[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122959-en":3,"doc-seo-122959-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122959,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","An integrated machine-learning model to predict nucleosome architecture","We demonstrate that nucleosomes placed in gene bodies can be accurately located using signal decay theory with two emitters positioned at gene start and end. The resulting wave signals may occur in phase, producing well-defined nucleosome arrays, or in antiphase, yielding fuzzy architectures. The +1 and last (-last) nucleosomes align with transcription factor binding site–proximal regions and DNA physical features that impede nucleosome wrapping. Integrating machine learning with signal transmission theory enables basal nucleosome location prediction at accuracy comparable to MNase-seq experimental methods.","Nucleic Acids Research, 2024, 1–12 [https://doi.org/10.1093/nar/gkae689](https://doi.org/10.1093/nar/gkae689)  \nComputational Biology  \nAn integrated machine-learning model to predict nucleosome architecture  \nAlba Sala 1 ,†, Mireia Labrador 1 ,†, Diana Buitrago1 , Pau De Jorge 1 , Federica Battistini 1 ,2 , Isabelle Brun Heath 1 and Modesto Orozco 1 ,2 , *  \n1 Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain  \n2 Departament de Bioquímica i Biomedicina, Universitat de Barcelona, Barcelona, Spain  \n* To whom correspondence should be addressed. Tel: +34 93 40 37156; Fax: +35 93 403 7157; [Email: modesto.orozco@irbbarcelona.org](Email: modesto.orozco@irbbarcelona.org)[ ](Email: modesto.orozco@irbbarcelona.org)†The first two authors should be regarded as Joint First Authors.  \nAbstract  \nWe demonstrate that nucleosomes placed in the gene body can be accurately located from signal decay theory assuming two emitters located at the beginning and at the end of genes. These generated wave signals can be in phase (leading to well defined nucleosome arrays) or in antiphase (leading to fuzzy nucleosome architectures) . We found that the first (+1) and the last (-last) nucleosomes are contiguous to regions signaled by transcription factor binding sites and unusual DNA physical properties that hinder nucleosome wrapping. Based on these analyses, we developed a method that combines Machine Learning and signal transmission theory able to predict the basal locations of the nucleosomes with an accuracy similar to that of experimental MNase-seq based methods.  \nGraphical abstract  \nIntroduction  \nNucleosomes (the basic units of eukaryotic chromatin) are formed by 147 bp of duplex DNA wrapped around an octamer of histones (1), followed by a linker DNA where, in complex eukaryotic organisms, an additional histone (H1) can be bound (2). Nucleosomes are not randomly placed but maintain a defined architecture along the genome, with certain positions occupied by well-positioned nucleosomes while others are nucleosome-free (3–9) . Most significant nucleosome free regions (NFRs) are associated with the promoter regions of genes (upstream of the Transcription Start Sites, TSSs), the replication origins (ORIs) and the Transcription Termination Sites (TTSs) (10,11). The general consensus is that NFRs at TSSs are preferentially recognized by effector proteins in-  \nvolved in the regulation of gene activity, and the widths of these regions correlate with gene expression (12) . Furthermore, perturbation in nucleosome architectures associated to stress, changes in cell cycle phases, source of nutrients, or the cell metabolic cycle (6,11,13,14) proved the connection between nucleosome architecture and gene activity. The causality in this relationship is however unclear.  \nOver the last two decades, many efforts have been made to discover the main determinants of nucleosome positioning (15–20) . Several studies have suggested that DNA physical properties are crucial for defining nucleosome positioning, with NFRs characterized by sequences where the mechanical cost of wrapping DNA around nucleosomes is very high (10,13,21) . On the contrary, others have  \nReceived: December 1, 2023. Revised: July 17, 2024. Editorial Decision: July 21, 2024. Accepted: July 29, 2024  \n© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.  \nThis is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License  \n([https:](https://creativecommons.org/licenses/by-nc/4.0/)[//](https://creativecommons.org/licenses/by-nc/4.0/)[creativecommons.org](https://creativecommons.org/licenses/by-nc/4.0/)[/](https://creativecommons.org/licenses/by-nc/4.0/)[licenses](https://creativecommons.org/licenses/by-nc/4.0/)[/](https://creativecommons.org/licenses/by-nc/4.0/)[by-nc](https://creativecommons.org/licenses/by-nc/4.0/)[/](https://creativ","cbCaidLLo1T0wxPl","https://ap.wps.com/l/cbCaidLLo1T0wxPl","pdf",2694787,1,12,"English","en",105,"# Abstract\n## Introduction\n## Nucleosome positioning and NFRs\n## Determinants and chromatin regulation","[{\"question\":\"How does the model locate nucleosomes in gene bodies?\",\"answer\":\"It uses signal decay theory assuming two emitters at the beginning and end of genes to generate wave signals that reflect nucleosome organization.\"},{\"question\":\"What determines whether nucleosome arrays are well-defined or fuzzy?\",\"answer\":\"The model distinguishes between signals in phase, which lead to well defined nucleosome arrays, and signals in antiphase, which lead to fuzzy nucleosome architectures.\"},{\"question\":\"How is the prediction method validated or benchmarked?\",\"answer\":\"Predicted basal nucleosome locations achieve accuracy similar to experimental MNase-seq based methods.\"}]","An integrated machine-learning model to predict nucleosome architecture | 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does the model locate nucleosomes in gene bodies?","Question",{"text":75,"@type":76},"It uses signal decay theory assuming two emitters at the beginning and end of genes to generate wave signals that reflect nucleosome organization.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What determines whether nucleosome arrays are well-defined or fuzzy?",{"text":80,"@type":76},"The model distinguishes between signals in phase, which lead to well defined nucleosome arrays, and signals in antiphase, which lead to fuzzy nucleosome architectures.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the prediction method validated or benchmarked?",{"text":84,"@type":76},"Predicted basal nucleosome locations achieve accuracy similar to experimental MNase-seq based 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