Finding an optimal sequencing strategy to detect short and long genetic variants in a human genome Article Swipe
Robert Eveleigh
,
Sarah J. Reiling
,
José Héctor Gálvez
,
Mathieu Bourgey
,
Jiannis Ragoussis
,
Guillaume Bourque
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.05.30.656631
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.05.30.656631
Advances in DNA sequencing have transformed genomics, enabling comprehensive insights into human genetic variation. While short-read sequencing (SRS) remains dominant due to its high accuracy and affordability, its limitations in complex genomic regions have spurred the adoption of long-read sequencing (LRS) platforms, such as those from Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT). Despite these advances, there is still a lack of systematic, large-scale benchmarking of variant calling performance across diverse platforms, variant types, sequencing depths, and genomic contexts. Here, we present a comprehensive benchmark of sequencing technologies and variant calling algorithms, evaluating their performance in detecting single nucleotide polymorphisms (SNPs), insertions/deletions (indels), and structural variants (SVs). We show that while SRS combined with DeepVariant or DRAGEN offers excellent small variant detection in well-mapped regions, LRS technologies significantly outperform SRS in complex regions and SV detection. PacBio achieves high SNP and smaller SV accuracy even at moderate coverage, while ONT excels in detecting large SVs. The SV callers Dysgu and SVIM emerged as top performers across LRS datasets. Our results highlight that no single platform is optimal for all variant types or regions: SRS remains optimal for high-throughput small variant detection in accessible regions, whereas LRS is critical for capturing SVs and resolving difficult-to-map loci. These findings offer practical guidance for selecting sequencing technologies, coverage and variant calling strategies tailored to specific research or clinical goals, contributing to more accurate and cost-effective genomic analyses. Highlights ● State-of-the-art short variant calling algorithms are highly comparable but focus on precision and sensitivity differently ● Short-read technologies outperform long-read technologies for small SNP and InDels, with the exception of difficult-to-map variants. ● To capture the majority of variants, a minimum coverage of 15x for PacBio, 20x for SRS, or 30x for ONT is required. However, optimal coverage depends on zygosity, variant type, and the region of interest. ● Long-read technologies outperform short-read technologies for all validation sets tested for deletions and insertions in all size categories.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.05.30.656631
- https://www.biorxiv.org/content/biorxiv/early/2025/05/30/2025.05.30.656631.full.pdf
- OA Status
- green
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410922688
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410922688Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2025.05.30.656631Digital Object Identifier
- Title
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Finding an optimal sequencing strategy to detect short and long genetic variants in a human genomeWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-30Full publication date if available
- Authors
-
Robert Eveleigh, Sarah J. Reiling, José Héctor Gálvez, Mathieu Bourgey, Jiannis Ragoussis, Guillaume BourqueList of authors in order
- Landing page
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https://doi.org/10.1101/2025.05.30.656631Publisher landing page
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https://www.biorxiv.org/content/biorxiv/early/2025/05/30/2025.05.30.656631.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2025/05/30/2025.05.30.656631.full.pdfDirect OA link when available
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Human genome, Computational biology, Genetics, Genome, Biology, DNA sequencing, GeneTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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37Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.analyses. | 235 |
| abstract_inverted_index.benchmark | 86 |
| abstract_inverted_index.capturing | 201 |
| abstract_inverted_index.contexts. | 80 |
| abstract_inverted_index.coverage, | 149 |
| abstract_inverted_index.datasets. | 169 |
| abstract_inverted_index.deletions | 318 |
| abstract_inverted_index.detecting | 98, 154 |
| abstract_inverted_index.detection | 123, 192 |
| abstract_inverted_index.excellent | 120 |
| abstract_inverted_index.exception | 266 |
| abstract_inverted_index.genomics, | 7 |
| abstract_inverted_index.highlight | 172 |
| abstract_inverted_index.interest. | 305 |
| abstract_inverted_index.long-read | 39, 257 |
| abstract_inverted_index.practical | 210 |
| abstract_inverted_index.precision | 249 |
| abstract_inverted_index.required. | 292 |
| abstract_inverted_index.resolving | 204 |
| abstract_inverted_index.selecting | 213 |
| abstract_inverted_index.variants, | 276 |
| abstract_inverted_index.variants. | 269 |
| abstract_inverted_index.zygosity, | 298 |
| abstract_inverted_index.Highlights | 236 |
| abstract_inverted_index.Short-read | 254 |
| abstract_inverted_index.accessible | 194 |
| abstract_inverted_index.algorithms | 242 |
| abstract_inverted_index.comparable | 245 |
| abstract_inverted_index.detection. | 137 |
| abstract_inverted_index.evaluating | 94 |
| abstract_inverted_index.insertions | 320 |
| abstract_inverted_index.nucleotide | 100 |
| abstract_inverted_index.outperform | 130, 256, 309 |
| abstract_inverted_index.performers | 166 |
| abstract_inverted_index.platforms, | 42, 73 |
| abstract_inverted_index.sequencing | 4, 17, 40, 76, 88, 214 |
| abstract_inverted_index.short-read | 16, 310 |
| abstract_inverted_index.strategies | 220 |
| abstract_inverted_index.structural | 106 |
| abstract_inverted_index.validation | 314 |
| abstract_inverted_index.variation. | 14 |
| abstract_inverted_index.Biosciences | 48 |
| abstract_inverted_index.DeepVariant | 116 |
| abstract_inverted_index.algorithms, | 93 |
| abstract_inverted_index.categories. | 324 |
| abstract_inverted_index.differently | 252 |
| abstract_inverted_index.large-scale | 65 |
| abstract_inverted_index.limitations | 29 |
| abstract_inverted_index.performance | 70, 96 |
| abstract_inverted_index.sensitivity | 251 |
| abstract_inverted_index.systematic, | 64 |
| abstract_inverted_index.transformed | 6 |
| abstract_inverted_index.well-mapped | 125 |
| abstract_inverted_index.Technologies | 53 |
| abstract_inverted_index.benchmarking | 66 |
| abstract_inverted_index.contributing | 228 |
| abstract_inverted_index.technologies | 89, 128, 255, 258, 308, 311 |
| abstract_inverted_index.comprehensive | 9, 85 |
| abstract_inverted_index.polymorphisms | 101 |
| abstract_inverted_index.significantly | 129 |
| abstract_inverted_index.technologies, | 215 |
| abstract_inverted_index.affordability, | 27 |
| abstract_inverted_index.cost-effective | 233 |
| abstract_inverted_index.high-throughput | 189 |
| abstract_inverted_index.State-of-the-art | 238 |
| abstract_inverted_index.difficult-to-map | 205, 268 |
| abstract_inverted_index.insertions/deletions | 103 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.23055792 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |