Dawson, E. T. | Search bioRxiv

bioRxiv2021

Vcflib and tools for processing the VCF variant call format

Since its introduction in 2011 the variant call format (VCF) has been widely adopted for processing DNA and RNA variants in practically all population studies -- as well as in somatic and germline mutation studies. VCF can present single nucleotide variants, multi-nucleotide variants, insertions and deletions, and simple structural variants called against a reference genome. Here we present over 125 useful and much used free and open source software tools and libraries, part of vcflib tools and bio-vcf. We also highlight cyvcf2, hts-nim and slivar tools. Application is typically in the comparison, filtering, normalisation, smoothing, annotation, statistics, visualisation and exporting of variants. Our tools run daily and invisibly in pipelines and countless shell scripts. Our tools are part of a wider bioinformatics ecosystem and we consider it very important to make these tools available as free and open source software to all bioinformaticians so they can be deployed through software distributions, such as Debian, GNU Guix and Bioconda. vcflib, for example, was installed over 40,000 times and bio-vcf was installed over 15,000 times through Bioconda by December 2020. We shortly discuss the design of VCF, lessons learnt, and how we can address more complex variation that can not easily be represented by the VCF format. All source code is published under free and open source software licenses and can be downloaded and installed from https://github.com/vcflib. Author summaryMost bioinformatics workflows deal with DNA/RNA variations that are typically represented in the variant call format (VCF) -- a file format that describes mutations (SNP and MNP), insertions and deletions (INDEL) against a reference genome. Here we present a wide range of free and open source software tools that are used in biomedical sequencing workflows around the world today.

bioinformatics↗

bioRxiv2017

Sequence variation aware genome references and read mapping with the variation graph toolkit

Reference genomes guide our interpretation of DNA sequence data. However, conventional linear references are fundamentally limited in that they represent only one version of each locus, whereas the population may contain multiple variants. When the reference represents an individuals genome poorly, it can impact read mapping and introduce bias. Variation graphs are bidirected DNA sequence graphs that compactly represent genetic variation, including large scale structural variation such as inversions and duplications.1 Equivalent structures are produced by de novo genome assemblers.2,3 Here we present vg, a toolkit of computational methods for creating, manipulating, and utilizing these structures as references at the scale of the human genome. vg provides an efficient approach to mapping reads onto arbitrary variation graphs using generalized compressed suffix arrays,4 with improved accuracy over alignment to a linear reference, creating data structures to support downstream variant calling and genotyping. These capabilities make using variation graphs as reference structures for DNA sequencing practical at the scale of vertebrate genomes, or at the topological complexity of new species assemblies.

genomics↗