Deep Learning Deciphers the Related Role of Master Regulators and G-Quadruplexes in Tissue Specification

Deep Learning Deciphers the Related Role of Master Regulators and G-Quadruplexes in Tissue Specification | 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 Deep Learning Deciphers the Related Role of Master Regulators and G-Quadruplexes in Tissue Specification Artem Bashkatov, Andrey Andreasyan, Dmitry Konovalov, Alan Herbert, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6165621/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract G-quadruplexes (GQs) are non-canonical DNA structures encoded by G-flipons that may play critical roles in gene regulation and chromatin structure. Here, we explore the role of G-flipons in tissue specification. We present a deep learning-based framework for the genome-wide G-flipon predictions across 14 human tissue types. The model was trained with high confidence experimental maps of GQ forming sequences and ATAC-seq peaks, conjoined with the location of RNA polymerase, histones, and transcription factor binding sites. The training set was based on level 4-6 Endoquad GQ annotated sequences with predictions validated using the complete level 1-6 dataset. To identify tissue-specific regulatory patterns, we classified GQ promoter predictions as either 'core' or ‘tissue-specific’. The predicted GQ-dependent, tissue-specific expression of genes was confirmed using DAVID gene ontology tools and validated using the TissueEnrich and GTEx datasets. We further explored interactions between GQ structures and master regulator genes (MRGs) in promoter regions, revealing a notable overlap of MRGs and predicted sites of GQ formation, with colocation of both features within the same tissue-specific DNase hypersensitivity site and with proteins that modulate R-loop formation. Collectively, the findings highlight the transactions between G-flipons with MRG during development that underlie tissue specification. Biological sciences/Biological techniques/Bioinformatics Biological sciences/Biological techniques/Biological models/Genetic models Biological sciences/Molecular biology/Transcription/Transcriptional regulatory elements Biological sciences/Biological techniques/Software G-quadruplex Flipons R-loops Tissue Differentiation Chromatin Deep Learning Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.xlsx SupplementaryTable2.xlsx SupplementaryTable3.xlsx SupplementaryTable4.xlsx SupplementaryTable5.xlsx SupplementaryTable6.pdf SupplementaryTable7.xlsx SupplementaryFigure1.pdf SupplementaryMethods.pdf SupplementaryMaterial1.zip Cite Share Download PDF Status: Published Journal Publication published 02 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 07 May, 2025 Reviews received at journal 02 May, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviews received at journal 19 Apr, 2025 Reviewers agreed at journal 02 Apr, 2025 Reviewers invited by journal 27 Mar, 2025 Editor assigned by journal 27 Mar, 2025 Editor invited by journal 11 Mar, 2025 Submission checks completed at journal 07 Mar, 2025 First submitted to journal 05 Mar, 2025 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. 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R-loops, Tissue Differentiation, Chromatin, Deep Learning","lastPublishedDoi":"10.21203/rs.3.rs-6165621/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6165621/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"G-quadruplexes (GQs) are non-canonical DNA structures encoded by G-flipons that may play critical roles in gene regulation and chromatin structure. Here, we explore the role of G-flipons in tissue specification. We present a deep learning-based framework for the genome-wide G-flipon predictions across 14 human tissue types. The model was trained with high confidence experimental maps of GQ forming sequences and ATAC-seq peaks, conjoined with the location of RNA polymerase, histones, and transcription factor binding sites. The training set was based on level 4-6 Endoquad GQ annotated sequences with predictions validated using the complete level 1-6 dataset. To identify tissue-specific regulatory patterns, we classified GQ promoter predictions as either 'core' or ‘tissue-specific’. The predicted GQ-dependent, tissue-specific expression of genes was confirmed using DAVID gene ontology tools and validated using the TissueEnrich and GTEx datasets. We further explored interactions between GQ structures and master regulator genes (MRGs) in promoter regions, revealing a notable overlap of MRGs and predicted sites of GQ formation, with colocation of both features within the same tissue-specific DNase hypersensitivity site and with proteins that modulate R-loop formation. 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