Dynamont: A comprehensive cross-species comparison of ONT segmentation tools

BACKGROUND: Oxford Nanopore Technologies (ONT) sequencing enables direct, long-read sequencing of DNA and RNA, preserving nucleotide modifications. During basecalling, deep neural networks translate raw nanopore signals into nucleotide sequences, internally segmenting the signal to align it with the corresponding bases. This is a challenging task due to uneven motor protein rotation, signal variability, low-quality reads, and the presence of nucleotide modifications. However, the signal to nucleotide assignment is critical for novel downstream signal analysis. Existing tools, such as Tombo Resquiggle, f5c Eventalign, f5c Resquiggle, and Uncalled4, operate after basecalling and rely on event-based segmentation and mapping approaches that often fail to align low-quality or modified reads and lack confidence estimates for segmentation accuracy. RESULTS: Here, we present a large-scale comparative study in which 5 segmentation tools, including our novel tool Dynamont, are applied to 16 ONT-sequenced datasets spanning different kingdoms of life. Overall, we segmented 160,000 reads and evaluated the tools' performance on a combination of 12 signal and downstream assembly metrics. Our study is accompanied by a comprehensive and extensible supplement that summarizes all datasets, execution instructions, and evaluation results. We score the segmentation results using an aggregated metric score, created from all our analyzed metrics. CONCLUSIONS: No tool delivered the best results for all datasets. We recommend a careful choice and normalization of evaluation metrics to select the best segmentation tool as a critical step in the process of ONT signal segmentation. Across nearly all RNA datasets, Dynamont outperforms other segmentation tools in terms of aggregated metric scores. For DNA datasets, however, the performance is more variable, with mixed results observed across tools.