Multi-scale hybrid correction of noisy long reads

Multi-scale hybrid correction of noisy long reads | 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 Article Multi-scale hybrid correction of noisy long reads Yuansheng Liu, Yicai Zhang, Yichen Li, Sisi Yuan, Xiao Luo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7401457/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 Long-read sequencing technologies have significantly enhanced genome resolution capabilities, but their inherent high error rate (1-15%) still constrains constrains the accuracy of assembly and other downstream analyses. Existing error correction methods struggle to balance the conflict between suppressing sequencing errors and preserving true biological variations, often leading to over-correction or loss of critical genomic signals. To address this, our study developed a novel hybrid error correction tool, DADEC, which synergistically integrates the global sequence context of De Bruijn Graph (DBG) with the local precision advantages of Multiple Sequence Alignment through a three-stage innovative architecture: (i) Dominant error elimination via high-confidence DBG correction; (ii) Haplotypeaware MSA refinement to filter residual errors with short-read support; (iii) Recovery of low-abundance biological signals using supplementary DBG correction. Validated across diverse datasets, DADEC reduced the error rate by an average of approximately 97.3%, significantly outperforming mainstream tools, demonstrating exceptional robustness particularly in complex scenarios. It also enhanced assembly contiguity, yielding more complete and continuous sequences, and effectively promoted strain-level metagenomic classification. Compared to the second-best performing tool, the False Discovery Rate and False Negative Rate were reduced by 73.8% and 84.8%, respectively. DADEC breakthroughy resolves the core conflict in the error correction field, thereby advancing the application of long-read technologies in complex genomic research. Biological sciences/Computational biology and bioinformatics/Data processing Biological sciences/Computational biology and bioinformatics/Computational models Biological sciences/Computational biology and bioinformatics/Software De Bruijn Graph Error Correction Haplotype-aware Long-read sequencing Multiple Sequence Alignment Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryFile.pdf Supplementary Material nrsoftwarepolicy.pdf Software and Code checklist nrreportingsummary.pdf Reporting summary Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7401457","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":509251944,"identity":"c608ecf9-c368-4caf-96a6-f220d6400fcf","order_by":0,"name":"Yuansheng Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYLACxgYgwQ4kPzaA+QZEamFmbGCcSaIWIOIlRovBjeRnD3/usMmTd2Zuk7bdcSexgb15mwRDzR08WtLMjXnPpBUbHmZsk8498yyxgedYmQTDsWd4tCSYSTO2HU7c2AzSAmQ0SOSYSTA2HMajJf2b5M+2/xAtliAt8m8IaQGaydt2IHE+M1ALI9gWHvxaJM+8KZPmbUtO3MDM2GzZ23bYuI0nrdgi4RhuLXzH07cBHWaXOL+9/eGNn22HZfvZD2+88aEGtxaFAzAXwhhsICIBpwYGBvkGdMYoGAWjYBSMAnQAAJQCWUVEgAcKAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-7680-3155","institution":"Hunan University","correspondingAuthor":true,"prefix":"","firstName":"Yuansheng","middleName":"","lastName":"Liu","suffix":""},{"id":509251945,"identity":"dab15865-91ae-479f-ad1e-6c3de68aaac8","order_by":1,"name":"Yicai Zhang","email":"","orcid":"","institution":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, Hunan, China.","correspondingAuthor":false,"prefix":"","firstName":"Yicai","middleName":"","lastName":"Zhang","suffix":""},{"id":509251946,"identity":"c7a5cec9-325e-41e3-98f2-946137e5b8b8","order_by":2,"name":"Yichen Li","email":"","orcid":"","institution":"Hunan University","correspondingAuthor":false,"prefix":"","firstName":"Yichen","middleName":"","lastName":"Li","suffix":""},{"id":509251947,"identity":"480611f1-548b-45f4-8cf4-51dd016aa046","order_by":3,"name":"Sisi Yuan","email":"","orcid":"","institution":"Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Sisi","middleName":"","lastName":"Yuan","suffix":""},{"id":509251948,"identity":"135ab321-1ade-4fce-b94e-b203222dc992","order_by":4,"name":"Xiao Luo","email":"","orcid":"https://orcid.org/0000-0002-3914-359X","institution":"Hunan University","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2025-08-18 16:09:24","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-7401457/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7401457/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96244578,"identity":"c35513e9-0b64-4ff5-bc2c-25a00caf828d","added_by":"auto","created_at":"2025-11-19 07:18:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":603520,"visible":true,"origin":"","legend":"Article File","description":"","filename":"Multiscalehybridcorrectionofnoisylongreads.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7401457/v1_covered_ec4569a0-e557-436c-8e1e-addc979e966e.pdf"},{"id":90561700,"identity":"8eaaf957-d0ec-4df2-82f0-838147b93113","added_by":"auto","created_at":"2025-09-04 06:22:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":632106,"visible":true,"origin":"","legend":"Supplementary Material","description":"","filename":"SupplementaryFile.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7401457/v1/ace3b3dc3fd981fbbba60c0b.pdf"},{"id":90561702,"identity":"7f1abd7a-07c3-45a0-822a-a58a36be7a53","added_by":"auto","created_at":"2025-09-04 06:22:53","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1316917,"visible":true,"origin":"","legend":"Software and Code checklist","description":"","filename":"nrsoftwarepolicy.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7401457/v1/3e5a795ecb94a8b33fe3a008.pdf"},{"id":90561701,"identity":"48aceb7b-79bb-469a-9211-53d4c9222ae7","added_by":"auto","created_at":"2025-09-04 06:22:53","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1665361,"visible":true,"origin":"","legend":"Reporting summary","description":"","filename":"nrreportingsummary.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7401457/v1/87b3f23b675a3fac1519ae76.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Multi-scale hybrid correction of noisy long reads","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"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":"De Bruijn Graph, Error Correction, Haplotype-aware, Long-read sequencing, Multiple Sequence Alignment","lastPublishedDoi":"10.21203/rs.3.rs-7401457/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7401457/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Long-read sequencing technologies have significantly enhanced genome resolution capabilities, but their inherent high error rate (1-15%) still constrains constrains the accuracy of assembly and other downstream analyses. Existing error correction methods struggle to balance the conflict between suppressing sequencing errors and preserving true biological variations, often leading to over-correction or loss of critical genomic signals. To address this, our study developed a novel hybrid error correction tool, DADEC, which synergistically integrates the global sequence context of De Bruijn Graph (DBG) with the local precision advantages of Multiple Sequence Alignment through a three-stage innovative architecture: (i) Dominant error elimination via high-confidence DBG correction; (ii) Haplotypeaware MSA refinement to filter residual errors with short-read support; (iii) Recovery of low-abundance biological signals using supplementary DBG correction. Validated across diverse datasets, DADEC reduced the error rate by an average of approximately 97.3%, significantly outperforming mainstream tools, demonstrating exceptional robustness particularly in complex scenarios. It also enhanced assembly contiguity, yielding more complete and continuous sequences, and effectively promoted strain-level metagenomic classification. Compared to the second-best performing tool, the False Discovery Rate and False Negative Rate were reduced by 73.8% and 84.8%, respectively. DADEC breakthroughy resolves the core conflict in the error correction field, thereby advancing the application of long-read technologies in complex genomic research.","manuscriptTitle":"Multi-scale hybrid correction of noisy long reads","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-04 06:22:49","doi":"10.21203/rs.3.rs-7401457/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":"3f442576-7a8a-4aee-9323-5e54e3d54996","owner":[],"postedDate":"September 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54087009,"name":"Biological sciences/Computational biology and bioinformatics/Data processing"},{"id":54087010,"name":"Biological sciences/Computational biology and bioinformatics/Computational models"},{"id":54087011,"name":"Biological sciences/Computational biology and bioinformatics/Software"}],"tags":[],"updatedAt":"2025-11-14T11:21:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-04 06:22:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7401457","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7401457","identity":"rs-7401457","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}