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DNA methylation-calling tools for Oxford Nanopore sequencing: a survey and human epigenome-wide evaluation

BACKGROUND: Nanopore long-read sequencing technology greatly expands the capacity of long-range, single-molecule DNA-modification detection. A growing number of analytical tools have been developed to detect DNA methylation from nanopore sequencing reads. Here, we assess the performance of different...

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Autores principales: Liu, Yang, Rosikiewicz, Wojciech, Pan, Ziwei, Jillette, Nathaniel, Wang, Ping, Taghbalout, Aziz, Foox, Jonathan, Mason, Christopher, Carroll, Martin, Cheng, Albert, Li, Sheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8524990/
https://www.ncbi.nlm.nih.gov/pubmed/34663425
http://dx.doi.org/10.1186/s13059-021-02510-z
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author Liu, Yang
Rosikiewicz, Wojciech
Pan, Ziwei
Jillette, Nathaniel
Wang, Ping
Taghbalout, Aziz
Foox, Jonathan
Mason, Christopher
Carroll, Martin
Cheng, Albert
Li, Sheng
author_facet Liu, Yang
Rosikiewicz, Wojciech
Pan, Ziwei
Jillette, Nathaniel
Wang, Ping
Taghbalout, Aziz
Foox, Jonathan
Mason, Christopher
Carroll, Martin
Cheng, Albert
Li, Sheng
author_sort Liu, Yang
collection PubMed
description BACKGROUND: Nanopore long-read sequencing technology greatly expands the capacity of long-range, single-molecule DNA-modification detection. A growing number of analytical tools have been developed to detect DNA methylation from nanopore sequencing reads. Here, we assess the performance of different methylation-calling tools to provide a systematic evaluation to guide researchers performing human epigenome-wide studies. RESULTS: We compare seven analytic tools for detecting DNA methylation from nanopore long-read sequencing data generated from human natural DNA at a whole-genome scale. We evaluate the per-read and per-site performance of CpG methylation prediction across different genomic contexts, CpG site coverage, and computational resources consumed by each tool. The seven tools exhibit different performances across the evaluation criteria. We show that the methylation prediction at regions with discordant DNA methylation patterns, intergenic regions, low CG density regions, and repetitive regions show room for improvement across all tools. Furthermore, we demonstrate that 5hmC levels at least partly contribute to the discrepancy between bisulfite and nanopore sequencing. Lastly, we provide an online DNA methylation database (https://nanome.jax.org) to display the DNA methylation levels detected by nanopore sequencing and bisulfite sequencing data across different genomic contexts. CONCLUSIONS: Our study is the first systematic benchmark of computational methods for detection of mammalian whole-genome DNA modifications in nanopore sequencing. We provide a broad foundation for cross-platform standardization and an evaluation of analytical tools designed for genome-scale modified base detection using nanopore sequencing. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02510-z.
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spelling pubmed-85249902021-10-22 DNA methylation-calling tools for Oxford Nanopore sequencing: a survey and human epigenome-wide evaluation Liu, Yang Rosikiewicz, Wojciech Pan, Ziwei Jillette, Nathaniel Wang, Ping Taghbalout, Aziz Foox, Jonathan Mason, Christopher Carroll, Martin Cheng, Albert Li, Sheng Genome Biol Research BACKGROUND: Nanopore long-read sequencing technology greatly expands the capacity of long-range, single-molecule DNA-modification detection. A growing number of analytical tools have been developed to detect DNA methylation from nanopore sequencing reads. Here, we assess the performance of different methylation-calling tools to provide a systematic evaluation to guide researchers performing human epigenome-wide studies. RESULTS: We compare seven analytic tools for detecting DNA methylation from nanopore long-read sequencing data generated from human natural DNA at a whole-genome scale. We evaluate the per-read and per-site performance of CpG methylation prediction across different genomic contexts, CpG site coverage, and computational resources consumed by each tool. The seven tools exhibit different performances across the evaluation criteria. We show that the methylation prediction at regions with discordant DNA methylation patterns, intergenic regions, low CG density regions, and repetitive regions show room for improvement across all tools. Furthermore, we demonstrate that 5hmC levels at least partly contribute to the discrepancy between bisulfite and nanopore sequencing. Lastly, we provide an online DNA methylation database (https://nanome.jax.org) to display the DNA methylation levels detected by nanopore sequencing and bisulfite sequencing data across different genomic contexts. CONCLUSIONS: Our study is the first systematic benchmark of computational methods for detection of mammalian whole-genome DNA modifications in nanopore sequencing. We provide a broad foundation for cross-platform standardization and an evaluation of analytical tools designed for genome-scale modified base detection using nanopore sequencing. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02510-z. BioMed Central 2021-10-18 /pmc/articles/PMC8524990/ /pubmed/34663425 http://dx.doi.org/10.1186/s13059-021-02510-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 Research
Liu, Yang
Rosikiewicz, Wojciech
Pan, Ziwei
Jillette, Nathaniel
Wang, Ping
Taghbalout, Aziz
Foox, Jonathan
Mason, Christopher
Carroll, Martin
Cheng, Albert
Li, Sheng
DNA methylation-calling tools for Oxford Nanopore sequencing: a survey and human epigenome-wide evaluation
title DNA methylation-calling tools for Oxford Nanopore sequencing: a survey and human epigenome-wide evaluation
title_full DNA methylation-calling tools for Oxford Nanopore sequencing: a survey and human epigenome-wide evaluation
title_fullStr DNA methylation-calling tools for Oxford Nanopore sequencing: a survey and human epigenome-wide evaluation
title_full_unstemmed DNA methylation-calling tools for Oxford Nanopore sequencing: a survey and human epigenome-wide evaluation
title_short DNA methylation-calling tools for Oxford Nanopore sequencing: a survey and human epigenome-wide evaluation
title_sort dna methylation-calling tools for oxford nanopore sequencing: a survey and human epigenome-wide evaluation
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8524990/
https://www.ncbi.nlm.nih.gov/pubmed/34663425
http://dx.doi.org/10.1186/s13059-021-02510-z
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