Variation graph toolkit improves read mapping by representing genetic variation in the reference
Open accessPublished version of a preprint
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.
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Incorporation of more variation helps mitigate reference bias and improves alignment of both modern and aDNA reads.
“Crucially, the incorporation of more variation helps mitigate reference bias, and this mitigation has been shown to improve the alignment of both modern and aDNA reads [9, 16].”
From <tt>SAFARI</tt> : pangenome alignment of ancient DNA using purine/pyrimidine encodings (2025) · cites this paper as [16]
The paper reports that mapping reads containing non-reference variants to a variation graph improves performance over mapping them to a linear reference, and that graph mapping reduces reference bias (Abstract; page unavailable). It studies simulated and contemporary sequencing reads, but our copy does not report improved alignment of ancient-DNA (aDNA) reads; a search for “ancient” found no such result. (LLM, conf=0.87)
Experimental — accuracy not yet measuredThe variation graph toolkit (vg) showed that read mapping to graphs reduces reference bias and better recovers complex variants.
“The variation graph toolkit (vg) showed that read mapping to graphs reduces reference bias and better recovers complex variants”
From Topological Approaches in Animal Comparative Genomics (2026) · cites this paper as [206]
The paper reports that vg mapping to a population graph nearly balances reference- and alternate-allele coverage, while linear-reference mapping increasingly misses reads containing longer insertions (Abstract; no page given). It also reports better mapping of reads containing non-reference variants. These results support reduced reference bias, but do not demonstrate better recovery or calling of complex variants themselves. (LLM, conf=0.84)
Experimental — accuracy not yet measuredPangenomic methods have rapidly progressed such that it is now practical to propose that common genomic analyses use a pangenome.
“Pangenomic methods have rapidly progressed over the past few years13–15 such that it is now practical to propose that common genomic analyses use a pangenome.”
From A draft human pangenome reference. (2023) · cites this paper as [13]
There are hundreds of thousands of non-reference indels in a typical human genome, and these have a greater effect on read mapping than SNPs.
“despite there being hundreds of thousands of non-reference indels in a typical human genome [16], and these having a greater affect on read mapping than SNPs [17]”
From Removing reference bias and improving indel calling in ancient DNA data analysis by mapping to a sequence variation graph (2020) · cites this paper as [17]
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- Authors
- Garrison, E., Sirén, J., Novak, A. M., Hickey, G., Eizenga, J. M., Dawson, E. T., Jones, W., Garg, S., Markello, C., Lin, M. F., Paten, B., & Durbin, R.
- Affiliations
- Wellcome Sanger Institute (GB)
- University of California, Santa Cruz (US)
- University of Cambridge (GB)
- Max Planck Institute for Informatics (DE)
- DNAnexus (United States) (US)
- Corresponding author
- Garrison, Erik
- Durbin, Richard
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2018-08-20
- Volume
- 36
- Issue
- 9
- Pages
- 875-879
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC6126949
- OpenAlex
- W2888300707
- Cited by
- 821
- References
- 41
- Citations by year
- 2026: 124 · 2025: 139 · 2024: 132 · 2023: 84 · 2022: 84 · 2021: 91 · 2020: 80 · 2019: 67 · 2018: 18 · 2012: 2
- FWCI
- 23.03
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Biotechnology (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- W. M. Keck Foundation (DT06172015)
- Simons Foundation (35190, SFLIFE# 35190)
- Wellcome Trust (207492, 207492/Z/17/Z, 090851, 109083/Z/15/Z, 109083, /Z/15/Z, 206194)
- Cambridge Trust
- National Institutes of Health
- National Heart, Lung, and Blood Institute (U01HL137183)
- National Human Genome Research Institute (T32HG008345, U41HG007234, U54HG007990)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Computer Science › Artificial Intelligence › Algorithms and Data Compression
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fractal and DNA sequence analysis
- Keywords
- variation graphs, read mapping, reference genome, reference bias, structural variants, human genome, de novo assembly, population genetic variation
- MeSH terms
- Computer Simulation, DNA, Humans, Genetic Variation
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