Last updated: 2020-09-01

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Input Parameters

library(tidyverse); library(magrittr)
dartvcfInput<-paste0("/workdir/marnin/DCas19_4403/Report_4403_VCF_Version_6.csv")
dartcountsInput<-paste0("/workdir/marnin/DCas19_4403/Report_4403_Counts_Version_6_updated.csv")
outName<-paste0("/workdir/marnin/DCas19_4403/DCas19_4403_102419")
nskipvcf<-2
nskipcounts<-3
ncores<-75 # using more than a few could be VERY memory intensive

Components of function

Read and format the 4432 counts/vcf files

# convertDart2vcf<-function(dartvcfInput,dartcountsInput,outName,
#                           nskipvcf=2,nskipcounts=3,ncores){
#rm(vcf,readCounts); gc()
vcf<-read.table(dartvcfInput,
                stringsAsFactors = F,skip = nskipvcf, header = T, sep = "\t", comment.char = "")
dim(vcf) # [1] 13603   654
readCounts<-read.csv(dartcountsInput, stringsAsFactors = F,header = T,skip=nskipcounts)
colnames(vcf)[10:length(colnames(vcf))]<-colnames(readCounts)[43:length(colnames(readCounts))]

vcf %<>% 
    mutate(X.CHROM=gsub("Chromosome","",X.CHROM)) %>% 
    rename(Pos=POS) %>% 
    mutate(Chr=as.numeric(gsub("Chromosome","",X.CHROM)),
           Pos=as.numeric(Pos)) 


# dim(readCounts)
# readCounts %>% .[,1:30] %>% str
# readCounts[1:5,1:30]
# readCounts[1:10,] %>% select(SNP,CloneID,AlleleID,Chrom_Cassava_v61,SNP_ChromPos_Cassava_v61)
# vcf[1:10,1:5]
# Note: The ID column in "vcf" is the value for the ALT allele in AlleleID of "readCounts"
readCounts %<>% 
  mutate(RefAlt=ifelse(SNP=="","REF","ALT"),
         Chr=as.numeric(gsub("Chromosome","",Chrom_Cassava_v61)),
         Pos=as.numeric(SNP_ChromPos_Cassava_v61))

Add a SNPindex

Add a unique value “SNPindex” to each SNP in the vcf and readCounts df’s For readCounts, this is going to be the best way to keep track of pairs of rows. Since in many cases, multiple CloneID can point to same Chr-Pos-Alleles and it’s otherwise unclear which pair of rows should go together when subsetting downstream.

dim(vcf) # [1] 13603  655
dim(readCounts) # [1] 27206  690
vcf %<>% 
  mutate(SNPindex=1:nrow(.))
readCounts %<>% 
  mutate(SNPindex=sort(rep(1:(nrow(.)/2),2)))
readCounts %<>% 
  separate(AlleleID,c("tmp","Alleles"),sep=-3,remove = F) %>% 
  select(-tmp) %>% 
  separate(Alleles,c("REF","ALT"),">",remove = F)
vcf %<>%
  separate(ID,c("tmp","Alleles"),sep=-3,remove = F) %>%
  select(-tmp)
vcf %<>% 
    separate(ID,c("CloneID","tmp"),"[|]",remove = F,extra = 'merge') %>% 
    mutate(CloneID=as.numeric(CloneID)) %>% 
    select(-tmp) %>% 
    rename(AlleleID=ID)
readCounts %>% 
  dplyr::select(Chr,Pos,REF,ALT,Alleles,RefAlt,SNPindex,SNP,CloneID,AlleleID) %>% head
vcf %>% 
  dplyr::select(Chr,Pos,ID,REF,ALT,Alleles,SNPindex) %>% head

readCounts and vcf long by sample

# Add required VCF fields
## First have to do some data transformation and 
## create some of the meta-data fields in a VCF, e.g. QUAL, FILTER INFO. 
readCounts %<>%
  arrange(Chr,Pos,SNPindex,CloneID,AlleleID,RefAlt) %>% 
  mutate(QUAL=".",
         FILTER=".",
         INFO=".",
         FORMAT="GT:AD:DP:PL",
         SNP_ID=paste0("S",Chr,"_",Pos))
vcf %<>%
  arrange(Chr,Pos,SNPindex,CloneID,AlleleID) %>% 
  mutate(QUAL=".",
         FILTER=".",
         INFO=".",
         FORMAT="GT:AD:DP:PL",
         SNP_ID=paste0("S",Chr,"_",Pos))
vcf %>% 
  select(Chr,Pos,SNPindex,CloneID,AlleleID,Alleles,REF,ALT,QUAL,FILTER,INFO,FORMAT,SNP_ID) %>% head
readCounts %>% filter(RefAlt=="ALT") %>% 
  select(Chr,Pos,SNPindex,CloneID,AlleleID,Alleles) %>% slice(1:10)

[NEW] Prune duplicate and multi-allelic sites

sites2keep<-vcf %>% 
    count(Chr,Pos) %>% 
    ungroup() %>% 
    filter(n==1) %>% 
    select(-n) %>% 
    semi_join(
        readCounts %>% 
            count(Chr,Pos) %>% 
            arrange(desc(n)) %>% 
            filter(n==2) %>% 
            select(-n)) # 12049 sites

vcf %<>% semi_join(sites2keep)
readCounts %<>% semi_join(sites2keep)
table((readCounts %>% filter(RefAlt=="ALT") %$% SNPindex)==vcf$SNPindex)

sampleIDsFromDartVCF<-colnames(vcf) %>% 
  .[!. %in% c("X.CHROM","Pos","AlleleID","Alleles","REF","ALT","QUAL","FILTER","INFO","FORMAT",
              "Chr","SNPindex","CloneID","SNP_ID","SNP")]
head(sampleIDsFromDartVCF); tail(sampleIDsFromDartVCF)

tmp_counts <-readCounts %>% 
  .[,c("Chr","Pos","SNPindex","SNP_ID",
       "CloneID","AlleleID","RefAlt","Alleles","QUAL","FILTER","INFO","FORMAT",sampleIDsFromDartVCF)] %>% 
    semi_join(sites2keep)
dim(tmp_counts) # [1] 24098   657
tmp_counts %<>%
  gather(FullSampleName,ReadCount,sampleIDsFromDartVCF)
dim(tmp_counts) # [1] 15543210       14
tmp_counts %<>%
  select(-AlleleID) %>% 
  spread(RefAlt,ReadCount) 
dim(tmp_counts) # [1] 7771605      13
head(tmp_counts)
tmp_counts %<>%
  rename(AltCount=ALT,
         RefCount=REF)
dim(tmp_counts) # [1] 7771605      13

vcf_long<-vcf %>% 
    .[,c("Chr","Pos","SNPindex","SNP_ID",
         "CloneID","Alleles","REF","ALT","QUAL","FILTER","INFO","FORMAT",sampleIDsFromDartVCF)] %>% 
    gather(FullSampleName,GT,sampleIDsFromDartVCF)
dim(vcf_long) # [1] 7771605      14
table(vcf_long$CloneID %in% tmp_counts$CloneID) # all true

Add GT from vcf to the counts

I use the DArT genotype call (GT) for the PLINK IBD step.
PLINK requires genotype calls.
Later, I impute in GL mode, so GTs are ignored.

tmp_counts %<>%
  inner_join(vcf_long)
dim(tmp_counts); head(tmp_counts)

Calc PL field

# AD+DP fields
## Now we can calc DP and formate the VCF field "AD" (e.g. "21,0" for 21 reference reads and 0 alt. allele reads)
tmp_counts %<>% 
  mutate(DP=AltCount+RefCount,
         AD=paste0(RefCount,",",AltCount))
tmp1<-tmp_counts %>% 
    filter(!is.na(AltCount),
           !is.na(RefCount))
tmp<-tmp1; rm(tmp1)
tmp %>% head

# Calc. genotype likelihoods

## Genotype likelihoods calculated according to: 
### http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1000862#s4
## Converted to Normalized Phred Scores according to: 
### https://gatkforums.broadinstitute.org/gatk/discussion/5913/math-notes-how-pl-is-calculated-in-haplotypecaller
## Truncate low Phred probabilities (high Phred scores) to 
### 255 max according to TASSEL's convention (Jeff Glaubitz, pers. communication).

#ref<-171; alt<-171; error<-0.001
calcPL<-function(ref,alt,error=0.001){
    # ref and alt arguments are read counts for ref and alt allele, repsectively
    dp<-ref+alt
    # for values >170, factorial() returns 'inf'
    # Since it means essentially 100% probability of a genotype... 
    # set DP to 169 cieling, ref/alt to equiv. allele proportions
    if(dp>=170){ ref<-169*(ref/dp); alt<-169*(alt/dp); dp<-169 }
    gl_RefRef<-(factorial(dp)/(factorial(ref)*factorial(alt)))*(1-(0.75*error))^ref*(error/4)^(alt)
    gl_RefAlt<-(factorial(dp)/(factorial(ref)*factorial(alt)))*(0.5-(0.25*error))^(ref+alt)*(error/4)^(0)
    gl_AltAlt<-(factorial(dp)/(factorial(ref)*factorial(alt)))*(1-(0.75*error))^alt*(error/4)^(ref)
    phredScale<--10*log10(c(gl_RefRef,gl_RefAlt,gl_AltAlt))
    minPhred<-min(phredScale)    
    normPhred<-round(phredScale-minPhred,0)
    normPhred[which(normPhred>=255)]<-255
    normPhred<-paste0(normPhred,collapse = ",")
    if(dp==0){ normPhred<-"." }
    return(normPhred)
  }
require(furrr); options(mc.cores=ncores); plan(multiprocess)
tmp %<>% 
  mutate(PL=future_map2_chr(RefCount,AltCount,~calcPL(ref=.x,alt=.y)))
tmp %>% head 
dim(tmp) # [1] 7771605      19

Final VCF format

tmp %<>% 
  mutate(FORMATfields=paste(GT,AD,DP,PL,sep=":")) %>% 
  select(Chr,Pos,SNP_ID,REF,ALT,QUAL,FILTER,INFO,FORMAT,FullSampleName,FORMATfields) %>% 
  spread(FullSampleName,FORMATfields) %>% 
  arrange(Chr,Pos) %>% 
  rename(`#CHROM`=Chr,
         POS=Pos,ID=SNP_ID)
dim(tmp) # [1] 12049   654
tmp[1:5,1:20] 

Write to disk

options("scipen"=1000, "digits"=4) 
# for a few SNPs, position kept printing in sci notation e.g. 1e3, screws up Beagle etc., this avoids that (I hope)
write_lines(header,
            path=paste0(outName,".vcf"))
write.table(tmp,
            paste0(outName,".vcf"),
            append = T,sep = "\t",row.names=F, col.names=T, quote=F)
# Save sitesWithAlleles
tmp %>% 
  rename(CHROM=`#CHROM`) %>% 
  select(CHROM:ALT) %>% 
  write.table(.,file=paste0(outName,".sitesWithAlleles"),
              row.names=F)
# Save sample list
write.table(sampleIDsFromDartVCF,file=paste0(outName,".samples"),
            row.names = F, col.names = F, quote = F)

# BGzip
system(paste0("cat ",outName,".vcf ",
              "| bgzip -c > ",outName,".vcf.gz"))
system(paste0("rm ",outName,".vcf"))

sessionInfo()