Last updated: 2020-03-30

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
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    Modified:   analysis/upsetter_DF.Rmd

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
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 ──
✔ 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()

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")

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

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
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

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

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")

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

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