ArticleGenomics, proteomics & bioinformatics2022
Robust Benchmark Structural Variant Calls of An Asian Using State-of-the-art Long-read Sequencing Technologies.
Article in Genomics, proteomics & bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- GrassSV - hybrid method to detect structural variants in high throughput DNA-seq data.PLoS computational biology · 2026Article
- Evaluating data requirements for high-quality haplotype-resolved genomes for creating robust pangenome references.Genome biology · 2024Article
- Article
- SVhawkeye: an ultra-fast software for user-friendly visualization of targeted structural fragments from BAM files.Frontiers in genetics · 2024Article
- Haplotype-resolved assemblies and variant benchmark of a Chinese Quartet.Genome biology · 2023Article
- Quartet DNA reference materials and datasets for comprehensively evaluating germline variant calling performance.Genome biology · 2023Article
- Haplotype-resolved Chinese male genome assembly based on high-fidelity sequencing.Fundamental research · 2022Article
- Profiling the Genome-Wide Landscape of Short Tandem Repeats by Long-Read Sequencing.Frontiers in genetics · 2022Article
- Accurate haplotype-resolved assembly reveals the origin of structural variants for human trios.Bioinformatics (Oxford, England) · 2021Article
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Authors and funding
21 authors.
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Abstract
The importance of structural variants (SVs) for human phenotypes and diseases is now recognized. Although a variety of SV detection platforms and strategies that vary in sensitivity and specificity have been developed, few benchmarking procedures are available to confidently assess their performances in biological and clinical research. To facilitate the validation and application of these SV detection approaches, we established an Asian reference material by characterizing the genome of an Epstein-Barr virus (EBV)-immortalized B lymphocyte line along with identified benchmark regions and high-confidence SV calls. We established a high-confidence SV callset with 8938 SVs by integrating four alignment-based SV callers, including 109× Pacific Biosciences (PacBio) continuous long reads (CLRs), 22× PacBio circular consensus sequencing (CCS) reads, 104× Oxford Nanopore Technologies (ONT) long reads, and 114× Bionano optical mapping platform, and one de novo assembly-based SV caller using CCS reads. A total of 544 randomly selected SVs were validated by PCR amplification and Sanger sequencing, demonstrating the robustness of our SV calls. Combining trio-binning-based haplotype assemblies, we established an SV benchmark for identifying false negatives and false positives by constructing the continuous high-confidence regions (CHCRs), which covered 1.46 gigabase pairs (Gb) and 6882 SVs supported by at least one diploid haplotype assembly. Establishing high-confidence SV calls for a benchmark sample that has been characterized by multiple technologies provides a valuable resource for investigating SVs in human biology, disease, and clinical research.
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