Systematic benchmarking of tools for CpG methylation detection from nanopore sequencing

Yuen, ZW-S; Srivastava, A; Daniel, R; McNevin, D; Jack, C; Eyras, E

Systematic benchmarking of tools for CpG methylation detection from nanopore sequencing

Yuen, ZW-S Srivastava, A Daniel, R McNevin, D

Jack, C Eyras, E

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Publisher:: NATURE RESEARCH
Publication Type:: Journal Article
Citation:: Nature Communications, 2021, 12, (1), pp. 3438
Issue Date:: 2021-06-08

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Field	Value	Language
dc.contributor.author	Yuen, ZW-S
dc.contributor.author	Srivastava, A
dc.contributor.author	Daniel, R
dc.contributor.author	McNevin, D https://orcid.org/0000-0003-1665-3367
dc.contributor.author	Jack, C
dc.contributor.author	Eyras, E
dc.date.accessioned	2022-01-14T06:03:31Z
dc.date.available	2021-05-17
dc.date.available	2022-01-14T06:03:31Z
dc.date.issued	2021-06-08
dc.identifier.citation	Nature Communications, 2021, 12, (1), pp. 3438
dc.identifier.issn	2041-1723
dc.identifier.issn	2041-1723
dc.identifier.uri	http://hdl.handle.net/10453/153128
dc.description.abstract	DNA methylation plays a fundamental role in the control of gene expression and genome integrity. Although there are multiple tools that enable its detection from Nanopore sequencing, their accuracy remains largely unknown. Here, we present a systematic benchmarking of tools for the detection of CpG methylation from Nanopore sequencing using individual reads, control mixtures of methylated and unmethylated reads, and bisulfite sequencing. We found that tools have a tradeoff between false positives and false negatives and present a high dispersion with respect to the expected methylation frequency values. We described various strategies to improve the accuracy of these tools, including a consensus approach, METEORE ( https://github.com/comprna/METEORE ), based on the combination of the predictions from two or more tools that shows improved accuracy over individual tools. Snakemake pipelines are also provided for reproducibility and to enable the systematic application of our analyses to other datasets.
dc.format	Electronic
dc.language	eng
dc.publisher	NATURE RESEARCH
dc.relation.ispartof	Nature Communications
dc.relation.isbasedon	10.1038/s41467-021-23778-6
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.mesh	Benchmarking
dc.subject.mesh	CRISPR-Associated Protein 9
dc.subject.mesh	CpG Islands
dc.subject.mesh	Cytosine
dc.subject.mesh	DNA
dc.subject.mesh	DNA Methylation
dc.subject.mesh	Escherichia coli
dc.subject.mesh	Genome, Bacterial
dc.subject.mesh	Nanopore Sequencing
dc.subject.mesh	ROC Curve
dc.subject.mesh	Escherichia coli
dc.subject.mesh	Cytosine
dc.subject.mesh	DNA
dc.subject.mesh	ROC Curve
dc.subject.mesh	DNA Methylation
dc.subject.mesh	CpG Islands
dc.subject.mesh	Genome, Bacterial
dc.subject.mesh	Benchmarking
dc.subject.mesh	CRISPR-Associated Protein 9
dc.subject.mesh	Nanopore Sequencing
dc.title	Systematic benchmarking of tools for CpG methylation detection from nanopore sequencing
dc.type	Journal Article
utslib.citation.volume	12
utslib.location.activity	England
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Strength - CFS - Centre for Forensic Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	open_access	*
dc.date.updated	2022-01-14T06:03:28Z
pubs.issue	1
pubs.publication-status	Published online
pubs.volume	12
utslib.citation.issue	1

Abstract:

DNA methylation plays a fundamental role in the control of gene expression and genome integrity. Although there are multiple tools that enable its detection from Nanopore sequencing, their accuracy remains largely unknown. Here, we present a systematic benchmarking of tools for the detection of CpG methylation from Nanopore sequencing using individual reads, control mixtures of methylated and unmethylated reads, and bisulfite sequencing. We found that tools have a tradeoff between false positives and false negatives and present a high dispersion with respect to the expected methylation frequency values. We described various strategies to improve the accuracy of these tools, including a consensus approach, METEORE ( https://github.com/comprna/METEORE ), based on the combination of the predictions from two or more tools that shows improved accuracy over individual tools. Snakemake pipelines are also provided for reproducibility and to enable the systematic application of our analyses to other datasets.

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http://hdl.handle.net/10453/153128