Genotyping structural variants in pangenome graphs using the vg toolkit

Abstract

Structural variants (SVs) remain challenging to represent and study relative to point mutations despite their demonstrated importance. We show that variation graphs, as implemented in the vg toolkit, provide an e ective means for leveraging SV catalogs for short-read SV genotyping experiments. We benchmarked vg against state-of-the-art SV genotypers using three sequence-resolved SV catalogs generated by recent long-read sequencing studies. In addition, we use assemblies from 12 yeast strains to show that graphs constructed directly from aligned de novo assemblies improve genotyping compared to graphs built from intermediate SV catalogs in the VCF format.real Illumina reads and a pangenome built from SVs discovered in recent long-read sequencing studies [21,22,23,5], We also compared vg's performance with state-of-the-art SV genotypers: SVTyper[3], Delly Genotyper[4], BayesTyper[19], Paragraph[20] and . Across the datasets we tested, which range in size from 26k to 97k SVs, vg is the best performing SV genotyper on real short-read data for all SV types in the majority of cases. Finally, we demonstrate that a pangenome graph built from the alignment of de novo assemblies of diverse Saccharomyces cerevisiae strains improves SV genotyping performance. Results Structural variation in vgWe used vg to implement a straightforward SV genotyping pipeline. Reads are mapped to the graph and used to compute the read support for each node and edge (see Supplementary Information for a description of the graph formalism). Sites of variation within the graph are then identi ed using the snarl decomposition as described in [24]. These sites correspond to intervals along the reference paths (ex. contigs or chromosomes) which are embedded in the graph. They also contain nodes and edges deviating from the reference path, which represent variation at the site. For each site, the two most supported paths spanning its interval (haplotypes) are determined, and their relative supports used to produce a genotype at that site (Figure 1a). The pipeline is described in detail in Methods. We rigorously evaluated the accuracy of our method on a variety of datasets, and present these results in the remainder of this section.

Bovine breed-specific augmented reference graphs facilitate accurate sequence read mapping and unbiased variant discovery

“…We constructed bovine variation-aware reference graphs using a Hereford-based linear reference sequence as backbone and variants that were filtered for allele frequency in four cattle breeds. Using both simulated and real short read data, our findings corroborate that a variation-aware reference facilitates accurate read mapping and unbiased sequence variant genotyping [22,23,[32][33][34].…”

“…Sequence variant genotyping from graph-based alignments using vg call is currently somewhat limited to structural variants [32]. In order to detect SNPs and Indels from the variation-aware reference graph using widely-used sequence variant genotyping methods, we had to make the graph-based alignments compatible with linear coordinates.…”

“…[52]) is not available in cattle. Recent studies indicated that large structural variants can be identified accurately from genome graphs [32,[53][54][55]. Eventually, a bovine genome graph that unifies multiple breed-specific haplotype-resolved genome assemblies and their sites of variation might provide access to sources of variation that are currently neglected when short sequencing reads are aligned to a linear reference sequence [56,57].…”

Paragraph: A graph-based structural variant genotyper for short-read sequence data

“…Paragraph works by aligning and genotyping reads on a local sequence graph constructed for each targeted SV. This approach is different from other proposed and most existing graph methods that create a single whole-genome graph and align all reads to this large graph 18,39 . A whole-genome graph may be able to rescue reads from novel insertions that are misaligned to other parts of the genome in the original linear reference, however, the computational cost of building such a graph and performing alignment against this graph is very high.…”

Identification and Genotyping of Transposable Element Insertions From Genome Sequencing Data

“…Since SV genotyping tools can be applied to TE genotyping with additional TE annotation, we introduce some of the tools in this review to facilitate the development of better tools using this approach. Paragraph (Chen et al., 2019) and GraphTyper2 (Eggertsson et al., 2019) genotype SVs; vg toolkit (Hickey et al., 2020) genotypes SVs, SNVs, and indels; and SVJedi (Lecompte, Peterlongo, Lavenier, & Lemaitre, 2019) genotypes SVs for long‐read sequencing data. These graph alignment tools construct a genome graph based on detected SVs and align reads to it (Fig.…”