GraphK-LR: Enhancing Long-read Metagenomic Binning with Read-overlap Graphs Across Microbial Kingdoms

GraphK-LR: Enhancing Long-read Metagenomic Binning with Read-overlap Graphs Across Microbial Kingdoms | 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 GraphK-LR: Enhancing Long-read Metagenomic Binning with Read-overlap Graphs Across Microbial Kingdoms Nethmi Ranasinghe, Sathsarani Aththanayaka, Jayathri Ranasinghe, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7390699/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: Metagenomics, the study of genetic material from environmental samples, relies on binning - the process of grouping DNA sequences from the same organism to disentangle complex species mixtures. Recently, metagenomics has shown a rising interest in using long-reads from third-generation sequencing technologies to overcome the limitations of short-reads. These long-reads contain species-specific signals for direct grouping into taxonomic bins prior to assembly. Previous studies have successfully used nucleotide composition and coverage for binning long-reads. The advent of less error-prone sequencing technologies has paved the way for incorporating additional information to enhance binning accuracy. In this paper, we introduce GraphK-LR, a long-read binning refiner that uses connectivity information between the reads and machine-learning-based graph techniques to refine potentially misclassified reads from an initial binning tool. Additionally, our tool uses marker-gene-based kingdom-level analysis to address the challenge of species from different microbial kingdoms being present in the same metagenomic sample, making it complex to bin using existing tools. This approach is inspired by the multitude of short-read refiners, addressing the gap in the unavailability of refining tools for long reads. Results: We evaluated the tool using publicly available mock community datasets sequenced with Oxford Nanopore R.10.x chemistry, initially binned using the existing tools OBLR and LRBinner. Upon refinement, we observed a marginal improvement of 2-3\% in binning accuracy, which indicates that both these tools are highly effective at correctly binning reads. Another long-reads binning tool named SemiBin2, discarded nearly 20\% of reads while binning, and after refinement we observed a significant improvement ranging from 20-30\% in evaluation criteria. These results demonstrate that GraphK-LR adds an additional layer of accuracy over the binning tools, particularly in cases with unclassified reads. Conclusion: Although there is still room for further enhancement, our tool represents an important initial step in exploring the capacity to further improve the accuracy of long-read binning by combining existing methods with more sophisticated techniques. The underlying concept of GraphK-LR holds the potential to advance long-read-based metagenomics analyses across a wide range of applications. The source code for GraphK-LR can be found at https://github.com/NethmiRanasinghe/GraphK-LR. Long-reads Metagenomic binning Marker-genes Read overlap graphs Graph Neural Network Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryGraphKLR.docx 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. 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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-7390699","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":510482883,"identity":"73c00250-cfab-4c31-a1ba-9b49b291c00b","order_by":0,"name":"Nethmi Ranasinghe","email":"","orcid":"","institution":"University of Peradeniya","correspondingAuthor":false,"prefix":"","firstName":"Nethmi","middleName":"","lastName":"Ranasinghe","suffix":""},{"id":510482885,"identity":"5b8a9b37-4376-46b6-9e9b-d6cbb52d35e1","order_by":1,"name":"Sathsarani Aththanayaka","email":"","orcid":"","institution":"University of 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Recently, metagenomics has shown a rising interest in using long-reads from third-generation sequencing technologies to overcome the limitations of short-reads. These long-reads contain species-specific signals for direct grouping into taxonomic bins prior to assembly. Previous studies have successfully used nucleotide composition and coverage for binning long-reads. The advent of less error-prone sequencing technologies has paved the way for incorporating additional information to enhance binning accuracy. In this paper, we introduce GraphK-LR, a long-read binning refiner that uses connectivity information between the reads and machine-learning-based graph techniques to refine potentially misclassified reads from an initial binning tool. Additionally, our tool uses marker-gene-based kingdom-level analysis to address the challenge of species from different microbial kingdoms being present in the same metagenomic sample, making it complex to bin using existing tools. This approach is inspired by the multitude of short-read refiners, addressing the gap in the unavailability of refining tools for long reads.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: We evaluated the tool using publicly available mock community datasets sequenced with Oxford Nanopore R.10.x chemistry, initially binned using the existing tools OBLR and LRBinner. Upon refinement, we observed a marginal improvement of 2-3\\% in binning accuracy, which indicates that both these tools are highly effective at correctly binning reads. Another long-reads binning tool named SemiBin2, discarded nearly 20\\% of reads while binning, and after refinement we observed a significant improvement ranging from 20-30\\% in evaluation criteria. These results demonstrate that GraphK-LR adds an additional layer of accuracy over the binning tools, particularly in cases with unclassified reads.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion: Although there is still room for further enhancement, our tool represents an important initial step in exploring the capacity to further improve the accuracy of long-read binning by combining existing methods with more sophisticated techniques. The underlying concept of GraphK-LR holds the potential to advance long-read-based metagenomics analyses across a wide range of applications. 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