Guided by data: adaptive sampling for targeted sequencing

When Matt Loose and colleagues at the University of Nottingham, UK, set out to find a faster way to classify brain tumours, they didn’t start with a new microscope; they started with a new way of thinking about sequencing.

Their tool, ROBIN, uses adaptive sampling — a targeted Oxford Nanopore sequencing technique where you can decide, molecule by molecule, which DNA strands to keep reading and which to eject. The result was remarkable: during surgery, the team generated molecular data essential to classify central nervous system (CNS) tumours in as little as two hours¹. Typically, with standard of care testing, these results take days or even weeks to generate, arriving long after surgery has taken place. Usually, neurosurgeons must make the decision about how much of the tumour to remove without knowing the type of tumour they are removing.

This research demonstrates what the future of molecular diagnosis using adaptive sampling could look like: a world where surgical decisions happen in real time, being guided by data that arrives quickly enough to influence what happens next.

In this Nanopore Know-How blog, we will explore how scientists are using adaptive sampling to redefine targeted sequencing as something programmable and flexible rather than static.

If you want to learn more about what adaptive sampling is and how it’s different from traditional enrichment methods, which depend on pre-designed primers/probes or complex lab prep, read our introduction to adaptive sampling Nanopore Know-How blog first.

Real-time insight in cancer research

In cancer research, speed matters. The vision set out by ROBIN is being echoed elsewhere. Patel et al. developed Rapid-CNS², another workflow that uses adaptive sampling to characterise CNS tumours, and achieved methylation-based classification within 30 minutes².

The same drive for rapid, information-rich sequencing is shaping paediatric oncology. Geoffrion et al. showed that within the first day of sequencing, adaptive sampling could capture 357 genes relevant to childhood cancer, and detect potentially clinically meaningful genomic, structural, and epigenomic alterations — in one go — across multiple tumour types³.

Adaptive sampling also has the potential to help democratise access to diagnostic approaches, which is critical in resource-limited settings. Researchers using adaptive sampling to classify paediatric acute leukaemia in real time have demonstrated a reduced cost and turnaround time compared with the current standard of care⁴.

While these studies show how adaptive sampling can deliver answers fast, other researchers are using the same technology to explore why disease occurs in the first place.

In hereditary cancer research, teams have used adaptive sampling to enrich for key cancer predisposition genes, detecting both small variants and large rearrangements that were missed by conventional approaches, while also uncovering transposable elements associated with inherited risk^5–7. We have recently released the Oxford Nanopore Hereditary Cancer Panel, an end-to-end solution that targets 258 cancer predisposition genes and is ideal for this field of research.

From risk to individuality in human genomics

If understanding inherited cancer risk shows how genomic variation shapes susceptibility, then pharmacogenomics demonstrates how it shapes response. Building on the same principle of targeted, multidimensional sequencing, researchers are applying adaptive sampling to explore how genetic diversity influences the way people metabolise and tolerate medicines. This has the potential to support personalised medicine, enabling cost-effective and flexible pharmacogenomic testing without the need for fixed assays.

Scientists have enriched for large panels of pharmacogenes, capturing hundreds of variants across key drug metabolism and response pathways, and demonstrated accurate variant calling and haplotype reconstruction ^8,9. The pharmacogenomic (PGx) sequencing workflow with adaptive sampling from Oxford Nanopore delivers accurate enrichment of 375 PGx-associated genes, and is a ready-made solution for you to perform PGx variant calling.

Researchers are also applying adaptive sampling to understand how individual variation manifests in rare diseases. Genomics is already transforming rare disease diagnosis: by pinpointing the genetic cause of a condition, clinicians and families can move from uncertainty to answers, guiding treatment options, prognosis, and family planning. Yet, in many cases, existing tests fail to capture complex, repetitive, or epigenetically modified regions where these answers may lie.

Adaptive sampling enables research that is both comprehensive and personal, illuminating the unique genomic and epigenomic variants that drive rare and inherited disease. Crucially, it offers a faster and more flexible route to answers for those who need them most.

Teams such as Sebastian Lunke’s group at the Victorian Clinical Genetics Services, Australia, have demonstrated how adaptive sampling can confirm or clarify suspected complex variants in complicated diagnostic cases, showing ‘the potential to replace a whole range of otherwise quite cumbersome and expensive orthogonal diagnostic tests’¹⁰. By targeting specific genomic regions during sequencing, researchers can revisit samples with uncertain results and recover variants that other methods have missed.

At the family level, Fu et al. introduced Trio-barcoded ONT Adaptive Sampling (TBAS), sequencing research samples from parents and affected children together on a single PromethION Flow Cell¹¹. This method cut sequencing costs by more than half when compared with running each sample separately, and the genomic regions to be sequenced were tailored to each patient’s phenotype. The researchers identified potentially causative variants in 5/8 cases that were previously unsolved by Illumina short-read sequencing.

Reading RNA directly and selectively

Researchers can tailor transcriptomic studies with the same flexibility that adaptive sampling has brought to DNA, uncovering signals that would remain otherwise hidden. In a proof-of-concept study, Wang et al. combined direct RNA sequencing with adaptive sampling to deplete all currently annotated Candida albicans transcripts and detect those that were previously unknown, including novel antisense transcripts¹².

Oxford Nanopore sequencing is currently the only technology that can read RNA molecules directly, preserving base modifications and providing a complete picture of how genes are expressed. When paired with adaptive sampling, researchers can now use this unique capability to target specific transcripts or deplete abundant ones dynamically, just like Wang et al. did. They can direct sequencing effort toward what matters most, even if it’s present in trace amounts.

Targeting what matters in microbial and metagenomic research

In complex or host-dominated metagenomic samples, researchers can use adaptive sampling to direct sequencing capacity toward low-abundance organisms, antibiotic-resistance genes, or entire pathogen genomes. By focusing sequencing, researchers can uncover the significant targets, helping to advance microbial genomics for global health impact.

Whole-genome sequencing of pathogens is an important tool for surveillance of infectious diseases; however, it can be challenging to isolate them from human DNA in real-world samples. De Meulenaere et al. and Hewel et al. used adaptive sampling to sequence the genomes of Plasmodium parasites and mpox virus, respectively, directly from patient research samples^13,14. The teams achieved complete or near-complete genome recovery that could address common infectious disease research questions.

Wrenn and Drown used adaptive sampling to target antimicrobial resistance (AMR) genes in a mock community as a proof-of-concept study for the surveillance of AMR in environmental microbial communities, which can act as AMR reservoirs. They found that adaptive sampling can enrich for AMR genes and suggested that such an approach could better inform preventative public health action¹⁵.

Plasmids also play a key role in the spread of AMR genes and Ulrich et al. used adaptive sampling to significantly enrich for low-abundance plasmids in known bacterial isolates, improving the quality of de novo plasmid assemblies and reducing sequencing time¹⁶.

Completing the picture

Adaptive sampling can do more than enrich targets; it can actively complete genomes, bringing long-standing assembly projects to a true finish. In plant genomics, adaptive sampling has been combined with ultra-long reads to close the final gaps in genomes, delivering complete telomere-to-telomere (T2T) assemblies for species such as Arabidopsis¹⁷ .

At the same time, researchers have developed bioinformatics strategies such as Cornetto to apply the same principle more broadly, dynamically directing sequencing effort to unsolved regions of a nascent assembly and producing highly complete genomes across human and non-human species¹⁸.

Guided by data

Adaptive sampling is redefining how scientists think about sequencing. It makes targeted sequencing programmable and flexible — a matter of editing a file, not redesigning a panel — and lets you decide which questions matter most.

Across species and disciplines, from tumour classification to plant genome assembly, the same idea holds: adaptive sampling delivers fast, clear, and comprehensive answers. Because sometimes, the most powerful discoveries come not from reading everything, but from reading with purpose.

See how you can get started with adaptive sampling

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