One option seems to be converting to VEP format, with something like this:

cut -f 1-5 $YOURFILE | perl -nae 'if($F[3] eq "-"){$F[2]=$F[1];$F[1]=$F[1]+1;};print join("\t",(@F[0..2],$F[3] . "/" . $F[4])) . "\n"'

(assumes input is tsv like chr1 123 124 - AA)

Then annotating with VEP to get a nicely formed VCF. Still interested in other answers that don't require a full annotation step.

My first thought was also bcftools norm like dariober suggested. But I couldn't make it work, to handle - or . as REF/ALT.

So here is a solution with awk, samtools faidx and parallel.

First prepare the header data for a valid vcf file:

$ echo "##fileformat=VCFv4.2" > output.vcf  
$ awk '{print "##contig=<ID="$1",length="$2">" >> "output.vcf"}' genome.fa.fai
$ echo "#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO"  >> output.vcf

Now let's do some magic with awk, samtools faidx and parallel. What has to be done, is to get the reference base before the position reported in your file, to convert -/TAT to A/ATAT or TA/- to GTA/G

$ awk -v OFS="\t" '{print $1, $2-1, $2, $4}' mutations.txt | parallel --colsep "\t" 'samtools faidx genome.fa {1}:{2}-{2} | awk -v OFS="\t" -v chr={1} -v start0={2} -v start1={3} -v genotype={4} -f makevcf.awk'

The makevcf.awk looks like this:

NR==2 { 
    split(genotype, gt, "/");
    if (gt[1] == "-") {
        print chr, start0, ".", $1, $1gt[2], ".", ".", ".";
    } else if (gt[2] == "-") {
        print chr, start0, ".", $1gt[1], $1, ".", ".", ".";
    } else {
        print chr, start1, ".", gt[1], gt[2], ".", ".", ".";
    }
}

I would recommend to run bcftools sortand bcftools norm on the new vcf file:

$ bcftools sort output.vcf|bcftools norm -f genome.fa > final.vcf

fin swimmer

EDIT: I assume that the first value of the given genotype is REF and the second ALT. But is this correct?

Hi, I couldn't find one so I wrote a simple R script that does exactly that- converting from a (A -> "-") notation to proper VCF notation (GA -> G) with a leading base:

https://github.com/orr161/VCF-indel-converter (available as a notebook w instructions too)

Basically, using BSgenome (of Bioconductor) to create a function that can fetch the leading base/sequence from hg19 (can be changed):

findbase <- function(chr, pos, len=0) { letter <- toString(Hsapiens[[chr]][pos:(pos+len)]) return(letter) }

and then iterating over the file as a dataframe.

Or.