Last updated: 2020-05-25

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/DeandNumPAS.Rmd
    Modified:   analysis/DiffTop2SecondDom.Rmd
    Modified:   analysis/DirSelectionKhan.Rmd
    Modified:   analysis/EffectSizeRelationshipFixDir.Rmd
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/PTM_analysis.Rmd
    Modified:   analysis/TotalDomStructure.Rmd
    Modified:   analysis/TotalVNuclearBothSpecies.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/changeMisprimcut.Rmd
    Modified:   analysis/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/dInforContent.Rmd
    Modified:   analysis/diffExpression.Rmd
    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/mRNADecay.Rmd
    Modified:   analysis/miRNAanalysis.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/phastCon.Rmd
    Modified:   analysis/pol2.Rmd
    Modified:   analysis/signalsites_doublefilter.Rmd
    Modified:   analysis/speciesSpecific.Rmd

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Rmd 4111a48 brimittleman 2020-05-25 which eQTL

I have noticed that the intronic apaQTL relationship with eQTLs are in the opposite direction from the interspecies relationship between delta PAS and expression effect size.

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()

First step:

-get the human specific and chimp allele for apaQTLs and eQTLs

bcftools annotate

lift human vcf

Before I get too far. I need to make sure the apaQTL PAS are tested in this study.

interect the QTL pas with the new ones. Then see if the QTL PAS are in this set.

mkdir ../data/QTLPASoverlap
apaQTLPAS=read.table("../data/liftover_files/APAPAS_GeneLocAnno.5perc.hg19lifted_extended.sort.bed", col.names = c("chr", "start", "end","apaName", "score","strand"))

write.table(apaQTLPAS, "../data/QTLPASoverlap/allPASfromapaQTL.bed", sep="\t", col.names = F, row.names = F, quote = F)


CompPAS=read.table("../data/PAS_doubleFilter/PAS_doublefilter_either_HumanCoordHummanUsage.sort.bed", col.names = c("chr","start", "end", "name", "score", "strand"))
bedtools intersect -sorted  -s -wa -wb -a ../data/PAS_doubleFilter/PAS_doublefilter_either_HumanCoordHummanUsage.sort.bed -b ../data/liftover_files/APAPAS_GeneLocAnno.5perc.hg19lifted_extended.sort.bed  > ../data/QTLPASoverlap/OverlappingPASbothprojects.txt
Overlap=read.table("../data/QTLPASoverlap/OverlappingPASbothprojects.txt", col.names = c(colnames(CompPAS),colnames(apaQTLPAS) ))%>% select(name, score,apaName ) %>% separate(apaName,into=c("num", "geneloc"),sep=":") %>% mutate(peak=paste("peak", num,sep="")) %>% separate(geneloc,into=c("gene", "loc"), sep="_")


Overlap %>% filter(gene=="TMEM156")
         name score   num    gene    loc      peak
1 human255238 0.020 96732 TMEM156    end peak96732
2 human255253 0.130 96738 TMEM156 intron peak96738
3 human255254 0.084 96738 TMEM156 intron peak96738
4 human255282 0.128 96746 TMEM156 intron peak96746

PAS with apaQTL

apaQTL= read.table("../../apaQTL/data/apaQTLs/NuclearQTLs_PeakSNP.txt",stringsAsFactors = F, col.names = c("peak", "snp", "gene"))


Overlap_qtl=Overlap %>% inner_join(apaQTL,by=c("peak","gene"))
Overlap_qtl %>% select(gene) %>% unique() %>% nrow()
[1] 310
Overlap_qtl %>% group_by(loc) %>% summarise(n())
# A tibble: 5 x 2
  loc    `n()`
  <chr>  <int>
1 cds       15
2 end       32
3 intron    98
4 utr3     279
5 utr5       5

I will be able to compare 98 intronic and 279 3’ UTR sites if the genotype info exists.

310 QTLs

do these genes have eQTL info?

Look in the nominal for the expression relationships at these sites:

overlapgenesnp= Overlap_qtl %>% select(gene, snp) %>% unique()



eQTL=read.table("../../apaQTL/data/molQTLs/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.AllNomRes.GeneName.txt", col.names = c("gene", "snp", "dist", "pval", "slope"),stringsAsFactors = F) %>% inner_join(overlapgenesnp, by=c("gene","snp"))

nrow(eQTL)
[1] 323

323 of these relationships have been tested in eQTL:

are any of these significant.

eQTL_sig= eQTL %>% filter(pval <0.05)

nrow(eQTL_sig)
[1] 64
eQTL_sig %>% select(gene) %>% unique() %>% nrow()
[1] 54

Are any of these intronic:

eQTL_sig_apaloc= eQTL_sig %>% inner_join(Overlap_qtl,by=c('gene', 'snp' )) 
eQTL_sig_apaloc_intron=eQTL_sig_apaloc %>% filter(loc=="intron")

nrow(eQTL_sig_apaloc_intron)
[1] 13
eQTL_sig_apaloc_intron
       gene         snp   dist        pval     slope        name score
1  C10orf88   rs7904973   3167 6.81696e-04 -0.511360  human51879 0.096
2    MTHFSD rs115018779  17711 9.05013e-09 -2.171840 human137593 0.048
3      YES1  rs78139339  14529 7.79311e-03 -0.368041 human153201 0.054
4      YES1  rs78139339  14529 7.79311e-03 -0.368041 human153202 0.130
5   PHACTR4  rs74064856  99467 1.14867e-03 -0.527008   human4946 0.074
6    ZNF264   rs4801440   3509 1.37992e-04 -0.501385 human171312 0.048
7     HIBCH     rs11542  15227 2.27510e-03 -0.296270 human197292 0.172
8      LIPH  rs62291862  37477 1.07623e-02 -0.627503 human248946 0.230
9     NR2C1 rs139153325  50064 2.31758e-02 -0.465992  human84280 0.054
10  TMEM156   rs2711981  70892 8.25279e-04 -0.825601 human255282 0.128
11  SLC35B3  rs17143705   8203 4.41774e-03 -0.582024 human294988 0.022
12 TBC1D22B  rs57137816   3614 1.81026e-02 -0.571407 human299689 0.074
13     NOM1   rs4716445 -10180 2.76954e-04 -0.637932 human336792 0.072
      num    loc       peak
1   19682 intron  peak19682
2   52510 intron  peak52510
3   59589 intron  peak59589
4   59589 intron  peak59589
5    2127 intron   peak2127
6   67214 intron  peak67214
7   76393 intron  peak76393
8   94458 intron  peak94458
9   31713 intron  peak31713
10  96746 intron  peak96746
11 110709 intron peak110709
12 113003 intron peak113003
13 126312 intron peak126312

12 gene snp pairs we could even test…

lift vcf




java -jar $PICARD CreateSequenceDictionary REFERENCE=/project2/gilad/briana/genome_anotation_data/Chimp_genome/panTro6.fa OUTPUT=/project2/gilad/briana/genome_anotation_data/Chimp_genome/panTro6.dict

Try to lift the VCF file to chimp.

DO this interactivly with the loaded module.

#!/bin/bash


#SBATCH --job-name=liftVCF
#SBATCH --output=liftVCF.out
#SBATCH --error=lliftVCF.err
#SBATCH --time=10:00:00
#SBATCH --partition=gilad
#SBATCH --nodelist=midway-l16b-31 
#SBATCH --mem=550G
#SBATCH --mail-type=END

module load picard 

#test chrom 4
java -jar $PICARD LiftoverVcf I=/project2/gilad/briana/li_genotypes/genotypesYRI.gen.proc.5MAF.chr4_test.vcf O=../data/QTLPASoverlap/Ch4_geno2pantro.vcf   CHAIN=../data/chainFiles/hg19ToPanTro6.over.chain.gz  REJECT=../data/QTLPASoverlap/Ch4_rejected_variants.vcf  R=/project2/gilad/briana/genome_anotation_data/Chimp_genome/panTro6.fa

VCF version, for input source: file:///project2/gilad/briana/li_genotypes/genotypesYRI.gen.proc.5MAF.chr4_test.vcf

had to add ##fileformat=VCFv4.2 to the vcf head

To get help, see http://broadinstitute.github.io/picard/index.html#GettingHelp Exception in thread “main” java.lang.IllegalStateException: Key INFO found in VariantContext field INFO at 4:11961 but this key isn’t defined in the VCFHeader. We require all VCFs to have complete VCF headers by default.

known examples:

I know the chimp allele for the TMEM156- rs2711981 QTL is T.

look at this gene using example plot boxplot:

human have increased usage of the PAS. this is opposite of the chimp allele in the APA.

Meta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F)  %>% dplyr::select(PAS, chr, start,end, loc)
DiffIso= read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T,stringsAsFactors = F) %>% inner_join(Meta, by=c("chr", 'start','end')) 
DiffIso %>% filter(PAS=="human255282") %>% select(PAS, Human, Chimp,deltaPAU)
          PAS     Human      Chimp  deltaPAU
1 human255282 0.2881221 0.03638857 0.2517335

apaQTL info:

pid nvar shape1 shape2 dummy sid dist npval slope ppval bpval bh 4:39029993:39030080:TMEM156_intron_+_peak96746 157 0.887098 26.4876 44.6684 rs2711981 9264 3.17459e-09 1.49328 0.000999001 1.22893e-06 0.000368808094594595

permRes=read.table("/project2/gilad/briana/apaQTL/data/apaQTLPermuted_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear_permResBH.txt", header = T)

1.560980

../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_chr$i.gz

../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_chr4.gz

How many of the intronic apaQTLs are sig in this work

Overlap_qtl_int=Overlap_qtl %>% filter(loc=="intron") %>% rename("PAS"=name)
DiffIsoSig= DiffIso %>% filter(SigPAU2=="Yes")

DiffIsoSigOverlapint= Overlap_qtl_int %>% inner_join(DiffIsoSig, by=c("PAS"))
Warning: Column `PAS` joining factor and character vector, coercing into
character vector
DiffIsoSigOverlapint
           PAS score    num   gene.x  loc.x       peak         snp   chr
1   chimp71069 0.056  28480    BICD1 intron  peak28480   rs1673864 chr12
2   human85038 0.176  31979    DRAM1 intron  peak31979   rs2138257 chr12
3  human146639 0.220  57020    KPNB1 intron  peak57020   rs8071832 chr17
4  human153202 0.130  59589     YES1 intron  peak59589  rs78139339 chr18
5  human167137 0.312  65165   ZNF146 intron  peak65165  rs11882933 chr19
6  human215583 0.914  82852   IFNGR2 intron  peak82852   rs9974603 chr21
7  human255282 0.128  96746  TMEM156 intron  peak96746   rs2711981  chr4
8  human260442 0.694  98569     AFF1 intron  peak98569   rs7677039  chr4
9  human294636 0.224 110580 LY86-AS1 intron peak110580 rs115728940  chr6
10 human326781 0.054 122625     CDK6 intron peak122625   rs6954290  chr7
11 human367209 0.082 137569     ABL1 intron peak137569   rs2855195  chr9
       start       end       gene.y      logef       Chimp     Human
1   32129860  32130060        BICD1  1.1574666 0.107015575 0.3074286
2  101901369 101901569        DRAM1 -0.6646054 0.560896771 0.3183421
3   47663804  47664004        KPNB1  4.1745016 0.002241554 0.2931332
4     727582    727782         YES1  3.2502413 0.008852500 0.3137472
5   36230417  36230617       ZNF146  1.2125051 0.127344309 0.3701575
6   33404298  33404498       IFNGR2  7.3012431 0.079748414 0.9145805
7   39028361  39028561      TMEM156  1.8975291 0.036388571 0.2881221
8   87071835  87072035 LOC105377320  1.2883239 0.522502000 0.7754384
9    6562291   6562491     LY86-AS1  0.9426266 0.291261376 0.5247326
10  92653768  92653968         CDK6  1.9718073 0.046790926 0.4049052
11 130881699 130881899         ABL1  2.6052641 0.001477165 0.2132200
     deltaPAU     p.adjust SigPAU2  loc.y
1   0.2004130 3.393258e-03     Yes intron
2  -0.2425547 7.408353e-05     Yes    cds
3   0.2908917 1.241188e-06     Yes intron
4   0.3048947 3.675671e-09     Yes intron
5   0.2428132 1.578414e-06     Yes intron
6   0.8348321 8.876451e-14     Yes intron
7   0.2517335 3.261011e-05     Yes   utr5
8   0.2529364 1.999108e-05     Yes intron
9   0.2334712 2.632432e-05     Yes intron
10  0.3581143 5.071769e-08     Yes intron
11  0.2117428 3.903033e-07     Yes intron

checking BICD1 example:

rs1673864 - chimp is a T allele.

This is in the correct direction.

DRAM1 rs2138257 chimp allele is T

KPNB1_intron

rs8071832 chimp allele is C

YES1

rs78139339- cant find snp.

ZNF146 rs11882933

chimp is C allele (wrong dir)

rs9974603 IFNGR2

chimp is C allele (correct dir.)

AFF1 - no comp apa plot

rs7677039

LY86-AS1

rs115728940- chimp is an A wrong dir

CDK6- no QTL plot

ABL1-no qtl plot

Made figures in illustrator on comp.


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  colorspace_1.3-2
 [5] generics_0.0.2   htmltools_0.3.6  yaml_2.2.0       utf8_1.1.4      
 [9] rlang_0.4.0      later_0.7.5      pillar_1.3.1     glue_1.3.0      
[13] withr_2.1.2      modelr_0.1.2     readxl_1.1.0     plyr_1.8.4      
[17] munsell_0.5.0    gtable_0.2.0     workflowr_1.6.0  cellranger_1.1.0
[21] rvest_0.3.2      evaluate_0.12    knitr_1.20       httpuv_1.4.5    
[25] fansi_0.4.0      broom_0.5.1      Rcpp_1.0.4.6     promises_1.0.1  
[29] scales_1.0.0     backports_1.1.2  jsonlite_1.6     fs_1.3.1        
[33] hms_0.4.2        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      magrittr_1.5    
[41] lazyeval_0.2.1   crayon_1.3.4     whisker_0.3-2    pkgconfig_2.0.2 
[45] xml2_1.2.0       lubridate_1.7.4  assertthat_0.2.0 rmarkdown_1.10  
[49] httr_1.3.1       rstudioapi_0.10  R6_2.3.0         nlme_3.1-137    
[53] git2r_0.26.1     compiler_3.5.1