Ortho Exon evaluation as an annotation

Last updated: 2020-03-31

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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.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File	Version	Author	Date	Message
Rmd	8280da5	brimittleman	2020-03-31	add distance plots
html	1274a02	brimittleman	2020-03-30	Build site.
html	469010e	brimittleman	2020-03-30	Build site.
Rmd	ef52dfc	brimittleman	2020-03-30	add all overlap orthoexon
html	3d4dd47	brimittleman	2020-03-27	Build site.
Rmd	f92b89c	brimittleman	2020-03-26	add ortho exon and new mm

library(tidyverse)

── Attaching packages ────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

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── Conflicts ───────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

I am still concerned about annotation. I want to see if using the ortho exon file as an annotation tool would be more appropriate. I want to look at it and see if there is a way to combine exons by gene and see if the spaces inbetween could be introns.

First look at the human one.

HumanOrtho=read.table("/project2/gilad/kenneth/OrthoExonPartialMapping/human.noM.gtf", sep="\t")

Parse this into a bed file with a python script.

mkdir ../data/OrthoExonBed
python SAF2Bed.py /project2/gilad/kenneth/OrthoExonPartialMapping/human.noM.gtf ../data/OrthoExonBed/human.noM.bed

sort -k1,1 -k2,2n ../data/OrthoExonBed/human.noM.bed > ../data/OrthoExonBed/human.noM.sort.bed

All PAS

sbatch overlapPASandOrthoexon.sh

Results:

PASMeta=read.table("../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt",header = T,stringsAsFactors = F) %>% select(PAS, gene,loc)
OverlapPAS=read.table("../data/OrthoExonBed/allPASinOrtho.bed", col.names = c("chr", "start", "end", "PAS", "score", "strand"),stringsAsFactors = F)%>% group_by(PAS) %>% summarize(n=n())%>%  mutate(OE="In") %>% inner_join(PASMeta,by="PAS")
nrow(OverlapPAS)

[1] 25621

NotOverlapPAS=read.table("../data/OrthoExonBed/allPAS_NOT_inOrtho.bed", col.names = c("chr", "start", "end", "PAS", "score", "strand"),stringsAsFactors = F)%>% group_by(PAS) %>% summarize(n=n())%>%  mutate(OE="OUT") %>% inner_join(PASMeta,by="PAS")
nrow(NotOverlapPAS)

[1] 16697

ALLPAS_ortho=OverlapPAS %>% bind_rows(NotOverlapPAS)

ggplot(ALLPAS_ortho, aes(x=OE, by=loc,fill=loc)) +geom_bar(stat="count",position = "dodge") + labs(x="Is PAS overlapping ortho exon", title="All PAS in OrthoExon",y="Number of PAS")+ scale_fill_brewer(palette = "Dark2")

Version	Author	Date
469010e	brimittleman	2020-03-30

Ok this is the expected distribution, We expect the coding and 3’ UTRs to be in the ortho exon file. What is more interesting is genes with and without exons in the ortho exon.

Take this to the gene level.

OrthoBed=read.table("../data/OrthoExonBed/human.noM.sort.bed", col.names = c("chr","start","end","gene", "nExon","strand"),stringsAsFactors = F) %>% group_by(gene) %>% summarise(nExon=n())

Now I look to see which of the PAS are in genes not in the ortho exon.

PASMeta_GeneOE= PASMeta %>% mutate(OE=ifelse(gene %in% OrthoBed$gene, "Yes", "No"))

PASMeta_GeneOEgene= PASMeta_GeneOE %>% group_by(gene, OE) %>% summarise()

ggplot(PASMeta_GeneOEgene, aes(x=OE, fill=OE))+ geom_histogram(stat="count") + labs(x="Is gene in Ortho Exon", y="Genes") + scale_fill_brewer(palette = "Dark2")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Version	Author	Date
469010e	brimittleman	2020-03-30

Look at the genes without:

PASMeta_GeneOE_NO= PASMeta_GeneOE %>% filter(OE=="No")
nrow(PASMeta_GeneOE_NO)

[1] 5293

ggplot(PASMeta_GeneOE_NO,aes(x=loc, fill=OE)) + geom_bar(stat="count")

Version	Author	Date
469010e	brimittleman	2020-03-30
3d4dd47	brimittleman	2020-03-27

Genesnotin=PASMeta_GeneOE_NO %>% group_by(gene) %>% summarise(n=n())

1000 genes not in ortho exons.

#sno
Genesnotin %>% filter(grepl("SNO",gene)) %>% nrow()

[1] 112

#linc
Genesnotin %>% filter(grepl("LINC",gene)) %>% nrow()

[1] 63

#loc
Genesnotin %>% filter(grepl("LOC",gene)) %>% nrow()

[1] 327

Are these the genes with different dominant PAS.

allPAS= read.table("../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T) 
ChimpPASwMean =allPAS %>% dplyr::select(-Human)
HumanPASwMean =allPAS %>% dplyr::select(-Chimp)

Chimp_Dom= ChimpPASwMean %>%
  group_by(gene) %>%
  top_n(1,Chimp) %>% 
  mutate(nPer=n()) %>% 
  filter(nPer==1) %>% 
  dplyr::select(gene,loc,PAS,Chimp) %>% 
  rename(ChimpLoc=loc, ChimpPAS=PAS)

Human_Dom= HumanPASwMean %>% 
  group_by(gene) %>% 
  top_n(1, Human) %>% 
  mutate(nPer=n()) %>% 
  filter(nPer==1) %>% 
  dplyr::select(gene,loc,PAS,Human) %>% 
  rename(HumanLoc=loc, HumanPAS=PAS)


#merge

BothDom= Chimp_Dom %>% inner_join(Human_Dom,by="gene")
DifDom= BothDom %>% filter(ChimpPAS!=HumanPAS)

Plot this before I remove those genes.

DifDom_before=DifDom %>% select(gene, ChimpLoc, HumanLoc) %>% gather("species","loc",-gene)

ggplot(DifDom_before, aes(x=loc, by=species, fill=species))+geom_histogram(position = "dodge",stat = "count")+ labs(title="Location of PAS with different Dominant",y="PAS")+scale_fill_brewer(palette = "Dark2")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Version	Author	Date
469010e	brimittleman	2020-03-30

Remove the not ortho genes:

DifDom_after=DifDom_before %>% anti_join(Genesnotin,by="gene")

Warning: Column `gene` joining factor and character vector, coercing into
character vector

ggplot(DifDom_after, aes(x=loc, by=species, fill=species))+geom_histogram(position = "dodge",stat = "count")+ labs(title="Location of PAS with different Dominant\n after removing genes not in orthoexon",y="PAS")+scale_fill_brewer(palette = "Dark2")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Version	Author	Date
469010e	brimittleman	2020-03-30

That didnt help.

Check the matching ones (are these in ortho exon)

SameDom= BothDom %>% filter(ChimpPAS==HumanPAS)
nrow(SameDom)

[1] 7638

SameDom_after=SameDom %>% anti_join(Genesnotin,by="gene")

Warning: Column `gene` joining factor and character vector, coercing into
character vector

nrow(SameDom_after)

[1] 6906

Where did I lose genes.

6906/7638

[1] 0.9041634

3432/4028

[1] 0.8520357

Lose more in the different dominant than in the same dominant. Percent lost=

(7638-6906)/7638

[1] 0.09583661

(4028-3432)/4028

[1] 0.1479643

MultiMap

I will check if some of the problem genes, I found in my pipeline are in this.

ChimpMM=read.table("../data/multimap/Chimp_Uniq_multimapPAS.txt", stringsAsFactors = F, header = T)
HumanMM=read.table("../data/multimap/Human_Uniq_multimapPAS.txt", stringsAsFactors = F, header = T)
BothMM=read.table("../data/multimap/Both_multimapPAS.txt",stringsAsFactors = F, header = T)


AllMM=ChimpMM %>% bind_rows(HumanMM) %>% bind_rows(BothMM)

I will overlap this with the ortho exon file. I will look at those that overlap and those that do not. I need a bed file

AllMM_bed=AllMM %>% mutate(Name=paste(gene, PAS,loc, MultiMap, sep=":")) %>% select(chr, start,end, Name, Human, strandFix)

write.table(AllMM_bed,"../data/multimap/allMM.bed",col.names = F, quote = F, row.names = F, sep="\t")

sort -k1,1 -k2,2n ../data/multimap/allMM.bed > ../data/multimap/allMM.sort.bed

Overlap.

sbatch overlapMMandOrthoexon.sh

Results:

InOrtho=read.table("../data/OrthoExonBed/allMMinOrtho.bed", col.names = c("chr", "start", "end", "name", "score", "strand")) %>% group_by(name) %>% summarize(n=n())%>% separate(name, into=c("gene", "PAS","loc", "MM"),sep=":") %>% mutate(OE="In")
NotInOrtho=read.table("../data/OrthoExonBed/allMM_NOT_inOrtho.bed", col.names = c("chr", "start", "end", "name", "score", "strand")) %>% group_by(name) %>% summarize(n=n()) %>% separate(name, into=c("gene", "PAS","loc", "MM"),sep=":")  %>% mutate(OE="Out")

AllOrthores=InOrtho %>% bind_rows(NotInOrtho)

ggplot(AllOrthores, aes(x=OE, by=loc,fill=loc)) +geom_bar(stat="count",position = "dodge") + labs(x="Is PAS overlapping ortho exon", title="PAS impacted by multimapping and ortho exon",y="Number of PAS")+ scale_fill_brewer(palette = "Dark2")

Version	Author	Date
469010e	brimittleman	2020-03-30

Proportion:

AllOrthores %>% group_by(OE) %>% summarise(n=n())

# A tibble: 2 x 2
  OE        n
  <chr> <int>
1 In      831
2 Out     578

#look only at utr  

AllOrthores %>% filter(loc=="utr3") %>% group_by(OE) %>% summarise(n=n())

# A tibble: 2 x 2
  OE        n
  <chr> <int>
1 In      492
2 Out     120

Look at the UTR sequences that are in:

AllOrthores %>% filter(OE=="In", loc=="utr3")

# A tibble: 492 x 6
   gene    PAS         loc   MM        n OE   
   <chr>   <chr>       <chr> <chr> <int> <chr>
 1 AARS2   human285010 utr3  Human     2 In   
 2 ABCB10  human30678  utr3  Both      2 In   
 3 ABHD17A human153108 utr3  Both      8 In   
 4 ABR     chimp125493 utr3  Human    10 In   
 5 ACAD8   chimp63259  utr3  Chimp     6 In   
 6 ACAD8   human66039  utr3  Chimp     6 In   
 7 ACTB    human299134 utr3  Both      7 In   
 8 ADAM9   chimp303728 utr3  Human     8 In   
 9 ADAM9   chimp303729 utr3  Human     8 In   
10 ADH5    human247010 utr3  Chimp     2 In   
# … with 482 more rows

distance to next annotated ortho exon boundry

I can look at the intronic PAS in both species to see how far they are from an ortho exon.

I need to merge exons.

sort -k1,1 -k2,2n ../data/OrthoExonBed/chimp.noM.bed >  ../data/OrthoExonBed/chimp.noM.sort.bed 
bedtools merge -s -c 4,5,6 -o distinct,count,distinct  -i  ../data/OrthoExonBed/chimp.noM.sort.bed >  ../data/OrthoExonBed/chimp.noM.sort.merged.bed 
bedtools merge -s -c 4,5,6 -o distinct,count,distinct  -i  ../data/OrthoExonBed/human.noM.sort.bed >  ../data/OrthoExonBed/human.noM.sort.merged.bed 

mkdir ../data/TestAnnoBiasOE

Now I can map all of the intronic PAS

For intronic PAS in genes in the ortho exon, filter the bed files

PASMeta_GeneOE_intronYes= PASMeta_GeneOE %>% filter(loc=="intron", OE=="Yes")

HumanBed=read.table("../data/PAS_doubleFilter/PAS_doublefilter_either_HumanCoordHummanUsage.sort.bed", col.names = c("chr", 'start','end','PAS','usage','strand'),stringsAsFactors = F) %>% semi_join(PASMeta_GeneOE_intronYes, by="PAS")

write.table(HumanBed, "../data/TestAnnoBiasOE/HumanIntronicGeneinOE.bed",col.names = F, row.names = F, quote = F, sep="\t")

ChimpBed=read.table("../data/PAS_doubleFilter/PAS_doublefilter_either_ChimpCoordChimpUsage.sort.bed", col.names = c("chr", 'start','end','PAS','usage','strand'),stringsAsFactors = F) %>% semi_join(PASMeta_GeneOE_intronYes, by="PAS")

write.table(ChimpBed, "../data/TestAnnoBiasOE/ChimpIntronicGeneinOE.bed",col.names = F, row.names = F, quote = F, sep="\t")

Find the closest:

same strand. look only upstream (-id) to say closest annotated 5’ splice site. then do only downstream to say closest annotated 3’ splice site.

sbatch ClosestorthoEx.sh

Look at upstream

If the gene is a different gene, make it 0

HumanUpstream=read.table("../data/TestAnnoBiasOE/HumanUpstream.intronic.txt",stringsAsFactors = F, col.names = c("chrPAS", "startPAS", "endPAS", "PAS", "HumanUsage", "strandPAS", "chrExon","startExon","endExon", "Orthogene", "n","strandIntron", "UpstreamdistancePAS2Exon")) %>% inner_join(PASMeta,by="PAS") %>% mutate(Fixed=ifelse(gene==Orthogene, UpstreamdistancePAS2Exon,0))
HumanDownstream=read.table("../data/TestAnnoBiasOE/HumanDownstream.intronic.txt",stringsAsFactors = F, col.names = c("chrPAS", "startPAS", "endPAS", "PAS", "HumanUsage", "strandPAS", "chrExon","startExon","endExon", "Orthogene", "n","strandIntron", "DownstreamdistancePAS2Exon")) %>% inner_join(PASMeta,by="PAS") %>% mutate(Fixed=ifelse(gene==Orthogene, DownstreamdistancePAS2Exon,0))



HumanBoth=as.data.frame(cbind(HumanUpstream[,1:6],gene=HumanUpstream[,14], "UpstreamHuman"=HumanUpstream[,16],"DownstreamHuman"=HumanDownstream[,16])) %>% 
  mutate(DominanceHuman=ifelse(PAS %in% BothDom$HumanPAS, "yes","no"))

Chimp:

ChimpUpstream=read.table("../data/TestAnnoBiasOE/ChimpUpstream.intronic.txt",stringsAsFactors = F, col.names = c("chrPAS", "startPAS", "endPAS", "PAS", "ChimpUsage", "strandPAS", "chrExon","startExon","endExon", "Orthogene", "n","strandIntron", "UpstreamdistancePAS2Exon"))  %>% inner_join(PASMeta,by="PAS") %>% mutate(Fixed=ifelse(gene==Orthogene, UpstreamdistancePAS2Exon,0))
ChimpDownstream=read.table("../data/TestAnnoBiasOE/ChimpDownstream.intronic.txt",stringsAsFactors = F, col.names = c("chrPAS", "startPAS", "endPAS", "PAS", "ChimpUsage", "strandPAS", "chrExon","startExon","endExon", "Orthogene", "n","strandIntron", "DownstreamdistancePAS2Exon"))  %>% inner_join(PASMeta,by="PAS") %>% mutate(Fixed=ifelse(gene==Orthogene, DownstreamdistancePAS2Exon,0))
ChimpBoth=as.data.frame(cbind(PAS=ChimpUpstream[,4], 'UpstreamChimp'=ChimpUpstream[,16],'DownstreamChimp'=ChimpDownstream[,16])) %>% 
  mutate(DominanceChimp=ifelse(PAS %in% BothDom$ChimpPAS, "yes","no"))

Join together:

BothSpeciesDistance= HumanBoth %>% inner_join(ChimpBoth, by="PAS")

Warning: Column `PAS` joining character vector and factor, coercing into
character vector

BothSpeciesDistance$UpstreamChimp=as.numeric(as.character(BothSpeciesDistance$UpstreamChimp))
BothSpeciesDistance$DownstreamChimp=as.numeric(as.character(BothSpeciesDistance$DownstreamChimp))

Look at distance based on human dominance then chimp dominance

ggplot(BothSpeciesDistance, aes(x=UpstreamHuman, y=UpstreamChimp, col=DominanceHuman)) + geom_point(alpha=.3)+ geom_abline(aes(slope=1,intercept=0))

ggplot(BothSpeciesDistance, aes(x=UpstreamHuman, y=UpstreamChimp, col=DominanceHuman)) + geom_point(alpha=.3) + ylim(c(-10000,0)) +xlim(c(-10000,0)) + geom_abline(aes(slope=1,intercept=0)) + scale_color_brewer(palette = "Dark2")

Warning: Removed 1651 rows containing missing values (geom_point).

I want to color by dominant in human, chimp, both neither

BothSpeciesDistance_dom=BothSpeciesDistance %>% mutate(Dominance=ifelse(DominanceHuman=="yes", ifelse(DominanceChimp=="yes", "Both", "human"), ifelse(DominanceChimp=="yes", "chimp", "Neither")))

Upstream Plots:

ggplot(BothSpeciesDistance_dom, aes(x=UpstreamHuman, y=UpstreamChimp, col=Dominance)) + geom_point(alpha=.3)+ geom_abline(aes(slope=1,intercept=0)) + geom_smooth(method="lm", alpha=.1)+scale_color_brewer(palette = "Dark2")

ggplot(BothSpeciesDistance_dom, aes(x=UpstreamHuman, y=UpstreamChimp, col=Dominance)) + geom_point(alpha=.3)+ geom_abline(aes(slope=1,intercept=0))+ylim(c(-50000,0)) +xlim(c(-50000,0)) + scale_color_brewer(palette = "Dark2") + geom_smooth(method="lm", alpha=.1)

Warning: Removed 122 rows containing non-finite values (stat_smooth).

Warning: Removed 122 rows containing missing values (geom_point).

Warning: Removed 1 rows containing missing values (geom_smooth).

Downstream

ggplot(BothSpeciesDistance_dom, aes(x=DownstreamHuman, y=DownstreamChimp, col=Dominance)) + geom_point(alpha=.3)+ geom_abline(aes(slope=1,intercept=0)) + scale_color_brewer(palette = "Dark2") + geom_smooth(method="lm", alpha=.1) + geom_smooth(method="lm", alpha=.1)

There are a lot of PAS that are far from an exon on the human side.

ggplot(BothSpeciesDistance_dom, aes(x=DownstreamHuman, y=DownstreamChimp, col=Dominance)) + geom_point(alpha=.3)+ geom_abline(aes(slope=1,intercept=0))+ylim(c(0,50000)) +xlim(c(0,50000)) + scale_color_brewer(palette = "Dark2") + geom_smooth(method="lm", alpha=.01)

Warning: Removed 395 rows containing non-finite values (stat_smooth).

Warning: Removed 395 rows containing missing values (geom_point).

Warning: Removed 3 rows containing missing values (geom_smooth).

Look at the top examples:

BothSpeciesDistance_dom_TopDiffUp=BothSpeciesDistance_dom %>% mutate(Updiff=abs(UpstreamHuman-UpstreamChimp)) %>% select(PAS, gene, Updiff) %>% arrange(desc(Updiff))

head(BothSpeciesDistance_dom_TopDiffUp)

          PAS    gene Updiff
1  human34001  SFMBT2  51262
2 chimp298751  ACTR3C  34423
3 human296634   TFB1M  33486
4 human175470 ANKRD36  31986
5 chimp298752  ACTR3C  31871
6 human175469 ANKRD36  31766

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         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       workflowr_1.6.0    cellranger_1.1.0  
[22] rvest_0.3.2        evaluate_0.12      labeling_0.3      
[25] knitr_1.20         httpuv_1.4.5       fansi_0.4.0       
[28] broom_0.5.1        Rcpp_1.0.2         promises_1.0.1    
[31] scales_1.0.0       backports_1.1.2    jsonlite_1.6      
[34] fs_1.3.1           hms_0.4.2          digest_0.6.18     
[37] stringi_1.2.4      grid_3.5.1         rprojroot_1.3-2   
[40] cli_1.1.0          tools_3.5.1        magrittr_1.5      
[43] lazyeval_0.2.1     crayon_1.3.4       whisker_0.3-2     
[46] pkgconfig_2.0.2    xml2_1.2.0         lubridate_1.7.4   
[49] assertthat_0.2.0   rmarkdown_1.10     httr_1.3.1        
[52] rstudioapi_0.10    R6_2.3.0           nlme_3.1-137      
[55] git2r_0.26.1       compiler_3.5.1

Ortho Exon evaluation as an annotation

Briana Mittleman

3/17/2020

All PAS

MultiMap

distance to next annotated ortho exon boundry