[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122030-en":3,"doc-seo-122030-105":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},122030,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Decoding biology with massively parallel reporter assays and machine learning","Massively parallel reporter assays (MPRAs) quantify how sequence variation influences gene expression, enabling interrogation of molecular phenotypes at scale through sequencing readouts. Machine learning models integrate MPRA-derived information to generalize beyond training sequences, yielding quantitative views of cis-regulatory codes that control gene expression. The review emphasizes cis-regulatory MPRAs for cotranscriptional and post-transcriptional regulation, covering alternative splicing, cleavage and polyadenylation, translation, and mRNA decay, and discusses how these approaches support variant stratification and design of synthetic regulatory elements.","UC Irvine  \nUC Irvine Previously Published Works  \nTitle  \nDecoding biology with massively parallel reporter assays and machine learning.  \nPermalink  \n[https://escholarship.org/uc/item/7zt7r2xj](https://escholarship.org/uc/item/7zt7r2xj)  \nJournal  \nGenes & Development, 38(17-20)  \nAuthors  \nLa Fleur, Alyssa  \nShi, Yongsheng Seelig, Georg  \nPublication Date  \n2024-10-16  \nDOI  \n10.1101/gad.351800.124  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nDecoding biology with massively parallel reporter assays and machine learning  \nAlyssa La Fleur,1 Yongsheng Shi,2 and Georg Seelig1,3  \n1Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, USA; 2Department of Microbiology and Molecular Genetics, School of Medicine, University of California, Irvine, Irvine, California 92697, USA; 3Department of Electrical & Computer Engineering, University of Washington, Seattle, Washington 98195, USA  \nMassively parallel reporter assays (MPRAs) are powerful tools for quantifying the impacts of sequence variation on gene expression. Reading out molecular phenotypes with sequencing enables interrogating the impact of sequence variation beyond genome scale. Machine learning models integrate and codify information learned from MPRAs and enable generalization by predicting sequences outside the training data set. Models can provide a quantitative understanding of cis-regulatory codes controlling gene expression, enable variant stratification, and guide the design of synthetic regulatory elements for applications from synthetic biology to mRNA and gene therapy. This review focuses on cis-regulatory MPRAs, particularly those that interrogate cotranscriptional and post-transcriptional processes: alternative splicing, cleavage and polyadenylation, translation, and mRNA decay.  \nIntroduction and historical perspective  \nA key challenge in the postgenomic era is understanding the relationship between genomic sequence and biological function. In particular, a thorough understanding of how cis-regulatory codes govern protein production is critical to linking genetic variation to gene expression changes or designing synthetic regulatory elements for applications from mRNA therapy to synthetic biology. Although significant progress has been made constructing these sequence-to-function links, many challenges remain. Gene expression is a multistep process, and cis-regulatory information controlling it is densely encoded, making it difficult to disentangle regulatory codes controlling different processes. Simultaneously, regulatory information controlling a process is often spread across multiple coding and noncoding regions. Moreover, the human genome contains a finite number of genes to learn  \n[Keywords: gene regulation; machine learning; massively parallel reporter assays]  \nCorresponding author: [gseelig@uw.edu](gseelig@uw.edu), [yongshes@uci.edu](yongshes@uci.edu)  \nArticle published online ahead of print. Article and publication date are online at [http://www.genesdev.org/cgi/doi/10.1101/gad.351800.124. Free](http://www.genesdev.org/cgi/doi/10.1101/gad.351800.124. Free)ly available online through the Genes & Development Open Access option.  \ncis-regulatory codes from. Although human population genetic variation can provide additional data, there is a high degree of sequence similarity between individuals, severely limiting sequence representation (Starita et al. 2017) .  \nMassively parallel reporter assays (MPRAs) are a powerful approach for studying gene regulation, overcoming some of the limitations above (Kinney and McCandlish 2019; Trauernicht et al. 2020; Gallego Romero and Lea 2023) . In an MPRA, the activity of a biological process of interest is monitored based on reporter expression. A high degree of sequence variation is introduced into this reporter to generate a reporter library. Libraries are delivered into cell ","cbCaidM76ivLdVZP","https://ap.wps.com/l/cbCaidM76ivLdVZP","pdf",968209,1,24,"English","en",105,"# Introduction and historical perspective\n## Cis-regulatory codes and postgenomic sequence-to-function challenges\n## MPRA principles and molecular phenotype readout\n## Conceptual origins of MPRA-like pooled selection and mutagenesis","[{\"question\":\"What do massively parallel reporter assays (MPRAs) measure in gene regulation studies?\",\"answer\":\"MPRAs measure the activity of a biological process through reporter expression, where sequence variation introduced into reporter regions generates a reporter library. Molecular phenotypes are then quantified using high-throughput sequencing, often leveraging barcodes as proxies.\"},{\"question\":\"How does machine learning improve interpretation of MPRA results?\",\"answer\":\"Machine learning models integrate and codify information learned from MPRAs to predict sequence behavior outside the training set. This enables quantitative understanding of cis-regulatory codes, supports variant stratification, and helps guide design of synthetic regulatory elements.\"},{\"question\":\"Which cis-regulatory processes are highlighted in this review of MPRA approaches?\",\"answer\":\"The review focuses on cis-regulatory MPRAs that probe cotranscriptional and post-transcriptional processes. It specifically discusses alternative splicing, cleavage and polyadenylation, translation, and mRNA decay.\"}]","Decoding biology with massively parallel reporter assays and machine learning | PDF",1785808392,60,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"decoding-biology-with-massively-parallel-reporter-assays-and-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/decoding-biology-with-massively-parallel-reporter-assays-and-machine-learning/122030/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What do massively parallel reporter assays (MPRAs) measure in gene regulation studies?","Question",{"text":75,"@type":76},"MPRAs measure the activity of a biological process through reporter expression, where sequence variation introduced into reporter regions generates a reporter library. Molecular phenotypes are then quantified using high-throughput sequencing, often leveraging barcodes as proxies.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does machine learning improve interpretation of MPRA results?",{"text":80,"@type":76},"Machine learning models integrate and codify information learned from MPRAs to predict sequence behavior outside the training set. This enables quantitative understanding of cis-regulatory codes, supports variant stratification, and helps guide design of synthetic regulatory elements.",{"name":82,"@type":73,"acceptedAnswer":83},"Which cis-regulatory processes are highlighted in this review of MPRA approaches?",{"text":84,"@type":76},"The review focuses on cis-regulatory MPRAs that probe cotranscriptional and post-transcriptional processes. It specifically discusses alternative splicing, cleavage and polyadenylation, translation, and mRNA decay.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":29,"slug":108},5,"Comic","comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]