{"id":5518,"date":"2026-07-30T09:13:50","date_gmt":"2026-07-30T13:13:50","guid":{"rendered":"https:\/\/med.virginia.edu\/genome-sciences\/?p=5518"},"modified":"2026-08-17T18:43:19","modified_gmt":"2026-08-17T22:43:19","slug":"genome-sciences-zang-lab-creates-machine-learning-tool-to-improve-accuracy-of-genomics-research","status":"publish","type":"post","link":"https:\/\/med.virginia.edu\/genome-sciences\/2026\/07\/30\/genome-sciences-zang-lab-creates-machine-learning-tool-to-improve-accuracy-of-genomics-research\/","title":{"rendered":"Genome Sciences&#8217; Zang Lab Creates Machine-Learning Tool To Improve Accuracy of Genomics Research"},"content":{"rendered":"<section class=\"container-parent w-full\">\n<div class=\"mx-4 @lg:mx-6\">\n<div class=\"mx-auto w-full max-w-screen-2xl\">\n<div class=\"mx-auto w-full max-w-article rounded-lg overflow-clip bg-uvah-neutral-25\">\n<figure><img decoding=\"async\" class=\"f-image aspect-ratio-[3\/2]\" src=\"https:\/\/cdn-assets-dynamic.frontify.com\/8000198\/eyJhc3NldF9pZCI6Mzc4NDksInNjb3BlIjoiYXNzZXQ6dmlldyJ9:uva-health:Qi_67PNjY1C5syHtdZBVf8nTeGqk65YvzYulOk6btLs\" alt=\"Portrait of Chongzhi Zang\" \/><figcaption class=\"uva-p1 text-primary p-4 tablet:p-6\">\n<p class=\"uva-p1 text-secondary \">Chongzhi Zang, PhD,\u00a0and colleagues have created a free tool that will help scientists avoid false leads in genomic research.<\/p>\n<\/figcaption><\/figure>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n<section class=\"container-parent w-full\">\n<div class=\"mx-auto w-full max-w-screen-2xl\">\n<div class=\"mx-4 @tablet:mx-14 @lg:!mx-22\">\n<div>\n<p class=\"uva-p1 text-secondary mb-6 max-w-article mx-auto\"><strong>Department of Genome Sciences<\/strong> scientists in <strong>Chongzhi Zang<\/strong>&#8216;s lab have identified a widespread source of error in a popular method for studying the genome \u2013 our genetic instructions \u2013 and created a machine-learning tool to correct it.<\/p>\n<p class=\"uva-p1 text-secondary mb-6 max-w-article mx-auto\">The free tool could improve the reliability of both conventional and single-cell data generated using this method, giving researchers a clearer view of how gene activity is controlled in health and disease and providing stronger foundations for future diagnostic and drug-development efforts.<\/p>\n<p class=\"uva-p1 text-secondary mb-6 max-w-article mx-auto\">Although nearly every cell in the body contains the same DNA sequence, different cells use different sets of genes to maintain their identities and functions. Much of that control comes from the \u201cepigenome\u201d\u00a0\u2013\u00a0chemical modifications and structural features of chromosomes that influence whether genes are turned on or off without changing the DNA sequence itself. The CUT&amp;Tag (Cleavage Under Targets &amp; Tagmentation) method can map these epigenomic features efficiently from very small samples and even from individual cells. However, the UVA researchers, led by <strong>Chongzhi Zang, PhD<\/strong>, found a \u201chidden bias\u201d in the method that creates artifacts resembling genuine biological signals.<\/p>\n<p class=\"uva-p1 text-secondary mb-6 max-w-article mx-auto\">\u201cThe DNA sequence is like the sheet music. The epigenome determines which notes to play, when they are played and by what instruments,\u201d said <strong>Zang<\/strong>, of <strong>UVA\u2019s Department of Genome Sciences<\/strong> and director of computational genomics at UVA Comprehensive Cancer Center. \u201cWhen a technical artifact looks like a real signal, researchers can be led toward the wrong biological mechanism. That risk is especially serious in single-cell data, where the true signals are already sparse.\u201d<\/p>\n<h2 class=\"uva-h2 text-primary mb-6 max-w-article mx-auto\">Finding and Fixing Hidden Bias<\/h2>\n<p class=\"uva-p1 text-secondary mb-6 max-w-article mx-auto\">CUT&amp;Tag has many advantages over prior methods and has quickly become popular among scientists. But it also has weaknesses that have gone unnoticed until recently. The assay relies on an enzyme called Tn5 transposase, which naturally favors open, accessible regions of the genome. <strong>Zang<\/strong> and colleagues found that this preference can create misleading and confusing research results that the scientists described as \u201csevere\u201d after examining nearly 300 published datasets.<\/p>\n<p class=\"uva-p1 text-secondary mb-6 max-w-article mx-auto\">Having discovered the unexpected scope of the problem, <strong>Zang<\/strong> and his team developed PATTY (Propensity Analyzer for Tn5 Transposase Yielded bias), a tool that uses machine learning to correct the CUT&amp;Tag bias. It is effective both\u00a0in conventional data from\u00a0many cells and in sparser single-cell data, the scientists report.<\/p>\n<p class=\"uva-p1 text-secondary mb-6 max-w-article mx-auto\">\u201cDetecting true signals in noisy data is like finding a needle in a haystack, and it is even more difficult when many pieces of hay look like real needles,\u201d <strong>Zang<\/strong> said. \u201cPATTY does not detect signals simply by subtracting a background. It learns how a real needle differs from hay and uses the learned model to reduce the artifact while preserving real signals.\u201d<\/p>\n<p class=\"uva-p1 text-secondary mb-6 max-w-article mx-auto\"><strong>Zang<\/strong> and his colleagues have made PATTY available as a free, open-source software package at Github (<a class=\"uva-a \" href=\"https:\/\/github.com\/zang-lab\/PATTY?utm_campaign=mim&amp;utm_source=sfmc&amp;utm_medium=email&amp;utm_content=text\" data-mhc-done=\"1\">https:\/\/github.com\/zang-lab\/PATTY<\/a>) and Zenodo (<a class=\"uva-a \" href=\"https:\/\/doi.org\/10.5281\/zenodo.20078737?utm_campaign=mim&amp;utm_source=sfmc&amp;utm_medium=email&amp;utm_content=text\" data-mhc-done=\"1\">https:\/\/doi.org\/10.5281\/zenodo.20078737<\/a>). They hope their tool will improve genomic research and eventually help get new diagnostics and medicines to patients faster, a major mission of UVA\u2019s new Paul and Diane Manning Institute of Biotechnology.<\/p>\n<p class=\"uva-p1 text-secondary mb-6 max-w-article mx-auto\">\u201cWe believe that bias correction should become a routine part of CUT&amp;Tag analysis,\u201d <strong>Zang<\/strong> said. \u201cCleaner data can keep scientists from wasting time and resources pursuing technical artifacts and can make real biological differences easier to see. More broadly, PATTY provides a conceptual framework for correcting similar biases in other genomic technologies.\u201d<\/p>\n<h2 class=\"uva-h2 text-primary mb-6 max-w-article mx-auto\">Findings Published<\/h2>\n<p class=\"uva-p1 text-secondary mb-6 max-w-article mx-auto\">The researchers have described the development of PATTY in an\u00a0<a class=\"uva-a \" href=\"https:\/\/doi.org\/10.1038\/s41467-026-73599-8?utm_campaign=mim&amp;utm_source=sfmc&amp;utm_medium=email&amp;utm_content=text\" data-mhc-done=\"1\">article in the scientific journal Nature Communications<\/a>. The article is open access, meaning it is free to read. The UVA research team consisted of <strong>Sheng&#8217;en Shawn Hu<\/strong>, <strong>Qingying Chen<\/strong>, <strong>Megan C. Grieco<\/strong>, <strong>Mengxue Tian<\/strong> and <strong>Zang<\/strong>. Collaborators Zhangli Su and Anindya Dutta of the University of Alabama at Birmingham and Lin Liu of Shanghai Jiao Tong University also contributed. The scientists have no financial interest in the work.<\/p>\n<p class=\"uva-p1 text-secondary mb-6 max-w-article mx-auto\">The research was supported by the National Institutes of Health, grants R35GM133712, R21HG012981, R00CA259526 and R01CA060499.<\/p>\n<p><em>Originally published July 20, 2026 in UVAHealth&#8217;s <a href=\"https:\/\/www.uvahealth.com\/news\/machine-learning-tool-improves-accuracy-of-genomics-research?utm_campaign=mim&amp;utm_source=sfmc&amp;utm_medium=email&amp;utm_content=text\">Medicine in Motion<\/a>.<\/em><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n","protected":false},"excerpt":{"rendered":"<p>Chongzhi Zang, PhD,\u00a0and colleagues have created a free tool that will help scientists avoid false leads in genomic research. Department of Genome Sciences scientists in Chongzhi Zang&#8216;s lab have identified a widespread source of error in a popular method for studying the genome \u2013 our genetic instructions \u2013 and created a machine-learning tool to correct [&hellip;]<\/p>\n","protected":false},"author":1287,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":"","_members_access_role":[],"_members_access_error":"","_links_to":"","_links_to_target":""},"categories":[3,8],"tags":[110,197,181,201,189,200,111],"class_list":["post-5518","post","type-post","status-publish","format-standard","hentry","category-homepage-feed","category-people","tag-chongzhi-zang","tag-department-news","tag-genome-sciences","tag-megan-c-grieco","tag-mengxue-tian","tag-qingying-chen","tag-shengen-hu"],"acf":false,"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Genome Sciences&#039; Zang Lab Creates Machine-Learning Tool To Improve Accuracy of Genomics Research - Department of Genome Sciences<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/med.virginia.edu\/genome-sciences\/2026\/07\/30\/genome-sciences-zang-lab-creates-machine-learning-tool-to-improve-accuracy-of-genomics-research\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Genome Sciences&#039; Zang Lab Creates Machine-Learning Tool To Improve Accuracy of Genomics Research - Department of Genome Sciences\" \/>\n<meta property=\"og:description\" content=\"Chongzhi Zang, PhD,\u00a0and colleagues have created a free tool that will help scientists avoid false leads in genomic research. 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