Awesome

Tip and tricks for VCF files

Usefull tools

Samtools organisation and repositories

File format specification
Bcftools github page
Bcftools webpage
Bcftools man page

Compilation (from here):

git clone --branch=develop git://github.com/samtools/htslib.git
git clone --branch=develop git://github.com/samtools/bcftools.git
git clone --branch=develop git://github.com/samtools/samtools.git
cd bcftools; make
cd ../samtools; make
cd ../htslib; make

Other tools

R bioconductor package Rsamtools
vcflib github page
vt wiki and github page
bedtools documentation and github page
PyVCF github page
VCFtools webpage and github page. It has been mostly replaced with bcftools but some commands are still only available in VCFtools (in particular vcf-annotate)

Use R VariantAnnotation bioconductor package

All the commands below assume the package VariantAnnotation has been loaded into R using library(VariantAnnotation).

Replace INFO/DP field with GENO/DP field

vcf <- readVcf("test.vcf", "hg19")
info(vcf)$DP=geno(vcf)$DP
writeVcf(vcf,"test.vcf")

Create a new INFO field

Here it's called DP_Tand filled with . (dot represent missing values in VCF files) but it could be anything you like.

vcf <- readVcf("test.vcf", "hg19")
newInfo <- DataFrame(Number=1, Type="Integer",Description="DP in normal",row.names="DP_N")
info(header(vcf)) <- rbind(info(header(vcf)), newInfo)
info(vcf)$DP_N="."
writeVcf(vcf,"test.vcf")

Split a multi-sample VCF into n-single sample VCFs

vcf_file = "test.vcf"
for (cur_sample in samples(scanVcfHeader(vcf_file))) {
  writeVcf(readVcf(vcf_file, "hg19",ScanVcfParam(sample = cur_sample)),paste(cur_sample,".vcf",sep = ""))
}

Manually processing VCF in R

Look at these functions too: https://github.com/sahilseth/vcfparser

Extract expected Q-value from a needlestack calling

Once a needlestack calling has been launch, user can want to compute what would be the Q-value of a particular sample at a particular position. This could help controlling the false negative rate.
Given a VCF chunk from VariantAnnotation bioconductor package, a sample ID and a particular mutation, this function extract the corresponding Q-value.

Loading a VCF file as a data frame

On Unix systems (Mac or Linux), automatically pipe it with grep and sed to remove the header.

my_vcf=read.table(pipe("grep -v '^##' test.vcf | sed s/^#//"),stringsAsFactors=F,header=T,sep="\t")

On Windows, you can manually remove the header lines (starting with ##) and also the # character from the line containing the column names. After that you can read it using:

my_vcf=read.table("test_noheader.vcf",stringsAsFactors=F,header=T,sep="\t")

Two R functions to extract values from INFO or GENOTYPE fields

Gist: https://gist.github.com/mfoll/a4dfbb92068dc559f130

get_info=function(info,field,num=T) {
  get_single_info=function(single_info,field) {
    grep_res=grep(paste("^",field,"=",sep=""),unlist(strsplit(single_info,";")),value=T)
    if (length(grep_res)>0) strsplit(grep_res,"=")[[1]][2] else NA
  }
  res=unlist(lapply(info,get_single_info,field))
  if (num) as.numeric(res) else res
}

get_genotype=function(genotype,format,field,num=T) {
  get_single_genotype=function(single_genotype,format,field) {
    single_res=unlist(strsplit(single_genotype,":"))[which(unlist(strsplit(format,":"))==field)]
    if (length(single_res)>0) single_res else NA
  }
  res=unlist(lapply(genotype,get_single_genotype,format,field))
  if (num) as.numeric(res) else res
}

Both function are vectorized (i.e. you can give them a vector of INFO fields or a vector of GENOTYPE fields. The genotype field requires that you give the format of the field (for example "GT:AO:DP"). In both functions the field argument indicates which field you want to extract. By default the result is converted to a numeric value, unless you specify num=FALSE when you call the functions.

Get genotype columns and sample names:

# list of columns containing sample specific data
GT_cols=(which(names(my_vcf)=="FORMAT")+1):ncol(my_vcf)
# extract sample names
SM=names(my_vcf)[GT_cols]

Get number of variants for each position in a VCF file

Following function returns, for each position in the input VCF file, the number of samples having a QVALUE higher than the input threshold (QVAL_thr, default=50), i.e. the number of variants.

# after reading a VCF with read.table(), see last example
get_number_of_variants <- function(vcf, QVAL_thr = 50){
  unlist(lapply(1:nrow(vcf), function(i) {
    all_QVAL = unlist(lapply((which(colnames(vcf)=="FORMAT")+1) : ncol(vcf),
                             function(id) get_genotype(vcf[i,id], vcf[i,"FORMAT"], field = "QVAL") ))
    sum(all_QVAL>=QVAL_thr)
  }))
}

Using all the above

This assumes that you have a my_vcf data frame loaded, the two functions above and the objects GT_cols and SM.

Extract the variant type (TYPE) from all lines from the INFO field:
```
get_info(my_vcf$INFO,"TYPE",num=F)
```

Use it to fikter only variants with TYPE=snv:

my_vcf[which(get_info(my_vcf$INFO,"TYPE",num=F)=="snv"),]

Extract coverage (DP) of each sample at a given line (1 here):
```
get_genotype(my_vcf[1,GT_cols],my_vcf$FORMAT[1],"DP")
```
Extract coverage of all lines of a given sample (MY_SAMPLE here):
```
get_genotype(my_vcf[,"MY_SAMPLE"],my_vcf$FORMAT[1],"DP")
```

You can replace "MY_SAMPLE" with SM[1] to take the first sample without typing manually its name (usefull if you have only one for example).

Plot the distribution of allelic fraction from the first sample (assuming AO and DP fields are available in the genotype column):
```
AO=get_genotype(my_vcf[,SM[1]],my_vcf$FORMAT[1],"AO")
DP=get_genotype(my_vcf[,SM[1]],my_vcf$FORMAT[1],"DP")
hist(AO/DP)
```

Built a TSV file, from VCF to extract a particular field from FORMAT

extract_FORMAT_vcf.r is a script which extract, for each variant in the VCF, and for each sample, the value of a field in FORMAT.
Example of command line:

Rscript extract_FORMAT_vcf.r --input_vcf=testepic.vcf.gz --field=DS

Manually processing VCF in bash

Split a n-samples VCF

This bash script splits a big VCF from n samples into n VCF with file name = sample name (save these lines into big_VCF_to_samples.sh)

Gist : https://gist.github.com/tdelhomme/cb28dec176b55c43e887

#!/bin/bash

if [ $# -eq 0 ]; then #if no provided parameters
  echo 'usage : ./big_VCF_to_samples.sh <input big VCF> <result folder>'
else
  mkdir -p $2
  IFS= read -a array <<< $(grep "#CHROM" $1 | head -1 | awk '{for(i=10;i<=NF;++i)print $i}')
  samples=${array[0]}
  for i in `seq 1 $(echo "$samples" | wc -w)`;
  do
    bcftools view -s $(echo "$samples" | cut -d" " -f$i) $1 > $2"/"$(echo "$samples" | cut -d" " -f$i).vcf
  done
fi

Generate a sorted and merged BED file from positions in a VCF

awk '{ if (!/^#/) print $1"	"$2"	"$2+1}' input.vcf | sort -k1,1 -k2,2n | bedtools merge -i stdin

Compute number of positions in a bed file

cat mybedfile.bed | awk -F'\t' 'BEGIN{SUM=0}{ SUM+=$3-$2+1 }END{print SUM}'

Get the samples ID from a VCF

grep "^#CHROM" input.vcf | tr '\t' '\n' | grep -v -E '#CHROM|POS|ID|REF|ALT|QUAL|FILTER|INFO|FORMAT'

Downsample a VCF

Here a zipped one (assuming the header is less than 10000 lines), from which we randomly extract 1 million lines:

(zcat human_9606_b150_GRCh38p7.vcf.gz | head -n 10000 | zgrep ^# ; zgrep -v ^# human_9606_b150_GRCh38p7.vcf.gz | shuf -n 1000000 | LC_ALL=C sort -k1,1V -k2,2n) | gzip > human_9606_b150_GRCh38p7_small.vcf.gz