ArticlePloS one2022
Different structural variant prediction tools yield considerably different results in Caenorhabditis elegans.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Evaluating beta-tubulin variants as predictors of benzimidazole resistance across Caenorhabditis nematodes.PLoS pathogens · 2026Article
- Transposable elements and heterochromatic regions are enriched for structural variation and sequence divergence in the genome of wild-type Caenorhabditis elegans.G3 (Bethesda, Md.) · 2025Article
- The impact of FASTQ and alignment read order on structural variant calling from long-read sequencing data.PeerJ · 2024Article
- Comprehensive detection of structural variation and transposable element differences between wild type laboratory lineages ofbioRxiv : the preprint server for biology · 2023Article
- De Novo Structural Variations of Escherichia coli Detected by Nanopore Long-Read Sequencing.Genome biology and evolution · 2023Article
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4 authors.
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Abstract
The accurate characterization of structural variation is crucial for our understanding of how large chromosomal alterations affect phenotypic differences and contribute to genome evolution. Whole-genome sequencing is a popular approach for identifying structural variants, but the accuracy of popular tools remains unclear due to the limitations of existing benchmarks. Moreover, the performance of these tools for predicting variants in non-human genomes is less certain, as most tools were developed and benchmarked using data from the human genome. To evaluate the use of long-read data for the validation of short-read structural variant calls, the agreement between predictions from a short-read ensemble learning method and long-read tools were compared using real and simulated data from Caenorhabditis elegans. The results obtained from simulated data indicate that the best performing tool is contingent on the type and size of the variant, as well as the sequencing depth of coverage. These results also highlight the need for reference datasets generated from real data that can be used as 'ground truth' in benchmarks.
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