# SVJedi-graph: using a variation graph to improve structural variant genotyping with long reads

Sandra Romain, Claire Lemaitre

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Sandra Romain, Claire Lemaitre. SVJedi-graph: using a variation graph to improve structural variant genotyping with long reads. DSB 2022 - Data Structure for Bioinformatics, Jun 2022, Dusseldorf, Germany. hal-05204293

HAL Id: hal-05204293

https://inria.hal.science/hal-05204293v1

Submitted on 8 Aug 2025

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# SVJedi-graph: using a variation graph to improve structural variant genotyping with long reads

Sandra ROMAIN¹ and Claire LEMAITRE¹

Université de Rennes 1, Inria, IRISA, 35000, Rennes, France

Corresponding author: claire.lemaitre@inria.fr

## Abstract

Structural variants (SVs) are genomic rearrangements of at least 50 bp and of various types, namely deletions, insertions, inversions, duplications and translocations. Genotyping SVs in individuals and populations is of particular interest as they can be associated with diseases or polymorphism in agronomic traits. Due to their large size range and frequent localization in repeated regions, long reads appear to be more suited to study SVs than short reads. As such, our team previously released SVJedi [1], one of the first SV genotypers using long reads. SVJedi's method of representing independently both SV's allelic sequences reduced reference bias and improved genotyping accuracy. However, the method failed to genotype closely located or overlapping SVs due to redundancy in representative allelic sequences. A more efficient way of representing SV alleles for genotyping while avoiding redundancy is to use a variation graph, but existing methods are restricted to short read data [2,3,4].

In response, we developed SVJedi-graph, a long read SV genotyper that uses a variation graph to represent SV alleles. In our method, we build a variation graph from a reference genome and a given set of SVs. The SV breakpoints (i.e. start and end positions) are extracted from the set and the genome sequence is split at each breakpoint into non-overlapping fragments. Each fragment becomes a node in the graph, and edges are added between nodes to form the reference path of the genome as well as the alternative path for each SV. Additional nodes are added for insertions. Then, the long reads are mapped on the variation graph using GraphAligner [5] and the resulting alignments are filtered on their quality and whether or not they overlap an edge (breakpoint) in the graph, which indicates the allele supported by the read. The most likely genotype for each SV is estimated from the ratio between the number of reads supporting each allele.

SVJedi-graph can genotype deletions, insertions, inversions and intra-chromosomal translocations. Running SVJedi-graph on simulated sets of deletions showed that the use of a variation graph was able to restore the genotyping quality on close and overlapping SVs. For instance, with a simulated set of deletions that had another close deletion 0 to 50 bp apart, SVJedi-graph showed a genotyping rate (proportion of SVs with a predicted genotype) of 99.9% and an accuracy (proportion of accurate genotype predicted among all predicted genotypes) of 99.0%, compared to a genotyping rate of 78.9% and an accuracy of 97.3% with SVJedi on the same dataset. We also tested our method on a real gold standard dataset of Genome In A Bottle (human, individual HG002), and were able to obtain a higher genotyping rate than SVJedi on the same data (97.4% against 90.2%), with a similar or slightly better accuracy (92.9% against 92.2%). SVJedi-graph is distributed under an AGPL license and available on GitHub at https://github.com/SandraLouise/SVJedi-graph.

## References

[1] L. Lecompte et al. SVJedi: genotyping structural variations with long reads. *Bioinformatics*, 36(17):4568-4575, 2020.

[2] S. Chen et al. Paragraph: a graph-based structural variant genotyper for short-read sequence data. *Genome Biology*, 20(1):291, 2019.

[3] H. P. Eggertsson et al. GraphTyper2 enables population-scale genotyping of structural variation using pangenome graphs. *Nature Communications*, 10(1):5402, 2019.

[4] G. Hickey et al. Genotyping structural variants in pangenome graphs using the vg toolkit. *Genome Biology*, 21(1):35, 2020.

[5] M. Rautiainen and T. Marschall. GraphAligner: rapid and versatile sequence-to-graph alignment. *Genome Biology*, 21(1):253, 2020.