Alagna Nicolò, Mündnich Stefan, Miedema Johannes, Pastore Stefan, Lehmann Lioba, Wierczeiko Anna, Friedrich Johannes, Walz Lukas, Jörg Marko, Butto Tamer, Friedland Kristina, Helm Mark, Gerber Susanne
Institute of Human Genetics, University Medical Center Mainz, Mainz 55128, Germany.
Institute of Pharmaceutical and Biomedical Science (IPBS), Johannes Gutenberg University Mainz, Mainz 55128, Germany.
Nucleic Acids Res. 2025 Jul 19;53(14). doi: 10.1093/nar/gkaf673.
RNA modifications play a crucial role in various cellular functions. Here, we present ModiDeC, a deep-learning-based classifier able to identify and distinguish multiple RNA modifications (N6-methyladenosine, inosine, pseudouridine, 2'-O-methylguanosine, and N1-methyladenosine) using direct RNA sequencing. Alongside ModiDeC, we provide an extensive database of in vitro-transcribed and synthetic sequences generated with both the new RNA004 chemistry and the old RNA002 kit. We show that RNA modifications can be accurately recognized and distinguished across different sequence motifs using synthetic data as well as in HEK293T cells and human blood samples. ModiDeC comes with a graphical user interface and an Epi2ME pipeline that allows easy customization and adaptation to specific research questions, such as learning and classifying additional RNA modifications and further sequence motifs. The reproducibility across samples, together with the low rate of false positives, underscores the potential of ModiDeC as a powerful tool for advancing the analysis of the epitranscriptome and RNA modification.
RNA修饰在各种细胞功能中起着至关重要的作用。在此，我们展示了ModiDeC，这是一种基于深度学习的分类器，能够使用直接RNA测序来识别和区分多种RNA修饰（N6-甲基腺苷、肌苷、假尿苷、2'-O-甲基鸟苷和N1-甲基腺苷）。除了ModiDeC，我们还提供了一个广泛的数据库，该数据库包含使用新的RNA004化学方法和旧的RNA002试剂盒生成的体外转录和合成序列。我们表明，使用合成数据以及在HEK293T细胞和人类血液样本中，可以跨不同的序列基序准确识别和区分RNA修饰。ModiDeC配备了图形用户界面和Epi2ME管道，可轻松定制并适应特定的研究问题，例如学习和分类其他RNA修饰以及进一步的序列基序。样本间的可重复性以及低假阳性率，突出了ModiDeC作为推进表观转录组和RNA修饰分析的强大工具的潜力。
