PanTE: A Comprehensive Framework for Transposable Element Discovery in Graph-based Pangenomes

PanTE: A Comprehensive Framework for Transposable Element Discovery in Graph-based Pangenomes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Brief Communication PanTE: A Comprehensive Framework for Transposable Element Discovery in Graph-based Pangenomes Yiwen Wang, Shuo Cao, Zhenya Liu, Yuting Liu, Zhongqi Liu, Wenqi Ma, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5867196/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Transposable element (TE) annotation is crucial for understanding genetics, genomics and evolution, yet current methods struggle to identify TEs in graph-based pangenomes. We developed a framework PanTE to construct accurate and representative TE libraries for both single genomes and graph pangenomes. PanTE is the first of its kind capable of being directly applied to graph-based pangenomes to build population-level TE libraries. By partially reimplementing RepeatModeler2 and integrating key innovations, including graph pangenome disassembly, alignment-free LTR structure detection, a machine learning-based classifier and efficiency-boosting strategies, PanTE outperformed RepeatModeler2 by efficiently handling large genomes, detecting high-abundance TEs and LTR-retrotransposons, and providing robust TE classification with superior computational efficiency. Compared to EDTA, it annotated ~ 26% more TEs in the grapevine genome and achieved up to 13 times faster runtimes in the wheat genome. PanTE represents a significant advancement in population-wide TE discovery, making it particularly valuable for pangenomic studies. Biological sciences/Computational biology and bioinformatics/Software Biological sciences/Computational biology and bioinformatics/Sequence annotation Full Text Supplementary Files Supplementarytables.xlsx Supplementary tables SupplementaryMaterial.docx Supplementary Material Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5867196","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Brief Communication","associatedPublications":[],"authors":[{"id":407436274,"identity":"9d0bfcac-ba85-45a4-849b-6dae26f1ffd4","order_by":0,"name":"Yiwen 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