[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125927-en":3,"doc-seo-125927-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125927,2336474459895,"Aria","https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916",8,"Research & Report","Prediction of DNA i-motifs via machine learning - iM-Seeker","i-Motifs (iMs) are cytosine-rich DNA secondary structures with diverse folding statuses and strengths across the genome. Existing work relies heavily on biophysical experiments, and dedicated computational methods are lacking to jointly predict iM folding and stability. This study presents iM-Seeker, a machine learning pipeline combining a Balanced Random Forest classifier for folding status using genome-wide CUT&Tag data and an Extreme Gradient Boosting regressor for folding strength from biophysical datasets, including literature and in-house measurements.","Nucleic Acids Research, 2024, 1–10 [https://doi.org/10.1093/nar/gkae092](https://doi.org/10.1093/nar/gkae092)  \nComputational Biology  \nPrediction of DNA i-motifs via machine learning  \nBibo Yang1 ,†, Dilek Guneri2 ,†, Haopeng Yu 1 , *,†, Elisé P. Wright3 , Wenqian Chen2 , Zoë A. E. Waller 2 , * and Yiliang Ding 1 , *  \n1 Department of Cell and Developmental Biology, John Innes Centre, Norwich Research Park, Norwich NR4 7UH, UK  \n2 School of Pharmacy, University College London, London WC1N 1AX, UK  \n3 Molecular Physiology School of Medicine, and Molecular Medicine Research Group, University of Western Sydney, Campbelltown, NSW 1797, Australia  \n* To whom correspondence should be addressed. Tel: +44 1603 450266; [Email: yiliang.ding@jic.ac.uk](Email: yiliang.ding@jic.ac.uk)  \nCorrespondence may also be addressed to Haopeng Yu. Email: [haopeng.yu@jic.ac.uk](haopeng.yu@jic.ac.uk)  \nCorrespondence may also be addressed to Zoë A. E. Waller. Email: [z.waller@ucl.ac.uk](z.waller@ucl.ac.uk)  \n†The first three authors should be regarded as Joint First Authors.  \nAbstract  \ni-Motifs (iMs), are secondary structures formed in cytosine-rich DNA sequences and are involved in multiple functions in the genome. Although putative iM forming sequences are widely distributed in the human genome, the folding status and strength of putative iMs vary dramatically. Much previous research on iM has focused on assessing the iM folding properties using biophysical experiments. However, there are no dedicated computational tools for predicting the folding status and strength of iM structures. Here, we introduce a machine learning pipeline, iM-Seeker, to predict both folding status and structural stability of DNA iMs. The programme iM-Seeker incorporates a Balanced Random Forest classifier trained on genome-wide iMab antibody-based CUT&Tag sequencing data to predict the folding status and an Extreme Gradient Boosting regressor to estimate the folding strength according to both literature biophysical data and our in-house biophysical experiments. iM-Seeker predicts DNA iM folding status with a classification accuracy of 81% and estimates the folding strength with coefficient of determination ( R2 ) of 0.642 on the test set. Model interpretation confirms that the nucleotide composition of the C-rich sequence significantly affects iM stability, with a positive correlation with sequences containing cytosine and thymine and a negative correlation with guanine and adenine.  \nGraphical abstract  \nIntroduction  \nNucleotides are the basic units that form DNA and RNA, two key molecules in the central dogma. DNA encodes genetic information, which is transcribed to mRNA and then translated to protein. In addition to this transfer of information, DNA and RNA can form complex structures, which can play crucial functional roles in organisms. Besides the canonical WatsonCrick double-helical B-form structure, DNA can form noncanonical secondary structures such as G-quadruplexes (G4s) and i-Motifs (iMs) . G4s are four-stranded structures formed from G-rich sequences and are stabilised by Hoogsteen hydrogen bonding between guanines (1) . iMs are also four-stranded structures, but formed from cytosine C-rich regions that are stabilised by hemi-protonated C-C base pairs (C+ :C) (2,3) . Complementary G-rich and C-rich sequences can form G4s  \nand iMs interdependently during distinct cellular processes (4) . As a non-canonical structure, iMs are indicated to play an important role in the genome. There are an increasing number of in vitro and in celluo studies that report evidence that iMs could fold in promotor region of certain genes, telomeres and untranslated regions. They have also been implicated asa regulatory element associated with the cell cycle, transcription, chromatin remodelling, as well as transposable element dynamics (5–7) .  \nCommonly, computational analysis of putative iMs is limited to indirect identification by searching for potential complementary G4 sequen","cbCaip0ZfXq6X2sc","https://ap.wps.com/l/cbCaip0ZfXq6X2sc","pdf",1557006,6,1,10,"English","en",105,"# Introduction\n## Noncanonical DNA structures and i-motifs\n## Computational identification of putative i-motifs\n## Limits of current computational analysis","[{\"question\":\"What problem does iM-Seeker address in DNA i-motif research?\",\"answer\":\"It provides a dedicated computational pipeline to predict both the folding status and structural stability (folding strength) of DNA i-motifs, which earlier approaches lacked.\"},{\"question\":\"How does iM-Seeker predict i-motif folding status?\",\"answer\":\"It uses a Balanced Random Forest classifier trained on genome-wide CUT\\u0026Tag sequencing data based on iMab antibody signals.\"},{\"question\":\"What models and data are used to estimate i-motif folding strength?\",\"answer\":\"It employs an Extreme Gradient Boosting regressor, trained using literature biophysical data together with in-house biophysical experiments.\"}]","Prediction of DNA i-motifs via machine learning - iM-Seeker | PDF",1785902079,25,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"prediction-of-dna-i-motifs-via-machine-learning-im-seeker","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/prediction-of-dna-i-motifs-via-machine-learning-im-seeker/125927/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-25","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What problem does iM-Seeker address in DNA i-motif research?","Question",{"text":77,"@type":78},"It provides a dedicated computational pipeline to predict both the folding status and structural stability (folding strength) of DNA i-motifs, which earlier approaches lacked.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How does iM-Seeker predict i-motif folding status?",{"text":82,"@type":78},"It uses a Balanced Random Forest classifier trained on genome-wide CUT&Tag sequencing data based on iMab antibody signals.",{"name":84,"@type":75,"acceptedAnswer":85},"What models and data are used to estimate i-motif folding strength?",{"text":86,"@type":78},"It employs an Extreme Gradient Boosting regressor, trained using literature biophysical data together with in-house biophysical experiments.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,116,121,124,129,132,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":22,"slug":134},"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":108,"slug":138},19,"General","general"]