[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81850-en":3,"doc-seo-81850-105":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},81850,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Enabling Fast, Efficient, and Low-Cost Genomic and Metagenomic Analyses via Storage-Centric System Designs","Massive volumes of genomic and metagenomic sequence data create two bottlenecks: data movement burden from large, low-reuse storage reads, and data preparation overhead when compressed sequences must be decompressed and reformatted before analysis. This work introduces storage-centric system designs that execute (meta)genomic processing inside storage, enabling highly compressed storage and high-performance access while reducing movement, computation, and preparation costs. The paper presents GenStore, MegIS, GRAINS, and SAGe to improve performance, energy efficiency, and cost-efficiency simultaneously.","Enabling Fast, Efficient, and Low-Cost Genomic and Metagenomic Analyses via Storage-Centric System Designs  \nNika Mansouri Ghiasi Onur Mutlu  \nSAFARI Research Group  \nETH Zürich  \narXiv :2607 .02552v1 [ cs .AR] 26 Jun 2026  \nAbstract  \nDue to the challenges of analyzing and storing massive volumes of genomic and metagenomic sequence data, significant efforts have been made to accelerate (meta)genomic analyses and store sequence data compressed. Despite the benefits of these techniques, we identify two major outstanding problems in accessing stored sequence data and supplying it to the analysis units: (i) the data movement bottleneck due to moving large amounts oflow-reuse data from storage and the unnecessary burden on the rest ofthe system, and (ii) the data preparation bottleneck, where compressed sequence data needs to be first decompressed and formatted before analysis.  \nWe present customized storage-centric systems, which efficiently (i) analyze (meta)genomic data inside storage, and (ii) enable highlycompressed storage and high-performance access of large-scale sequence data, thereby alleviating the overheads of data movement, computation, and data preparation. First, we introduce GenStore, an in-storage processing system that filters genomic data not requiring expensive computation directly inside storage. Second, we propose MegIS, an in-storage processing system that significantly reduces the data movement overhead of metagenomic analysis. Third, we introduce GRAINS, a storage-centric system for analysis on largescale (meta)genomic graphs in storage. Fourth, we propose SAGe, an algorithm-architecture co-design for highly-compressed storage and high-performance access of sequence data.  \nWe demonstrate that the proposed systems significantly (e.g., by one to two orders of magnitude) improve performance, energy efficiency, and cost-efficiency, all at the same time. We hope these systems facilitate broader adoption of (meta)genomics and inspire research on other data-intensive domains in health and life sciences.  \n1 Genomic and Metagenomic Analyses  \nGenome sequence analysis, which examines the genomic information of living organisms and other biological entities, plays an important role in many fields, such as tracking outbreaks ofcommunicable diseases [1–26], personalized and precision medicine [27– 54], cancer research [55–101], pathogen monitoring for food safety [102, 103], agriculture [104–119], scientific discovery [120– 122], and biodiversity conservation [123, 124] . To analyze genomic information computationally, a sample of an organism’s or a biological entity’s genetic material, typically DNA or RNA that has been reverse-transcribed into DNA [125, 126], undergoes a process called sequencing [127–169] . Sequencing converts the information from DNA molecules to digital data. Current sequencing technologies cannot sequence long DNA molecules end-to-end. Instead, state-of-the-art sequencers generate randomly-and redundantlysampled smaller and inexact DNA fragments, called reads. Sets of genomic reads (called read sets) are then used in genomic analysis. Traditional genomics analyzes sequences of a genomic sample from  \nan individual (or a small group of individuals) of the same known species.  \nSince sometimes a sample contains organisms or biological entities with different species present in a common environment (e.g., human gut, soil, or oceans), genomic analysis is complemented by metagenomic analysis [117, 121, 122, 170–177]. Metagenomic analysis refers to the study of the genome sequences of various organisms or biological entities with different species present in a common environment. Since metagenomics deals with genome sequences whose species are not known in advance in many cases, it requires comparisons of the target sequences against large databases of many reference genomes. Metagenomics has led to groundbreaking advances in many fields, such as precision medicine [178–183], urgent clinical setting","cbCaiarxY8PhEpbU","https://ap.wps.com/l/cbCaiarxY8PhEpbU","pdf",781964,5,1,14,"English","en",105,"# Genomic and Metagenomic Analyses\n# Prior Work on Improving the Analysis and Storage of (Meta)Genomic Sequence Data\n## Challenges and growth of sequence data\n## Directions: accelerate analysis and improve storage","[{\"question\":\"What two key bottlenecks does the document identify in handling genomic and metagenomic sequence data?\",\"answer\":\"It identifies (i) a data movement bottleneck caused by moving large amounts of low-reuse data from storage and (ii) a data preparation bottleneck where compressed sequences must be decompressed and formatted before analysis.\"},{\"question\":\"How do the proposed storage-centric systems address these bottlenecks?\",\"answer\":\"They analyze (meta)genomic data inside storage, allowing highly compressed storage with high-performance access, which reduces overhead from data movement, computation, and data preparation.\"},{\"question\":\"What systems are introduced, and what is each one responsible for?\",\"answer\":\"GenStore filters genomic data inside storage to avoid expensive computation, MegIS reduces data movement overhead for metagenomic analysis, GRAINS supports in-storage analysis on large-scale (meta)genomic graphs, and SAGe co-designs algorithms and architecture for highly compressed storage and high-performance access.\"}]","Enabling Fast, Efficient, and Low-Cost Genomic and Metagenomic Analyses via Storage-Centric System Designs | PDF",1784176638,35,{"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},"enabling-fast-efficient-and-low-cost-genomic-and-metagenomic-analyses-via-storage-centric-system-designs","",{"@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/enabling-fast-efficient-and-low-cost-genomic-and-metagenomic-analyses-via-storage-centric-system-designs/81850/",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","2026-07-30","2026-07-16",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 two key bottlenecks does the document identify in handling genomic and metagenomic sequence data?","Question",{"text":77,"@type":78},"It identifies (i) a data movement bottleneck caused by moving large amounts of low-reuse data from storage and (ii) a data preparation bottleneck where compressed sequences must be decompressed and formatted before analysis.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How do the proposed storage-centric systems address these bottlenecks?",{"text":82,"@type":78},"They analyze (meta)genomic data inside storage, allowing highly compressed storage with high-performance access, which reduces overhead from data movement, computation, and data preparation.",{"name":84,"@type":75,"acceptedAnswer":85},"What systems are introduced, and what is each one responsible for?",{"text":86,"@type":78},"GenStore filters genomic data inside storage to avoid expensive computation, MegIS reduces data movement overhead for metagenomic analysis, GRAINS supports in-storage analysis on large-scale (meta)genomic graphs, and SAGe co-designs algorithms and architecture for highly compressed storage and high-performance access.","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},[95,99,103,107,111,116,121,124,129,132,136],{"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":20,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"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":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":20,"slug":139},19,"General","general"]