Published in Cell Genomics, the study was led by Peter Doris, PhD, director of the Center for Human Genetics at The Brown Foundation Institute of Molecular Medicine within McGovern Medical School at UTHealth Houston. The researchers used advanced long-read DNA sequencing to construct a telomere-to-telomere assembly of the brown rat genome. Unlike earlier genome drafts, which contained gaps and unresolved repetitive regions, the new assembly provides continuous sequences extending from one telomere—the protective DNA structure at a chromosome’s end—to the other.

The completed genome revealed that the rat’s genetic architecture is considerably more complex than previously recognized. The team identified more than 60 genes that had not been accurately captured in earlier reference genomes. Many of these genes lie in regions that are difficult to sequence because they contain repeated or nearly identical DNA segments. Some appear to be involved in immunity and other biological processes, raising the possibility that missing genetic information has contributed to incomplete or misleading interpretations of rat-based disease research.

One of the most surprising discoveries involved the rat’s X and Y chromosomes. In humans and most other mammals, these chromosomes contain a shared segment called the pseudoautosomal region, or PAR. The PAR contains genes present on both the X and Y chromosomes, allowing the two chromosomes to pair during the formation of reproductive cells and to replicate correctly. Although the X and Y chromosomes differ substantially, this shared region acts as a genetic bridge between them.

The researchers found that the brown rat has lost these PAR genes from its sex chromosomes. Instead, the genes have moved to ordinary, non-sex chromosomes. The team also identified newly organized DNA sequences that appear to allow the rat’s X and Y chromosomes to pair in a head-to-tail configuration, rather than the head-to-head arrangement seen in most other mammals. This finding suggests that the mechanics of rat reproduction have evolved along a distinct genetic pathway, despite the animal’s close relevance to human biology.

“Sexual reproduction in the rat can take place, but it’s not taking place in exactly the same way that it is in humans,” Doris said. The unusual chromosome structure would have been difficult to detect without a highly accurate genome assembly, because incomplete reference sequences can obscure rearrangements and make genes appear to be missing, misplaced, or incorrectly duplicated.

The new work also addresses a long-standing problem in genetic disease research. Researchers often compare the genomes of laboratory rats with those of other strains or with disease-associated genetic regions, but missing segments can make it difficult to determine which DNA differences are biologically meaningful. Gene duplications are especially challenging: when two copies are nearly identical, conventional sequencing methods may collapse them into a single sequence. Yet duplicated genes can acquire different functions, allowing one copy to retain an original role while the other becomes specialized.

To capture this diversity, the team assembled eight reference-quality genomes from different brown rat strains. These assemblies were combined into a pangenome—a comprehensive genetic resource that represents variation across multiple individuals rather than treating one genome as the definitive standard. The rat pangenome contains approximately 7% more sequence than the previously available reference genome, revealing genetic regions that would otherwise remain invisible. Scientists can now examine a gene across several rat strains and determine whether its sequence, copy number, or biological function varies between animals.

Such variation may have direct implications for laboratory studies. A gene involved in digestion, for example, may have been duplicated in some rats, with one copy retaining a digestive function while the other becomes involved in immune activity. If researchers use different strains without accounting for these differences, they may obtain conflicting results or fail to reproduce findings. The pangenome provides a framework for identifying these differences before they influence an experiment, potentially improving the reliability of studies that use rats to investigate human disease.

The rat genome consists of 22 chromosome pairs, and the new assembly describes each chromosome in an unbroken sequence. By filling the gaps in the genetic “map,” the study gives researchers a more precise way to locate disease-associated variants, study chromosome evolution, and compare rat biology with human biology. The resource is expected to support future research into cardiovascular and metabolic disease, kidney function, inflammation, immunity, and neurological disorders. Alongside Doris, the study included Yaming Zhu of UTHealth Houston and collaborators from the University of Kentucky, the National Institutes of Health, the University of Louisville, and The Jackson Laboratory.

Subject of Research: Genetic sequencing, genome assembly, pangenomics, chromosome biology, and biomedical rat research

Article Title: Telomere-to-telomere genome assembly and a pangenome for the rat

News Publication Date: 6-Aug-2026

Web References: https://www.cell.com/cell-genomics/fulltext/S2666-979X(26)00143-6

References: Cell Genomics, “Telomere-to-telomere genome assembly and a pangenome for the rat”

Image Credits: Photo by UTHealth Houston; Peter Doris, PhD, director of the Center for Human Genetics at The Brown Foundation Institute of Molecular Medicine within McGovern Medical School at UTHealth Houston.

Keywords: Brown rat, rat genome, telomere-to-telomere assembly, pangenome, genomics, genetics, sex chromosomes, pseudoautosomal region, gene duplication, disease research, biomedical research, long-read sequencing

Cancer mutations are alterations in the DNA of tumour cells that are absent from healthy tissue. Finding these changes accurately is essential for understanding how cancers develop, tracking disease progression and selecting treatments tailored to individual patients. Yet the task is technically demanding. Tumour samples often contain a mixture of cancerous and normal cells, while mutations can occur in repetitive or structurally complex sections of the genome that are difficult to reconstruct from short fragments of DNA.

Most existing somatic-variant callers—the software tools used to distinguish tumour mutations from inherited genetic differences—were created primarily for short-read sequencing. Short-read platforms produce large numbers of highly accurate fragments, but each fragment covers only a small portion of the genome. Long-read sequencing, by contrast, generates much longer DNA molecules that can span repetitive sequences, structural rearrangements and other difficult regions. This broader view can reveal genomic changes that would otherwise remain hidden, although it also creates new computational challenges.

ClairS tackles these challenges with a neural-network architecture trained to interpret the complex signals produced by long-read tumour-normal sequencing. The system compares DNA data from a tumour with a matched normal sample and searches for small somatic variants, including single-nucleotide changes and short insertions or deletions. Rather than relying only on fixed rules, the model learns patterns associated with genuine tumour mutations, sequencing errors and differences caused by the proportion of cancer cells present in a sample.

One of the most innovative aspects of ClairS is the way its developers generated training data. High-quality tumour-normal datasets are scarce, expensive to produce and difficult to obtain in sufficient quantities. To overcome this limitation, the researchers mixed sequencing data from normal human samples to create synthetic tumour-normal pairs. The process allowed them to simulate a wide range of biological and technical conditions, including different tumour purities, sequencing depths and mutation burdens.

This synthetic-data strategy gives the model access to an effectively unlimited supply of realistic training examples. Tumour purity is particularly important because a mutation may appear in only a small fraction of the DNA molecules analysed. If the cancer cells represent a minor component of a biopsy, the signal from a true mutation can be overwhelmed by normal DNA. By exposing ClairS to simulated samples with varying levels of tumour purity, the researchers trained it to recognise weak but meaningful mutation signals under conditions that resemble real clinical specimens.

The team evaluated ClairS using datasets from several cancer types, including breast cancer, lung cancer, melanoma and pancreatic cancer cell lines. Across different sequencing conditions, the algorithm showed high accuracy in detecting small somatic mutations. Its performance was particularly important in regions where long reads provide an advantage, because the extended DNA fragments can preserve the genomic context needed to distinguish a true mutation from a technical artefact.

Unlike many experimental algorithms that remain confined to academic demonstrations, ClairS has already been incorporated into the official somatic-variant-calling workflow of Oxford Nanopore Technologies. The integration places the method inside a practical commercial analysis pipeline and could speed its adoption by researchers and clinical genomics laboratories. Although further validation will be needed before any tool is used routinely for patient diagnosis or treatment decisions, the development represents a significant step toward making long-read cancer analysis more accessible.

“Long-read sequencing is transforming how we study cancer genomes, especially in regions that were previously difficult to analyse,” said Professor Ruibang Luo, the study’s senior researcher and an Associate Professor at HKU’s School of Computing and Data Science. “ClairS makes it possible to train powerful AI models even when real cancer training data is limited, supporting more reliable cancer mutation discovery from long-read sequencing data.”

The work also illustrates a broader shift in biomedical AI. Many medical algorithms are limited not by a lack of computational power, but by the shortage of accurately labelled clinical data. ClairS demonstrates how carefully designed simulations can provide a practical bridge between limited real-world samples and the enormous diversity of conditions encountered in biology. By combining long-read sequencing with deep learning and scalable synthetic-data generation, the method could help researchers build more complete cancer genomes, uncover mutations missed by traditional approaches and advance the development of precision oncology. The study, published in Nature Methods, is open source, allowing the wider genomics community to inspect, reproduce and further develop the technology.

Subject of Research: Computational simulation/modeling

Article Title: ClairS: a deep-learning method for long-read tumor–normal pair somatic small variant calling

News Publication Date: 1 July 2026

Web References: https://github.com/HKU-BAL/ClairS

References: Nature Methods. DOI: 10.1038/s41592-026-03152-4

Image Credits: The University of Hong Kong

Keywords: ClairS, cancer mutations, long-read sequencing, deep learning, artificial intelligence, somatic variant calling, tumour genomics, precision medicine, bioinformatics, Oxford Nanopore Technologies