BLink-seq is presented, a novel Haplotagging method that is compatible with standard short-read next-generation sequencing platforms, is locally reproducible with low-cost reagents, and is scalable for high-throughput sample processing.
Abstract
Information about segregating haplotypes and structural variation (SV) can be extremely rich for a variety of applications in population genomics but remains largely inaccessible for many non-model species. Of the available methods, linked-read sequencing is especially promising for its low cost and scalability, but its adoption remains limited. One existing linked-read method is Haplotagging, which barcodes sequencing reads to reconstruct long molecules that encode haplotype information, with the potential to generate phased whole-genome data and detect structural variants. In this study, we present BLink-seq, a novel Haplotagging method that is compatible with standard short-read next-generation sequencing platforms, is locally reproducible with low-cost reagents, and is scalable for high-throughput sample processing. We optimized library preparation parameters, explored their relationship to linked-read library metrics, and validated phasing performance and structural variant detection in two evolutionary extremes: an experimental Drosophila melanogaster cross of inbred lines carrying known inversions, and four Atlantic silverside (Menidia menidia) parent-offspring trios sourced from highly outbred, wild-caught populations. We then applied our protocol to a cohort of 376 silversides to demonstrate its scalability and potential for SV detection and genotype imputation. Using BLink-seq, we generated chromosome-scale phased blocks and identified known inversions in both validation datasets. We discovered previously uncharacterized structural complexity within a known adaptive inversion on silverside chromosome 11, demonstrating that linked-read data can refine our understanding of SV architecture beyond what short reads alone can resolve. Finally, we provide a user guide for researchers interested in using BLink-seq.
A single genomic assay that delivers complete information across variant classes remains an aspirational goal. Currently, researchers and clinicians rely on an inefficient, expensive combination of short-read sequencing for single-nucleotide variants (SNVs) and small indels, comparative genomic hybridization (CGH) arrays for copy number variants (CNVs), and optical mapping and long-read sequencing for complex rearrangements, limiting the full potential of genomic discovery. To address these issues, TruPath Genome provides a one-test-for-all solution. By combining PCR-free whole-genome sequencing (WGS) with proximity-mapped read technology, it achieves high-resolution detection of SNVs and indels alongside long-range phasing for CNVs and structural variant (SV) refinement. We applied TruPath Genome on six clinical samples that were previously resolved by conventional methods. Across the cohort, TruPath Genome delivered coverage and variant-calling performance comparable to conventional WGS while achieving superior long-range phasing and enabling precise breakpoint resolution for clinically relevant structural events. This highlights TruPath Genome’s potential to consolidate genomic testing pipelines, accelerate diagnosis, and expand access to advanced genomic insights. Furthermore, its ultra-long-range data facilitates telomere-to-telomere assemblies and pangenome development, advancing our understanding of genome biology at an unprecedented scale.
This work describes the full spectrum of genetic variation and shows that while 99% of the variants between any two genomes are single base-pair substitutions, 88% of the euchromatic variant base pairs are SVs, including insertions, deletions, duplications, and inversions.
J. Lin, J. Gustafson, J. Wertz et al.· medRxiv· 0 citations
Abstract Accurate genotyping accelerates crop improvement, yet long-read sequencing remains underused in breeding due to cost. We present a scalable long-read low-pass (LRLP) sequencing framework for high-throughput variant discovery and trait mapping. Using PacBio HiFi reads in an allotetraploid peanut (Arachis hypogaea; AABB, 2n = 4x = 40) MAGIC population, we generated both LRLP and short-read low-pass (SRLP) data. At comparable depths, LRLP achieved substantially greater whole-genome and gene–space coverage than SRLP. Data were analyzed using both a single-reference genome and an 18-parent pangenome graph constructed with KhufuPan, a new tool for graph-based genotyping. Across analytical approaches, LRLP consistently identified more SNPs, indels (2–1,000 bp), and structural variants (>1 kb) than SRLP, improving genotype resolution and selection accuracy, particularly for large structural variants. By reducing cost barriers and increasing variant discovery in complex genomes, LRLP provides a practical path for deploying advanced genomics in under-resourced and orphan crops critical to global food security.
Kendall Lee, W. Korani, S. Pokhrel et al.· G3· 0 citations
Abstract Long-read sequencing (LRS) has driven a transition in microbial genomics, overcoming the assembly fragmentation inherent to short-read sequencing. This review elucidates the impact of LRS across isolate genomics, metagenomics, and multi-omics domains. By spanning extensive repetitive regions, LRS facilitates the reconstruction of circular chromosomes and precisely resolves mobile genetic elements (MGEs). In metagenomics, LRS enables strain-level resolution, the recovery of circular metagenome-assembled genomes, and the precise localization of MGEs within host replicons. Furthermore, the single-molecule, amplification-free properties of LRS provide enhanced resolution of native epigenetic modifications and full-length transcriptomes. Despite these advancements, widespread implementation remains constrained by multidimensional challenges, including stringent high-molecular-weight DNA requirements, depth deficits, and computational overhead. Nevertheless, LRS is increasingly becoming the method of choice for isolate genomics and metagenomics. As detection technologies and algorithms progress, LRS will further improve our ability to decipher the structural and functional diversity of microbial ecosystems.
Xing Rao, Yu-He Gu, Gabriella et al.· GigaScience· 0 citations
A step-by-step protocol for snpArcher, a Snakemake-based workflow that takes raw sequencing reads and a reference genome as input and produces a filtered, joint-called VCF suitable for downstream population genomic analysis, is presented.
Cade Mirchandani, Abdelmajid Omarjee, Guillaume Achaz et al.· Molecular biology and evolut...· 0 citations
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