{"id":33701,"date":"2026-01-27T22:08:17","date_gmt":"2026-01-27T22:08:17","guid":{"rendered":"https:\/\/www.tun.com\/home\/?p=33701"},"modified":"2026-01-27T22:08:40","modified_gmt":"2026-01-27T22:08:40","slug":"new-hku-ai-tools-boost-cancer-mutation-detection-and-rna-research","status":"publish","type":"post","link":"https:\/\/www.tun.com\/home\/new-hku-ai-tools-boost-cancer-mutation-detection-and-rna-research\/","title":{"rendered":"New HKU AI Tools Boost Cancer Mutation Detection and RNA Research"},"content":{"rendered":"\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-uagb-blockquote uagb-block-e7eb3fc3 uagb-blockquote__skin-border uagb-blockquote__stack-img-none\"><blockquote class=\"uagb-blockquote\"><div class=\"uagb-blockquote__content\">Engineers at the University of Hong Kong have built two deep-learning tools that make it easier to spot cancer-linked mutations and decode RNA. The open-source algorithms could expand access to precision medicine and speed genomic discovery.<\/div><footer><div class=\"uagb-blockquote__author-wrap uagb-blockquote__author-at-left\"><\/div><\/footer><\/blockquote><\/div>\n\n\n\n<div class=\"wp-block-group is-content-justification-space-between is-nowrap is-layout-flex wp-container-core-group-is-layout-0dfbf163 wp-block-group-is-layout-flex\"><div style=\"font-size:16px;\" class=\"has-text-align-left wp-block-post-author\"><div class=\"wp-block-post-author__content\"><p class=\"wp-block-post-author__name\">The University Network<\/p><\/div><\/div>\n\n\n<div class=\"wp-block-uagb-social-share uagb-social-share__outer-wrap uagb-social-share__layout-horizontal uagb-block-ee584a31\">\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-ec619ce7\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/www.facebook.com\/sharer.php?u=\" tabindex=\"0\" role=\"button\" aria-label=\"facebook\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 512 512\"><path d=\"M504 256C504 119 393 8 256 8S8 119 8 256c0 123.8 90.69 226.4 209.3 245V327.7h-63V256h63v-54.64c0-62.15 37-96.48 93.67-96.48 27.14 0 55.52 4.84 55.52 4.84v61h-31.28c-30.8 0-40.41 19.12-40.41 38.73V256h68.78l-11 71.69h-57.78V501C413.3 482.4 504 379.8 504 256z\"><\/path><\/svg><\/span><\/span><\/span><\/div>\n\n\n\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-32d99934\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/twitter.com\/share?url=\" tabindex=\"0\" role=\"button\" aria-label=\"twitter\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 512 512\"><path d=\"M389.2 48h70.6L305.6 224.2 487 464H345L233.7 318.6 106.5 464H35.8L200.7 275.5 26.8 48H172.4L272.9 180.9 389.2 48zM364.4 421.8h39.1L151.1 88h-42L364.4 421.8z\"><\/path><\/svg><\/span><\/span><\/span><\/div>\n\n\n\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-1d136f14\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/www.linkedin.com\/shareArticle?url=\" tabindex=\"0\" role=\"button\" aria-label=\"linkedin\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 448 512\"><path d=\"M416 32H31.9C14.3 32 0 46.5 0 64.3v383.4C0 465.5 14.3 480 31.9 480H416c17.6 0 32-14.5 32-32.3V64.3c0-17.8-14.4-32.3-32-32.3zM135.4 416H69V202.2h66.5V416zm-33.2-243c-21.3 0-38.5-17.3-38.5-38.5S80.9 96 102.2 96c21.2 0 38.5 17.3 38.5 38.5 0 21.3-17.2 38.5-38.5 38.5zm282.1 243h-66.4V312c0-24.8-.5-56.7-34.5-56.7-34.6 0-39.9 27-39.9 54.9V416h-66.4V202.2h63.7v29.2h.9c8.9-16.8 30.6-34.5 62.9-34.5 67.2 0 79.7 44.3 79.7 101.9V416z\"><\/path><\/svg><\/span><\/span><\/span><\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n<p>Two new artificial intelligence tools from The University of Hong Kong promise to make it easier, faster and cheaper to detect genetic mutations tied to cancer and to decode the RNA messages that keep our cells running.<\/p>\n\n\n\n<p>Researchers in HKU\u2019s Faculty of Engineering have developed ClairS-TO and Clair3-RNA, a pair of deep-learning algorithms that sharpen the analysis of long-read DNA and RNA sequencing data. The tools are designed to tackle long-standing bottlenecks in cancer diagnostics and RNA-based genomic research, and both studies &#8212; <a href=\"https:\/\/www.nature.com\/articles\/s41467-025-64547-z\" target=\"_blank\" rel=\"noopener\" title=\"\">ClairS-TO<\/a> and <a href=\"https:\/\/www.nature.com\/articles\/s41467-025-67237-y\" target=\"_blank\" rel=\"noopener\" title=\"\">Clair3-RNA<\/a> &#8212; are published in Nature Communications.<\/p>\n\n\n\n<p>The work is led by Ruibang Luo, an associate professor in HKU\u2019s School of Computing and Data Science, whose lab focuses on bioinformatics algorithms and clinical informatics. Luo\u2019s team has spent years building the Clair series, a family of AI\u2013driven genomic tools that has become widely used in the field.<\/p>\n\n\n\n<p>The latest additions push that effort further, according to Luo.<\/p>\n\n\n\n<p>\u201cClairS-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,\u201d Luo said in a news release.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Why long-read sequencing matters<\/h3>\n\n\n\n<p>Genetic sequencing technologies read the order of DNA or RNA letters in our cells. Long-read sequencing, a newer generation of this technology, can capture continuous stretches of genetic material, revealing complex regions and structural changes that short-read methods can miss.<\/p>\n\n\n\n<p>Those long reads are especially valuable in cancer, where tumors often carry a tangled mix of mutations, and in RNA studies, which probe how genes are turned on and off. But the same richness that makes long-read data powerful also makes it hard to interpret. Distinguishing real mutations from technical errors or natural RNA editing has been a major challenge.<\/p>\n\n\n\n<p>The HKU team built ClairS-TO and Clair3-RNA to address those pain points.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">A new way to read tumor DNA without normal tissue<\/h3>\n\n\n\n<p>ClairS-TO is aimed squarely at cancer diagnostics. Traditionally, labs compare DNA from a patient\u2019s tumor with DNA from their healthy tissue to identify somatic mutations \u2014 changes that arise in the tumor but are not present in the rest of the body. That \u201ctumor-normal\u201d pairing helps filter out harmless inherited variants.<\/p>\n\n\n\n<p>In practice, though, a matched normal sample is not always available. It may be too costly or invasive to collect, or the patient\u2019s healthy tissue may not have been stored. That can limit access to high-quality genomic testing, particularly in resource-constrained settings.<\/p>\n\n\n\n<p>ClairS-TO is designed to work with tumor-only samples. It uses a dual-network deep-learning architecture: one network focuses on confirming genuine mutations, while a second network is trained to reject sequencing errors and other noise. By learning patterns in long-read tumor data, the system can infer which changes are likely to be true somatic variants even without a normal sample for comparison.<\/p>\n\n\n\n<p>This approach can make tumor DNA analysis more cost-effective and practical when sample material is limited. In clinical settings, that could mean more patients can receive detailed genomic profiling of their cancers, which in turn can guide targeted therapies and clinical trial enrollment.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">First deep-learning variant caller for long-read RNA<\/h3>\n\n\n\n<p>While ClairS-TO tackles DNA in tumors, Clair3-RNA focuses on RNA, the molecule that carries genetic instructions from DNA to the cell\u2019s protein-making machinery.<\/p>\n\n\n\n<p>RNA sequencing reveals which genes are active, in what forms, and at what levels. Long-read RNA sequencing goes a step further by capturing full-length transcripts, making it easier to see how exons are pieced together and to detect rare or complex isoforms.<\/p>\n\n\n\n<p>However, RNA comes with its own complications. Cells naturally edit some RNA molecules, and sequencing technologies can introduce errors. Both can masquerade as mutations, making it difficult to pinpoint true genetic variants.<\/p>\n\n\n\n<p>Clair3-RNA is described as the world\u2019s first deep-learning-based small variant caller built specifically for long-read RNA sequencing. It uses advanced neural network models to distinguish real mutations from biological noise and RNA editing events. That allows researchers and clinicians to analyze gene expression and genetic variants at the same time, with higher confidence.<\/p>\n\n\n\n<p>In practical terms, Clair3-RNA could help scientists study how mutations affect RNA processing, identify disease-associated variants directly from RNA, and better understand how gene activity changes in conditions such as cancer, neurological disorders and immune diseases.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Building on a widely used AI toolkit<\/h3>\n\n\n\n<p>ClairS-TO and Clair3-RNA extend the existing Clair series, which already includes Clair3, an industry-standard tool for long-read variant calling. The Clair algorithms are known for their speed, accuracy and robustness, and they are released as open-source software.<\/p>\n\n\n\n<p>According to HKU, the Clair tools have been downloaded more than 400,000 times and are widely adopted by leading research institutes and sequencing companies around the world. That broad uptake means new capabilities can spread quickly into both research and clinical pipelines.<\/p>\n\n\n\n<p>For students and early-career scientists, the Clair series also offers a hands-on example of how computer science and engineering can directly impact medicine. Deep-learning models, once associated mainly with image recognition or language processing, are now central to how researchers read and interpret the genome.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What comes next<\/h3>\n\n\n\n<p>The HKU team\u2019s latest work highlights how AI can make cutting-edge genomics more accessible. By reducing the need for matched normal samples and by taming the complexity of RNA data, ClairS-TO and Clair3-RNA could lower barriers for hospitals and labs that want to adopt long-read sequencing.<\/p>\n\n\n\n<p>Future directions are likely to include further training on diverse patient populations, integration with clinical reporting systems, and expansion to other types of genomic variation. As long-read sequencing technologies continue to improve and drop in cost, tools like these will be critical for turning raw data into actionable insights.<\/p>\n\n\n\n<p>For patients, the long-term promise is more accurate cancer diagnoses and more personalized treatment plans. For researchers, it is a clearer view of how DNA and RNA changes shape health and disease \u2014 and a faster path from genomic discovery to real-world impact.<\/p>\n\n\n\n<div style=\"height:13px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p class=\"has-text-align-left\"><strong>Source:<\/strong> <a href=\"https:\/\/www.hku.hk\/press\/press-releases\/detail\/28903.html\" target=\"_blank\" rel=\"noopener\" title=\"\">The City University of Hong Kong<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Engineers at the University of Hong Kong have built two deep-learning tools that make it easier to spot cancer-linked mutations and decode RNA. The open-source algorithms could expand access to precision medicine and speed genomic discovery.<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"single-no-separators","format":"standard","meta":{"_acf_changed":false,"_uag_custom_page_level_css":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[8],"tags":[531],"class_list":["post-33701","post","type-post","status-publish","format-standard","hentry","category-ai","tag-university-of-hong-kong"],"acf":[],"aioseo_notices":[],"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"1536x1536":false,"2048x2048":false},"uagb_author_info":{"display_name":"The University Network","author_link":"https:\/\/www.tun.com\/home\/author\/funky_junkie\/"},"uagb_comment_info":0,"uagb_excerpt":"Engineers at the University of Hong Kong have built two deep-learning tools that make it easier to spot cancer-linked mutations and decode RNA. The open-source algorithms could expand access to precision medicine and speed genomic discovery.","_links":{"self":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts\/33701","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/comments?post=33701"}],"version-history":[{"count":7,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts\/33701\/revisions"}],"predecessor-version":[{"id":33728,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts\/33701\/revisions\/33728"}],"wp:attachment":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/media?parent=33701"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/categories?post=33701"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/tags?post=33701"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}