HKU Develops ClairS to Enhance Cancer Mutation Detection Using Long-Read Sequencing
A research team at the University of Hong Kong, led by Professor Ruibang Luo, has developed ClairS, a deep-learning algorithm designed to improve the detection of cancer mutations using long-read sequencing. ClairS addresses the limitations of existing methods that often struggle with structurally complex regions of the human genome. By utilizing long-read sequencing, ClairS can reveal mutations that might otherwise be missed. The algorithm has been tested on various cancer cell lines, including breast, lung, and melanoma, demonstrating high accuracy in detecting small cancer mutations. ClairS has been integrated into Oxford Nanopore Technologies’ official somatic variant-calling workflow, marking a significant advancement in clinical genomics. 
