# SVJedi-Tag: a novel method for genotyping large inversions with linked-read data

Mélody Temperville, Fantine Benoit, Claire Mérot, Fabrice Legeai, Claire Lemaitre

► To cite this version:

Mélody Temperville, Fantine Benoit, Claire Mérot, Fabrice Legeai, Claire Lemaitre. SVJedi-Tag: a novel method for genotyping large inversions with linked-read data. JOBIM 2025 - Journées Ouvertes Biologie, Informatique et Mathématiques, Jul 2025, Bordeaux, France. pp.1-9. hal-05393269

HAL Id: hal-05393269

https://hal.science/hal-05393269v1

Submitted on 2 Dec 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# SVJedi-Tag : a novel method for genotyping large inversions with linked-read data

Mélody TEMPERVILLE<fnref n="1" />, Fantine BENOIT<fnref n="2" />, Claire MÉROT<fnref n="2" />, Fabrice LEGEAI<fnref n="1" />,<fnref n="3" /> and Claire LEMAITRE<fnref n="1" />

Corresponding author: melody.temperville@inria.fr

**Keywords** Structural Variants, sequencing data analysis, variant genotyping, population genomics

**Abstract** Structural Variants (SVs) are an important but overlooked aspect of genetic variation. In particular, inversions are known for their role in the evolution of biological diversity and particularly studied in non-model species using population data. One of the major steps in the study of SVs is genotyping. Linked-read data provide a cost-efficient alternative to long-reads to genotype many individuals, by combining the low sequencing cost of short reads with long-distance information thanks to the use of barcodes tagging long molecules. Whereas several methods have been proposed to discover SVs with linked-reads, there are currently no tool for genotyping with this type of sequencing data. In this paper, we present SVJedi-Tag, the first inversion genotyping method dedicated to linked-read data. We tested SVJedi-Tag on simulated and real linked-read data in the seaweed fly Coelopa frigida, and showed that SVJedi-Tag is able to genotype with high accuracy large inversions above 25 kb, with a read depth as low as 3X.

## Introduction

Accurately detecting and characterizing genetic variation is essential for all aspects of genomics including medical research, ecological and evolutionary genomics, application for food production, and conservation. While most studies have long focused on nucleotide substitutions, recent research showed that structural variants (SVs), i.e. changes in position, presence, and orientation of genomic fragments from a few bases up to several megabases (Mb), represent an important but overlooked aspect of genetic diversity [1]. Long-reads have made it easier to characterize SVs, demonstrating a strong impact on phenotypes, including disease and traits of agronomical interest [2]. Inversions are particularly important for the evolution of biodiversity and are extensively studied in a context of conservation and evolutionary biology [3,4]. Most of those applications, such as genotype-phenotype association and population genomics, requires population studies with many individuals [5], for which the use of long-reads is not cost-efficient while the use of short-reads is not accurate enough, raising the need for alternative type of data. Linked reads combine both the high quality and low cost of short-read sequencing with the long-distance information of long reads. This technology is based on the attachment of barcodes to a long DNA molecule (usually from 5 to 100kb) during library preparation before fragmentation for short-read sequencing [6]. This long-distance information helps scaffolding during genome assembly and identifying large structural changes [7,8]. Several technologies produce linked read data, such as stLFR [6], TELL-seq [9] and, Haplotagging [10]. Haplotagging was optimized for multiplexing and it is thus particularly adequate for large datasets in ecological and population studies.

<footnote n="1">¹ Univ Rennes, Inria, CNRS, IRISA - UMR 6074, F-35000 Rennes, France</footnote>

<footnote n="2">² Université de Rennes, CNRS, ECOBIO - UMR6553, 35000 Rennes, France</footnote>

<footnote n="3">³ IGEPP, INRAE, Institut Agro, University of Rennes, 35653 Le Rheu, France</footnote>
Structural variants are often studied in two steps: detection and genotyping. Detection aims at characterising SVs present in an individual or population relatively to a reference genome. Genotyping, on the other hand, seeks to determine the presence or absence of each allele of the SVs previously identified in a genome. Detection is generally performed with the highest quality of data, such as long-reads with high depth, which is usually available on a limited number of individuals. Conversely, genotyping needs to be carried out on a larger number of individuals to provide stronger statistical support for analysis and thus uses more cost-effective data such as short-reads [11]. From that point of view, linked-reads thus provides a major advantage by combining a low sequencing cost, allowing to accommodate a large number of individuals, high sequencing quality, and long-range information allowing to capture kb-long molecules spanning over the breakpoints. Several tools exist to detect structural variants with linked-reads data, such as LEVIATHAN [12], Valor [13], Aquila [14], NAIBR [15] or Wrath [16], but there exists no tool for genotyping already discovered structural variants with linked-read data.

This paper presents SVJedi-Tag, the first tool dedicated to inversion genotyping with linked-read data. SVJedi-Tag analyses specific barcode (molecule tags) signals to estimate the presence or absence of an inversion in each individual. We tested our tool on simulated and real haplotagging data from the seaweed fly *Coelopa Frigida*, a species bearing adaptive polymorphic inversions [17].

# Materials and methods

## Method

**Barcode signal for inversion genotyping** Linked-reads are produced from long DNA molecules that are individually associated with microbeads coated with unique barcodes. They are then fragmented into barcode-associated short reads [6]. All reads originating from the same DNA molecule share the same barcode. This sequencing technology is therefore characterised by the size of the long DNA molecule (ranging from 1 to 100kb), the number of molecules per barcode (which is close to 1 for haplotagging data) and the average coverage of each molecule by reads (i.e. the number of reads per molecule, usually ranging from 2 to 10). All these characteristics vary depending on the dataset, the quality of the DNA and the library preparation. In linked-read data, the original long molecule is typically covered by a few reads randomly distributed over it [10]. That means that the full molecule cannot simple be re-assembled from its barcoded reads. However, after mapping the reads to a reference genome, long-range information can be recovered from the distribution of similar barcodes (belonging to the same molecule). Hence, to determine if a DNA segment is inverted or not in a sequenced individual, we can extract information from the long molecules that span the inversion breakpoints. SVJedi-tag therefore looks for particular patterns of barcode distributions around the inversion breakpoints to genotype each individual.

SVJedi-Tag is based on three major steps, as shown in Figure 1. First, a variation graph is created from a catalogue of inversions and a reference genome. Then, linked-reads are aligned on this variation graph. Finally, inversions are genotyped by analyzing the barcode distribution around the inversion breakpoints on the graph.

**Step 1: Variation graph** Instead of mapping the reads to a linear reference genome, we chose to represent the different SV alleles in a variation graph to better represent close and overlapping
variants, following the approach used in the long read genotyper SVJedi-graph [18]. The first step of our method constructs a variation graph (in GFA format) from a catalog of structural variants and a reference genome. In this graph, each node represents a sequence, the edges symbolize the adjacencies between sequences observed in an allele, and each combination of alleles is represented by a path in the graph.

**Step 2: Alignment** As there is no read-to-graph alignment tool dedicated to linked-read data, we use the short-read mapper VG Giraffe [19] (version 1.43.0), which ignores barcode information during alignment. The alignment process generates a GAF file that contains alignment information for each read on the graph (such as node, position on the node, size, quality, etc.).

**Step 3: Genotyping** To estimate which allele is present in the sample, we count the amount of barcodes supporting each allele around the inversion breakpoints. For each inversion, we define four regions on the inversion node and its adjacent nodes, in which we will register the observed barcodes: *Left* for the region on the adjacent node at the left of the inversion, *Begin* for the start of the inversion node, *End* for the end of the inversion node, and *Right* for the region on the adjacent node at the right of the inversion. The length of the regions is fixed and is governed by a parameter, whose default value is set to 10 Kb, which is a typical long molecule length (Figure 2).

Based on read alignments, we output a list of barcode found in each of the four regions. A given barcode can belong to three classes of signal: Reference, Alternative and Undetermined. The 'Reference' and 'Alternative' classes are informative because reads which share this same barcode are only found in two regions on either side of an arc of the graph, that is an allele-specific adjacency. The 'Undetermined' class corresponds to barcodes that are found in more than two regions or in both regions of the inversion node and are therefore non-informative. These 'Undetermined' signals result from molecules either including in the inverted segment not spanning any breakpoint, or that are larger than the inversion and span both breakpoints.

Depending on their proximity to other SVs, the adjacent nodes can be smaller than the region length (10 Kb). In this case, a given region can be defined on more than one node, by traversing the graph with a depth-first search so that each different paths has the wanted length (10 Kb). If the inversion node is smaller than twice the region length, then the length of the *Begin* and *End* regions is reduced to half the length of the inversion, so that these two regions do not overlap.

Finally, for each inversion, an allelic ratio is calculated as the number of barcodes supporting each allele. The three possible genotypes for a diploid individual are then called using fixed thresholds: below 0.2, the inversion will be genotyped as homozygous reference, above 0.8, homozygous alternative, and between these two thresholds, heterozygous.

**Implementation** SVJedi-Tag is implemented in Python 3. The code is available on GitHub (https://github.com/Mtemperville/SVJedi-Tag). SVJedi-Tag takes as input data: a file containing inversions in VCF format, which can also include other types of SVs that could impact genotyping (e.g. insertion close to a breakpoint), a linked-reads file (fastq /fastq.gz /fq /fq.gz) and a reference genome (fastq/fa/fna). The output file is a VCF file containing the predicted genotypes for all input inversions.
SVJedi-Tag also produces intermediate files containing the variation graph (GFA) and the alignment file (GAF), which can be used to launch the tool by skipping steps already performed.

## Application data

We tested SVJedi-Tag on three types of datasets: (1) simulated linked-reads with simulated inversions; (2) real linked-reads with simulated inversions and (3) real linked-reads with a real 3 Mb known inversion. For the real linked-read data, we used haplotagging data from 14 individuals of *Coelopa frigida* with different sequencing depths ranging from 1X to 7X and the associated reference genome of *Coelopa frigida* (250Mb).

We simulated inversions in the *Coelopa frigida* reference genome, restricted to the first five chromosomes (190Mb). For test (1), we first sampled uniformly 60 non-overlapping 50kb-long segments along the genome. Then we generated 2 haploid *C. frigida* genomes using Visor hack [20], by inverting segments from two different subsets of the 60 segments: 20 segments were inverted in both haplotypes (expected homozygous alternative genotype), 20 other segments were inverted in only one of the two haplotypes (expected heterozygous genotypes). The 20 remaining non-inverted segments represent expected homozygous reference genotypes. We then use both haploid genomes to simulate linked-reads with Visor Xenia [20] using several linked-read parameters inspired from the characteristics of the real haplotagging data (estimated using the LR_Stat script included in the SVJedi-Tag repository).

For test (2), we randomly determined the positions of 40 inversions and modified the *C. frigida* reference genome by inverting half of them in the genome using Visor Hack. Real individuals are thus expected to be homozygous alternative for the 20 inversions put in the reference genome and homozygous reference for the 20 other inversions. We repeated this simulation for five fixed inversion lengths: 10 kb, 25 kb, 50 kb, 100 kb, 500 kb and 1 Mb. Finally, for set of inversions, we ran SVJedi-Tag with the VCF containing the 40 inversions of a given length and the empirical linked-reads from 14 *C. frigida* samples.

For test (3), to assess the accuracy of the method on real data and real inversion, we genotyped a known 3 Mb inversion of *Coelopa Frigida* [17] located on chromosome LG4 (*Cf-Inv(4.1)*) with coordinates 1393751-4456396 determined using long-reads, and previously genotyped by PCR in the 14 individuals.

## Results

Tests with simulated data and 50kb-long simulated inversions demonstrated that SVJedi-Tag has a high genotyping rate (96%) and a high genotyping accuracy (92%) despite shallow sequencing depth. In fact, we simulated linked-read data with characteristics comparable to real data obtained from *Coelopa frigida*, i.e. an average sequencing depth of 2X, an original molecule length of 10kb and a molecule coverage of 0.05 (around 6 reads on average per molecule). We were able to assign a genotype to 57 out of the 60 simulated 50 Kb inversions. The remaining three inversions could not be genotyped due to an insufficient number of informative barcodes (here, minimal value of 1). Out of the 57 genotyped inversions, 53 were assigned the true genotype, resulting in a genotyping accuracy of 93 % (see also the distributions of the estimated allelic ratio for the three expected genotypes in Figure 3).
Genotyping performances depend on the linked-read sequencing characteristics: the more reads per molecule, the better the accuracy, with 72 % and 100 % accuracy for molecule coverages of 0.02 and 0.3 respectively (Table 1). Genotyping performances also vary with the length of the region in where barcodes are counted, a parameter which is optimal at values close to the molecule length (Table 1).

To evaluate SVJedi-Tag on more realistic datasets, we simulated homozygous inversions in the reference genome (see Methods) and tested genotyping in empirical haplotagging linked-reads from 14 *Coelopa frigida* samples. For simulated inversions of 50 Kb, at least 38 inversions out of 40 were genotyped in all samples (min genotyping rate of 95 %), and 12 out of 14 samples obtain a 100 % genotyping accuracy (see Figure 4). The other two samples show a lower genotyping accuracy of 95-97% probably explained by their lower sequencing depth ($< 2X$). It is worth noting that the ungenotyped inversions were randomly simulated in low-mappability regions.

When varying the length of inversions to genotype, we obtained 100 % accuracy and rate for inversions larger than 100 Kb for all real haplotagging samples (Figure 4). However, the accuracy drops slightly to 87% for 25kb-long inversions, and strongly for 10kb-long inversions. This pattern is explained by a higher number of "uninformative" molecules spanning the whole inverted segment when the inversions are shorter, as well as to the noise associated to the low coverage of reads per molecule.

Last, genotyping with empirical haplotagging data a known 3 Mb inversion of *Coelopa frigida* located on LG4 (*CF-Inv-4.1*) showed 100% accuracy for the 14 individuals previously genotyped by PCR.

## Discussion

Our results on simulated and empirical data show that SVJedi-Tag performs very well for genotyping large inversions (>50kb) using haplotagging linked-reads, including for data with shallow depth (2-5X). This is a real strength to perform future studies on chromosomal inversions in large population datasets as used in genotype-phenotype studies (GWAS) and ecological genomics. In particular, SVJedi-tag is able to genotype Mb-scale inversions with complex breakpoints, a type of variant which is increasingly studied but hardly accessible with standard methods. Indeed, the inversions most studied for their evolutionary or phenotypic impact are often at least several hundred Kb long and contain several genes.

Genotyping accuracy is improved by different parameters, such as a higher sequencing depth, longer DNA molecules, and well-adjusted length of the genotyping regions. Yet, the number of reads per molecule seems to be the parameter with the greatest impact on genotyping accuracy, and further molecular development to enhance this coverage would likely be beneficial to SV detection and genotyping.

Most genotyping errors were due to allelic ratio being just below or above the threshold. A future improvement will therefore consist in relying on a probabilistic model to infer genotype likelihoods instead. Future developments of SVJedi-tag include testing different parameters to improve genotyping of smaller inversions and exploring the impact of the distance between inversions. SVJedi-tag will also be tested on other empirical linked-read datasets with known inversions to better optimize
inversion genotyping in non-model species. Finally, in the future, we would like to extend the genotyping method to other types of structural variants.

## Acknowledgements

We thank the GenOuest bioinformatics core facility for providing the computing infrastructure. We are grateful to GenSeq platform (Université de Montpellier) and MGX platform (CNRS Montpellier) for the haplotagging data on C. frigida. C. Mérot is supported by an ERC-starting grant Evol-SV.

## Figures and Tables

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Overview of the three main steps of SVjedi-Tag.</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Illustration of barcode distributions around the inversion breakpoints. Barcodes with the same colour belong to the same initial DNA molecule. Three different classes of signal can be identified: barcodes supporting the alternative allele with a long molecule spanning the alternative allele adjacencies (in the pink box), barcodes supporting the reference allele with a long molecule spanning the reference allele adjacencies (in the blue box), and non-informative barcodes for which the long molecules span no breakpoints or both breakpoints (in the grey box).</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Boxplot of estimated allelic ratios predicted by SVJedi-Tag as a function of the expected genotype for inversions of 50 Kb in real and simulated linked-reads. (0/0: Homozygote reference, 1/1: Homozygote alternative, 0/1: Heterozygote) (Sim: simulate linked-reads, Real: real haplotagging linked-reads).</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Genotyping accuracy for inversions simulated in the reference genome and genotyped in empirical linked-reads from 14 Coelopa frigida samples as a function of inversion length. For each inversion length, 40 inversions were simulated. Each sample is represented by a line whose color reflects sequencing depth, varying between 0.9X and 7.7X.</figcaption></figure>
<table><thead><tr><th></th><th>Percentage of molecule<br>covered by reads</th><th>Region length<br>parameter value</th><th>Genotyping<br>accuracy</th><th>Genotyping<br>rate</th></tr></thead><tbody><tr><td rowspan="4">Molecule size 8 kb</td><td>0.02</td><td>10 Kb</td><td>80%</td><td>80%</td></tr><tr><td>0.05</td><td>10 Kb</td><td>85%</td><td>95%</td></tr><tr><td>0.1</td><td>10 Kb</td><td>89%</td><td>96%</td></tr><tr><td>0.2</td><td>10 Kb</td><td>89%</td><td>98%</td></tr><tr><td rowspan="4">Molecule size 10 kb</td><td>0.02</td><td>10 Kb</td><td>68%</td><td>88%</td></tr><tr><td>0.05</td><td>10 Kb</td><td>92%</td><td>96%</td></tr><tr><td>0.1</td><td>10 Kb</td><td>94%</td><td>98%</td></tr><tr><td>0.2</td><td>10 Kb</td><td>96%</td><td>98%</td></tr><tr><td rowspan="3">Molecule size 10 kb</td><td>0.05</td><td>1 Kb</td><td>72%</td><td>46%</td></tr><tr><td>0.05</td><td>5 Kb</td><td>92%</td><td>91%</td></tr><tr><td>0.05</td><td>15 Kb</td><td>93%</td><td>98%</td></tr></tbody></table>

**Tab. 1.** Accuracy and genotyping rate obtained with SVJedi-Tag on simulated linked-read data, for different simulated molecule length (8 or 10 kb), molecule coverage (0.02, 0.05, 0.1 and 0.2, for each molecule length) and different values for the region length parameter (on data with molecule length of 10 kb and molecule coverage of 0.05).

## References

[1] Mérot C, Oomen RA, Tigano A, Wellenreuther M. A Roadmap for Understanding the Evolutionary Significance of Structural Genomic Variation. Trends in Ecology & Evolution. 2020 Jul;35(7):561-72. Publisher: Elsevier. Available from: https://www.cell.com/trends/ ecology-evolution/abstract/S0169-5347(20)30076-8.

[2] Mahmoud M, Gobet N, Cruz-Dávalos DI, Mounier N, Dessimoz C, Sedlazeck FJ. Structural variant calling: the long and the short of it. Genome Biology. 2019 Nov;20(1):246. Available from: https://doi.org/10.1186/s13059-019-1828-7.

[3] Davey JW, Barker SL, Rastas PM, Pinharanda A, Martin SH, Durbin R, et al. No evidence for maintenance of a sympatric Heliconius species barrier by chromosomal inversions. Evolution Letters. 2017 June;1(3):138-54. Available from: https://www.ncbi.nlm.nih.gov/pmc/ articles/PMC6122123/.

[4] Wellenreuther M, Bernatchez L. Eco-Evolutionary Genomics of Chromosomal Inversions. Trends in Ecology & Evolution. 2018 June;33(6):427-40. Available from: https://www. sciencedirect.com/science/article/pii/S0169534718300788.

[5] Hooper DM, McDiarmid CS, Powers MJ, Justyn NM, Kučka M, Hart NS, et al. Spread of Yellow-Bill-Color Alleles Favored by Selection in the Long-Tailed Finch Hybrid System. Current Biology. 2024 December;34(23):5444-56.e8. Available from: https://linkinghub.elsevier.com/ retrieve/pii/S0960982224013733.

[6] Wang O, Chin R, Cheng X, Wu MKY, Mao Q, Tang J, et al. Efficient and unique cobarcoding of second-generation sequencing reads from long DNA molecules enabling cost-effective and accurate sequencing, haplotyping, and de novo assembly. Genome Research. 2019 May;29(5):798-808. Available from: https://genome.cshlp.org/content/29/5/798.

[7] Weisenfeld NI, Kumar V, Shah P, Church DM, Jaffe DB. Direct Determination of Diploid Genome Sequences. Genome Research. 2017 May;27(5):757-67. Available from: http://genome. cshlp.org/content/27/5/757.

[8] Yeo S, Coombe L, Warren RL, Chu J, Birol I. ARCS: Scaffolding Genome Drafts with Linked Reads. Bioinformatics. 2018 March;34(5):725-31. Available from: https://doi.org/10.1093/ bioinformatics/btx675.

[9] Chen Z, Pham L, Wu TC, Mo G, Xia Y, Chang PL, et al. Ultralow-input single-tube linked-read library method enables short-read second-generation sequencing systems to routinely generate
highly accurate and economical long-range sequencing information. Genome Research. 2020 June;30(6):898-909. Available from: https://genome.cshlp.org/content/30/6/898.

[10] Meier Jl, Salazar PA, Kučka M, Davies RW, Dréau A, Aldás I, et al. Haplotype tagging reveals parallel formation of hybrid races in two butterfly species. Proceedings of the National Academy of Sciences of the United States of America. 2021 June;118(25):e2015005118. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8237668/.

[11] Duan X, Pan M, Fan S. Comprehensive Evaluation of Structural Variant Genotyping Methods Based on Long-Read Sequencing Data. BMC Genomics. 2022 April;23(1):324. Available from: https://doi.org/10.1186/s12864-022-08548-y.

[12] Morisse P, Legeai F, Lemaitre C. LEVIATHAN: efficient discovery of large structural variants by leveraging long-range information from Linked-Reads data. bioRxiv; 2021. Preprint available under CC BY-ND 4.0 License. Available from: https://www.biorxiv.org/content/10.1101/2021.03.25.437002v1.

[13] Karaoğlanoğlu F, Ricketts C, Ebren E, Rasekh ME, Hajirasouliha I, Alkan C. VALOR2: characterization of large-scale structural variants using linked-reads. Genome Biology. 2020 March;21(1):72. Available from: https://doi.org/10.1186/s13059-020-01975-8.

[14] Zhou X, Zhang L, Weng Z, Dill DL, Sidow A. Aquila enables reference-assisted diploid personal genome assembly and comprehensive variant detection based on linked reads. Nature Communications. 2021 February;12:1077. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7889865/.

[15] Elyanow R, Wu HT, Raphael BJ. Identifying structural variants using linked-read sequencing data. Bioinformatics. 2018 January;34(2):353-60. Available from: https://doi.org/10.1093/bioinformatics/btx712.

[16] Orteu A, Kucka M, Gordon IJ, Ng'iru I, van der Heijden ESM, Talavera G, et al. Transposable Element Insertions Are Associated with Batesian Mimicry in the Pantropical Butterfly Hypolimnas misippus. Molecular Biology and Evolution. 2024 March;41(3):msae041. Available from: https://doi.org/10.1093/molbev/msae041.

[17] Mérot C, Berdan EL, Cayuela H, Djambazian H, Ferchaud AL, Laporte M, et al. Locally Adaptive Inversions Modulate Genetic Variation at Different Geographic Scales in a Seaweed Fly. Molecular Biology and Evolution. 2021 September;38(9):3953-71. Available from: https://doi.org/10.1093/molbev/msab143.

[18] Romain S, Lemaitre C. SVJedi-graph: improving the genotyping of close and overlapping structural variants with long reads using a variation graph. Bioinformatics. 2023 June;39(Suppl 1):i270-8. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10311344/.

[19] Pangenomics enables genotyping of known structural variants in 5202 diverse genomes | Science. Available from: https://www.science.org/doi/10.1126/science.abg8871.

[20] Bolognini D, Sanders A, Korbel JO, Magi A, Benes V, Rausch T. VISOR: a versatile haplotype-aware structural variant simulator for short- and long-read sequencing. Bioinformatics. 2020 February;36(4):1267-9. Available from: https://doi.org/10.1093/bioinformatics/btx719.