A little longer, a lot better: simulation-guided exploration of extended-length single-end barcoded reads for structural variant detection

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ID: 328281
2026
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Abstract
Abstract Accurate detection of genetic variants, including single nucleotide polymorphisms (SNPs), small insertions and deletions (INDELs), and structural variants (SVs), is essential for comprehensive genomic analysis. While short-read sequencing performs well for SNP and INDEL detection, it remains limited in resolving SVs, particularly in complex genomic regions, due to its short read length. Linked-read sequencing technologies, such as single-tube Long Fragment Read (stLFR), partially address this limitation by incorporating molecular barcodes to provide long-range information. In this study, we evaluate conventional paired-end linked reads (PE100_stLFR) and explore a conceptual extension: long single-end barcoded reads of 500 bp (SE500_stLFR) and 1000 bp (SE1000_stLFR). We developed stLFR-sim, a Python-based simulator that reproduces the stLFR workflow and enables realistic benchmarking. Using a high-quality T2T assembly of HG002, we generated multiple datasets across 12 sequencing configurations. SVs were called using Aquila_stLFR (v2) and benchmarked against the Genome in a Bottle (GIAB) HG002 SV truth set with Truvari. We show that simulated PE100_stLFR has the same trade-off pattern between precision and recall in SV calling compared to real data. Increasing read length consistently improves SV detection accuracy, with SE1000_stLFR achieving the best performance among the evaluated stLFR configurations and showing competitive performance relative to ICLR- and pangenome-based approaches, while approaching the performance of long-read methods. Collectively, our results highlight the potential of extended-length single-end barcoded reads for improving SV detection and demonstrate how simulation can be used to evaluate prospective linked-read sequencing designs.
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openalex_W7212034228 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Can Luo, Yichen Henry Liu, Han Liu, Zhenmiao Zhang, Lu Zhang, Brock A Peters, Xin Zhou
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Journal Bioinformatics advances
Year 2026
DOI
10.1093/bioadv/vbag267
URL
https://academic.oup.com/bioinformaticsadvances/advance-article-pdf/doi/10.1093/bioadv/vbag267/71000577/vbag267.pdf
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