Repeat and haplotype aware error correction in nanopore sequencing reads with DeChat

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This paper presents DeChat, a repeat- and haplotype-aware error correction method designed for Nanopore R10 reads basecalled with high-accuracy or super-accuracy models (reported error rates below 2%). Using a hybrid strategy that combines de Bruijn graphs with variant-aware multiple sequence alignment, DeChat aims to avoid read overcorrection so that true variants in repeats and haplotypes are preserved while sequencing errors are corrected. Benchmarking shows substantially fewer residual errors (several-fold to two orders of magnitude) versus state-of-the-art methods, and applying DeChat improves genome assembly across multiple aspects. This paper does not explicitly state additional limitations in the provided text beyond its positioning as a method tailored to Nanopore R10 high-accuracy basecalling. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Abstract Error self-correction is a pivotal first step in the analysis of long-read sequencing data. However, most existing methods for this purpose are primarily tailored for noisy sequencing data with error rates exceeding 5%, often collapsing true variants in repeats and haplotypes. Alternatively, some methods are heavily optimized for PacBio HiFi reads, leaving a gap in methods specifically designed for Nanopore R10 reads basecalled with high accuracy or super accuracy models, which typically have error rates below 2%. Here, we introduce DeChat, a novel approach specifically designed for Nanopore R10 reads. DeChat enables repeat- and haplotype-aware error correction, leveraging the strengths of both de Bruijn graphs and variant-aware multiple sequence alignment to create a synergistic approach. This approach avoids read overcorrection, ensuring that variants in repeats and haplotypes are preserved while sequencing errors are accurately corrected. Benchmarking experiments reveal that reads corrected using DeChat exhibit substantially fewer errors, ranging from several times to two orders of magnitude lower, compared to the current state-of-the-art approaches. Furthermore, the application of DeChat for error correction significantly improves genome assembly across various aspects. DeChat is implemented as a highly efficient, standalone, and user-friendly software and is publicly available at https://github.com/LuoGroup2023/DeChat
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Repeat and haplotype aware error correction in nanopore sequencing reads with DeChat | 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 Brief Communication Repeat and haplotype aware error correction in nanopore sequencing reads with DeChat Xiao Luo, Yichen Li, Enlian Chen, Jialu Xu, Wenhai Zhang, Xiangxiang Zeng, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4384428/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Dec, 2024 Read the published version in Communications Biology → Version 1 posted You are reading this latest preprint version Abstract Error self-correction is a pivotal first step in the analysis of long-read sequencing data. However, most existing methods for this purpose are primarily tailored for noisy sequencing data with error rates exceeding 5%, often collapsing true variants in repeats and haplotypes. Alternatively, some methods are heavily optimized for PacBio HiFi reads, leaving a gap in methods specifically designed for Nanopore R10 reads basecalled with high accuracy or super accuracy models, which typically have error rates below 2%. Here, we introduce DeChat, a novel approach specifically designed for Nanopore R10 reads. DeChat enables repeat- and haplotype-aware error correction, leveraging the strengths of both de Bruijn graphs and variant-aware multiple sequence alignment to create a synergistic approach. This approach avoids read overcorrection, ensuring that variants in repeats and haplotypes are preserved while sequencing errors are accurately corrected. Benchmarking experiments reveal that reads corrected using DeChat exhibit substantially fewer errors, ranging from several times to two orders of magnitude lower, compared to the current state-of-the-art approaches. Furthermore, the application of DeChat for error correction significantly improves genome assembly across various aspects. DeChat is implemented as a highly efficient, standalone, and user-friendly software and is publicly available at https://github.com/LuoGroup2023/DeChat Biological sciences/Computational biology and bioinformatics Biological sciences/Computational biology and bioinformatics/Genome informatics Biological sciences/Computational biology and bioinformatics/Software Long reads Error correction de Bruijn graphs Nanopore sequencing Haplotype Full Text Additional Declarations There is NO Competing Interest. Supplementary Files supplement.pdf supplementary information Cite Share Download PDF Status: Published Journal Publication published 19 Dec, 2024 Read the published version in Communications Biology → 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. 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