Fig. 3: Application of pum6a for m6A detection in ONT direct RNA sequencing data in HEK293T cells.
a Schematic diagram of the pum6a model tailored for m6A detection from ONT direct RNA sequencing. The conceptual structure of the workflow was inspired by previous works by Hendra et al. (Nature Methods)32 and Zhong et al. (Nature Communications)47, and independently designed and integrated. This figure was adapted from Hendra et al. (Nature Methods, 2022, https://doi.org/10.1038/s41592-022-01666-1) and Zhong et al. (Nature Communications, 2023, https://doi.org/10.1038/s41467-023-37596-5), both published under a CC-BY license (https://creativecommons.org/licenses/by/4.0/). Modifications were made. b Distribution of m6A modification sites identified in HEK293T cells across four experiment protocols. c, d Comparison of pum6a’s performance with EpiNano, MINES, Nanom6A, and Tombo using ROC (c), and PR curves (d) for datasets with at least 3 reads. e, f, ROC (e), and PR curves (f) for datasets with at least 5 reads, comparing pum6a with additional methods including ELIGOS. g, h ROC (g), and PR curves (h) for datasets with at least 20 reads, incorporating m6anet into the comparison. i Summary of precision, recall, and F1 scores for all evaluated models. j Precision analysis of the top 18,000 m6A sites across four protocols, showing pum6a’s superior precision.
