[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-384104-105":59,"doc-detail-384104-en":129},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":122,"head_meta":124,"extra_data":126,"updated_unix":128},105,"en","predicting-regional-somatic-mutation-rates-using-dna-motifs","Predicting regional somatic mutation rates using DNA motifs","","Locus-specific epigenetic regulation remains unresolved, with a key mechanism being recruitment of epigenetic enzymes to genomic loci by DNA binding factors that recognize sequence motifs (epi-motifs). Using DNA motifs, including transcription factor motifs and epi-motifs, as surrogates for epigenetic signals, the study predicts somatic mutation rates across 13 cancers at ~23 kbp resolution. An interpretable contextual regression model learns a universal relationship between mutations and DNA motifs and identifies high-impact motifs such as TP53 and epi-motifs linked to H3K9me3. The work also detects tumor regions with elevated mutation rates beyond expectations and uses these cancer-related regions for cancer type classification, while revealing distinct motif contributions between cancer-related and cancer-independent regions.",{"@graph":69,"@context":121},[70,84,104],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/predicting-regional-somatic-mutation-rates-using-dna-motifs/384104/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":98,"encodingFormat":97,"isAccessibleForFree":99,"interactionStatistic":100},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/predicting-regional-somatic-mutation-rates-using-dna-motifs/384104.png","ImageObject",300,407,{"name":92,"@type":93},"McQueen","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-24",true,{"@type":101,"interactionType":102,"userInteractionCount":8},"InteractionCounter",{"@type":103},"ViewAction",{"@type":105,"mainEntity":106},"FAQPage",[107,113,117],{"name":108,"@type":109,"acceptedAnswer":110},"What are epi-motifs and how are they used in the study?","Question",{"text":111,"@type":112},"Epi-motifs are DNA sequence motifs recognized by DNA binding factors that recruit epigenetic enzymes to specific loci. The study uses known TF motifs and epi-motifs as surrogates for epigenetic signals to predict somatic mutation rates.","Answer",{"name":114,"@type":109,"acceptedAnswer":115},"What model is introduced to connect DNA motifs with mutation rates?",{"text":116,"@type":112},"The study implements an interpretable neural network called contextual regression. It learns the relationship between mutations and DNA motifs at kilobase resolution across 13 cancers.",{"name":118,"@type":109,"acceptedAnswer":119},"How do the researchers use high-mutation regions for cancer understanding?",{"text":120,"@type":112},"They identify genomic regions where mutation rates are significantly higher than predicted values in each tumor. These cancer-related regions can then accurately predict cancer types, and the work analyzes motif contributions to mutation signatures.","https://schema.org",{"og:url":83,"og:type":123,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":125,"canonical":83},"index,follow",{"doc_id":127,"site_id":62},384104,1790293444,{"code":4,"msg":5,"data":130},{"doc_id":127,"user_id":131,"nickname":92,"user_avatar":132,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":133,"file_id":134,"file_url":135,"file_type":136,"file_size":137,"view_count":8,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":138,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144},5909890329169,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","PLOS COMPUTATIONAL BIOLOGY  \nOPEN ACCESS  \nCitation: Liu C, Wang Z, Wang J, Liu C, Wang M, Ngo V, et al. (2023) Predicting regional somatic mutation rates using DNA motifs. PLoS Comput Biol 19(10): e1011536 . [https://doi.org/10.1371/](https://doi.org/10.1371/)[ ](https://doi.org/10.1371/)[journal.pcbi.1011536](journal.pcbi.1011536)  \nEditor: Li Shen, Icahn School of Medicine at Mount Sinai, UNITED STATES  \nReceived: April 2, 2023  \nAccepted: September 20, 2023  \nPublished: October 2, 2023  \nCopyright: © 2023 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData Availability Statement: All relevant data are within the manuscript and its Supporting Information files. Software availability: The code is available from GitHub ([https://github.com/Wang](https://github.com/Wang)lab-UCSD/SomaticMutation) .  \nFunding: This work was partially supported by the NIH (R01HG009626 to W.W.) . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.  \nCompeting interests: The authors have declared that no competing interests exist.  \nRESEARCH ARTICLE  \nPredicting regional somatic mutation rates using DNA motifs  \nCong Liu1☯, Zengmiao Wang2☯, Jun Wang1☯, Chengyu Liu1, Mengchi Wang3, Vu Ngo3, Wei Wang1,3,4 *  \n1 Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California, United States of America, 2 State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China, 3 Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, California, United States of America, 4 Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, California, United States of America  \n☯ These authors contributed equally to this work.  \n* [wei-wang@ucsd.edu](wei-wang@ucsd.edu)  \nAbstract  \nHow the locus-specificity of epigenetic modifications is regulated remains an unanswered question. A contributing mechanism is that epigenetic enzymes are recruited to specific loci by DNA binding factors recognizing particular sequence motifs (referred to as epi-motifs) . Using these motifs to predict biological outputs depending on local epigenetic state such as somatic mutation rates would confirm their functionality. Here, we used DNA motifs including known TF motifs and epi-motifs as a surrogate of epigenetic signals to predict somatic mutation rates in 13 cancers at an average 23kbp resolution. We implemented an interpretable neural network model, called contextual regression, to successfully learn the universal relationship between mutations and DNA motifs, and uncovered motifs that are most impactful on the regional mutation rates such as TP53 and epi-motifs associated with H3K9me3 . Furthermore, we identified genomic regions with significantly higher mutation rates than the expected values in each individual tumor and demonstrated that such cancerrelated regions can accurately predict cancer types. Interestingly, we found that the same mutation signatures often have different contributions to cancer-related and cancer-independent regions, and we also identified the motifs with the most contribution to each mutation signature.  \nAuthor summary  \nLocus-specific epigenetic modifications play critical roles in various biological processes. However, it remains elusive how proteins and their binding motifs regulate such locusspecific epigenetic patterns. A contributing mechanism is that epigenetic enzymes are recruited to specific loci by DNA binding factors recognizing particular sequence motifs (referred to as epi-motifs) . Using these motifs to predict biological outputs depending on local epigenetic stat","cbCaitxbJ1bFYcHN","https://ap.wps.com/l/cbCaitxbJ1bFYcHN","pdf",2927896,25,"English","# Abstract\n## Author summary\n## Introduction","[{\"question\":\"What are epi-motifs and how are they used in the study?\",\"answer\":\"Epi-motifs are DNA sequence motifs recognized by DNA binding factors that recruit epigenetic enzymes to specific loci. The study uses known TF motifs and epi-motifs as surrogates for epigenetic signals to predict somatic mutation rates.\"},{\"question\":\"What model is introduced to connect DNA motifs with mutation rates?\",\"answer\":\"The study implements an interpretable neural network called contextual regression. It learns the relationship between mutations and DNA motifs at kilobase resolution across 13 cancers.\"},{\"question\":\"How do the researchers use high-mutation regions for cancer understanding?\",\"answer\":\"They identify genomic regions where mutation rates are significantly higher than predicted values in each tumor. These cancer-related regions can then accurately predict cancer types, and the work analyzes motif contributions to mutation signatures.\"}]","Predicting regional somatic mutation rates using DNA motifs | PDF",1790261248,63]