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

By Erik K. Garrison, Jouni Sirén, Adam M. Novak, Glenn Hickey, Jordan M. Eizenga, Eric T. Dawson, W. Jones, Shilpa Garg, Charles Markello, Michael F. Lin and 2 more. Nature Biotechnology, 2018

681 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a702a8d2dfa922c30d1393a40c9ae20319bd73d0

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Citations in the corpus

ExpansionHunter: a sequence-graph-based tool to analyze variation in short tandem repeat regions (2019, methodology): “The implementation uses sequence graphs (Garrison et al. 2018; Paten et al. 2017; Dilthey et al. 2015) as a general and flexible model of each target locus.”
Insights into human genetic variation and population history from 929 diverse genomes (2019, background): “However, some curves, including those between the Central American Pima and the South American Karitiana, between Han Chinese and the Siberian Yakut, or between the European Sardinians and the Near Eastern Druze, do not deviate appreciably from those expected under instant splits.”
Pangenomics enables genotyping known structural variants in 5,202 diverse genomes (2021, methodology): “Simulated read sets were mapped to the graphs using Giraffe, VG-MAP ( 10 ), HISAT2 ( 11 ), and GraphAligner ( 12 ).”
A draft human pangenome reference (2022, methodology): “ChIP-seq analysis We aligned H3K4me1, H3K27ac and ATAC-seq obtained from monocyte-derived macrophages from 30 individuals (Groza et al., 2022) using vg map (Garrison et al., 2018) to the hg38 reference genome graph and to the HPRC genome graph.”
The design and construction of reference pangenome graphs with minigraph (2020, methodology): “he stable coordinate. Li et al. Page 4 of12 Sequence-to-graph mapping Our incremental graph construction algorithm relies on genome-to-graph alignment (Fig.2b). As existing sequence-to-graph aligners [31,38] do not work with chromosome-long query sequences, we adapted minimap2 [39] for our purpose a”
Long-read human genome sequencing and its applications (2020, neutral): “The next steps will likely involve the development of graph-based reference genomes using new standards, such as Variant Graph Toolkit 159 .”