To be, or not to be an intron evidence from entropy-based machine learning

To be, or not to be an intron evidence from entropy-based machine learning | 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 Article To be, or not to be an intron evidence from entropy-based machine learning Alessio Mancini, Emanuela Merelli, Marco Piangerelli, Sandra Pucciarelli, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5897883/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 Alternative splicing (AS) of introns is a key mechanism contributing to proteomic diversity. It enables the generation of multiple mRNAs variants from a single gene sequence, which are subsequently translated into the distinct protein isoforms. Intron retention (IR) is a specific type of AS in which introns remain unspliced in the mature mRNA. The process of intron splicing is regulated by cis-regulatory elements that recruit small non-coding RNAs and heterogeneous nuclear ribonucleoproteins (hnRNP), collectively forming the spliceosome. However, the precise mechanism or “code” governing the splicing pattern of any primary transcript remains not completely understood. In this study, we present the use of explainable Machine Learning (xML) models to investigate the mecahnism underlying intron retention in mature mRNA. Intronic sequences obtained were analyzed from species within the ciliate genus Tetrahymena, providing unique insights into the IR process that are biased by tissue-specific factors. Various features of the intronic sequences were examined, including the absence of repetitive nucleotide motifs-quantified as “entropy”-the GC content, and the complexity of the secondary structures as estimated by the Lempel-Ziv (LZ) measure. Our findings indicate that the key distinguishing features of retained introns (RIs) compared to constitutively spliced introns include the reduced presence of repetitive nucleotide motifs within the intronic sequences and the compactness of secondary structures near the 3’ splice sites. These features appear to weaken splicing signals, impairing the recognition of intronic sequences and resulting in IR. In conclusion, our work offers insights into the regulatory code underlying intron retention in other organisms and highlights its potential role in modulating phenotypic plasticity. This supports a framework for understanding the epigenetic mechanisms of stress response and environmental adaptation, aligning with the “Lamarckian” perspective of evolutionary biology. Biological sciences/Computational biology and bioinformatics Biological sciences/Molecular biology Full Text Additional Declarations No competing interests reported. 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-5897883","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":409084330,"identity":"9db05d8a-dcdf-497f-8b1b-37a7231a545c","order_by":0,"name":"Alessio Mancini","email":"","orcid":"","institution":"University of Camerino","correspondingAuthor":false,"prefix":"","firstName":"Alessio","middleName":"","lastName":"Mancini","suffix":""},{"id":409084331,"identity":"1c22873a-d240-4db7-b3ed-9df26633045e","order_by":1,"name":"Emanuela Merelli","email":"","orcid":"","institution":"University of Camerino","correspondingAuthor":false,"prefix":"","firstName":"Emanuela","middleName":"","lastName":"Merelli","suffix":""},{"id":409084332,"identity":"d08bcd0e-b9b5-4286-8203-07879428070f","order_by":2,"name":"Marco Piangerelli","email":"","orcid":"","institution":"University of Camerino","correspondingAuthor":false,"prefix":"","firstName":"Marco","middleName":"","lastName":"Piangerelli","suffix":""},{"id":409084333,"identity":"761b3a41-0b69-40f2-8350-1e1bfc981504","order_by":3,"name":"Sandra Pucciarelli","email":"data:image/png;base64,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","orcid":"","institution":"University of Camerino","correspondingAuthor":true,"prefix":"","firstName":"Sandra","middleName":"","lastName":"Pucciarelli","suffix":""},{"id":409084334,"identity":"8e289fad-6921-4d94-93d2-e9dc331271d3","order_by":4,"name":"Leonardo Vito","email":"","orcid":"","institution":"University of Camerino","correspondingAuthor":false,"prefix":"","firstName":"Leonardo","middleName":"","lastName":"Vito","suffix":""}],"badges":[],"createdAt":"2025-01-24 19:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5897883/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5897883/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78516513,"identity":"03c884ad-46a0-478d-8fb5-732354dd0c2f","added_by":"auto","created_at":"2025-03-14 10:54:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":18920870,"visible":true,"origin":"","legend":"","description":"","filename":"MancinietalScientificReportscheckeddata.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5897883/v1_covered_7d6adacf-e2b0-44e7-ab8d-38e6100b5d2f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"To be, or not to be an intron evidence from entropy-based machine learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5897883/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5897883/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Alternative splicing (AS) of introns is a key mechanism contributing to proteomic diversity. It enables the generation of multiple mRNAs variants from a single gene sequence, which are subsequently translated into the distinct protein isoforms. Intron retention (IR) is a specific type of AS in which introns remain unspliced in the mature mRNA. The process of intron splicing is regulated by cis-regulatory elements that recruit small non-coding RNAs and heterogeneous nuclear ribonucleoproteins (hnRNP), collectively forming the spliceosome. However, the precise mechanism or “code” governing the splicing pattern of any primary transcript remains not completely understood. In this study, we present the use of explainable Machine Learning (xML) models to investigate the mecahnism underlying intron retention in mature mRNA. Intronic sequences obtained were analyzed from species within the ciliate genus Tetrahymena, providing unique insights into the IR process that are biased by tissue-specific factors. 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