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.

Center for Alzheimer's and Related Dementias National Institute on Aging and National Institute of Neurological Disorders and Stroke National Institutes of Health Bethesda MD USA

Central European Institute of Technology Masaryk University 625 00 Brno Czech Republic

Centre for Molecular Medicine and Biobanking University of Malta MSD 2080 Msida Malta

Laboratory of Genetics and Genomics National Institute on Aging Intramural Research Program National Institutes of Health Baltimore MD 21224 USA

National Centre for Biomolecular Research Faculty of Science Masaryk University 625 00 Brno Czech Republic

NAR Genom Bioinform. 2024 Aug 29;6(3):lqae116. doi: 10.1093/nargab/lqae116. PubMed

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