In eukaryotes, genes produce a variety of distinct RNA isoforms, each with potentially unique protein products, coding potential or regulatory signals such as poly(A) tail and nucleotide modifications. Assessing the kinetics of RNA isoform metabolism, such as transcription and decay rates, is essential for unraveling gene regulation. However, it is currently impeded by lack of methods that can differentiate between individual isoforms. Here, we introduce RNAkinet, a deep convolutional and recurrent neural network, to detect nascent RNA molecules following metabolic labeling with the nucleoside analog 5-ethynyl uridine and long-read, direct RNA sequencing with nanopores. RNAkinet processes electrical signals from nanopore sequencing directly and distinguishes nascent from pre-existing RNA molecules. Our results show that RNAkinet prediction performance generalizes in various cell types and organisms and can be used to quantify RNA isoform half-lives. RNAkinet is expected to enable the identification of the kinetic parameters of RNA isoforms and to facilitate studies of RNA metabolism and the regulatory elements that influence it.

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

bioRxiv. 2023 Nov 17:2023.11.17.567581. doi: 10.1101/2023.11.17.567581. PubMed

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