For genomic and epigenomic research to scale, sequencing methods must balance accuracy, cost, and throughput. Short-read approaches that rely on high genomic coverage (≥30x) often achieve precision but at the cost of throughput and accessibility, limiting their utility in clinical diagnostics, large-scale studies, and real-time disease monitoring.
Low-pass long-read sequencing (LRS) offers a powerful alternative to traditional high-coverage short-read methods. By spanning large, complex genomic regions and preserving native epigenetic information, LRS enables accurate detection of structural variants and methylation patterns, even at coverage levels below 10x. This capability is accelerating insights in oncology, biomarker discovery, and personalized medicine, while significantly reducing sequencing costs and complexity. In this article, we explore how low-pass LRS delivers high-impact results with fewer reads, unlocking broader utility across both research and clinical applications.
Short-read sequencing (SRS) depends on fragmenting, amplifying, and reassembling small DNA fragments (typically 150–300 bp), which introduces bias and limits resolution in complex genomic regions1,2. High GC content, repeat elements, and structural complexity reduce mapping accuracy, while the amplification process itself can distort true sequence composition3,4. As a result, high (≥30x) sequencing depth is often required to generate accurate consensus sequences with short reads, representing an average signal across many reads to correct for noise, errors, and artifacts. However, this level of depth comes at a cost: increased reagent use, longer turnaround times, and greater computational demands.
Adding methylation profiling to short-read workflows introduces even more complexity. Capturing methylation typically requires chemical conversion (e.g., bisulfite treatment) or enrichment steps, as well as separate library preparation and sequencing protocols5. These additions not only raise the cost and time requirements but also limit the ability to scale across larger cohorts or integrate epigenetic data into routine workflows. In sum, SRS struggles to deliver unbiased, cost-effective insights when applied to structurally complex or methylation-rich regions, especially when scalability is a core requirement.
By sequencing long, unamplified strands of DNA or RNA, LRS avoids the pitfalls of PCR amplification6,7,8. This allows accurate resolution of structural variants and base modifications, including all major types of DNA methylation, directly from the native molecule9. In structurally complex genomic regions, this results in superior accuracy and deeper insights.LRS has been shown to identify large deletions, translocations, and inversions using hundreds, not millions, of reads10. In one WBL case study, nanopore-based LRS at 5x coverage identified over 6,000 differentially methylated CpG islands across two tissue types—capturing 2.4x more than traditional arrays¹¹. These results demonstrate that low-pass LRS can provide technical advantages and biological breadth and efficiency even at reduced coverage.
Low-pass LRS reduces reagent usage and cycle counts, directly lowering costs while preserving high data quality12. Genome skimming, very low-pass sequencing, with nanopore sequencing at coverage levels below 0.05x has shown effective for global methylation profiling across vertebrate and plant species 13,14,15. These studies confirmed reproducible detection of conserved methylation patterns with minimal coverage, validating the approach as a fast and economic tool for epigenomic screening. In these studies, low-pass LRS produced comparable methylation results to more complex bisulfite-based methods, with a fraction of the passes required. This efficiency opens the door to larger cohort studies, rapid diagnostic workflows, and resource-conscious applications.
Low-pass LRS is particularly well suited for oncology. In high-grade ovarian and prostate cancers, patient-specific large structural variants have been identified with just 1.5–5x genomic coverage15. These breakpoints were validated with PCR and used to track disease recurrence via circulating tumor DNA (ctDNA). In some cases, ctDNA monitoring with LRS detected tumor progression earlier than traditional markers like PSA (prostate-specific antigen), a commonly used blood-based biomarker16.
Low-coverage LRS is also proving valuable in transcriptomic and epigenetic tumor profiling. In a recent study comparing shallow nanopore RNA-seq to traditional short-read transcriptome profiling across multiple tumor types, LRS achieved strong concordance despite using 36-fold fewer reads. It accurately recapitulated key features, including signaling pathway activity, kinase expression, tumor microenvironment composition, and gene fusions, that are often difficult to resolve with short-read approaches20.
.Additionally, low-pass LRS enabled epigenomic profiling of 189 tumors and 41 matched normal samples, revealing hypermethylation of the BRCA1 and RAD51C gene promoters, tumor suppressors and key regulators of DNA repair, in cancer types where this had not been previously observed22,23,24. These findings expand the potential use of PARP inhibitors and platinum-based therapies beyond their traditional indications. More broadly, they illustrate how low-pass LRS can uncover previously unrecognized molecular features that inform treatment decisions, reveal new therapeutic opportunities, and support more personalized cancer care. By surfacing clinically actionable insights with fewer inputs, lower cost, and faster turnaround, low-pass LRS is opening new doors in translational oncology.
Low-pass long-read sequencing offers a practical and scalable approach to genetic and epigenetic analysis. By delivering high-resolution insights with fewer sequencing reads, it enables efficient detection of structural variants and methylation patterns, even in complex regions of the genome.
As the technology matures, low-pass LRS is poised to play an increasingly important role in translational research, diagnostics, and precision oncology—unlocking clinically meaningful insights in oncology, diagnostics, and population-scale studies at a fraction of the cost and complexity of traditional methods
