Enhancing variant detection in complex genomes: leveraging linked reads for robust SNP, Indel, and structural variant analysis

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 closely matches real data, validating the simulation framework. Increasing read length consistently improves SV detection accuracy, with SE1000_stLFR achieving the best performance and approaching long-read methods while outperforming short-read and pangenome-based approaches. Collectively, our results highlight the strong potential of long single-end barcoded reads for improving SV detection, and suggest that even modest increases in read length, when combined with barcode information, can provide a cost-effective and practical strategy for enhancing future sequencing technologies and SV discovery.