At present, more than 150 kinds of RNA modified bases have been reported in the biological world, among which N6-methyladenine (m6A) is the most abundant in vertebrates, and it is also the most thoroughly studied modified base. At present, m6A detection is mainly based on next-generation sequencing methods, which have problems such as false positives, complicated operations, and limited to site detection. So nanopore sequencing technology (Oxford Nanopore Technologies, ONT) has become an ideal replacement method. The ONT sequencing platform directly sequences DNA or RNA by monitoring the current change caused by a single molecule passing through a nanopore embedded in a synthetic polymer membrane. The current generated by modified base perforation may be different from the corresponding classical base, so it can be based on The detected current difference is used to detect modified bases including m6A. Since then, more than a dozen computational tools have been developed (Fig. 1) to identify the position of m6A and determine its stoichiometry from direct RNA sequencing (DRS).
Figure 1 The tools, process and classification of m6A detection using ONT DRS platform
Although many highly sophisticated, advanced computational tools have been developed to detect and quantify m6A, studies that thoroughly evaluate and compare these tools are currently lacking. To fill this gap, the research team published a paper "Systematic comparison of tools used for m6A mapping from nanopore direct RNA sequencing" in Nature Communications on April 5, 2023.
These tools were comprehensively evaluated in multiple validation sets (Fig. 2) using multi-species multi-replicate (two replicates each of mouse embryonic cells and Arabidopsis thaliana) samples as evaluation data (Fig. 2), including the use of two continuous evaluation metrics (Receiver Operating Characteristic (Receiver Operating Characteristic, ROC) and precision-recall rate (Precision Recall, PR) curve) quantitatively evaluate their performance, compare the accuracy of the top sites they detect, and their The accuracy, recall and F1 score of the detected sites under the optimal threshold. It turns out that most tools have a trade-off between precision and recall.
Figure 2 Performance evaluation of ONT tool capabilities for detecting m6A
In addition, this study evaluates the inherent bias of these tools in the detection process and demonstrates that introducing negative control samples can improve the performance of most tools. In addition, it was also found that the detection ability varied between different motifs, which was due to the fact that on some sequences, current differences were not easily detected. For tools that can quantify m6A, wide variance was observed in their metrological estimates, even though acceptable results could be obtained in some motifs. Sequencing depth and modification stoichiometry are two other important influencing factors (Figure 3).
Figure 3 Further analysis of the ONT tool for detecting m6A
Taken together, the study provides insight into the computational tools currently used to detect m6A based on ONT DRS data and highlights the potential for further improvement of these tools, which may form the basis for future studies. The School of Life Sciences, Sun Yat-sen University is the first author's unit. This work was supported by the Key Research and Development Program of the Ministry of Science and Technology, the National Natural Science Foundation of China and the Shenzhen Bay Scholars Program.
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