CMDTarget: sRNA Target Prediction with Comparative Denoising

CMDTarget: sRNA Target Prediction with Comparative Denoising | 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 Method Article CMDTarget: sRNA Target Prediction with Comparative Denoising Yunfan Jin, Yu Li, Zhi John Lu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9610537/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 Accurate identification of sRNA targets is critical for elucidating sRNA functions, yet current computational methods remain limited in predictive performance. Here, we present CMDTarget (Comparative Modeling-based Denoising for sRNA Target prediction), a comparative genomic framework that explicitly incorporates clade-specific interaction energy, Hfq-binding propensity predicted by deep learning, and phylogenetic information with a flexible evolution model. Across nine bacterial species with experimentally supported interactions, CMDTarget consistently outperforms existing methods. By explicit modeling of clade-specific properties, CMDTarget reveals the evolutionary turnover of sRNA-target interactions. Implemented as a scalable Snakemake workflow, CMDTarget enables genome-scale prediction and evolutionary analysis of sRNA regulatory networks. sRNA target prediction sRNA evolution Full Text Additional Declarations No competing interests reported. Supplementary Files TableS1.xlsx TableS2.xlsx TableS3.xlsx SupplementaryInformation.docx 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-9610537","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":637418399,"identity":"3fee0350-f2c4-4827-83c9-282de7a4242d","order_by":0,"name":"Yunfan Jin","email":"","orcid":"","institution":"Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Yunfan","middleName":"","lastName":"Jin","suffix":""},{"id":637418400,"identity":"e4000d6d-f74d-4b05-8d7b-1151f67b9029","order_by":1,"name":"Yu Li","email":"","orcid":"","institution":"Tsinghua 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