ArticleGigaScience2019
Evaluation of computational genotyping of structural variation for clinical diagnoses.
Article in GigaScience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- A systematic assessment of machine learning for structural variant filtering.bioRxiv : the preprint server for biology · 2026Article
- SV-MeCa: an XGBoost-based meta-caller approach for structural variant calling from short-read data.BMC bioinformatics · 2025Article
- The promise and challenges of characterizing genome-wide structural variants: A case study in a critically endangered parrot.Molecular ecology resources · 2025Article
- Closing the gaps, and improving somatic structural variant analysis and benchmarking using CHM13-T2T.Genome research · 2025Article
- K-mer analysis of long-read alignment pileups for structural variant genotyping.Nature communications · 2025Article
- K-mer analysis of long-read alignment pileups for structural variant genotyping.bioRxiv : the preprint server for biology · 2024Article
- Detection of mosaic and population-level structural variants with Sniffles2.Nature biotechnology · 2024Article
- Leaf: an ultrafast filter for population-scale long-read SV detection.Genome biology · 2024Article
- The benefit of a complete reference genome for cancer structural variant analysis.medRxiv : the preprint server for health sciences · 2024Article
- NPSV-deep: a deep learning method for genotyping structural variants in short read genome sequencing data.Bioinformatics (Oxford, England) · 2024Article
- Structural genomic variation and migratory behavior in a wild songbird.Evolution letters · 2023Article
- MSV: a modular structural variant caller that reveals nested and complex rearrangements by unifying breakends inferred directly from reads.Genome biology · 2023Article
- Contrasting Patterns of Single Nucleotide Polymorphisms and Structural Variation Across Multiple Invasions.Molecular biology and evolution · 2023Article
- Comprehensive evaluation of structural variant genotyping methods based on long-read sequencing data.BMC genomics · 2022Article
- Integrating whole-genome sequencing with multi-omic data reveals the impact of structural variants on gene regulation in the human brain.Nature neuroscience · 2022Article
- Non-coding regulatory elements: Potential roles in disease and the case of epilepsy.Neuropathology and applied neurobiology · 2022Review
- Towards accurate and reliable resolution of structural variants for clinical diagnosis.Genome biology · 2022Review
- Population-scale genotyping of structural variation in the era of long-read sequencing.Computational and structural biotechnology journal · 2022Review
- Hidden biases in germline structural variant detection.Genome biology · 2021Article
- Expanding the conservation genomics toolbox: Incorporating structural variants to enhance genomic studies for species of conservation concern.Molecular ecology · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundStructural variation (SV) plays a pivotal role in genetic disease. The discovery of SVs based on short DNA sequence reads from next-generation DNA sequence methods is error-prone, with low sensitivity and high false discovery rates. These shortcomings can be partially overcome with extensive orthogonal validation methods or use of long reads, but the current cost precludes their application for routine clinical diagnostics. In contrast, SV genotyping of known sites of SV occurrence is relatively robust and therefore offers a cost-effective clinical diagnostic tool with potentially few false-positive and false-negative results, even when applied to short-read DNA sequence data.
resultsWe assess 5 state-of-the-art SV genotyping software methods, applied to short-read sequence data. The methods are characterized on the basis of their ability to genotype different SV types, spanning different size ranges. Furthermore, we analyze their ability to parse different VCF file subformats and assess their reliance on specific metadata. We compare the SV genotyping methods across a range of simulated and real data including SVs that were not found with Illumina data alone. We assess sensitivity and the ability to filter initial false discovery calls. We determined the impact of SV type and size on the performance for each SV genotyper. Overall, STIX performed the best on both simulated and GiaB based SV calls, demonstrating a good balance between sensitivity and specificty.
conclusionOur results indicate that, although SV genotyping software methods have superior performance to SV callers, there are limitations that suggest the need for further innovation.
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Registered trials
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