[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-383287-105":59,"doc-detail-383287-en":130},{"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":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","a-deep-learning-approach-reveals-unexplored-landscape-of-viral-expression-in-cancer","A deep learning approach reveals unexplored landscape of viral expression in cancer","","About 15% of human cancer cases are attributed to viral infections, yet most studies rely on aligning tumor RNA reads to known-virus databases, limiting detection of divergent viruses and rapid characterization of tumor viromes. This work introduces viRNAtrap, an alignment-free deep learning pipeline that identifies viral reads and assembles viral contigs. Applied to 14 cancer types from TCGA, it reveals unexpected divergent viruses and endogenous viruses linked to poor overall survival, enabling study of viral infections across clinical conditions.",{"@graph":69,"@context":122},[70,84,105],{"@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/a-deep-learning-approach-reveals-unexplored-landscape-of-viral-expression-in-cancer/383287/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/a-deep-learning-approach-reveals-unexplored-landscape-of-viral-expression-in-cancer/383287.png","ImageObject",300,407,{"name":92,"@type":93},"anakgang17","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-27","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":14},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What limitation affects current approaches to studying virus expression in tumors?","Question",{"text":112,"@type":113},"Most methods align tumor RNA-seq reads to databases of known viruses, which restricts identification of divergent viruses and slows tumor virome characterization.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What is viRNAtrap and what does it do?",{"text":117,"@type":113},"viRNAtrap is an alignment-free pipeline that uses a deep learning model to discriminate viral RNA-seq reads and leverages model scores to assemble viral contigs.",{"name":119,"@type":110,"acceptedAnswer":120},"How was viRNAtrap validated and applied in this study?",{"text":121,"@type":113},"The method was applied to 14 cancer types from TCGA, evaluated by comparing findings against sequences in three viral databases, and used for exploratory analysis of the viral expression landscape in the human cancer transcriptome.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},383287,1790520783,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":14,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":41},962090883568,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Article [https://doi.org/10.1038/s41467-023-36336-z](https://doi.org/10.1038/s41467-023-36336-z)  \nA deep learning approach reveals unexplored landscape of viral expression in cancer  \nReceived: 20 August 2022  \n\n| Accepted: 25 January 2023 |\n| --- |\n| |\n| Check for updates |\n\nAbdurrahman Elbasir1, Ying Ye1, Daniel E. Schäffer 1,2, Xue Hao 1,  \nJayamanna Wickramasinghe1, Konstantinos Tsingas1,3, Paul M. Lieberman 1  \n,  \nQi Long3, Quaid Morris 4, Rugang Zhang 1, Alejandro A. Schäffer 5 & Noam Auslander 1   \nAbout 15% of human cancer cases are attributed to viral infections. To date, virus expression in tumor tissues has been mostly studied by aligning tumor RNA sequencing reads to databases of known viruses. To allow identification of divergent viruses and rapid characterization of the tumor virome, we develop viRNAtrap, an alignment-free pipeline to identify viral reads and assemble viral contigs. We utilize viRNAtrap, which is based on a deep learning model trained to discriminate viral RNAseq reads, to explore viral expression in cancers and apply it to 14 cancer types from The Cancer Genome Atlas (TCGA). Using viRNAtrap, we uncover expression ofunexpected and divergent viruses that have not previously been implicated in cancer and disclose human endogenous viruses whose expression is associated with poor overall survival. The viRNAtrap pipeline provides a way forward to study viral infections associated with different clinical conditions.  \nViral infections have a causal role in~15% of all cancer cases worldwide1. Viruses linked to cancer are generally divided into direct carcinogens, which drive an oncogenic transformation through viral oncogene expression, and indirect carcinogens, which may lead to cancer through mutagenesis associated with infection and inflammation. To date, seven viruses have been classified as direct carcinogenic agents in humans2. Among these, the high-risk subtypes of human papillomavirus (HPV) are the causative agent of ~5% of human cancers. Chronic hepatitis B virus(HBV)or hepatitis C virus (HCV)infections are associated with most hepatocellular carcinoma cases. More recently, advances in sequencing technologies have contributed to a better appreciation of the high burden of viral infections in cancer, exemplified by Kaposi’s sarcoma herpesvirus and the Merkel cell polyomavirus, which were discovered based on nucleic acid subtraction to cause Kaposi’s sarcoma and Merkel cell carcinoma, respectively2. The discovery of oncogenic viruses, starting with the Rous sarcoma virus3, has been critical for understanding mechanisms driving cancer  \nevolution and for improving cancer prevention and intervention strategies. However, the burden of viral infections in cancer is thought to remain underappreciated by much ofthe cancer research community4.  \nSince the advent of next-generation sequencing, new viral strains are typically identified from large-scale DNA or RNA sequencing data based on sequence similarity to known viruses. The Cancer Genome Atlas (TCGA) has become a principal resource for the identification of viral sequences in cancer tissues. Several studies screened TCGA DNA sequencing data to characterize known viruses in cancers5, and analyze host integration sites for viruses such as HBV that integrate into the human genome6. Other studies used RNA sequencing to screen for known viruses in the human transcriptome7–10, and to discover novel viral isolates10. Most recently, a few studies combined DNA and RNA sequencing to quantify the presence of known cancer-associated viruses in human cancers11,12. However, the set of sequenced viral clades and the set of viral clades known to infect humans are both incomplete. Viruses and cancers have rapidly evolving genomes, and a  \n1The Wistar Institute, Philadelphia, PA 19104, USA. 2Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA. 3University of Pennsylvania, Philadelphia, PA, USA. 4Computational and Systems Biology, Sloan","cbCairykSI0APN9P","https://ap.wps.com/l/cbCairykSI0APN9P","pdf",1567548,12,"English","# Background and motivation\n## Limitations of alignment-based viral detection\n## Viral roles in cancer\n# viRNAtrap framework\n## Deep learning model for viral RNA-seq reads\n## Alignment-free identification and contig assembly\n# Application to TCGA cancers\n## Selection of 14 cancer types\n## Building viral databases and evaluations\n## Discoveries: divergent viruses and survival-associated endogenous viruses","[{\"question\":\"What limitation affects current approaches to studying virus expression in tumors?\",\"answer\":\"Most methods align tumor RNA-seq reads to databases of known viruses, which restricts identification of divergent viruses and slows tumor virome characterization.\"},{\"question\":\"What is viRNAtrap and what does it do?\",\"answer\":\"viRNAtrap is an alignment-free pipeline that uses a deep learning model to discriminate viral RNA-seq reads and leverages model scores to assemble viral contigs.\"},{\"question\":\"How was viRNAtrap validated and applied in this study?\",\"answer\":\"The method was applied to 14 cancer types from TCGA, evaluated by comparing findings against sequences in three viral databases, and used for exploratory analysis of the viral expression landscape in the human cancer transcriptome.\"}]","A deep learning approach reveals unexplored landscape of viral expression in cancer | PDF",1790255703]