Systematic benchmarking of tools for CpG methylation detection from nanopore sequencing

• Published in: Nature communications 2021-06, Vol.12 (1), p.3438-3438, Article 3438
• Main Authors: Yuen, Zaka Wing-Sze, Srivastava, Akanksha, Daniel, Runa, McNevin, Dennis, Jack, Cameron, Eyras, Eduardo
• Format: Article
• Language: English
• Subjects: 45/23; 631/114/2785; 631/114/794; 631/1647/514/1948; 631/208/177; Accuracy; Algorithms; Benchmarking; Benchmarks; Bisulfite; CpG islands; CpG Islands - genetics; CRISPR-Associated Protein 9 - metabolism; Cytosine - metabolism; Deoxyribonucleic acid; DNA; DNA - metabolism; DNA methylation; DNA Methylation - genetics; DNA sequencing; Escherichia coli - genetics; Gene expression; Genome, Bacterial; Genomes; Humanities and Social Sciences; multidisciplinary; Nanopore Sequencing; ROC Curve; Science; Science (multidisciplinary)
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-021-23778-6

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. Several existing algorithms predict the methylation of DNA using Nanopore sequencing signals, but it is unclear how they compare in performance. Here, the authors benchmark the performance of several such tools, and propose METEORE, a consensus tool that improves prediction accuracy.