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Genome biology



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Genome Biol 2021 Oct 18; 22(1):295





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GM133562, CA034196, The Jackson Laboratory


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 ( ) 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.


The authors thank Dr. Charles Lee for scientific discussion and providing the access to the long-read sequencing data. The authors thank Dr. Derya Unutmaz for helpful discussion. The authors thank the members of Li Lab for discussions and thank Drs. Stephen Sampson and Kevin Seburn from The Jackson Laboratory Research Program Development group for editing this paper. The authors thank Dr. Chia-Lin Wei and Dr. Chew Yee Ngan from Genome Technologies at The Jackson Laboratory for Oxford Nanopore sequencing support and discussion. The authors thank The Jackson Liu et al. Genome Biology (2021) 22:295 Page 28 of 33 Laboratory Computational Sciences and Research IT teams for technical support, i.e., Shiny app deployment helped by Sandeep Namburi and Richard Yanicky. The authors thank Sarah Foxton, Zoe McDougall, and Monolina Binny from Oxford Nanopore Technologies for Oxford Nanopore Technologies information check. The authors thank Christina Chatzipantsiou and Sangram Keshari Sahu from Lifebit and Dr. Anne Deslattes Mays for supporting the scripts running on cloud computing platform. The authors thank Kevin J. Anderson, Kevin Johnson, and Magalie Collet for the data submission.

This article is licensed under a Creative Commons Attribution 4.0 International License.