStat)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2")+ labs(title="Reverse Liftover Results")
I need to now make sure they lifted to the same location. To do this I will overlap the reciprocal lifted PAS with the original files.
I subset the original files by the pas that have an exact match in the reverse map. I can do this by pas name- start:end
mkdir ../data/cleanPeaks_lifted
python filterPostLift.py ../data/cleanPeaks_byspecies/human_APApeaks.ALLChrom.Filtered.Named.Cleaned_100bpreg.bed ../data/reverseLift/human_APApeaks_primarylift2Chimp_rev2Human.bed ../data/cleanPeaks_lifted/Human_PASregions.bed
python filterPostLift.py ../data/cleanPeaks_byspecies/chimp_APApeaks.ALLChrom.Filtered.Named.Cleaned_100bpreg.bed ../data/reverseLift/chimp_APApeaks_primarylift2Human_rev2Chimp.bed ../data/cleanPeaks_lifted/Chimp_PASregions.bedResults:
Human_recLift=read.table("../data/cleanPeaks_lifted/Human_PASregions.bed",stringsAsFactors = F, col.names = c("chr", "start","end", "name", "score", "strand"))
Chimp_recLift=read.table("../data/cleanPeaks_lifted/Chimp_PASregions.bed",stringsAsFactors = F,col.names = c("chr", "start","end", "name", "score", "strand"))
originalH=unliftedH + liftedH
originalC=unliftedC + liftedC
#human
nrow(Human_recLift)/originalH[1] 0.9629937#chimp
nrow(Chimp_recLift)/originalC[1] 0.9675151Reciprocal lift:
96% reciprocally lifted over
lift the chimp ones back to human
sbatch recLiftchim2human.shJoin the results: If they are discovered in both say so.
I need to intersect these with bedtools to know when a PAS is
sort -k1,1 -k2,2n ../data/cleanPeaks_lifted/Chimp_PASregions_humanCoord.bed > ../data/cleanPeaks_lifted/Chimp_PASregions_humanCoord.sort.bed
sort -k1,1 -k2,2n ../data/cleanPeaks_lifted/Human_PASregions.bed > ../data/cleanPeaks_lifted/Human_PASregions.sort.bedI used 75% overlap for the observed in both. I had it report number of basepair overlap so i can evaluate this. If it is bimodal i will change this. (i chose 75% because its 50bp or within 1 read.)
sbatch intersectLiftedPAS.shOverlapBoth_Test=read.table("../data/cleanPeaks_lifted/PASregions_identifiedbothTEST.txt", col.names = c("Hchr", "Hstart","Hend", "Hname", "Hscore", "Hstrand", "Cchr", "Cstart", "Cend", "Cname", "Cscore", "Cstrand", "overlap"),stringsAsFactors = F)
ggplot(OverlapBoth_Test, aes(x=overlap)) + geom_histogram(bins=30) + scale_y_log10() + geom_vline(xintercept = 125) + labs(title="Test for how many basepairs of 200 overlap", y="Number of Sites", x="Number of Overlaped bases")
125/200[1] 0.625I will go with 62.5% overlap. I can prepare the files to make a full set.
OverlapBoth=read.table("../data/cleanPeaks_lifted/PASregions_identifiedboth.txt", col.names = c("Hchr", "Hstart","Hend", "Hname", "Hscore", "Hstrand", "Cchr", "Cstart", "Cend", "Cname", "Cscore", "Cstrand", "overlap"),stringsAsFactors = F) %>% mutate(meanScore=(Hscore+Cscore)/2, name=paste("Both", Hname, sep=":"), Bothname=paste(Hname, Cname, sep=":"))
overlap2= OverlapBoth %>% group_by(name) %>% filter(n()>1) %>% mutate(id = row_number()) %>% filter(id==2)
OverlapBoth_format=read.table("../data/cleanPeaks_lifted/PASregions_identifiedboth.txt", col.names = c("Hchr", "Hstart","Hend", "Hname", "Hscore", "Hstrand", "Cchr", "Cstart", "Cend", "Cname", "Cscore", "Cstrand", "overlap"),stringsAsFactors = F) %>% mutate(meanScore=(Hscore+Cscore)/2, name=paste("Both", Hname, sep=":"),Bothname=paste(Hname, Cname, sep=":")) %>% filter(!Bothname %in% overlap2$Bothname) %>% dplyr::select(Hchr,Hstart,Hend,name,meanScore,Hstrand)
HumanSpec=read.table("../data/cleanPeaks_lifted/PASregions_identifiedHuman.txt", col.names = c("Hchr", "Hstart","Hend", "Hname", "meanScore", 'Hstrand'),stringsAsFactors = F) %>% mutate(name=paste("Human", Hname, sep=":")) %>% dplyr::select(Hchr, Hstart,Hend, name, meanScore, Hstrand)
ChimpSpec=read.table("../data/cleanPeaks_lifted/PASregions_identifiedChimp.txt", col.names = c("Hchr", "Hstart","Hend", "Cname", "meanScore", 'Hstrand'),stringsAsFactors = F) %>% mutate(name=paste("Chimp", Cname, sep=":")) %>% dplyr::select(Hchr, Hstart,Hend, name, meanScore, Hstrand)Join all of these and plot characteristics
AllPAS=as.data.frame(rbind(OverlapBoth_format,HumanSpec,ChimpSpec)) %>% separate(name, into=c("discovery", "PAS"))
ggplot(AllPAS, aes(x=discovery, fill=discovery)) + geom_bar(stat="count")+ scale_fill_brewer(palette = "Dark2")
ggplot(AllPAS, aes(x=discovery, fill=discovery)) + geom_bar(aes(y = (..count..)/sum(..count..)))+ scale_fill_brewer(palette = "Dark2")
| Version | Author | Date |
|---|---|---|
| aee923e | brimittleman | 2019-12-17 |
Plot the difference in scores for both and discovered in 1 or other:
ggplot(AllPAS, aes(x=discovery, fill=discovery, y=meanScore)) + geom_boxplot() + scale_y_log10()+ scale_fill_brewer(palette = "Dark2")
| Version | Author | Date |
|---|---|---|
| aee923e | brimittleman | 2019-12-17 |
This is expected. Those found in both will be used more often. I expect many of those only discovered in 1 will drop out at the 5% cutoff.
AllPAS_use=as.data.frame(rbind(OverlapBoth_format,HumanSpec,ChimpSpec))
write.table(AllPAS_use, "../data/cleanPeaks_lifted/AllPAS_postLift.bed", col.names = F, row.names = F, quote = F, sep = "\t") sort these:
sort -k1,1 -k2,2n ../data/cleanPeaks_lifted/AllPAS_postLift.bed > ../data/cleanPeaks_lifted/AllPAS_postLift.sort.bed
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
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 rlang_0.4.0 later_0.7.5
[10] pillar_1.3.1 glue_1.3.0 withr_2.1.2
[13] RColorBrewer_1.1-2 modelr_0.1.2 readxl_1.1.0
[16] plyr_1.8.4 munsell_0.5.0 gtable_0.2.0
[19] workflowr_1.6.0 cellranger_1.1.0 rvest_0.3.2
[22] evaluate_0.12 labeling_0.3 knitr_1.20
[25] httpuv_1.4.5 broom_0.5.1 Rcpp_1.0.2
[28] promises_1.0.1 scales_1.0.0 backports_1.1.2
[31] jsonlite_1.6 fs_1.3.1 hms_0.4.2
[34] digest_0.6.18 stringi_1.2.4 grid_3.5.1
[37] rprojroot_1.3-2 cli_1.1.0 tools_3.5.1
[40] magrittr_1.5 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