Decoding Cancer Screening: Oxford Nanopore vs. Illumina Sequencing

Why the Technology Behind Cancer Gene Panels Matters

Genetic screening for inherited cancer risk has become a cornerstone of modern precision medicine. By analyzing specific genes known to influence cancer susceptibility — such as BRCA1, BRCA2, TP53, PTEN, and the mismatch repair genes MLH1 and MSH2 — clinicians can identify individuals at high risk and guide preventive or targeted treatment strategies.

The American College of Medical Genetics and Genomics (ACMG) provides a list of such clinically actionable genes recommended for reporting.

Today, two main players dominate this space: Illumina (short-read sequencing) and Oxford Nanopore Technologies (ONT) (long-read sequencing). Both can reveal genetic variants that drive cancer, yet they differ profoundly in how they read DNA, how accurate they are, and what kinds of variants they can capture.

The Fundamental Difference: Short Reads vs. Long Reads

At its core, the distinction between Illumina and ONT lies in how they read DNA.

• Illumina sequencing cuts DNA into small fragments — usually around 150 base pairs long — and reads them one base at a time through a process called “sequencing by synthesis.” Each base emits a fluorescent signal that a camera detects. The result is a huge collection of short but highly accurate reads.

• Oxford Nanopore sequencing reads DNA in its natural, long form. A single DNA molecule passes through a microscopic protein pore, and as each nucleotide moves through, it changes the electrical current in a distinct way. Software then decodes this signal to determine the sequence.

• Illumina offers short but extremely precise reads.

• ONT offers long reads with more information, but each base call has a slightly higher chance of error.

Illumina: The Benchmark for Clinical Accuracy

Illumina sequencing is currently the gold standard in most diagnostic laboratories. Its per-base accuracy is exceptionally high — with an error rate below 0.1%. For single-nucleotide variants (SNVs) and small insertions or deletions (indels), Illumina’s reliability is unmatched.

Why Illumina is favored in clinical settings:

• It produces clean, consistent, and reproducible data.

• Variant-calling pipelines (like GATK, Mutect2, and DeepVariant) are well-validated and have established regulatory acceptance.

• Panels and exomes can reach very deep coverage (hundreds of reads per base), further reducing the chance of false positives.

For ACMG cancer panels, which largely screen for small variants in known genes, Illumina fits perfectly. However, it does have blind spots.

Because Illumina reads are short, they often struggle with complex genomic regions — such as those containing repetitive DNA, pseudogenes, or large structural rearrangements. For example, genes like PMS2 and CHEK2 have near-identical pseudogene copies, leading to ambiguous mapping. Similarly, structural changes like large deletions, duplications, or translocations can be fragmented across many short reads, making them difficult to detect.

Best for: SNVs and small indels

Limitations: Misses large structural variants and struggles in repetitive regions

Oxford Nanopore

Key advantages of ONT sequencing:

• Detects structural variants (SVs) directly — including large deletions, inversions, fusions, and complex rearrangements.

• Resolves repetitive and homologous regions where short reads misalign.

• Phases variants, meaning it can determine whether two mutations occur on the same chromosome or different ones — important in cancer predisposition analysis.

• Can also detect epigenetic modifications like DNA methylation, which influence gene expression and may serve as early cancer biomarkers.

In cancer research, where structural variation often plays a major role, ONT’s ability to “see” across these regions is invaluable.

However, ONT’s long-read strength comes with a trade-off. Its raw read accuracy (how correctly each base is called) is still slightly lower than Illumina’s. Most ONT errors involve insertions, deletions, or misreads in homopolymer regions (e.g., AAAAA). This can lead to false positives for small variants unless the data are carefully filtered and polished using advanced algorithms.

Recent tools — such as Medaka, Clair3, and PEPPER-Margin-DeepVariant — have improved accuracy substantially, and newer ONT chemistries now approach 99% base accuracy. Still, short reads remain more precise for small variant detection.

Best for: Structural variants, phasing, and methylation analysis

Limitations: Higher false-positive rate for SNVs unless filtered carefully

The False Positive Divide

In clinical sequencing, a false positive is a variant call that appears to be real but isn’t actually present in the DNA. Such errors can have serious consequences — from unnecessary follow-up tests to incorrect treatment recommendations.

When it comes to false positives, Illumina clearly has the advantage:

• Its short-read chemistry and mature bioinformatics pipelines produce extremely few false SNV calls.

• Most remaining errors arise from misalignment in repetitive regions, not sequencing mistakes.

ONT, while improving rapidly, still generates more false positives in small variant calling due to basecalling errors. These can be reduced by increasing sequencing depth, polishing reads, and requiring multiple read confirmations before accepting a variant.

Thus, for clinical cancer screening, where absolute accuracy matters more than completeness, Illumina remains the safer choice for SNV and small indel detection. ONT’s errors are not random, but systematic — meaning they can be modeled and filtered, yet still require confirmation by another method.

Comprehensiveness: The Long-Read Edge

While Illumina minimizes false positives, it can miss true positives when variants are large or complex. ONT, with its long reads, can directly detect:

• Large gene deletions or duplications

• Fusions and rearrangements

• Repeat expansions

• Methylation patterns

This makes ONT more comprehensive in terms of variant classes, even if each individual base call is slightly noisier. Many structural changes linked to cancer risk, such as copy number variations in BRCA1 or PTEN, may go undetected in short-read data but are obvious in long-read coverage maps.

In essence:

• Illumina → Fewer false positives, but may miss complex events (false negatives).

• ONT → Fewer false negatives for structural events, but requires careful filtering to reduce false positives.

Which Platform Gives Better Results?

The answer depends on the type of variant and the clinical question.

• For routine germline cancer screening (ACMG panels), Illumina provides unparalleled accuracy, maturity, and clinical acceptance.

• For research or extended diagnostic workflows, especially when structural variants or phasing are important, ONT offers insights that short reads cannot.

In practice, many leading centers now adopt a hybrid approach:

1. Use Illumina sequencing for accurate detection of SNVs and small indels.

2. Use ONT sequencing for structural variant detection, phasing, and methylation profiling.

3. Confirm clinically actionable variants by orthogonal validation — such as Sanger sequencing or digital PCR.

This hybrid strategy reduces false positives and increases overall sensitivity, giving clinicians a more complete genomic picture.

The Future: Convergence of Accuracy and Insight

The technological gap between Illumina and ONT is closing fast. ONT’s latest “Q20+” chemistry and improved basecallers have pushed accuracies above 99%, while Illumina continues to refine throughput and cost efficiency. As both evolve, we may soon see single workflows that combine the precision of Illumina with the comprehensiveness of ONT.

For now, however, the distinction remains clear:

• Illumina = Precision and reliability

• ONT = Depth and discovery

References:

1. Pei Y. et al. Genes (2024). “A Comparison of Structural Variant Calling from Short-Read and Nanopore-Based WGS Using Optical Genome Mapping as a Benchmark.”

2. Xu L. et al. Genome Research (2023). “Long-read sequencing identifies novel structural variations in colorectal cancer.”

3. Helal A.A. et al. (2022). “Evaluation of Variant Calling Tools for Oxford Nanopore Sequencing.”

4. Lemay M.A. et al. (2022). “Combined use of Oxford Nanopore and Illumina sequencing improves assemblies and variant calling.”

5. Stefan C.P. et al. (2022). “Comparison of Illumina and Oxford Nanopore sequencing data quality.”

6. ACMG Recommendations for Reporting Secondary Findings in Clinical Genomics, Version 3.2 (2023).