BloodCircR: A Comprehensive Database for Human Peripheral Blood Circular RNAs

BloodCircR: A Comprehensive Database for Human Peripheral Blood Circular RNAs | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article BloodCircR: A Comprehensive Database for Human Peripheral Blood Circular RNAs Shaoxun Yuan, Wanjun Gu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5764022/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Blood circular RNAs (circRNAs) are stable, noncoding RNAs with diverse functional roles. However, a comprehensive resource for blood-derived circRNAs is lacking. We developed BloodCircR , a database of circRNAs from human peripheral blood, to support research on circRNAs in disease and biomarker discovery. Methods: BloodCircR integrates circRNAs identified from 89 human blood RNA-seq datasets, comprising 5,430 samples across 58 diseases categorized into 16 groups. Full-length circRNAs were identified using CIRI-full and supplemented with data from public databases. The platform provides detailed annotations and tools for exploring circRNA expression and differential expression. Results: The database includes approximately 2.3 million circRNAs, of which over 1.7 million are exonic. Most circRNAs are full-length, with a substantial proportion derived from infectious disease datasets. Functional annotations suggest their interactions with miRNAs and RNA-binding proteins, while expression analysis supports the investigation of circRNA roles in disease. Conclusions: BloodCircR is a comprehensive resource for exploring circRNAs in human blood, offering significant insights into their potential as biomarkers and therapeutic targets. Bioinformatics BloodCircR circRNAs blood Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Circular RNAs (circRNAs) are a class of noncoding RNA molecules characterized by their covalently closed-loop structure, which lacks 5’ caps and 3’ polyadenylated tails, making them resistant to exonuclease-mediated degradation ( 1 ). This unique structural property confers high stability, rendering circRNAs particularly attractive as candidates for biomarker discovery in liquid biopsy ( 2 ) and as therapeutic targets ( 3 ). Emerging studies have shown that circRNAs perform diverse functions, such as acting as microRNA (miRNA) sponges, interacting with RNA-binding proteins (RBPs), and regulating transcriptional processes ( 4 ). Furthermore, circRNAs exhibit tissue-specific expression patterns and have been identified as key regulators in various diseases ( 5 – 7 ). Notably, their dysregulated expression in blood samples has been associated with several human diseases, including hepatocellular carcinoma ( 8 ), leukemia ( 9 , 10 ), COVID-19 ( 11 ), and tuberculosis ( 12 , 13 ). Consequently, a comprehensive investigation of the circRNA repertoire in human blood is essential for understanding their biological roles in disease and facilitating the development of circRNA biomarkers. Recent advances in high-throughput sequencing technologies and computational tools have greatly expanded our understanding of the circRNA repertoire ( 14 ). Comprehensive databases now catalog millions of circRNAs across tissue types and organisms ( 15 ). For example, circBase , the first circRNA database, lists nearly 100,000 circRNAs derived from 78 samples across five species ( 16 ). CIRCpedia extended this resource to approximately 260,000 circRNAs with detailed expression features from 185 samples spanning various cell types and tissues, including disease contexts ( 17 ). More recently, CircAtlas further expanded the catalog, encompassing over three million circRNAs from 2,672 samples across 10 vertebrate species, including more than 2.5 million full-length circRNAs ( 18 ). Specialized databases, such as TransCirc ( 19 ) and CircRNADisease ( 20 ), focus on specific functional or disease-related aspects of circRNAs. TransCirc catalogs 328,080 circRNAs with an emphasis on circRNA translation, while CircRNADisease contains nearly 7,000 experimentally validated circRNA‒disease entries across multiple species. Additionally, cancer-specific circRNA databases like MiOncoCirc ( 21 ) and CSCD2 ( 22 ) have identified over a million circRNAs from more than 1,000 cancer samples and cell lines. Despite these advances, a systematic resource dedicated to blood-specific circRNAs has been notably absent. Existing blood transcriptome resources, such as BloodSpot ( 23 ) and exoRBase ( 24 ), primarily focus on mRNAs or noncoding RNAs in exosomes or platelets, leaving a significant gap in the understanding of blood-derived circRNAs. BloodSpot , for instance, offers extensive gene expression data for blood cells but is limited to mRNAs. Similarly, exoRBase , ExMdb ( 25 ), and PltDB ( 26 ) emphasize expression landscapes of mRNAs, lncRNAs, and circRNAs in exosomes or platelets. This gap highlights the need for a dedicated resource to systematically identify and organize blood circRNAs. To address this need, we developed BloodCircR , a comprehensive database for circRNAs specific to human peripheral blood (Fig. 1 ). By systematically analyzing public high-throughput RNA sequencing (HT RNA-seq) datasets, BloodCircR curates blood circRNAs and provides detailed annotations, including circRNA structures, predicted binding sites for miRNAs and RBPs, and associated interaction networks. The database also incorporates standardized nomenclature and integrates circRNA IDs from existing databases, ensuring compatibility and promoting data sharing. Additionally, BloodCircR enables users to explore expression profiles across datasets or uploaded their own data for integrative analysis. With these features, BloodCircR serves as a centralized resource to advance circRNA research and enhance understanding of their roles in health and disease. Materials and Methods Data Collection and Processing We performed a manual search of the GEO ( 27 ) and ArrayExpress ( 28 ) databases to obtain transcriptomic data related to human diseases and phenotypes (Fig. 1 A). The inclusion criteria for dataset selection were: ( 1 ) human peripheral blood samples, ( 2 ) HT RNA-seq, ( 3 ) paired-end sequencing, and ( 4 ) use of rRNA depletion methods. Following these criteria, we compiled 89 RNA-seq datasets from 93 studies, encompassing 5,430 blood samples that represent 58 human diseases or phenotypes across 16 disease categories ( Supplementary Table S1 ). From these datasets, we identified blood circRNAs and reconstructed their internal structures using AQUARIUM-HB ( 29 ) (Fig. 1 B), a bioinformatics pipeline optimized for human blood HT RNA-seq data. AQUARIUM-HB utilizes CIRI-full to identify circRNAs ( 30 ). For circRNAs categorized as “ break ” or “ BSJ only ” transcripts by CIRI-full , we reconstructed full-length sequences using internal structure information from the FL-circAS ( 31 ) or IsoCirc ( 32 ) databases. By integrating circRNAs identified via CIRI-full with those curated from the FL-circAS or IsoCirc databases, AQUARIUM-HB constructs a comprehensive atlas of full-length blood circRNA transcripts. The pipeline also annotates circRNAs, quantifies their expression, and conducts expression analyses. General Information BloodCircR provides detailed annotations for all identified blood circRNAs (Fig. 1 C). Each circRNA is assigned a unique BloodCircR ID , formatted as “circ + host gene + transcript number”. To ensure compatibility with other platforms, we adopted the circRNA naming standard proposed by Chen et al . ( 33 ). Each circRNA’s uniform ID includes the “circ” prefix, its host gene name, circular exon annotations, and gene number, providing detailed structural information. Aliases ID s for each circRNA, sourced from existing databases (e.g., circBase , circAtlas , FL-circAS , TransCirc , and PltDB ) are also included. The origin of each circRNA is specified, including whether it was identified via CIRI-full or complemented with data from FL-circAS and/or IsoCirc . CircRNAs are classified into four confidence levels based on their reconstruction methods and detection frequencies: Level-1 circRNAs: Full-length sequences reconstructed via CIRI-full , and detected in at least five samples or deposited in FL-circAS and/or IsoCirc . Level-2 circRNAs: Full-length sequences reconstructed via CIRI-full but detected in fewer than five samples or not found in FL-circAS or IsoCirc ; blood circRNAs deposited in FL-circAS and/or IsoCirc but not detected in the analyzed samples. Level-3 circRNAs: Incomplete circRNAs reconstructed from full-length circRNAs in FL-circAS and/or IsoCirc . Level-4 circRNAs: Incomplete circRNAs reconstructed using human genome annotations. Functional Annotation BloodCircR provides extensive functional annotations, including miRNA binding sites, RBP binding sites, and circRNA–miRNA–RBP–mRNA interaction networks (Fig. 1 C). miRNA binding sites were predicted using TargetScan (v7.0) ( 34 ), based on miRNA conservation and 3' untranslated regions (3' UTRs) analysis. High-confidence RBP binding sites were identified via RBPmap (v1.2) ( 35 ), incorporating known RBP binding site data and sequence features. Interaction networks were constructed by integrating predicted miRNA and RBP binding sites on circRNAs and analyzing interactions between mRNAs and RBPs/miRNAs. Enrichment analysis on genes within the interaction network was conducted using the clusterProfiler package (version 4.6.2) ( 36 ) to explore Gene Ontology (GO) ( 37 ), Kyoto Encyclopedia of Genes and Genomes (KEGG) ( 38 ), and Reactome ( 39 ) pathways. To investigate protein‒protein interactions of circRNA host genes, the STRING database ( 40 ) was used, providing the top ten associated genes and corresponding enrichment results. Expression Analysis CircRNAs generally exhibit lower expression levels than their linear counterparts, with substantial variation across contexts. To elucidate regulatory mechanisms, we quantified circular and linear RNA transcripts simultaneously. CircRNA expression levels were analyzed across diseases at the isoform, back-splice junction (BSJ), and gene levels (Fig. 1 D), enabling the identification of disease-specific expression profiles and potential biomarkers. Within each disease category, overall circRNA expression differences were assessed across subgroups. Individual circRNAs were further analyzed at isoform, BSJ, and gene levels. Differential expression analysis was conducted to identify significantly altered circRNAs, and results were visualized to highlight notable expression patterns. Enrichment analyses were conducted on GO , KEGG , and Reactome pathways, supplemented by gene set enrichment analysis (GSEA) ( 41 ) using the clusterProfiler package and hallmark gene sets from the MSigDB database ( 42 ). These analyses provided insights into the biological functions and pathways associated with differentially expressed circRNAs. Database Deployment The BloodCircR database was deployed on a virtual machine running CentOS 7.9 ( Supplementary Figure S1 ). The backend employs MySQL (v5.7.44), while the web server uses Apache (v2.4.62) and PHP (v7.3.33). Interactive visualization tools, implemented using jQuery (v3.6.4), Apache ECharts (v5.4.3) ( 43 ), and R (v4.3.1), ensure a seamless user experience (Fig. 1 E). BloodCircR is publicly accessible at http://36.139.165.98:8002/ , where users can download detected circRNA information and explore the comprehensive dataset. Results Human Blood CircRNA Repertoire The current release of BloodCircR contains 2,298,044 circRNAs derived from human blood. These were constructed by integrating circRNAs identified from HT RNA-seq data with full-length circRNAs from the FL-circAS and IsoCirc databases. The majority (90.7%, 2,083,846 circRNAs) originate from 5,430 human blood samples (Fig. 2 A), while the remaining 9.3% (214,198 circRNAs) are exclusively sourced from the FL-circAS or IsoCirc databases (Fig. 2 B). These 5,430 samples span 58 human diseases or phenotypes categorized into 16 groups, with infectious diseases representing the largest category, comprising 3,000 samples (55.24%). Among the circRNAs derived from blood samples, 74.1% (1,544,889) were reconstructed in full length using CIRI-full (Fig. 2 B). The remaining 538,957 circRNAs were incompletely reconstructed, including 468,435 with breaks in their internal structure and 70,522 containing only BSJ sites (Fig. 2 B). Regarding confidence levels, 251,308 circRNAs are classified as Level-1 , the highest confidence level, as they were fully reconstructed by CIRI-full , detected in at least five samples or previously deposited in public databases (Fig. 2 C). More than half of the circRNAs (1,293,581) are categorized as Level- 2, representing newly identified circRNAs detected in fewer than five samples. CircRNAs incompletely reconstructed by CIRI-full are classified as Level-3 (302,751 circRNAs) or Level-4 (450,404 circRNAs), depending on whether they were complemented by existing full-length circRNAs or human gene annotations, respectively (Fig. 2 C). Exonic circRNAs dominate the repertoire, constituting 74.94% (1,722,207 circRNAs) (Fig. 2 D). Most circRNAs contain fewer than three exons (Fig. 2 E), and the majority are relatively short, with the largest proportion being approximately 200 nucleotides in length (Fig. 2 F). The majority of BSJs correspond to a single circRNA, while most genes produce 2 to 50 circRNAs (Fig. 2 G). Data Resources BloodCircR provides detailed information on studies and datasets related to human blood samples. Study details include publication information and associated datasets (Fig. 3 A), while dataset details encompass sample metadata and the number of detected circRNAs (Fig. 3 B). For each dataset, the platform also provides several analytical insights: (i) visualization of circRNA counts across samples; (ii) circRNA expression profiles across sample groups; (iii) sample proportion distributions for detected circRNAs; and (iv) key characteristics such as confidence levels and length distributions. CircRNA Details The BloodCircR platform features a user-friendly interface for exploring the human blood circRNA repertoire, with the following key functionalities: 1) Searchability : Users can search circRNAs by BloodCircR ID, BSJ ID, genomic location, circRNA type, host gene, and other identifiers (Fig. 4 A), enabling precise queries. 2) Internal structure : Full-length sequences and reconstructed structures for each circRNA are provided (Fig. 4 B). Short-read alignments can also be visualizd to validate CIRI-full reconstruction. 3) Functional annotations : Predicted miRNA binding sites, RBP binding sites, and circRNA–miRNA–RBP–mRNA interaction networks are available (Fig. 4 C). Protein–protein interaction (PPI) networks of host genes are included to highlight functional significance. 4) Expressions : Expression levels of circRNAs and their host genes across 16 disease categories are displayed (Fig. 4 D). Junction ratios at the BSJ site are also provided to examine splicing strength of back-splicing events. Expression Profiles BloodCircR includes three modules for exploring circRNA expressions across datasets and human phenotypes: 1) Overall circRNA expression : Allows comparisons of overall circRNA expression levels across sample groups within or between datasets (Fig. 5 A). Statistical significance is assessed using a t -test. 2) Single circRNA expression : Enables analysis of individual circRNAs at isoform, BSJ, or gene levels across groups (Fig. 5 B). Results are visualized as violin plots, with significance determined via a t -test. 3) Differential expression analysis : Identifies circRNAs significantly associated with specific diseases or phenotypes based on customizable thresholds for significance, including P values, fold changes, and detection ratios (Fig. 5 C). This module integrates gene set enrichment analysis with GO , KEGG , and Reactome pathways to reveal biological functions of differentially expressed circRNAs (Fig. 5 D). Differential expression patterns can be compared with those of linear mRNA counterparts to assess the regulatory roles of circRNAs in disease (Fig. 5 E). Additionally, users can upload their own circRNA expression data for custom analyses and comparisons with datasets available in BloodCircR (Fig. 5 F). Discussion In this study, we constructed BloodCircR , a comprehensive human blood circRNA database encompassing nearly 2.3 million blood-derived circRNAs, including over 1.5 million full-length sequences. This resource was developed from 89 human blood RNA-seq datasets comprising 5,430 samples, representing 58 human diseases or phenotypes across 16 disease categories. BloodCircR addresses a significant gap in circRNA research, particularly for RNA-seq data derived from human blood samples. Unlike existing databases, BloodCircR integrates large-scale RNA-seq data to provide comprehensive circRNA sequences along with detailed functional annotations. The database also offers robust tools for expression and differential expression analysis, enabling in-depth comparative studies of disease phenotypes and uncovering the roles of circRNAs in disease mechanisms. Additionally, the platform supports user-uploaded datasets for customized analyses, significantly enhancing its utility for the broader research community. In summary, BloodCircR functions as both a comprehensive database of human blood circRNAs and an integrative platform for circRNA expression analysis using RNA-seq data. This resource is essential for exploring the regulatory roles of blood circRNAs in diseases and provides numerous research opportunities for the development of blood-based biomarkers and therapeutic strategies. Future updates to BloodCircR will aim to expand the diversity of disease-related blood samples, incorporate additional analytical tools, and identify potential biomarkers to further advance the application of blood circRNAs in medical research. Declarations Acknowledgements This work was supported by the National Key R&D Program of China (Nos. 2022YFC3500200, 2022YFC3500202), the National Natural Science Foundation of China (Nos. 81930117, 82430122), and the Jiangsu Provincial Social Development and Clinical Frontier Technology Project (BE2023790). Contributions W.G. conceived the research. S.Y., X.B., and L.L. contributed to data collection and analysis. S.Y., X.B., and W.G. wrote the manuscript. All authors read, revised and approved the final version of the manuscript. Ethics declarations Not applicable. Competing interests The authors declare no competing interests. References Liu C-X, Chen L-L (2022) Circular RNAs: Characterization, cellular roles, and applications. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5764022","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":397651496,"identity":"b2483e97-17b2-419c-99ff-69c27626b82e","order_by":0,"name":"Shaoxun Yuan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Shaoxun","middleName":"","lastName":"Yuan","suffix":""},{"id":397651497,"identity":"c7255668-5e8a-414d-b102-9b99e464a161","order_by":1,"name":"Wanjun Gu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYJACAxDBz8CQAOEeIFaLZAMpWiD64CoJaTE4fvZAMe+O2sTNtxueSfzMYZDju5HA+LkAn5YzeQnGvGeOJ267cyBNsncbg7HkjQRm6Rl4tJgdyDEw5m07lrjtRkKaNOM2hsQNNxLYmHnwaTn/BqJl8wyIlnrCWm6AbalJ3CAB0ZJgQEiL/Y03BoZz2w4Yz7iRkGzZu03CcOaZh83S+LRI9ueYGbxtq5Ptn5GTeOPnNht5vuPJBz/j0wIEbMCoPAykeRKAhAQQMzbg18DAwPyAgaEOSLMfIKRyFIyCUTAKRigAABN6T/BSk4mXAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Wanjun","middleName":"","lastName":"Gu","suffix":""}],"badges":[],"createdAt":"2025-01-04 14:15:00","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5764022/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5764022/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73149571,"identity":"fb3cc002-5316-4bdd-9a6f-9ca96fd3bdb8","added_by":"auto","created_at":"2025-01-07 08:13:50","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1072788,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and functional modules of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBloodCircR\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e \u003cstrong\u003e(A) \u003c/strong\u003eIntegration of human blood circRNAs from 89 RNA-seq datasets. (\u003cstrong\u003eB\u003c/strong\u003e) Identification of full-length circRNAs using \u003cem\u003eCIRI-full\u003c/em\u003e, supplemented with data from public databases \u003cem\u003eFL-circAS\u003c/em\u003eand \u003cem\u003eIsoCirc\u003c/em\u003e. (\u003cstrong\u003eC\u003c/strong\u003e) Detailed annotations provided for each circRNA. (\u003cstrong\u003eD\u003c/strong\u003e) Tools for expression and differential expression analysis enabling in-depth exploration. \u003cstrong\u003e(E)\u003c/strong\u003e Customizable analyses supported by user-uploaded datasets for integrative analysis.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5764022/v1/d06d6ffaffe86aadb5ab794d.jpg"},{"id":73148203,"identity":"b6b6fc2c-e06d-42ea-8f96-ff660a8605a2","added_by":"auto","created_at":"2025-01-07 08:05:50","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2025035,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSummary statistics of the human blood circRNA repertoire in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBloodCircR\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e. (A) \u003c/strong\u003eDistribution of sample counts and circRNA counts across disease types. \u003cstrong\u003e(B) \u003c/strong\u003eSource distribution of all circRNAs. \u003cstrong\u003e(C)\u003c/strong\u003e Proportional distribution of circRNA confidence levels. \u003cstrong\u003e(D)\u003c/strong\u003e Distribution of circRNA types. \u003cstrong\u003e(E) \u003c/strong\u003eExon counts distribution for exonic circRNAs. \u003cstrong\u003e(F) \u003c/strong\u003eDistribution of circRNA transcript lengths. \u003cstrong\u003e(G) \u003c/strong\u003eNumber of circRNA isoforms per BSJ site and per host gene.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5764022/v1/ac7b91e162824e6744994dba.jpg"},{"id":73148207,"identity":"ab916c74-0220-4736-b39e-6b0322a0e768","added_by":"auto","created_at":"2025-01-07 08:05:50","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1683015,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResources of HT RNA-seq studies and datasets in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBloodCircR\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003eOverview of study components, including disease types, related datasets and publication details. \u003cstrong\u003e(B)\u003c/strong\u003e Key dataset components, including phenotype, sample metadata, and detected circRNAs: \u003cstrong\u003e(i)\u003c/strong\u003e Number of circRNAs identified in samples. \u003cstrong\u003e(ii)\u003c/strong\u003e CircRNA expression profiles in disease and control groups. \u003cstrong\u003e(iii)\u003c/strong\u003e Sample distributions of detected circRNAs. \u003cstrong\u003e(iv)\u003c/strong\u003e Characteristics of detected circRNAs.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5764022/v1/201603609c729f5f3399cc22.jpg"},{"id":73148212,"identity":"2eb3c71b-b231-482d-aab1-503b57025249","added_by":"auto","created_at":"2025-01-07 08:05:50","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2530132,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDetails of the human blood circRNA repertoire in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBloodCircR\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003eSearch options for circRNAs using multiple identifiers and parameters. \u003cstrong\u003e(B) \u003c/strong\u003eGeneral information for specific circRNAs, including genomic location and structure. \u003cstrong\u003e(C)\u003c/strong\u003eFunctional annotations, including predicted miRNA and RBP binding sites and interaction networks. \u003cstrong\u003e(D) \u003c/strong\u003eExpression levels of circRNAs across disease types, displayed at isoform, gene, and junction read ratio levels.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5764022/v1/cce9f91527556b1fa83af2ab.jpg"},{"id":73149574,"identity":"0810f063-b99e-4bca-8781-cc8b082eef43","added_by":"auto","created_at":"2025-01-07 08:13:50","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1896911,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression analysis in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBloodCircR\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e. (A) \u003c/strong\u003eComparison of total circRNA expression across different groups. \u003cstrong\u003e(B) \u003c/strong\u003eExpression analysis of individual circRNAs at isoform, BSJ, and gene levels. \u003cstrong\u003e(C)\u003c/strong\u003e Identification of differentially expressed circRNAs at isoform, BSJ, and gene levels. \u003cstrong\u003e(D) \u003c/strong\u003eEnrichment analyses, including \u003cem\u003eGO\u003c/em\u003e, \u003cem\u003eKEGG\u003c/em\u003e, and \u003cem\u003eReactome\u003c/em\u003e pathways. \u003cstrong\u003e(E) \u003c/strong\u003eComparison of expression differences between circRNAs and linear mRNAs, including functional differences. \u003cstrong\u003e(F)\u003c/strong\u003e Integrative analysis through user-uploaded circRNA expression data.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5764022/v1/1312636098be52107f60b2ef.jpg"},{"id":73149575,"identity":"d5be5765-0f05-46ad-9aef-2ec5af55a021","added_by":"auto","created_at":"2025-01-07 08:13:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9775258,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5764022/v1/8efdc1ec-993e-408d-b940-e6da9c64331a.pdf"},{"id":73149572,"identity":"395d3ac0-3ec1-4cd0-aa64-b940adac625c","added_by":"auto","created_at":"2025-01-07 08:13:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":341177,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S1. \u003c/strong\u003eArchitecture of the \u003cem\u003eBloodCircR\u003c/em\u003e database.\u003c/p\u003e","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-5764022/v1/3eff07815ac6a5b073999354.docx"},{"id":73149573,"identity":"6a50061f-3958-49bd-99ea-5b291d62529d","added_by":"auto","created_at":"2025-01-07 08:13:50","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":23761,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table S1. \u003c/strong\u003eSummary of the collected HT RNA-seq datasets in \u003cem\u003eBloodCircR\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5764022/v1/3cf07787faa71104b0bb4145.xlsx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eBloodCircR: A Comprehensive Database for Human Peripheral Blood Circular RNAs\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCircular RNAs (circRNAs) are a class of noncoding RNA molecules characterized by their covalently closed-loop structure, which lacks 5\u0026rsquo; caps and 3\u0026rsquo; polyadenylated tails, making them resistant to exonuclease-mediated degradation (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). This unique structural property confers high stability, rendering circRNAs particularly attractive as candidates for biomarker discovery in liquid biopsy (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) and as therapeutic targets (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Emerging studies have shown that circRNAs perform diverse functions, such as acting as microRNA (miRNA) sponges, interacting with RNA-binding proteins (RBPs), and regulating transcriptional processes (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Furthermore, circRNAs exhibit tissue-specific expression patterns and have been identified as key regulators in various diseases (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Notably, their dysregulated expression in blood samples has been associated with several human diseases, including hepatocellular carcinoma (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), leukemia (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), COVID-19 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), and tuberculosis (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Consequently, a comprehensive investigation of the circRNA repertoire in human blood is essential for understanding their biological roles in disease and facilitating the development of circRNA biomarkers.\u003c/p\u003e \u003cp\u003eRecent advances in high-throughput sequencing technologies and computational tools have greatly expanded our understanding of the circRNA repertoire (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Comprehensive databases now catalog millions of circRNAs across tissue types and organisms (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). For example, \u003cem\u003ecircBase\u003c/em\u003e, the first circRNA database, lists nearly 100,000 circRNAs derived from 78 samples across five species (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). \u003cem\u003eCIRCpedia\u003c/em\u003e extended this resource to approximately 260,000 circRNAs with detailed expression features from 185 samples spanning various cell types and tissues, including disease contexts (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). More recently, \u003cem\u003eCircAtlas\u003c/em\u003e further expanded the catalog, encompassing over three million circRNAs from 2,672 samples across 10 vertebrate species, including more than 2.5\u0026nbsp;million full-length circRNAs (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Specialized databases, such as \u003cem\u003eTransCirc\u003c/em\u003e (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) and \u003cem\u003eCircRNADisease\u003c/em\u003e (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), focus on specific functional or disease-related aspects of circRNAs. \u003cem\u003eTransCirc\u003c/em\u003e catalogs 328,080 circRNAs with an emphasis on circRNA translation, while \u003cem\u003eCircRNADisease\u003c/em\u003e contains nearly 7,000 experimentally validated circRNA‒disease entries across multiple species. Additionally, cancer-specific circRNA databases like \u003cem\u003eMiOncoCirc\u003c/em\u003e (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) and \u003cem\u003eCSCD2\u003c/em\u003e (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) have identified over a million circRNAs from more than 1,000 cancer samples and cell lines. Despite these advances, a systematic resource dedicated to blood-specific circRNAs has been notably absent. Existing blood transcriptome resources, such as \u003cem\u003eBloodSpot\u003c/em\u003e (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) and \u003cem\u003eexoRBase\u003c/em\u003e (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), primarily focus on mRNAs or noncoding RNAs in exosomes or platelets, leaving a significant gap in the understanding of blood-derived circRNAs. \u003cem\u003eBloodSpot\u003c/em\u003e, for instance, offers extensive gene expression data for blood cells but is limited to mRNAs. Similarly, \u003cem\u003eexoRBase\u003c/em\u003e, \u003cem\u003eExMdb\u003c/em\u003e (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), and \u003cem\u003ePltDB\u003c/em\u003e (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) emphasize expression landscapes of mRNAs, lncRNAs, and circRNAs in exosomes or platelets. This gap highlights the need for a dedicated resource to systematically identify and organize blood circRNAs.\u003c/p\u003e \u003cp\u003eTo address this need, we developed \u003cem\u003eBloodCircR\u003c/em\u003e, a comprehensive database for circRNAs specific to human peripheral blood (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). By systematically analyzing public high-throughput RNA sequencing (HT RNA-seq) datasets, \u003cem\u003eBloodCircR\u003c/em\u003e curates blood circRNAs and provides detailed annotations, including circRNA structures, predicted binding sites for miRNAs and RBPs, and associated interaction networks. The database also incorporates standardized nomenclature and integrates circRNA IDs from existing databases, ensuring compatibility and promoting data sharing. Additionally, \u003cem\u003eBloodCircR\u003c/em\u003e enables users to explore expression profiles across datasets or uploaded their own data for integrative analysis. With these features, \u003cem\u003eBloodCircR\u003c/em\u003e serves as a centralized resource to advance circRNA research and enhance understanding of their roles in health and disease.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Collection and Processing\u003c/h2\u003e \u003cp\u003eWe performed a manual search of the \u003cem\u003eGEO\u003c/em\u003e (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) and \u003cem\u003eArrayExpress\u003c/em\u003e (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) databases to obtain transcriptomic data related to human diseases and phenotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The inclusion criteria for dataset selection were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) human peripheral blood samples, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) HT RNA-seq, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) paired-end sequencing, and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) use of rRNA depletion methods. Following these criteria, we compiled 89 RNA-seq datasets from 93 studies, encompassing 5,430 blood samples that represent 58 human diseases or phenotypes across 16 disease categories (\u003cb\u003eSupplementary Table S1\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eFrom these datasets, we identified blood circRNAs and reconstructed their internal structures using \u003cem\u003eAQUARIUM-HB\u003c/em\u003e (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), a bioinformatics pipeline optimized for human blood HT RNA-seq data. \u003cem\u003eAQUARIUM-HB\u003c/em\u003e utilizes \u003cem\u003eCIRI-full\u003c/em\u003e to identify circRNAs (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). For circRNAs categorized as \u0026ldquo;\u003cem\u003ebreak\u003c/em\u003e\u0026rdquo; or \u0026ldquo;\u003cem\u003eBSJ only\u003c/em\u003e\u0026rdquo; transcripts by \u003cem\u003eCIRI-full\u003c/em\u003e, we reconstructed full-length sequences using internal structure information from the \u003cem\u003eFL-circAS\u003c/em\u003e (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) or \u003cem\u003eIsoCirc\u003c/em\u003e (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) databases. By integrating circRNAs identified via \u003cem\u003eCIRI-full\u003c/em\u003e with those curated from the \u003cem\u003eFL-circAS\u003c/em\u003e or \u003cem\u003eIsoCirc\u003c/em\u003e databases, \u003cem\u003eAQUARIUM-HB\u003c/em\u003e constructs a comprehensive atlas of full-length blood circRNA transcripts. The pipeline also annotates circRNAs, quantifies their expression, and conducts expression analyses.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGeneral Information\u003c/h3\u003e\n\u003cp\u003e \u003cem\u003eBloodCircR\u003c/em\u003e provides detailed annotations for all identified blood circRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Each circRNA is assigned a unique \u003cem\u003eBloodCircR ID\u003c/em\u003e, formatted as \u0026ldquo;circ\u0026thinsp;+\u0026thinsp;host gene\u0026thinsp;+\u0026thinsp;transcript number\u0026rdquo;. To ensure compatibility with other platforms, we adopted the circRNA naming standard proposed by Chen \u003cem\u003eet al\u003c/em\u003e. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Each circRNA\u0026rsquo;s \u003cem\u003euniform ID\u003c/em\u003e includes the \u0026ldquo;circ\u0026rdquo; prefix, its host gene name, circular exon annotations, and gene number, providing detailed structural information. \u003cem\u003eAliases ID\u003c/em\u003es for each circRNA, sourced from existing databases (e.g., \u003cem\u003ecircBase\u003c/em\u003e, \u003cem\u003ecircAtlas\u003c/em\u003e, \u003cem\u003eFL-circAS\u003c/em\u003e, \u003cem\u003eTransCirc\u003c/em\u003e, and \u003cem\u003ePltDB\u003c/em\u003e) are also included. The origin of each circRNA is specified, including whether it was identified via \u003cem\u003eCIRI-full\u003c/em\u003e or complemented with data from \u003cem\u003eFL-circAS\u003c/em\u003e and/or \u003cem\u003eIsoCirc\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eCircRNAs are classified into four confidence levels based on their reconstruction methods and detection frequencies:\u003c/p\u003e \u003cp\u003e \u003cem\u003eLevel-1\u003c/em\u003e circRNAs: Full-length sequences reconstructed via \u003cem\u003eCIRI-full\u003c/em\u003e, and detected in at least five samples or deposited in \u003cem\u003eFL-circAS\u003c/em\u003e and/or \u003cem\u003eIsoCirc\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cem\u003eLevel-2\u003c/em\u003e circRNAs: Full-length sequences reconstructed via \u003cem\u003eCIRI-full\u003c/em\u003e but detected in fewer than five samples or not found in \u003cem\u003eFL-circAS\u003c/em\u003e or \u003cem\u003eIsoCirc\u003c/em\u003e; blood circRNAs deposited in \u003cem\u003eFL-circAS\u003c/em\u003e and/or \u003cem\u003eIsoCirc\u003c/em\u003e but not detected in the analyzed samples.\u003c/p\u003e \u003cp\u003e \u003cem\u003eLevel-3\u003c/em\u003e circRNAs: Incomplete circRNAs reconstructed from full-length circRNAs in \u003cem\u003eFL-circAS\u003c/em\u003e and/or \u003cem\u003eIsoCirc\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cem\u003eLevel-4\u003c/em\u003e circRNAs: Incomplete circRNAs reconstructed using human genome annotations.\u003c/p\u003e\n\u003ch3\u003eFunctional Annotation\u003c/h3\u003e\n\u003cp\u003e \u003cem\u003eBloodCircR\u003c/em\u003e provides extensive functional annotations, including miRNA binding sites, RBP binding sites, and circRNA\u0026ndash;miRNA\u0026ndash;RBP\u0026ndash;mRNA interaction networks (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). miRNA binding sites were predicted using \u003cem\u003eTargetScan\u003c/em\u003e (v7.0) (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), based on miRNA conservation and 3' untranslated regions (3' UTRs) analysis. High-confidence RBP binding sites were identified via \u003cem\u003eRBPmap\u003c/em\u003e (v1.2) (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), incorporating known RBP binding site data and sequence features. Interaction networks were constructed by integrating predicted miRNA and RBP binding sites on circRNAs and analyzing interactions between mRNAs and RBPs/miRNAs. Enrichment analysis on genes within the interaction network was conducted using the \u003cem\u003eclusterProfiler\u003c/em\u003e package (version 4.6.2) (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) to explore \u003cem\u003eGene Ontology\u003c/em\u003e (GO) (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), \u003cem\u003eKyoto Encyclopedia of Genes and Genomes (KEGG)\u003c/em\u003e (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), and \u003cem\u003eReactome\u003c/em\u003e (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e) pathways. To investigate protein‒protein interactions of circRNA host genes, the \u003cem\u003eSTRING\u003c/em\u003e database (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) was used, providing the top ten associated genes and corresponding enrichment results.\u003c/p\u003e\n\u003ch3\u003eExpression Analysis\u003c/h3\u003e\n\u003cp\u003eCircRNAs generally exhibit lower expression levels than their linear counterparts, with substantial variation across contexts. To elucidate regulatory mechanisms, we quantified circular and linear RNA transcripts simultaneously. CircRNA expression levels were analyzed across diseases at the isoform, back-splice junction (BSJ), and gene levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD), enabling the identification of disease-specific expression profiles and potential biomarkers. Within each disease category, overall circRNA expression differences were assessed across subgroups. Individual circRNAs were further analyzed at isoform, BSJ, and gene levels. Differential expression analysis was conducted to identify significantly altered circRNAs, and results were visualized to highlight notable expression patterns. Enrichment analyses were conducted on \u003cem\u003eGO\u003c/em\u003e, \u003cem\u003eKEGG\u003c/em\u003e, and \u003cem\u003eReactome\u003c/em\u003e pathways, supplemented by gene set enrichment analysis (GSEA) (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) using the \u003cem\u003eclusterProfiler\u003c/em\u003e package and \u003cem\u003ehallmark\u003c/em\u003e gene sets from the \u003cem\u003eMSigDB\u003c/em\u003e database (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). These analyses provided insights into the biological functions and pathways associated with differentially expressed circRNAs.\u003c/p\u003e\n\u003ch3\u003eDatabase Deployment\u003c/h3\u003e\n\u003cp\u003eThe \u003cem\u003eBloodCircR\u003c/em\u003e database was deployed on a virtual machine running \u003cem\u003eCentOS\u003c/em\u003e 7.9 (\u003cb\u003eSupplementary Figure S1\u003c/b\u003e). The backend employs \u003cem\u003eMySQL\u003c/em\u003e (v5.7.44), while the web server uses \u003cem\u003eApache\u003c/em\u003e (v2.4.62) and \u003cem\u003ePHP\u003c/em\u003e (v7.3.33). Interactive visualization tools, implemented using \u003cem\u003ejQuery\u003c/em\u003e (v3.6.4), \u003cem\u003eApache ECharts\u003c/em\u003e (v5.4.3) (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), and \u003cem\u003eR\u003c/em\u003e (v4.3.1), ensure a seamless user experience (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003cem\u003eBloodCircR\u003c/em\u003e is publicly accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://36.139.165.98:8002/\u003c/span\u003e\u003cspan address=\"http://36.139.165.98:8002/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, where users can download detected circRNA information and explore the comprehensive dataset.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eHuman Blood CircRNA Repertoire\u003c/h2\u003e \u003cp\u003eThe current release of \u003cem\u003eBloodCircR\u003c/em\u003e contains 2,298,044 circRNAs derived from human blood. These were constructed by integrating circRNAs identified from HT RNA-seq data with full-length circRNAs from the \u003cem\u003eFL-circAS\u003c/em\u003e and \u003cem\u003eIsoCirc\u003c/em\u003e databases. The majority (90.7%, 2,083,846 circRNAs) originate from 5,430 human blood samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), while the remaining 9.3% (214,198 circRNAs) are exclusively sourced from the \u003cem\u003eFL-circAS\u003c/em\u003e or \u003cem\u003eIsoCirc\u003c/em\u003e databases (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). These 5,430 samples span 58 human diseases or phenotypes categorized into 16 groups, with infectious diseases representing the largest category, comprising 3,000 samples (55.24%). Among the circRNAs derived from blood samples, 74.1% (1,544,889) were reconstructed in full length using \u003cem\u003eCIRI-full\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The remaining 538,957 circRNAs were incompletely reconstructed, including 468,435 with breaks in their internal structure and 70,522 containing only BSJ sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Regarding confidence levels, 251,308 circRNAs are classified as \u003cem\u003eLevel-1\u003c/em\u003e, the highest confidence level, as they were fully reconstructed by \u003cem\u003eCIRI-full\u003c/em\u003e, detected in at least five samples or previously deposited in public databases (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). More than half of the circRNAs (1,293,581) are categorized as \u003cem\u003eLevel-\u003c/em\u003e2, representing newly identified circRNAs detected in fewer than five samples. CircRNAs incompletely reconstructed by \u003cem\u003eCIRI-full\u003c/em\u003e are classified as \u003cem\u003eLevel-3\u003c/em\u003e (302,751 circRNAs) or \u003cem\u003eLevel-4\u003c/em\u003e (450,404 circRNAs), depending on whether they were complemented by existing full-length circRNAs or human gene annotations, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Exonic circRNAs dominate the repertoire, constituting 74.94% (1,722,207 circRNAs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Most circRNAs contain fewer than three exons (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE), and the majority are relatively short, with the largest proportion being approximately 200 nucleotides in length (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). The majority of BSJs correspond to a single circRNA, while most genes produce 2 to 50 circRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Resources\u003c/h3\u003e\n\u003cp\u003e \u003cem\u003eBloodCircR\u003c/em\u003e provides detailed information on studies and datasets related to human blood samples. Study details include publication information and associated datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), while dataset details encompass sample metadata and the number of detected circRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). For each dataset, the platform also provides several analytical insights: (i) visualization of circRNA counts across samples; (ii) circRNA expression profiles across sample groups; (iii) sample proportion distributions for detected circRNAs; and (iv) key characteristics such as confidence levels and length distributions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCircRNA Details\u003c/h2\u003e \u003cp\u003eThe \u003cem\u003eBloodCircR\u003c/em\u003e platform features a user-friendly interface for exploring the human blood circRNA repertoire, with the following key functionalities: 1) \u003cb\u003eSearchability\u003c/b\u003e: Users can search circRNAs by \u003cem\u003eBloodCircR\u003c/em\u003e ID, \u003cem\u003eBSJ\u003c/em\u003e ID, genomic location, circRNA type, host gene, and other identifiers (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), enabling precise queries. 2) \u003cb\u003eInternal structure\u003c/b\u003e: Full-length sequences and reconstructed structures for each circRNA are provided (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Short-read alignments can also be visualizd to validate \u003cem\u003eCIRI-full\u003c/em\u003e reconstruction. 3) \u003cb\u003eFunctional annotations\u003c/b\u003e: Predicted miRNA binding sites, RBP binding sites, and circRNA\u0026ndash;miRNA\u0026ndash;RBP\u0026ndash;mRNA interaction networks are available (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). \u003cem\u003eProtein\u0026ndash;protein interaction (PPI)\u003c/em\u003e networks of host genes are included to highlight functional significance. 4) \u003cb\u003eExpressions\u003c/b\u003e: Expression levels of circRNAs and their host genes across 16 disease categories are displayed (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Junction ratios at the BSJ site are also provided to examine splicing strength of back-splicing events.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eExpression Profiles\u003c/h2\u003e \u003cp\u003e \u003cem\u003eBloodCircR\u003c/em\u003e includes three modules for exploring circRNA expressions across datasets and human phenotypes: 1) \u003cb\u003eOverall circRNA expression\u003c/b\u003e: Allows comparisons of overall circRNA expression levels across sample groups within or between datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Statistical significance is assessed using a \u003cem\u003et\u003c/em\u003e-test. 2) \u003cb\u003eSingle circRNA expression\u003c/b\u003e: Enables analysis of individual circRNAs at isoform, BSJ, or gene levels across groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Results are visualized as violin plots, with significance determined via a \u003cem\u003et\u003c/em\u003e-test. 3) \u003cb\u003eDifferential expression analysis\u003c/b\u003e: Identifies circRNAs significantly associated with specific diseases or phenotypes based on customizable thresholds for significance, including \u003cem\u003eP\u003c/em\u003e values, fold changes, and detection ratios (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). This module integrates gene set enrichment analysis with \u003cem\u003eGO\u003c/em\u003e, \u003cem\u003eKEGG\u003c/em\u003e, and \u003cem\u003eReactome\u003c/em\u003e pathways to reveal biological functions of differentially expressed circRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Differential expression patterns can be compared with those of linear mRNA counterparts to assess the regulatory roles of circRNAs in disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, users can upload their own circRNA expression data for custom analyses and comparisons with datasets available in \u003cem\u003eBloodCircR\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we constructed \u003cem\u003eBloodCircR\u003c/em\u003e, a comprehensive human blood circRNA database encompassing nearly 2.3\u0026nbsp;million blood-derived circRNAs, including over 1.5\u0026nbsp;million full-length sequences. This resource was developed from 89 human blood RNA-seq datasets comprising 5,430 samples, representing 58 human diseases or phenotypes across 16 disease categories. \u003cem\u003eBloodCircR\u003c/em\u003e addresses a significant gap in circRNA research, particularly for RNA-seq data derived from human blood samples. Unlike existing databases, \u003cem\u003eBloodCircR\u003c/em\u003e integrates large-scale RNA-seq data to provide comprehensive circRNA sequences along with detailed functional annotations. The database also offers robust tools for expression and differential expression analysis, enabling in-depth comparative studies of disease phenotypes and uncovering the roles of circRNAs in disease mechanisms. Additionally, the platform supports user-uploaded datasets for customized analyses, significantly enhancing its utility for the broader research community.\u003c/p\u003e \u003cp\u003eIn summary, \u003cem\u003eBloodCircR\u003c/em\u003e functions as both a comprehensive database of human blood circRNAs and an integrative platform for circRNA expression analysis using RNA-seq data. This resource is essential for exploring the regulatory roles of blood circRNAs in diseases and provides numerous research opportunities for the development of blood-based biomarkers and therapeutic strategies. Future updates to \u003cem\u003eBloodCircR\u003c/em\u003e will aim to expand the diversity of disease-related blood samples, incorporate additional analytical tools, and identify potential biomarkers to further advance the application of blood circRNAs in medical research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key R\u0026amp;D Program of China (Nos. 2022YFC3500200, 2022YFC3500202), the National Natural Science Foundation of China (Nos. 81930117, 82430122), and the Jiangsu Provincial Social Development and Clinical Frontier Technology Project (BE2023790).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eW.G. conceived the research. S.Y., X.B., and L.L. contributed to data collection and analysis. S.Y., X.B., and W.G. wrote the manuscript. All authors read, revised and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLiu C-X, Chen L-L (2022) Circular RNAs: Characterization, cellular roles, and applications. Cell 185:2016\u0026ndash;2034\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWen G, Zhou T, Gu W (2021) The potential of using blood circular RNA as liquid biopsy biomarker for human diseases. 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Visual Inf 2:136\u0026ndash;146\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Nanjing University of Chinese Medicine","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"BloodCircR, circRNAs, blood","lastPublishedDoi":"10.21203/rs.3.rs-5764022/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5764022/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Blood circular RNAs (circRNAs) are stable, noncoding RNAs with diverse functional roles. However, a comprehensive resource for blood-derived circRNAs is lacking. We developed \u003cem\u003eBloodCircR\u003c/em\u003e, a database of circRNAs from human peripheral blood, to support research on circRNAs in disease and biomarker discovery.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e \u003cem\u003eBloodCircR\u003c/em\u003e integrates circRNAs identified from 89 human blood RNA-seq datasets, comprising 5,430 samples across 58 diseases categorized into 16 groups. Full-length circRNAs were identified using \u003cem\u003eCIRI-full\u003c/em\u003e and supplemented with data from public databases. The platform provides detailed annotations and tools for exploring circRNA expression and differential expression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The database includes approximately 2.3 million circRNAs, of which over 1.7 million are exonic. Most circRNAs are full-length, with a substantial proportion derived from infectious disease datasets. Functional annotations suggest their interactions with miRNAs and RNA-binding proteins, while expression analysis supports the investigation of circRNA roles in disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e \u003cem\u003eBloodCircR\u003c/em\u003e is a comprehensive resource for exploring circRNAs in human blood, offering significant insights into their potential as biomarkers and therapeutic targets.\u003c/p\u003e","manuscriptTitle":"BloodCircR: A Comprehensive Database for Human Peripheral Blood Circular RNAs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-07 08:05:45","doi":"10.21203/rs.3.rs-5764022/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0b1b8e2d-eed2-4977-a785-369ab2b7f93f","owner":[],"postedDate":"January 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":42365271,"name":"Bioinformatics"}],"tags":[],"updatedAt":"2025-01-07T08:05:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-07 08:05:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5764022","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5764022","identity":"rs-5764022","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}