Researchers from HKU Engineering Develop Deep-learning Tools to Transform Cancer Diagnosis and Genomic Research
Researchers from the Faculty of Engineering at The University of Hong Kong (HKU) have developed two innovative deep-learning algorithms, ClairS-TO and Clair3-RNA, that significantly advance genetic mutation detection in cancer diagnostics and RNA-based genomic studies.
The pioneering research team, led by Professor Ruibang Luo from the School of Computing and Data Science, Faculty of Engineering, has unveiled two groundbreaking deep-learning algorithms—ClairS-TO and Clair3-RNA—set to revolutionise genetic analysis in both clinical and research settings. Leveraging long-read sequencing technologies, these tools significantly improve the accuracy of detecting genetic mutations in complex samples, opening new horizons for precision medicine and genomic discovery. Both research articles have been published in Nature Communications.
Long-read sequencing technologies capture continuous stretches of DNA and RNA, providing detailed insights into genetic information. However, interpreting this data, especially identifying mutations in challenging conditions, has remained a hurdle. The two new algorithms aim to overcome these obstacles, making genomic analysis faster, more accurate, and more accessible.
ClairS-TO addresses a critical challenge in cancer diagnostics: analysing tumour DNA without needing matched healthy tissue samples. Standard methods require both tumour and normal samples for comparison, which are not always available. Using a sophisticated dual-network approach—one to confirm genuine mutations and another to reject errors— ClairS-TO eliminates this requirement. This breakthrough allows for cost-effective, reliable tumour analysis even when sample material is limited, broadening access to precise cancer diagnostics.
Meanwhile, Clair3-RNA marks the world's first deep-learning-based small variant caller specifically tailored for long-read RNA sequencing. RNA editing and technical sequencing errors can easily confuse the identification of true genetic variants. Clair3-RNA employs advanced deep learning techniques to accurately distinguish real mutations from biological noise and editing, enabling researchers and clinicians to simultaneously analyse gene expression and mutations with exceptional accuracy.
These algorithms are the latest additions to the renowned Clair series, a suite of artificial intelligence (AI)-driven genomic tools developed by Professor Luo's team. The series, including the industry-standard Clair3, has become a cornerstone in the field of computational biology. Known for their speed, accuracy, and robustness, these open-source algorithms have amassed over 400,000 downloads. They are widely adopted by leading research institutes and sequencing companies globally, setting the benchmark for processing third-generation sequencing data.
Professor Ruibang Luo commented, "ClairS-TO and Clair3-RNA, along with other algorithms in the Clair series, have established a solid foundation for deep-learning-driven genetic mutation discovery, and accelerated the adoption of precision medicine and clinical genomics."
These advances represent a significant leap toward more accessible, accurate, and comprehensive genetic analysis. They hold the potential to improve cancer diagnosis, enable personalised medicine, and accelerate genomic research—delivering tangible benefits to patients and scientists around the world.
Link to papers:
"ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling"
https://www.nature.com/articles/s41467-025-64547-z
"Clair3-RNA: a deep learning-based small variant caller for long-read RNA sequencing data"
https://www.nature.com/articles/s41467-025-67237-y.
About Professor Ruibang Luo
Professor Ruibang Luo is an Associate Professor of the School of Computing and Data Science at the University of Hong Kong. He completed his PhD training in Bioinformatics with Professor Tak-Wah Lam at the University of Hong Kong (2010-2015), and his postdoctoral training with Professor Steven Salzberg and Professor Michael Schatz at the Center of Computational Biology, Johns Hopkins University (2016-2017).
Luo is a researcher working on bioinformatics algorithms and clinical informatics. He published more than 80 papers, with ten achieving over a thousand citations. He has been identified as Top 1% Scholars Worldwide by Clarivate Analytics since 2019, selected by Baidu Research as Worldwide Top 150 Chinese Young Scholars in AI, named Top 10 Innovators Under 35 Asia Pacific by MIT Technology Review in 2019, and recognised as 30 Under 30 Asia in Healthcare and Science by Forbes in 2017.
