Aug 21, 2018

Variation graph toolkit improves read mapping by representing genetic variation in the reference

Nature Biotechnology
Erik GarrisonRichard Durbin

Abstract

Reference genomes guide our interpretation of DNA sequence data. However, conventional linear references represent only one version of each locus, ignoring variation in the population. Poor representation of an individual's genome sequence impacts read mapping and introduces bias. Variation graphs are bidirected DNA sequence graphs that compactly represent genetic variation across a population, including large-scale structural variation such as inversions and duplications. Previous graph genome software implementations have been limited by scalability or topological constraints. Here we present vg, a toolkit of computational methods for creating, manipulating, and using 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, with improved accuracy over alignment to a linear reference, and effectively removing reference bias. These capabilities make using variation graphs as references for DNA sequencing practical at a gigabase scale, or at the topological complexity of de novo assemblies.

Mentioned in this Paper

Computer Software
In Silico
Genome
Sequence Determinations, DNA
Structure
Locus
Suffix brand of benzoylprop-ethyl
Genome, Human
Manipulating Function
Population Group

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