The central challenge the team confronted is a notorious one in human genetics. PKD1 shares extensive stretches of near-identical sequence with six pseudogenes located elsewhere on chromosome 16, remnants of duplicated DNA that have lost their protein-coding function. Any sequencing approach that reads the genome indiscriminately risks aligning true PKD1 reads to the wrong location or, worse, mistaking pseudogene sequence for genuine gene sequence, producing false calls that could mislead clinical decisions. To sidestep this interference, the researchers employed long-range PCR, a technique that selectively amplifies the functional PKD1 gene in large fragments before sequencing, effectively isolating the target from its deceptive copies.

Oxford Nanopore Technology has attracted growing interest in clinical genomics because of its accessibility. A MinION device is roughly the size of a large USB stick, requires comparatively modest capital investment, and can be run in laboratories far from major sequencing centers. The trade-off has always been accuracy: nanopore reads carry higher error rates than the short-read Illumina platforms that dominate clinical laboratories, because each DNA strand threads through a protein pore and is read base by base as electrical current fluctuations. Raw errors, however, are largely random rather than systematic, which means that reading the same molecule many times and applying sophisticated computational correction can drive the consensus error rate down to levels suitable for variant calling. The catch is that this correction demands careful bioinformatic optimization, and until now no validated, end-to-end pipeline existed specifically for PKD1 on the nanopore platform.

The Chilean team built their solution as a nine-stage bioinformatic pipeline, with each stage dedicated to a distinct computational task in the journey from raw electrical signal to final variant call. The stages encompass the standard architecture of modern long-read analysis: quality assessment of the raw data, basecalling that converts current measurements into DNA sequence, demultiplexing to sort reads by sample, adapter trimming and filtering to remove artifacts, alignment of reads to the human reference genome, post-alignment processing, variant calling, and annotation that interprets each variant’s likely biological consequence. Crucially, rather than adopting tools by default, the authors performed a systematic, criteria-based selection at every stage, comparing candidate programs against explicit performance benchmarks to choose the configuration best suited to the error profile of nanopore data derived from long-range PCR amplicons.

This structured development approach is what distinguishes the work from ad hoc bioinformatics. Nanopore pipelines are notoriously sensitive to parameter choices: the settings that work for whole-genome sequencing of bacteria may fail badly when applied to a single amplified human gene, where coverage is deep, read lengths are constrained by the PCR fragment size, and homopolymer stretches, runs of identical bases that nanopore sequencers historically struggled to measure, can distort indel calls. By evaluating tools stage by stage against defined criteria, the team produced a reproducible framework whose behavior can be understood and audited, an essential property for any pipeline intended for clinical use. The pipeline was also designed with computational efficiency in mind, so that it can run on modest hardware rather than demanding a high-performance computing cluster, although the authors acknowledged the Patagn supercomputer team for supporting their development work.

Validation proceeded in two independent cohorts with distinct purposes. The first consisted of eight samples in which PKD1 variants had already been confirmed by Sanger sequencing, the decades-old method that reads DNA through chain-termination chemistry and remains the reference standard in many clinical laboratories. The optimized nanopore pipeline achieved complete concordance with Sanger across all eight samples, correctly recovering every previously known variant without introducing false positives. This head-to-head agreement is the critical benchmark for any platform aspiring to clinical deployment, because it demonstrates that the computational corrections have neutralized the platform’s raw error rate to the point where the two technologies deliver identical answers on real patient material.

The second cohort provided the true test of diagnostic utility: 26 ADPKD patients who had never received a molecular diagnosis. When the pipeline was applied to their sequenced samples, it identified pathogenic or likely pathogenic PKD1 variants in 69 percent of cases, a detection rate consistent with the known genetics of ADPKD, in which PKD1 accounts for the large majority of cases with an identifiable cause and roughly a tenth of patients carry variants in PKD2 or other genes instead. Among the prioritized findings were six variants never before reported, expanding the catalog of known disease-causing mutations in the gene. To confirm these new calls, the researchers sent 18 prioritized variants for independent Sanger verification, and 17 were confirmed, a validation rate that underscores both the sensitivity of the pipeline and the value of orthogonal confirmation for novel discoveries.

The implications reach well beyond a single gene. ADPKD is one of the most common inherited kidney diseases worldwide, and targeted therapies now exist that can slow disease progression, making early molecular diagnosis clinically meaningful rather than merely informative. Yet in many countries, including Chile where this study was conducted, patients may wait years for genetic testing or never receive it, because sending samples to commercial laboratories abroad is costly and short-read sequencing of PKD1 requires careful strategies to handle the pseudogene problem. A pipeline that runs on a benchtop nanopore device, uses long-range PCR to guarantee specificity, and delivers Sanger-concordant results changes the calculus for public health systems, allowing regional hospitals and university laboratories to perform the analysis in-house at low infrastructure cost.

The work also offers a template for tackling other clinically important genes haunted by pseudogenes or other architectural complexities. Many disease genes share this burden, and the authors argue that their structured, criteria-based development approach, demonstrated diagnostic performance, and computational optimization make the framework applicable to genomic research and clinical settings requiring high-sensitivity analysis of large, complex genes. Because the pipeline is described as reproducible and its development logic is documented stage by stage, other groups can adapt it rather than rebuilding the analysis from scratch, accelerating the spread of long-read diagnostics into low-resource environments.

The study, conducted under ethical approval from the Research Ethics Committee of the Health Service of Valdivia and with informed consent from all participants recruited across Chilean health centers, was supported by Chile’s National Agency for Research and Development through FONDECYT Regular projects and by Innovation Fund for Competitiveness grants from the Regional Government of Los Ros. The authors credit collaboration among the Nephrology Laboratory of Universidad Austral de Chile, the Valdivia Base Hospital, the Centro de Investigacin Clnica Avanzada, and nephrologists across the country who connected patients with the research. As nanopore sequencing matures from a promising technology into a validated clinical tool, this pipeline stands as evidence that careful bioinformatics, not expensive hardware, can be the decisive factor in bringing precision diagnosis to patients who have historically been left waiting.

Subject of Research: Development and clinical validation of an Oxford Nanopore bioinformatic pipeline for detecting PKD1 variants in autosomal dominant polycystic kidney disease

Article Title: An optimized nanopore-based bioinformatic pipeline for PKD1 variant detection: development and validation with clinical samples

Article References: Millar, D., Gajardo, M., Poblete, B., Ubilla, R., Izquierdo, M., Medina, A., Lehmann, P., Flores, C., Krall, P., & Maturana, J. (2026). An optimized nanopore-based bioinformatic pipeline for PKD1 variant detection: development and validation with clinical samples. BMC Bioinformatics. https://doi.org/10.1186/s12859-026-06644-4

Image Credits: AI Generated

DOI: 10.1186/s12859-026-06644-4

Keywords: Oxford Nanopore Technology, PKD1, ADPKD, long-read sequencing, bioinformatic pipeline, variant detection, pseudogene, long-range PCR, Sanger sequencing, polycystic kidney disease, low-resource sequencing, clinical genomics

The clinical problem the team set out to address is well known in genomic medicine. Molecular diagnosis of rare disease currently plateaus at roughly fifty percent of patients, even after whole exome or whole genome sequencing. A substantial share of those unresolved cases involves variants that alter splicing, the process by which RNA transcripts are cut and re-joined to produce mature messenger RNA. Predicting whether a DNA change actually disrupts splicing in a patient’s tissues remains notoriously difficult, and variants of uncertain significance accumulate in reports without a practical way to test their effects directly.

Short-read RNA sequencing, which fragments transcripts into small pieces before reading them, often cannot show how exons connect across a whole transcript. Long-read platforms from Oxford Nanopore Technologies solve this by reading RNA-derived molecules end to end, revealing full isoform structures. Until now, however, long-read RNA approaches have typically demanded large control cohorts, complex bioinformatics and tissue samples that are hard to obtain, keeping them confined to specialist research laboratories rather than routine diagnostics.

RAPID, short for RNA Analysis Pipeline for Integrated Diagnostics, was designed from the outset to be diagnostically deployable. It is fully modular, covering the entire sample-to-answer journey, from primer design targeting genes of interest through nanopore sequencing to reproducible single-sample interpretation. Cases entered the workflow after exome or genome sequencing and multidisciplinary team review had narrowed the search to five or fewer candidate genes, meaning the sequencing effort could be focused and fast. Crucially, the method relies on accessible tissues such as blood and does not require large control datasets for interpretation.

The study applied the workflow to six probands with suspected monogenic neurometabolic disease, and the outcomes illustrate three distinct diagnostic scenarios. In two cases, targeted long-read RNA sequencing confirmed pathogenic splice disruption at the transcript level, converting uncertainty into a resolved molecular diagnosis. In one case, the RNA evidence argued against a candidate gene, prompting its exclusion and redirecting the diagnostic search. In the remaining three cases, transcript-level findings prioritised further DNA investigation, refining how the variants of uncertain significance should be weighed.

Technically, the workflow captured amplicons spanning the relevant exons and splice junctions of each candidate gene, generating reads long enough to assemble near-full-length isoform structures. The team demonstrated reproducibility by showing consistent isoform composition for a control gene across commercial blood RNA samples and an independent public long-read dataset, and they characterised the minimum read depth needed to detect transcripts at low fractional abundance. Quality control metrics, including read length and depth, were achieved using standard long-read sequencing infrastructure within a clinically relevant timeframe.

The authors also mapped their RNA results onto the established variant interpretation framework used by clinical scientists, in which aberrant splicing leading to frameshifts or premature stop codons, particularly in transcripts subject to nonsense-mediated decay, can support pathogenic classifications. By providing direct, mechanism-level evidence rather than in silico predictions, the workflow strengthens this evidence hierarchy and gives diagnostic laboratories a practical route to resolving variants that would otherwise remain reportable only as uncertain.

The significance for patients and families is considerable. A definitive molecular diagnosis can end a diagnostic odyssey that sometimes lasts decades, inform prognosis, guide surveillance and treatment, enable accurate genetic counselling, and open doors to targeted therapies and clinical trials. For a field in which half of patients still leave the sequencing process without answers, a rapid, cost-effective test that interrogates RNA directly addresses one of the largest remaining diagnostic gaps.

The researchers argue that RAPID shows long-read RNA sequencing can be implemented within existing diagnostic infrastructure, offering a scalable path to routine transcript-level assessment in clinical genomics. As nanopore sequencing becomes more widespread in NHS and hospital laboratories, workflows of this kind could shift splicing variant interpretation from probabilistic prediction to direct functional measurement. Supported by funding from the charity Sparks and conducted with appropriate ethical approvals and participant consent, the study points toward a future in which reading the transcript itself becomes a standard step in solving rare disease.

The biological importance of splicing in human disease provides useful context for why this approach matters. Introns interrupt most human genes, and their precise removal depends on short sequence signals at exon boundaries that are frequently disrupted by single-nucleotide changes lying well outside protein-coding regions. Because such variants often sit in positions that protein-prediction tools ignore, they can be classified as benign or left as uncertain even when they silently abolish a transcript. Deep intronic changes can also activate cryptic exon inclusion, an effect essentially invisible to standard exome analysis, which is one reason splice-altering variation has been described as a substantial hidden burden within the undiagnosed fraction of rare disease cohorts.

The interpretation framework applied in the study reflects a broader international movement toward using RNA evidence in clinical classification. Guidelines from professional bodies now allow aberrant splicing demonstrated at the transcript level to contribute to pathogenic classifications, provided the altered transcript is shown to escape nonsense-mediated decay or to produce a clearly deleterious product. This matters because a large proportion of loss-of-function variants predicted to trigger nonsense-mediated decay are already treated as pathogenic by default; demonstrating experimentally that a variant of uncertain significance produces the same consequence effectively moves it into that well-established category. Conversely, showing that a candidate variant leaves splicing intact can be equally decisive, as the gene-exclusion case in this cohort illustrates.

Tissue choice is a central consideration for any transcript-based diagnostic test. Genes are not expressed uniformly across the body, and the reference resources that underpin transcript interpretation, such as population-scale tissue atlases, show that many disease-relevant genes have their highest expression in tissues that cannot ethically or practically be sampled. Blood, however, is accessible, and the workflow’s reliance on peripheral blood RNA, collected in standard preservation tubes and stored frozen, means the logistics resemble those of routine clinical phlebotomy rather than specialist tissue procurement. The supplementary analyses showing that isoform composition of a control gene is consistent across commercial RNA references and public datasets speak to the reproducibility that regulators and accreditation bodies would expect of a deployable assay.

The depth requirements characterised by the team address a practical question that any diagnostic laboratory must answer before adopting such a test: how much sequencing is enough. Because a deleterious transcript may represent only a small fraction of all transcripts from a given gene, particularly when nonsense-mediated decay degrades the abnormal product rapidly, sensitivity at low fractional abundance is essential. Mapping the relationship between transcript rarity and required read depth gives laboratories a principled basis for setting sequencing targets and quality thresholds rather than relying on ad hoc criteria.

The study also sits within a distinctive UK genomic infrastructure. Several participating families had previously been sequenced through national genome programmes, and the ethical frameworks governing those programmes permit recontact of participants for follow-up sampling, a mechanism that allowed the researchers to obtain fresh RNA from already-investigated patients. This recontact pathway, coordinated through clinical interpretation partnerships, represents a model for how long-read RNA follow-up could be layered onto existing genomic medicine services without requiring patients to restart the consent and recruitment process from scratch.

From a health-systems perspective, the economics of the approach deserve emphasis. Whole genome sequencing has become affordable at scale, but the interpretation bottleneck, not the sequencing itself, now limits diagnosis. A targeted assay that sequences only a handful of genes consumes modest sequencing capacity on instruments that many hospital laboratories already operate or can readily access, and the modular design means primer sets can be redesigned quickly for each new case. The near-full-length isoform output also reduces interpretive ambiguity, because the analyst sees the complete exon connectivity of each transcript rather than inferring it from fragmented short reads.

The neurometabolic focus of the cohort is itself informative. Leukodystrophies and related neurodegenerative conditions of childhood often present with nonspecific imaging findings and progressive symptoms, and several of the genes implicated in these disorders are known to harbour splice-disrupting variants that confound standard pipelines. For families facing such progressive conditions, the speed of the workflow is not merely convenient; an earlier molecular answer can determine eligibility for emerging therapies, some of which are most effective when started before irreversible neurological damage accumulates.

Looking forward, the study suggests a tiered model of genomic diagnosis in which short-read sequencing remains the first-line discovery tool, while targeted long-read RNA analysis serves as a rapid functional triage step for the subset of cases with splice-relevant candidates. The authors’ demonstration that results can be obtained within a clinically relevant timeframe, using standard infrastructure and single-sample interpretation without large control cohorts, positions transcript-level evidence to become a routine component of multidisciplinary review rather than a research exception reserved for specially funded projects.

Subject of Research: A targeted long-read RNA sequencing workflow for functionally resolving splicing variants in rare disease diagnosis

Article Title: RAPID: a targeted long-read RNA workflow for functional resolution of splicing variants in rare disease

Article References: Montgomery, K.-A., Macpherson, H., Anderson, C., Wade, C., Gustavsson, E. K., Lynch, D. S., Wilson, L. C., Davison, J., Wakeling, E., Tuschl, K., Houlden, H., Clement, E., Mills, P. B., & Ryten, M. (2026). RAPID: a targeted long-read RNA workflow for functional resolution of splicing variants in rare disease. Genome Medicine. https://doi.org/10.1186/s13073-026-01754-3

Image Credits: AI Generated

DOI: 10.1186/s13073-026-01754-3

Keywords: long-read RNA sequencing, Oxford Nanopore Technologies, splicing variants, rare disease diagnostics, variants of uncertain significance, neurometabolic disease, transcriptomics, molecular diagnosis, clinical genomics, whole exome sequencing, RNA splicing, diagnostic workflow

AMP represents professionals who develop and perform molecular testing used to guide diagnosis, treatment, and disease monitoring. In a field where laboratory decisions can directly affect clinical outcomes, leadership is closely tied to scientific rigor, education, and community-wide standards for quality.

Tafe is known for expertise spanning thoracic and gynecologic cancers and for applying molecular diagnostics to improve patient stratification. She leads the Laboratory for Clinical Genomics and Advanced Technologies (CGAT) at Dartmouth Hitchcock and directs the molecular tumor board at the Dartmouth Cancer Center, where genomic findings are translated into actionable clinical insights.

Her work reflects a broader shift in oncology: moving from single-gene assumptions toward multigene, high-dimensional profiles that capture tumor heterogeneity. In practice, this requires careful interpretation frameworks, laboratory validation strategies, and continuous coordination between clinicians and molecular testing experts.

“I attended my first AMP meeting as a resident and was immediately smitten with the organization and the field of molecular diagnostics,” Tafe said, describing how the community shaped her professional path. She joined AMP in 2007 and later contributed nearly continuously through committees and elected roles, culminating in serving as AMP president in 2023.

Beyond organizational service, Tafe has contributed to AMP’s scientific output by reviewing papers for The Journal of Molecular Diagnostics since 2010. AMP leadership emphasizes not only governance, but also technical stewardship—helping ensure that education and standards evolve alongside emerging assays and analytical methods.

AMP Chief Executive Officer Laurie Menser, CAE, said Tafe’s leadership has strengthened educational programming, strategic initiatives, and the organization’s culture. Menser highlighted her commitment to mentoring future leaders as the field rapidly advances.

The Jeffrey A. Kant Leadership Award is named for the late Jeffrey A. Kant, M.D., Ph.D., one of AMP’s founding members, its first president, and the first recipient of the AMP Leadership Award. Tafe will receive the award and a medallion at the AMP 2026 Annual Meeting & Expo in November in Seattle.

Subject of Research: Molecular diagnostics leadership in oncology
Article Title: Laura J. Tafe Receives AMP 2026 Jeffrey A. Kant Leadership Award
Web References: https://www.amp.org
Image Credits: Courtesy of Laura J. Tafe, M.D
Keywords: molecular diagnostics, oncology, leadership award, tumor board, clinical genomics