Martinek Vlastimil, Martin Jessica, Belair Cedric, Payea Matthew J, Malla Sulochan, Alexiou Panagiotis, Maragkakis Manolis
Laboratory of Genetics and Genomics, National Institute on Aging, Intramural Research Program, National Institutes of Health, Baltimore, MD 21224, USA.
Central European Institute of Technology, Masaryk University, 625 00 Brno, Czech Republic.
bioRxiv. 2023 Nov 17:2023.11.17.567581. doi: 10.1101/2023.11.17.567581.
Quantification of the dynamics of RNA metabolism is essential for understanding gene regulation in health and disease. Existing methods rely on metabolic labeling of nascent RNAs and physical separation or inference of labeling through PCR-generated mutations, followed by short-read sequencing. However, these methods are limited in their ability to identify transient decay intermediates or co-analyze RNA decay with cis-regulatory elements of RNA stability such as poly(A) tail length and modification status, at single molecule resolution. Here we use 5-ethynyl uridine (5EU) to label nascent RNA followed by direct RNA sequencing with nanopores. We developed RNAkinet, a deep convolutional and recurrent neural network that processes the electrical signal produced by nanopore sequencing to identify 5EU-labeled nascent RNA molecules. RNAkinet demonstrates generalizability to distinct cell types and organisms and reproducibly quantifies RNA kinetic parameters allowing the combined interrogation of RNA metabolism and cis-acting RNA regulatory elements.
对RNA代谢动态进行定量分析对于理解健康和疾病状态下的基因调控至关重要。现有方法依赖于对新生RNA进行代谢标记，以及通过PCR产生的突变进行物理分离或标记推断，随后进行短读长测序。然而，这些方法在以单分子分辨率识别瞬时衰变中间体或与RNA稳定性的顺式调控元件（如聚腺苷酸尾长度和修饰状态）共同分析RNA衰变方面能力有限。在这里，我们使用5-乙炔基尿苷（5EU）标记新生RNA，随后通过纳米孔进行直接RNA测序。我们开发了RNAkinet，这是一种深度卷积循环神经网络，可处理纳米孔测序产生的电信号，以识别5EU标记的新生RNA分子。RNAkinet证明了其对不同细胞类型和生物体的通用性，并可重复地定量RNA动力学参数，从而实现对RNA代谢和顺式作用RNA调控元件的联合研究。
