One-liner to get distribution of the alternative allele numbers in a VCF file

The VCF format allows for multiple alternative alleles in a single variant record. The alternative alleles are specified as a comma-separated list of their bases, so one may easily estimate the distribution of the alternative allele numbers in a command line using the following one-line script:

bcftools query -f '%ALT\n' input.vcf.gz | \
    tr -d 'A-Za-z0-9<>:' | sort | uniq -c

Here we use bcftools query from the bcftools package for rapid extraction of alternative alleles from a VCF file. We need to know only the number of commas in each line, so we remove all other symbols using tr. Finally, we count lines containing the particular numbers of commas.

Example: 1000 Genomes variants on chromosome 22

Let us demonstrate the script using the VCF file of 1000 Genomes variants on chromosome 22. The file contains 1,103,547 variants, including 1,060,388 SNPs and 43,230 indels.

bcftools query -f '%ALT\n' \
    ALL.chr22.phase3_shapeit2_mvncall_integrated_v5.20130502.genotypes.vcf.gz | \
    tr -d 'A-Za-z0-9<>:' | sort | uniq -c

The script produced the following output.

6073 ,
 224 ,,
  38 ,,,
   9 ,,,,
   3 ,,,,,
   1 ,,,,,,,

According to the output, most of the variants in the file are biallelic (i.e., having a reference allele and a single alternative allele) and less than 1% of them are multiallelic. Most of the multiallelic variants are triallelic (i.e., having a reference allele and two alternative alleles) and only 275 multiallelic variants have more than two alternative alleles.

Obtaining probability of all variant calls being correct

The VCF format specifies quality scores (QUAL) for each variable position (variant) in a genome. The QUAL value is the Phred quality score for the assertion that alternative bases of a variant are correct, that is, \mathrm{QUAL} = -10 \log_{10} p, where p is the probability that the alternative base calls are wrong. Using the QUAL scores, one may easily calculate the probability that all variant calls in a VCF file are correct.

Here we give an equation for that probability, a Python script that implements it and an example of its usage.

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Combining a large number of VCF files

The bcftools and vcftools packages provide routines for merging or concatenating multiple VCF files. However, specifying a large number of input VCF files may terminate their processing because an operating system will not be able to keep so many files opened. This problem can be overcome by iterative combining of files: first, pairs of the original VCF files are processed, then pairs of the obtained files are processed and so on until we get the resulting VCF file.

Here we describe an iterative scheme for merging or concatenating VCF files using bcftools and GNU parallel and present a Python script that implements it.

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Obtaining scaffold positions on assembled chromosomes from NCBI Genome

NCBI Genome stores genomic assemblies of numerous species. Besides assembly sequences, it also contains the related auxiliary information, including AGP files that describe how large sequence objects (e.g., chromosomes) were assembled from smaller ones (e.g., scaffolds or contigs).

For some assemblies, their chromosome-from-scaffold AGP files may be missing although the chromosomes were assembled from the scaffolds. In that case, one may reconstruct the AGP file of scaffolds on chromosomes using chromosome-from-components and scaffold-from-components AGP files.

Further we describe how to perform such a reconstruction and present a Python script implementing it.

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