Last updated: 2020-10-09

Checks: 7 0

Knit directory: NaCRRI_2020GS/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20200826)

The command set.seed(20200826) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: bcbcec3

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version bcbcec3. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


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Unstaged changes:
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    Deleted:    analysis/Imputation_EMBRAPA_102419.Rmd
    Deleted:    analysis/ImputeDCas20_5360.Rmd
    Deleted:    analysis/Verify_gbs2dart_sampleMatches_EMBRAPA_102419.Rmd
    Deleted:    analysis/convertDCas19_4403_ToVCF_102419.Rmd
    Deleted:    analysis/convertDCas20_5360_ToVCF.Rmd

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/convertDArTseqLD_to_VCF_82719.Rmd) and HTML (docs/convertDArTseqLD_to_VCF_82719.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
html c6022b6 wolfemd 2020-10-09 Build site.
Rmd 4f8a229 wolfemd 2020-10-09 Publish imputations for 2020 of DCAs20_5360 (and 2019 code too) for

Is 4459 actually on V7?

library(tidyverse); library(magrittr);
system(paste0("cat /workdir/marnin/DCas19_4459/SNP_4459_3822_4078_4433_VCF.csv ",
              "| cut -f1-5 > ",
              "/workdir/marnin/DCas19_4459/SNP_4459_3822_4078_4433_VCF.sitesWithAlleles"))

dcas4459_sites<-read.table(paste0("/workdir/marnin/DCas19_4459/",
                                  "SNP_4459_3822_4078_4433_VCF.sitesWithAlleles"),
                           stringsAsFactors = F, header = F, skip = 3, sep = c("\t"))

dcas4351_sites<-read.table(paste0("~/DCas19_4351/DCas19_4351_SEQ_SNPs_blast_v6_VCF.sitesWithAlleles"),
                           stringsAsFactors = F, header = F, sep = c("\t"))

dim(dcas4459_sites) # [1] 13603     5
dim(dcas4351_sites) # [1] 13406     5
dcas4459_sites %>% 
  select(V3) %>% 
  semi_join(dcas4351_sites %>% select(V3)) %>% 
  dim # 5608 match based on the ID column...
dcas4459_sites %>% semi_join(dcas4351_sites) 
dcas4459_sites %>% 
  select(V1,V2,V4,V5) %>% 
  semi_join(dcas4351_sites %>% select(V1,V2,V4,V5)) %>% 
  dim # essentially none match based on chr, pos, ref, alt
 
# So 4459 is probably reported on V7 even though Andrzej said it was V6

dcas4459_sites %<>% 
  separate(V3,c("CloneID","Rest"),"[|]",extra = 'merge',remove = F) %>% 
  select(-Rest) %>% 
  dplyr::rename(CHROM=V1,
                POSv7=V2,
                ID=V3,
                REF=V4,
                ALT=V5) %>% 
  mutate(CHROM=as.numeric(gsub("Chromosome","",CHROM)),
         POSv7=as.numeric(POSv7)) %>% 
  select(CHROM,POSv7,ID,REF,ALT,CloneID)

dcas4351_sites %<>% 
  separate(V3,c("CloneID","Rest"),"[|]",extra = 'merge',remove = F) %>% 
  select(-Rest) %>% 
  rename(CHROM=V1,
         POSv6=V2,
         ID=V3,
         REF=V4,
         ALT=V5) %>% 
  mutate(CHROM=as.numeric(gsub("Chromosome","",CHROM)),
         POSv6=as.numeric(POSv6)) %>% 
  select(CHROM,POSv6,ID,REF,ALT,CloneID)

str(dcas4459_sites)
str(dcas4351_sites)

Intersection between 4459 (V7?) and 4351 (V6) using CloneID.

dcas4459_sites %>% 
  inner_join(dcas4351_sites) %>% str # 5592

Intersection with ImputationRefPanelII sites

Of the 4459 sites I can liftover to V6 positions, which intersect the RefPanelII SNPs?

library(furrr)
options(mc.cores=18)
plan(multiprocess)
sitesWithAlleles<-tibble(Chr=1:18) %>%
  mutate(SiteList=future_map(Chr,function(Chr){ 
        refpanel2part1<-read.table(file = paste0("/workdir/marnin/nextgenImputation2019/ImputationStageII_71219/chr",Chr,
                                        "_ImputationReferencePanel_StageIIpartI_72219.sitesWithAlleles"),
                           stringsAsFactors = F, header = F) 
    return(refpanel2part1)}))
sitesWithAlleles %<>% 
    unnest() %>% 
    rename(CHROM=V1,
           POS=V2,
           REF=V4,
           ALT=V5)
dcas4459_sites %>% 
  inner_join(dcas4351_sites) %>% 
  rename(POS=POSv6) %>% 
  semi_join(sitesWithAlleles) %>% #dim # 1808
  count(CHROM)

CHROM n 1 1 199 2 2 115 3 3 138 4 4 112 5 5 125 6 6 93 7 7 60 8 8 80 9 9 99 10 10 79 11 11 89 12 12 75 13 13 89 14 14 101 15 15 93 16 16 70 17 17 97 18 18 94

DCas19_4459 to VCF (custom / mod job)

Data to cbsulm12

scp -r DCas19_4459 mw489@cbsulm12.biohpc.cornell.edu:/workdir/mw489
scp -r /workdir/marnin/nextgenImputation2019/ImputationStageII_71219  mw489@cbsulm12.biohpc.cornell.edu:/workdir/mw489
scp -r /workdir/marnin/nextgenImputation2019/ImputationStageIII_72619  mw489@cbsulm12.biohpc.cornell.edu:/workdir/mw489
scp -r /workdir/marnin/nextgenImputation2019/DCas19_4301_DArTseqLD_AllSites_AllChrom_raw_70819* mw489@cbsulm12.biohpc.cornell.edu:/workdir/mw489
scp -r /workdir/marnin/nextgenImputation2019/DCas19_4301_SEQ_SNPs_blast_v6_* mw489@cbsulm12.biohpc.cornell.edu:/workdir/mw489

Site Lists

RefPanel

library(tidyverse); library(magrittr);
library(furrr)
options(mc.cores=18)
plan(multiprocess)
tibble(Chr=1:18) %>%
  mutate(sitesWithAlleles=future_map(Chr,function(Chr){ 
    system(paste0("zcat /workdir/mw489/ImputationStageIII_72619/chr",
                  Chr,"_RefPanelAndGSprogeny_ReadyForGP_72719.vcf.gz ",
                  "| cut -f1-5 > ",
                  "/workdir/mw489/ImputationStageIII_72619/chr",
                  Chr,"_RefPanelAndGSprogeny_ReadyForGP_72719.sitesWithAlleles"))}))
library(tidyverse); library(magrittr);
library(furrr)
options(mc.cores=18)
plan(multiprocess)
refpanel3_sites<-tibble(Chr=1:18) %>%
  mutate(SiteList=future_map(Chr,function(Chr){ 
        refpanel3<-read.table(file = paste0("/workdir/mw489/ImputationStageIII_72619/chr",Chr,
                                        "_RefPanelAndGSprogeny_ReadyForGP_72719.sitesWithAlleles"),
                           stringsAsFactors = F, header = F) 
    return(refpanel3)}))
refpanel3_sites %<>% 
    unnest() %>% 
    rename(CHROM=V1,
           POS=V2,
           REF=V4,
           ALT=V5)
dim(refpanel3_sites) # [1] 68814     6

4301

dcas4301sites<-read.csv(paste0("/workdir/mw489/",
                                "DCas19_4301_SEQ_SNPs_blast_v6_counts.csv"), 
                         stringsAsFactors = F,header = T) %>% 
    mutate(RefAlt=ifelse(SNP=="","REF","ALT"),
           Chr=as.numeric(gsub("Chromosome","",Chrom_Cassava_v61)),
           Pos=SNP_ChromPos_Cassava_v61) %>% 
  select(RefAlt,SNP,CloneID,AlleleID,Chr,Pos)
table(is.na(dcas4301sites$Chr))
dcas4301sites %<>% 
  separate(AlleleID,c("tmp","Alleles"),sep=-3,remove = F) %>% 
  select(-tmp) %>% #slice(1:40) %>% 
  group_by(CloneID,Pos,Alleles) %>% 
  nest() %>% 
  mutate(data=map(data,function(data){
    # I checked that there are only two rows per CloneID-Pos-Alleles combo
    # In some cases, noticed that Chr was NA for only one of the rows, 
    # but position and alleles were all identical. 
    # In those cases, replace NA with the Chr val. in the other row for that SNP
    x<-which(!is.na(data$Chr))
    if(length(x)==1){ 
      chr<-data$Chr[which(!is.na(data$Chr))]
      data %<>% mutate(Chr=chr)
    }
    return(data)
  })) %>% 
  unnest()
table(is.na(dcas4301sites$Chr))
which(is.na(dcas4301sites$Chr))[1:30]
dcas4301sites[c(136:139,141:145),]
dcas4301sites %<>% filter(!is.na(Chr))

4351

dcas4351sites<-read.csv(paste0("~/DCas19_4351/",
                                "DCas19_4351_SEQ_SNPs_blast_v6_counts.csv"), 
                         stringsAsFactors = F,header = T) %>% 
    mutate(RefAlt=ifelse(SNP=="","REF","ALT"),
           Chr=as.numeric(gsub("Chromosome","",Chrom_Cassava_v61)),
           Pos=SNP_ChromPos_Cassava_v61) %>% 
  select(RefAlt,SNP,CloneID,AlleleID,Chr,Pos)
table(is.na(dcas4351sites$Chr))
dcas4351sites %<>% 
  separate(AlleleID,c("tmp","Alleles"),sep=-3,remove = F) %>% 
  select(-tmp) %>% #slice(1:40) %>% 
  group_by(CloneID,Pos,Alleles) %>% 
  nest() %>% 
  mutate(data=map(data,function(data){
    # In some cases, noticed that Chr was NA for only one of the rows, 
    # but position and alleles were all identical. 
    # Replace NA with the Chr val. in the other row for that SNP
   x<-which(!is.na(data$Chr))
    if(length(x)==1){
      chr<-data$Chr[which(!is.na(data$Chr))]
      data %<>% mutate(Chr=chr)
    }
    return(data)
  })) %>% 
  unnest()
table(is.na(dcas4351sites$Chr))
dcas4351sites %<>% filter(!is.na(Chr))
dcas4351sites %>% 
  filter(RefAlt=="REF") %>% 
  select(-SNP) %>% 
  separate(Alleles,c("REF","ALT"),">") %>% 
  inner_join(
    dcas4301sites %>% 
      filter(RefAlt=="REF") %>% 
      select(-SNP) %>% 
      separate(Alleles,c("REF","ALT"),">")) %>% 
  semi_join(refpanel3_sites) %>% #dim # [1] 10860     7
  count(Chr)
 Chr     n

1 1 1133 2 2 664 3 3 684 4 4 698 5 5 641 6 6 662 7 7 421 8 8 517 9 9 505 10 10 634 11 11 600 12 12 484 13 13 534 14 14 751 15 15 503 16 16 438 17 17 513 18 18 478

Input Parameters

library(tidyverse); library(magrittr)
dartvcfInput<-paste0("/workdir/mw489/DCas19_4459/SNP_4459_3822_4078_4433_VCF.csv")
dartcountsInput<-paste0("/workdir/mw489/DCas19_4459/SNPs_counts_V6_4459_3822_4078_4433.csv")
outName<-paste0("/workdir/mw489/DCas19_4459/DCas19_4459_82719")
nskipvcf<-2
nskipcounts<-3
ncores<-90 # using more than a few could be VERY memory intensive

Components of function

Read and format the 4459 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  4543
vcf %<>% 
  rename(Pos=POS) %>% 
  mutate(Chr=as.numeric(gsub("Chromosome","",X.CHROM)),
         Pos=as.numeric(Pos)) 
  # filter(X.CHROM != ".",
  #        POS != ".") %>% 
 #  mutate_at(vars(Chr,POS),as.numeric)

readCounts<-read.csv(dartcountsInput, stringsAsFactors = F,header = T,skip=nskipcounts)
# 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.

#readCounts %>% select(RefAlt,Chr,Pos,SNP)
dim(vcf) # [1] 13603  4543
dim(readCounts) # [1] 27206  4579
vcf %>% 
  mutate(SNPindex=1:nrow(.)) %>% 
  select(X.CHROM:ALT,SNPindex) %>% head
readCounts %>% 
  mutate(SNPindex=sort(rep(1:(nrow(.)/2),2))) %>% 
  select(SNPindex,SNP,CloneID,AlleleID,Chrom_Cassava_v61,SNP_ChromPos_Cassava_v61) %>% head

vcf %<>% 
  mutate(SNPindex=1:nrow(.))
readCounts %<>% 
  mutate(SNPindex=sort(rep(1:(nrow(.)/2),2)))

Remove non-mapped SNPs / alleles

readCounts %>% 
  count(Chr,Pos,CloneID,SNPindex) %>% arrange(desc(n)) %$% table(n)
 #    1     2 
 # 2504 12351
readCounts %>% 
  count(Chr,Pos,CloneID,SNPindex) %>% 
  filter(n==1) %>% head

I was hoping that using “Chr,Pos,CloneID,SNPindex” would uniquify the readCounts such that there would be 2 rows for each unique combo. Instead, there were 2501 with only 1…

vcf %>% 
  select(Chr,Pos,ID,REF,ALT,SNPindex) %>% 
  filter(SNPindex==8419)
readCounts %>% 
  select(Chr,Pos,RefAlt,SNPindex,SNP,CloneID,AlleleID) %>% 
  filter(SNPindex==8419)

After inspecting a bunch of them: sometimes “Chrom_Cassava_v61” maps to a Chr for one row, but a scaffold for another. Or in others, one of them is simply NA and actually both Chr and Pos are NA in the VCF.

Going to exclude anything like this.

removeThese<-readCounts %>% 
  count(Chr,Pos,CloneID,SNPindex) %>% 
  filter(n==1) %$% SNPindex
removeTheseToo<-vcf$SNPindex[is.na(vcf$Chr)]

readCounts %<>% filter(!SNPindex %in% union(removeThese,removeTheseToo))
vcf %<>% filter(!SNPindex %in% union(removeThese,removeTheseToo))
dim(vcf) # [1] 11911  4545
dim(readCounts) # [1] 23822  4580

Add the “Pos” from 4301/4351

readCounts %<>% 
  separate(AlleleID,c("tmp","Alleles"),sep=-3,remove = F) %>% select(-tmp)
vcf %<>%
  separate(ID,c("tmp","Alleles"),sep=-3,remove = F) %>% select(-tmp)

Now I think I can add the 4301/4351 positions and subset to unique, RefPanel3 intersection before doing further manipulation of readCounts/vcf…

readCounts %>% 
  count(Chr,AlleleID) %>% arrange(desc(n))
vcf %>% 
  count(Chr,ID) %>% arrange(desc(n))
table(readCounts$AlleleID %in% dcas4351sites$AlleleID)
readCounts %>% 
  rename(Chr4459=Chr,
         Pos4459=Pos) %>% 
  select(Chr4459,Pos4459,SNPindex,RefAlt,CloneID,AlleleID) %>% head
vcf %>% 
  rename(Chr4459=Chr,
         Pos4459=Pos) %>% 
  select(Chr4459,Pos4459,SNPindex,ID,REF,ALT) %>% head

alleles2keep<-dcas4351sites %>% 
  select(Chr,Pos,RefAlt,CloneID,AlleleID) %>% #dim
  bind_rows(
    dcas4301sites %>% 
      select(Chr,Pos,RefAlt,CloneID,AlleleID)
  ) %>% #dim [1] 26244     5
  distinct #%>% #dim # [1] 26392     5
  # 
  # filter(RefAlt=="ALT") %>% # Choose ALT because it's AlleleID will match the VCF (see above)
  # select(-SNP) %>% 
  # separate(Alleles,c("REF","ALT"),">",remove=F)

readCounts %>% 
  rename(Chr4459=Chr,
         Pos4459=Pos) %>% 
  inner_join(alleles2keep) %>% #dim 
  select(Chr4459,Pos4459,Chr,Pos,SNPindex,RefAlt,CloneID,AlleleID) -> x 
table(x$Chr4459==x$Chr)
table(x$Pos4459==x$Pos)
rm(x)

readCounts %>% 
  rename(Pos4459=Pos) %>% 
  inner_join(alleles2keep) %>% #dim 
  select(Chr,Pos,Pos4459,SNPindex,RefAlt,CloneID,AlleleID) %>% dim

vcf %>% 
  rename(Pos4459=Pos,
         AlleleID=ID) %>% 
  inner_join(alleles2keep %>% filter(RefAlt=="ALT") %>% select(-RefAlt)) %>% 
  select(Chr,Pos,Pos4459,SNPindex,CloneID,AlleleID) %>% dim

readCounts %<>% 
  rename(Pos4459=Pos) %>% 
  inner_join(alleles2keep)

vcf %<>% 
  rename(Pos4459=Pos,
         AlleleID=ID) %>% 
  inner_join(alleles2keep %>% filter(RefAlt=="ALT") %>% select(-RefAlt))
dim(readCounts) # [1] 12162  4582
dim(vcf) # [1] 6081 4548

Remove non-unique Chr-Pos

Some Chr, Pos are duplicated. Doesn’t matter the reason, remove them.

vcf %>% 
  select(Chr,Pos,Pos4459,SNPindex,CloneID,AlleleID,REF,ALT) %>% 
  count(Chr,Pos) %>% arrange(desc(n)) %$% table(n)
# 1    2    3 
# 6002   38    1 

vcf %>% 
  select(Chr,Pos,Pos4459,SNPindex,CloneID,AlleleID,REF,ALT) %>% 
  semi_join(
    vcf %>% 
      count(Chr,Pos) %>% 
      filter(n==1) %>% 
      select(-n)
  ) %>% dim # [1] 6002    8
vcf %<>% 
  semi_join(
    vcf %>% 
      count(Chr,Pos) %>% 
      filter(n==1) %>% 
      select(-n)
  )
readCounts %<>%
  filter(SNPindex %in% vcf$SNPindex)
dim(vcf) # [1] 6002 4548
dim(readCounts) # [1] 12004  4582

###Subset to isect with RefPanel3 SNPs Now check the intersection with RefPanel3 and subset to only those SNPs

vcf %>% 
  select(Chr,Pos,Pos4459,SNPindex,CloneID,AlleleID,REF,ALT) %>% 
  semi_join(refpanel3_sites %>% 
              rename(Pos=POS) %>% 
              select(Chr,Pos,REF,ALT)) %>% dim # [1] 1786    8

vcf %<>% 
  semi_join(
    refpanel3_sites %>% 
              rename(Pos=POS) %>% 
              select(Chr,Pos,REF,ALT)
  )
readCounts %<>%
  filter(SNPindex %in% vcf$SNPindex)
dim(vcf) # [1] 1786 4548
dim(readCounts) # [[1] 3572 4582
readCounts %>% 
  count(Chr,Pos,SNPindex) %>% arrange(desc(n))
readCounts %>% 
  count(Chr,Pos) %>% arrange(desc(n))

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,Pos4459,SNPindex,CloneID,AlleleID,RefAlt) %>% 
  mutate(QUAL=".",
         FILTER=".",
         INFO=".",
         FORMAT="GT:AD:DP:PL",
         SNP_ID=paste0("S",Chr,"_",Pos))
vcf %<>%
  arrange(Chr,Pos,Pos4459,SNPindex,CloneID,AlleleID) %>% 
  mutate(QUAL=".",
         FILTER=".",
         INFO=".",
         FORMAT="GT:AD:DP:PL",
         SNP_ID=paste0("S",Chr,"_",Pos))
vcf %>% 
  select(Chr,Pos,Pos4459,SNPindex,CloneID,AlleleID,Alleles,REF,ALT,QUAL,FILTER,INFO,FORMAT,SNP_ID) %>% head
readCounts %>% filter(RefAlt=="ALT") %>% 
  select(Chr,Pos,Pos4459,SNPindex,CloneID,AlleleID,Alleles) %>% slice(1:10)

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

sampleIDsFromDartVCF<-colnames(vcf) %>% 
  .[!. %in% c("X.CHROM","Pos4459","AlleleID","Alleles","REF","ALT","QUAL","FILTER","INFO","FORMAT",
              "Chr","SNPindex","Pos","CloneID","SNP_ID")]
head(sampleIDsFromDartVCF); tail(sampleIDsFromDartVCF)

tmp <-readCounts %>% 
  .[,c("Chr","Pos","Pos4459","SNPindex","SNP_ID",
       "CloneID","AlleleID","RefAlt","Alleles","QUAL","FILTER","INFO","FORMAT",sampleIDsFromDartVCF)]
tmp %<>%
  gather(FullSampleName,ReadCount,sampleIDsFromDartVCF)
dim(tmp) # [1] 16195448       15
# tmp %>% 
#   count(Chr,Pos,Pos4459,SNPindex,SNP_ID,CloneID,AlleleID,RefAlt,Alleles) %>% arrange(desc(n)) %$% table(n)
tmp %<>%
  select(-AlleleID) %>% 
  spread(RefAlt,ReadCount) 
dim(tmp) # [1] 8097724      14
head(tmp)
tmp %<>%
  rename(AltCount=ALT,
         RefCount=REF)
dim(tmp) # [1] 8097724      14

vcf_long<-vcf %>% 
    .[,c("Chr","Pos","Pos4459","SNPindex","SNP_ID",
         "CloneID","Alleles","REF","ALT","QUAL","FILTER","INFO","FORMAT",sampleIDsFromDartVCF)] %>% 
    gather(FullSampleName,GT,sampleIDsFromDartVCF)
dim(vcf_long) # [1] 8097724      15

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 %<>%
  left_join(vcf_long)
tmp %>% head

Standard Pipeline code should work from here

# 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 %<>% 
  mutate(DP=AltCount+RefCount,
         AD=paste0(RefCount,",",AltCount))
tmp %>% head

tmp %>% 
  select(Chr,Pos,Pos4459,SNPindex,SNP_ID,CloneID,Alleles) %>% 
  distinct %>% 
  semi_join(refpanel3_sites %>% select(-CHROM) %>% rename(SNP_ID=V3)) %>% dim # [1] 1786    7

# 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] 8097724      20

# 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] 21554636       15
tmp[1:5,1:20] 

# 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"))
tmp[,1:5] %>% head
tmp[,1:5] %>% 
  semi_join(refpanel3_sites %>% 
              rename(`#CHROM`=CHROM,
                     ID=V3) %>% 
              select(-Chr))

Rsync to cbsurobbins

rsync --update --archive --verbose /workdir/mw489/DCas19_4459/ mw489@cbsurobbins.biohpc.cornell.edu:/workdir/marnin/DCas19_4459;

sessionInfo()