[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123975-en":3,"doc-seo-123975-105":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},123975,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Towards the genomic sequence code of DNA fragility for machine learning","Genomic DNA breakages and the resulting insertion and deletion mutations drive genome instability and are linked to disease. This study analyzes differences and shared signals across many breakage datasets to uncover sequence-linked rules governing DNA strand fragility. Results deconvolve sequence influence into short-, mid-, and long-range effects and highlight stressor-dependent changes in range and composition. A released DNAfragAIlib feature compendium supports genomic machine learning to model cancer-associated breakages and reveals context-specific patterns for structural variants, chromothripsis, and viral integration.","Towards the genomic sequence code of DNA fragility for machine learning  \nPatrick Pflughaupt  , Adib A. Abdullah  , Kairi Masuda  and Aleksandr B. Sahakyan  *  \nMRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 9DS, UK  \n* To whom correspondence should be addressed. Tel: +44 1865 222407; Email: [aleksandr.sahakyan@imm.ox.ac.uk](aleksandr.sahakyan@imm.ox.ac.uk)  \nAbstract  \nGenomic DNA breakages and the subsequent insertion and deletion mutations are important contributors to genome instability and linked diseases. Unlike the research in point mutations, the relationship between DNA sequence context and the propensity for strand breaks remains elusive. Here, by analyzing the differences and commonalities across myriads of genomic breakage datasets, we extract the sequence-linked rules and patterns behind DNA fragility. We show the overall deconvolution of the sequence influence into short- , mid-and long-range effects, and the stressor-dependent differences in defining the range and compositional effects on DNA fragility. We summarize and release our feature compendium as a library that can be seamlessly incorporated into genomic machine learning procedures, where DNA fragility is of concern, and train a generalized DNA fragility model on cancer-associated breakages. Structural variants (SVs) tend to stabilize regions in which they emerge, with the effect most pronounced for pathogenic SVs. In contrast, the effects of chromothripsis are seen across regions less prone to breakages. We find that viral integration may bring genome fragility, particularly for cancer-associated viruses. Overall, this work offers novel insights into the genomic sequence basis of DNA fragility and presents a powerful machine learning resource to further enhance our understanding of genome (in)stability and evolution.  \nGraphical abstract  \nIntroduction  \nGenomic insertion and deletion alterations, which occur through the formation of DNA strand breaks, are the second most significant DNA modifications after point mutations (1,2) . However, while the latter has been studied before (3,4), the short-and long-range sequence context patterns associated with DNA strand breakpoints have not been extensively interrogated through computational means. Nevertheless, several works have found associations of DNA strand breakpoints with non-B DNA conformations (5), cancer genes (6), mutations (7), abasic sites (8), chromatin packing (9), 3D genome organization (10) and the natural DNA decay processes (11). Early predictive models based on hidden Markov models and copy number variation data achieved coarse resolution (∼300 base pairs) for ∼400 breakpoints in the hu-  \nman genome (12) . This demonstrates the potential for highresolution, sequence-based prediction of DNA strand breaks. However, the advent of high-throughput sequencing technologies has enabled genome-wide mapping of DNA strand breakpoints at a nucleotide resolution (13–16), along with the availability of biological big data from both normal and pathological tissues (17–20) . As such, advances in machine learning techniques now allow the development of models for studying genome-wide endogenous and disease-related DNA strand breaks reported experimentally (7,21–24) .  \nThis work aims to examine the general principles of the influence of genomic sequence on DNA fragility in different physiological and pathological conditions. By analyzing the patterns and commonalities across 100 genomic breakage datasets, we revealed that DNA sequence can be broadly  \nReceived: March 18, 2024. Revised: September 20, 2024. Editorial Decision: September 28, 2024. Accepted: October 2, 2024 © 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 License ([https:](https://creativecom","cbCailcJc2jeqQ4Y","https://ap.wps.com/l/cbCailcJc2jeqQ4Y","pdf",12228890,1,19,"English","en",105,"# Introduction\n## Sequence context and DNA strand breaks\n## Machine learning for genome-wide fragility\n# Abstract\n## Sequence-linked rules and range deconvolution\n## DNAfragAIlib and fragility prediction\n## Findings on SVs, chromothripsis, and viral integration","[{\"question\":\"What problem does the study address about DNA fragility?\",\"answer\":\"The study focuses on how DNA sequence context influences the propensity for strand breaks, which remains unclear compared with well-studied point mutations.\"},{\"question\":\"How were DNA fragility rules extracted in this work?\",\"answer\":\"By analyzing differences and commonalities across numerous genomic breakage datasets, the work identifies sequence-linked patterns and decomposes sequence effects into short-, mid-, and long-range components.\"},{\"question\":\"What resource is released to support machine learning applications?\",\"answer\":\"The study summarizes and releases a feature compendium called DNAfragAIlib, designed for seamless integration into sequence-driven genomic machine learning procedures.\"}]","Towards the genomic sequence code of DNA fragility for machine learning | 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problem does the study address about DNA fragility?","Question",{"text":75,"@type":76},"The study focuses on how DNA sequence context influences the propensity for strand breaks, which remains unclear compared with well-studied point mutations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were DNA fragility rules extracted in this work?",{"text":80,"@type":76},"By analyzing differences and commonalities across numerous genomic breakage datasets, the work identifies sequence-linked patterns and decomposes sequence effects into short-, mid-, and long-range components.",{"name":82,"@type":73,"acceptedAnswer":83},"What resource is released to support machine learning applications?",{"text":84,"@type":76},"The study summarizes and releases a feature compendium called DNAfragAIlib, designed for seamless integration into sequence-driven genomic machine learning 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