Long-read sequencing provides larger pieces of the genomic puzzle, making it easier to assemble a complete picture of the genome. By combining long-read sequencing with pangenomes and artificial intelligence, researchers aim to significantly improve rare disease diagnostics.
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With the launch of the Human Genome Project in the 1990s, researchers set out to sequence the complete human genome. Although the project was successfully concluded in 2003, critical gaps remained in the sequence.
Scientists subsequently adopted whole genome sequencing techniques to reduce these blind spots, yet some gaps persisted. “In my teaching, I sometimes put the ‘W’ of ‘whole genome’ in brackets,” said Alexander Hoischen, a genomic technologies researcher at Radboud University. “The reality is that current genome sequencing still contains many holes.”
This challenge is particularly critical in diagnosing rare diseases, where researchers must look for variants outside of well-known disease-related genes. The lack of a single, comprehensive method to read the entire human genome forces patients to undergo a series of expensive tests. Yet, due to persistent gaps in sequencing, these tests still do not guarantee a definitive diagnosis.1
Alexander Hoischen, who researchers genomics technologies at Radboud University, employs long-read sequencing to diagnose rare diseases. Here, he is pictured next to some long-read sequencing machines.
© Radboud UMC
To address this gap, Hoischen and his team have spent the past few years exploring ways to supplement existing technologies, helping researchers interpret novel rare disease variants. They developed a long-read sequencing test with higher genome coverage, which successfully diagnosed more disease cases conclusively than conventional technologies.2
Building on these success, the researchers proposed a framework called near-perfect genome sequencing. This approach consolidates long-read sequencing with genome assembly from both chromosomes, more representative pangenome references, and AI-supported interpretation.3 This one-test paradigm has the potential to accelerate and improve rare disease diagnoses, transforming the genetic diagnostics landscape.
Short-Read Sequencing Gaps Limit Rare Disease Variant Discovery
Short-read sequencing is one of the most common methods in massively parallel sequencing, where researchers decode DNA broken down into small fragments of about 100 base pairs.4 Bioinformatic tools help reassemble these pieces, but the short reads may not map to the entirety of the reference genome. This leaves critical gaps and limits the technology’s application in identifying novel variants.
“Imagine the human genome being a jigsaw puzzle with a million pieces. The trouble with short reads is that they are tiny pieces,” explained Hoischen. Long-read sequencing, which involves reading fragments of up to 20,000 base pairs, helps overcome this challenge. “Long reads are just bigger pieces, so they are naturally much more intuitive and easier to work with,” said Hoischen. He added that this method also examines epigenetic modifications, which can turn genes on or off and cause diseases.
To test the feasibility of long-read genome sequencing, Hoischen and his colleagues compared its outcomes with those of conventional tests.3 Applying this sequencing to samples from more than 800 patients with rare diseases yielded a conclusive diagnosis in 160 cases. In contrast, standard-of-care diagnostic testing—which involved multiple tests—helped diagnose only 137 patients, highlighting the utility of long-read sequencing as a powerful, single-test approach for genetic diagnoses.
Integrating Pangenomics and AI to Achieve Rapid Genetic Answers
Despite this, Hoischen noted that long-read sequencing alone may not provide the complete picture. “The trouble with long reads only, with a single technology, is that the base pairs may not be long enough to fully piece the jigsaw puzzle together,” he said. While an additional sequencing method could offer a fuller picture, it would also significantly increase the cost of the diagnostic test.
A Single Molecule, Real-Time (SMRT) cell acts as the reaction chamber where millions of individual DNA molecules are sequenced simultaneously in real time.
© Radboud UMC
Therefore, Hoischen and his team proposed using long-read sequencing as the foundation, combined with diploid genome assembly to represent the individual genome. Coupling these technologies with pangenome data from diverse populations could help ensure equitable access to medical genomics, according to Hoischen.
While combining these methods can help researchers identify novel rare disease-associated variants, interpreting them remains a significant challenge. AI-based variant interpretation tools could help scale the process.5 “Even if we sequence the genome to perfection, we still need these AI tools and a few other concepts to make the interpretation near perfect as well,” explained Hoischen.
Looking ahead, Hoischen hopes that bioinformaticians and genomics researchers will join forces to adopt and improve near-perfect genome sequencing, which could significantly enhance rare disease diagnostics. “We would be able to help our doctors much more efficiently,” he said.
He added that methods like near-perfect genome sequencing could also bring unknown disease-causing variants into the spotlight, helping researchers identify more rare diseases. “We could help patients who deserve clearer answers much more rapidly and effectively. That is a massive hope.”
