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Nat Biotechnol. 2018 Oct;36(9):875-879. doi: 10.1038/nbt.4227. Epub 2018 Aug 20.

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

Author information

1
Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
2
UC Santa Cruz Genomics Institute, University of California, Santa Cruz, California, USA.
3
National Cancer Institute, Rockville, Maryland, USA.
4
Department of Genetics, University of Cambridge, Cambridge, UK.
5
Max-Planck-Institut für Informatik, Saarbrücken, Germany.
6
DNAnexus, Mountain View, California, USA.

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.

PMID:
30125266
PMCID:
PMC6126949
[Available on 2019-02-20]
DOI:
10.1038/nbt.4227

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