期刊
GENOME BIOLOGY
卷 22, 期 1, 页码 -出版社
BMC
DOI: 10.1186/s13059-021-02510-z
关键词
DNA methylation; Base modification; Long-read sequencing; Nanopore sequencing; Methylation calling
资金
- Jackson Laboratory for Genomic Medicine
- Leukemia Research Foundation New Investigator Grant
- Jackson Laboratory Director's Innovation Fund [19000-17-31, 19000-20-05]
- Jackson Laboratory Cancer Center New Investigator Award
- Jackson Laboratory Cancer Center Fast Forward Award
- National Institute of General Medical Sciences of the National Institutes of Health [R35GM133562]
- National Cancer Institute of the National Institutes of Health [P30CA034196]
This study systematically benchmarks computational methods for detecting mammalian whole-genome DNA modifications in nanopore sequencing for the first time. It provides a broad foundation for cross-platform standardization and evaluates analytical tools designed for genome-scale modified base detection using nanopore sequencing.
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.
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