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#' @dartvcfInput input name and path of "vcf" file from DArT
#' @dartcountsInput input name and path of counts file from DArT
#' @outName output path and name
#' @nskipvcf number of "VCF" rows to skip on read-in
#' @nskipcounts number of "counts file" rows to skip on read in
#' @ncores number of cores to use, could be VERY memory intensive
#' @dartVars chr vector, column names that _are not_ sample IDs in the read-counts file. I use this internally to assign the sampleIDs in the VCF file
library(tidyverse); library(magrittr)
dartvcfInput<-here::here("data/Report-DCas20-5360","Report_DCas20-5360_SNP_VCF_Version_6.csv")
dartcountsInput<-here::here("data/Report-DCas20-5360","Report_DCas20-5360_SNP_Counts_Version_6_final.csv")
outName<-here::here("data/Report-DCas20-5360","DCas20_5360")
# From report to report, there has been minor (NOT SO MINOR?) variation
# in format among files, requiring user input
# rather frustrating
# can't pipeline their stuff
nskipvcf<-2
nskipcounts<-3
ncores<-75
dartVars<-read.csv(dartcountsInput,
stringsAsFactors = F,
header = T,
skip=nskipcounts,
nrows = 1) %>% colnames(.) %>% .[1:26] Start manual. Check that the files read in according to previous code. Adjust code if necessary. Make a function and apply it to the input files.
vcf<-read.table(dartvcfInput,
stringsAsFactors = F,skip = nskipvcf, header = T, sep = "\t", comment.char = "")
readCounts<-read.csv(dartcountsInput, stringsAsFactors = F,header = T,skip=nskipcounts)
# dim(vcf)
# [1] 13603 3749
# dim(readCounts)
# [1] 27206 4105
# colnames(readCounts)[1:100]
# [1] "AlleleID" "CloneID"
# [3] "AlleleSequence" "TrimmedSequence"
# [5] "TrimmedSequence_plus_Strand" "Chrom_Cassava_v61"
# [7] "ChromPos_Cassava_v61" "SNP_ChromPos_Cassava_v61"
# [9] "AlnCnt_Cassava_v61" "AlnEvalue_Cassava_v61"
# [11] "Strand_Cassava_v61" "SeqDiff_Cassava_v61"
# [13] "SNP" "SnpPosition.on.Tag"
# [15] "CallRate" "OneRatioRef"
# [17] "OneRatioSnp" "FreqHomRef"
# [19] "FreqHomSnp" "FreqHets"
# [21] "PICRef" "PICSnp"
# [23] "AvgPIC" "AvgCountRef"
# [25] "AvgCountSnp" "RepAvg"
# [27] "X9624.09" "BGM.0004"
# [29] "BGM.0005" "BGM.0006"
# [31] "BGM.0014" "BGM.0018"
# [33] "BGM.0019" "BGM.0020"
# [35] "BGM.0021" "BGM.0022"
# [37] "BGM.0023" "BGM.0024"
# colnames(vcf)[1:30]
# [1] "X.CHROM" "POS" "ID"
# [4] "REF" "ALT" "QUAL"
# [7] "FILTER" "INFO" "FORMAT"
# [10] "X911118200001_A_1" "X911118200001_A_10" "X911118200001_A_11"
# [13] "X911118200001_A_12" "X911118200001_A_2" "X911118200001_A_3"
# [16] "X911118200001_A_4" "X911118200001_A_5" "X911118200001_A_6"
# [19] "X911118200001_A_7" "X911118200001_A_8" "X911118200001_A_9"
# [22] "X911118200001_B_1" "X911118200001_B_10" "X911118200001_B_11"
# [25] "X911118200001_B_12" "X911118200001_B_2" "X911118200001_B_3"
# [28] "X911118200001_B_4" "X911118200001_B_5" "X911118200001_B_6"
colnames(vcf)[10:length(colnames(vcf))]<-colnames(readCounts)[length(dartVars):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))Printed here, but available and sourced from code/ subdirectory.
convertDart2vcf<-function(dartvcfInput,dartcountsInput,outName,
nskipvcf=2,nskipcounts=3,ncores){
require(tidyverse); require(magrittr)
# Read and format the counts/vcf files -------------------
## Read in "VCF" (called genotypes from DArT)
## and "counts" files (read counts)
vcf<-read.table(dartvcfInput,
stringsAsFactors = F,skip = nskipvcf, header = T, sep = "\t", comment.char = "")
readCounts<-read.csv(dartcountsInput, stringsAsFactors = F,header = T,skip=nskipcounts)
## Liftover the sampleIDs from readCounts onto the VCF
colnames(vcf)[10:length(colnames(vcf))]<-colnames(readCounts)[length(dartVars):length(colnames(readCounts))]
## Formatting chrom and position info
vcf %<>%
mutate(X.CHROM=gsub("Chromosome","",X.CHROM)) %>%
rename(Pos=POS) %>%
mutate(Chr=as.numeric(gsub("Chromosome","",X.CHROM)),
Pos=as.numeric(Pos))
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.
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)
# 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))
# 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))
vcf %<>% semi_join(sites2keep)
readCounts %<>% semi_join(sites2keep)
sampleIDsFromDartVCF<-colnames(vcf) %>%
.[!. %in% c("X.CHROM","Pos","AlleleID","Alleles","REF","ALT","QUAL","FILTER","INFO","FORMAT",
"Chr","SNPindex","CloneID","SNP_ID","SNP")]
tmp_counts <-readCounts %>%
.[,c("Chr","Pos","SNPindex","SNP_ID",
"CloneID","AlleleID","RefAlt","Alleles","QUAL","FILTER","INFO","FORMAT",sampleIDsFromDartVCF)] %>%
semi_join(sites2keep)
# readCounts and vcf long by sample ------------------------------
tmp_counts %<>%
pivot_longer(cols = all_of(sampleIDsFromDartVCF),
names_to = "FullSampleName",
values_to = "ReadCount")
#gather(FullSampleName,ReadCount,sampleIDsFromDartVCF)
tmp_counts %<>%
select(-AlleleID) %>%
spread(RefAlt,ReadCount)
tmp_counts %<>%
rename(AltCount=ALT,
RefCount=REF)
vcf_long<-vcf %>%
.[,c("Chr","Pos","SNPindex","SNP_ID",
"CloneID","Alleles","REF","ALT","QUAL","FILTER","INFO","FORMAT",sampleIDsFromDartVCF)] %>%
pivot_longer(cols = all_of(sampleIDsFromDartVCF), names_to = "FullSampleName", values_to = "GT")
# gather(FullSampleName,GT,sampleIDsFromDartVCF)
# Add GT from vcf to the counts -----------------------------------
tmp_counts %<>%
inner_join(vcf_long)
# 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)
# 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)))
# 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)
# Header ----------------------------------------
header<-c("##fileformat=VCFv4.0",
"##FORMAT=<ID=GT,Number=1,Type=String,Description=\"Genotype\">",
"##FORMAT=<ID=AD,Number=.,Type=Integer,Description=\"Allelic depths for the reference and alternate alleles in the order listed\">",
"##FORMAT=<ID=DP,Number=1,Type=Integer,Description=\"Read Depth (only filtered reads used for calling)\">",
"##FORMAT=<ID=PL,Number=3,Type=Float,Description=\"Normalized, Phred-scaled likelihoods for AA,AB,BB genotypes where A=ref and B=alt; not applicable if site is not biallelic\">")
# 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"))
}source(here::here("code/","convertDart2vcf.R"))
convertDart2vcf(dartvcfInput,dartcountsInput,outName,
nskipvcf=2,nskipcounts=3,ncores)Split the genome-wide VCF into per-chromosome VCFs for imputation.
require(furrr); options(mc.cores=18); plan(multiprocess)
source(here::here("code","imputationFunctions.R"))
vcfIn<-here::here("data/Report-DCas20-5360","DCas20_5360.vcf.gz")
filters<-"--minDP 4 --maxDP 50" # because using GT not PL for impute (Beagle5)
outPath<-here::here("data/Report-DCas20-5360/")
outSuffix<-"DCas20_5360"
future_map(list(Chr=1:18),
~splitVCFbyChr(Chr=.),
vcfIn=vcfIn,filters=filters,
outPath=outPath,outSuffix=outSuffix)
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Mojave 10.14.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 whisker_0.4 knitr_1.29 magrittr_1.5
[5] here_0.1 R6_2.4.1 rlang_0.4.7 stringr_1.4.0
[9] tools_4.0.2 xfun_0.16 git2r_0.27.1 htmltools_0.5.0
[13] ellipsis_0.3.1 yaml_2.2.1 digest_0.6.25 rprojroot_1.3-2
[17] tibble_3.0.3 lifecycle_0.2.0 crayon_1.3.4 later_1.1.0.1
[21] vctrs_0.3.2 promises_1.1.1 fs_1.5.0 glue_1.4.1
[25] evaluate_0.14 rmarkdown_2.3 stringi_1.4.6 compiler_4.0.2
[29] pillar_1.4.6 backports_1.1.8 httpuv_1.5.4 pkgconfig_2.0.3