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

Nature Biotechnology volume 36, pages 875–879 (2018)Cite this article

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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 duplications1. Previous graph genome software implementations2,3,4 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 arrays5, 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.

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Figure 1: A region of a yeast genome variation graph.
Figure 2: Mapping accuracy for vg against the human genome.
Figure 3: Mapping short and long reads with vg to yeast genome references.

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Acknowledgements

E.G., J.S., and R.D. were funded by the Wellcome Trust (grants 206194 and 207492). E.T.D. was funded by an NIH Cambridge Trust studentship, and W.J. by a Wellcome Trust MGM studentship (109083/Z/15/Z). A.M.N., G.H., J.M.E., and B.P. were supported by the National Institutes of Health (5U41HG007234), the W.M. Keck Foundation (DT06172015) and the Simons Foundation (SFLIFE# 35190). We thank members of the GA4GH Reference Variation Working Group for support, ideas, and comments, and Hannes Eggertsson for assistance in the integration with GraphTyper.

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Authors and Affiliations

  1. Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK

    Erik Garrison, Jouni Sirén, Eric T Dawson, William Jones & Richard Durbin

  2. UC Santa Cruz Genomics Institute, University of California, Santa Cruz, California, USA

    Adam M Novak, Glenn Hickey, Jordan M Eizenga, Charles Markello & Benedict Paten

  3. National Cancer Institute, Rockville, Maryland, USA

    Eric T Dawson

  4. Department of Genetics, University of Cambridge, Cambridge, UK

    Eric T Dawson & Richard Durbin

  5. Max-Planck-Institut für Informatik, Saarbrücken, Germany

    Shilpa Garg

  6. DNAnexus, Mountain View, California, USA

    Michael F Lin

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  1. Erik Garrison
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Contributions

E.G. conceived and led the development of vg, J.S. developed the GCSA2 index, A.M.N., G.H., J.M.E., and E.T.D. contributed to the software, E.G., W.J., S.G., C.M., M.F.L., and R.D. contributed results and data analysis, R.D. and B.P. oversaw the project, and all contributed to the manuscript.

Corresponding authors

Correspondence to Erik Garrison or Richard Durbin.

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Competing interests

M.L. is an employee of, and E.G. consults for, DNAnexus Inc. R.D. holds shares in and consults for Congenica Ltd. and Dovetail Inc.

Integrated supplementary information

Supplementary Figure 1 ROC curves as in main figure 2a for vg graphs with different allele frequency inclusion thresholds.

ROC curves parameterised by mapping quality for 10M read pairs simulated from NA24385 as mapped by bwa mem, vg to various 1000GP pangenome references, and vg with a linear reference, using single end (se) or paired end (pe) mapping. Allele frequency thresholds for the various rows are from top to bottom 0 (all variants), 0.001, 0.01, 0.1. Within each row, the left plot is based on all reads, middle on reads simulated from segments with no genetic variants from the linear reference, right on reads simulated from segments containing variants. All reads may contain simulated sequencing errors.

Supplementary Figure 2 Relative performance of vg and bwa mem when mapping to a viral metagenome assembly graph.

Left: part of the assembly graph for an arctic viral metagenome23 assembled by minia and visualized by Bandage after complexity reduction in vg. Right: a scatterplot showing the score obtained when aligning each of 100,000 reads held out from the metagenomic assembly with bwa mem to the contigs of the assembly (y-axis) versus vg to the assembly graph (x-axis).

Supplementary Figure 3 Illustration of the unfolding process.

The starting graph (a) has an inverting edge leading from the forward to reverse strand of node 2. In (b) we unfold the graph and unroll with k greater than the length of the graph, which materializes the implied reverse strand as sequence on the forward strand of new nodes.

Supplementary Figure 4 Illustration of the unrolling process.

The starting graph (a) and a representation without sequences or sides to clarify the underlying structure (b). In (c) we have unrolled one step (k = 2). In (d), k = 4, (e) k = 10, and (f) k = 25.

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Supplementary Figures 1–4 (PDF 842 kb)

Life Sciences Reporting Summary (PDF 131 kb)

Supplementary Information

Supplementary Table 1, Supplementary Note, and Supplementary References (PDF 382 kb)

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Garrison, E., Sirén, J., Novak, A. et al. Variation graph toolkit improves read mapping by representing genetic variation in the reference. Nat Biotechnol 36, 875–879 (2018). https://doi.org/10.1038/nbt.4227

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