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Hybrid-hybrid correction of errors in long reads with HERO

Although generally superior, hybrid approaches for correcting errors in third-generation sequencing (TGS) reads, using next-generation sequencing (NGS) reads, mistake haplotype-specific variants for errors in polyploid and mixed samples. We suggest HERO, as the first “hybrid-hybrid” approach, to mak...

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Autores principales: Kang, Xiongbin, Xu, Jialu, Luo, Xiao, Schönhuth, Alexander
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10690975/
https://www.ncbi.nlm.nih.gov/pubmed/38041098
http://dx.doi.org/10.1186/s13059-023-03112-7
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author Kang, Xiongbin
Xu, Jialu
Luo, Xiao
Schönhuth, Alexander
author_facet Kang, Xiongbin
Xu, Jialu
Luo, Xiao
Schönhuth, Alexander
author_sort Kang, Xiongbin
collection PubMed
description Although generally superior, hybrid approaches for correcting errors in third-generation sequencing (TGS) reads, using next-generation sequencing (NGS) reads, mistake haplotype-specific variants for errors in polyploid and mixed samples. We suggest HERO, as the first “hybrid-hybrid” approach, to make use of both de Bruijn graphs and overlap graphs for optimal catering to the particular strengths of NGS and TGS reads. Extensive benchmarking experiments demonstrate that HERO improves indel and mismatch error rates by on average 65% (27[Formula: see text] 95%) and 20% (4[Formula: see text] 61%). Using HERO prior to genome assembly significantly improves the assemblies in the majority of the relevant categories. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-03112-7.
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spelling pubmed-106909752023-12-02 Hybrid-hybrid correction of errors in long reads with HERO Kang, Xiongbin Xu, Jialu Luo, Xiao Schönhuth, Alexander Genome Biol Method Although generally superior, hybrid approaches for correcting errors in third-generation sequencing (TGS) reads, using next-generation sequencing (NGS) reads, mistake haplotype-specific variants for errors in polyploid and mixed samples. We suggest HERO, as the first “hybrid-hybrid” approach, to make use of both de Bruijn graphs and overlap graphs for optimal catering to the particular strengths of NGS and TGS reads. Extensive benchmarking experiments demonstrate that HERO improves indel and mismatch error rates by on average 65% (27[Formula: see text] 95%) and 20% (4[Formula: see text] 61%). Using HERO prior to genome assembly significantly improves the assemblies in the majority of the relevant categories. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-03112-7. BioMed Central 2023-12-01 /pmc/articles/PMC10690975/ /pubmed/38041098 http://dx.doi.org/10.1186/s13059-023-03112-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Method
Kang, Xiongbin
Xu, Jialu
Luo, Xiao
Schönhuth, Alexander
Hybrid-hybrid correction of errors in long reads with HERO
title Hybrid-hybrid correction of errors in long reads with HERO
title_full Hybrid-hybrid correction of errors in long reads with HERO
title_fullStr Hybrid-hybrid correction of errors in long reads with HERO
title_full_unstemmed Hybrid-hybrid correction of errors in long reads with HERO
title_short Hybrid-hybrid correction of errors in long reads with HERO
title_sort hybrid-hybrid correction of errors in long reads with hero
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10690975/
https://www.ncbi.nlm.nih.gov/pubmed/38041098
http://dx.doi.org/10.1186/s13059-023-03112-7
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