Deep learning and direct sequencing of labeled RNA captures transcriptome dynamics

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14740%2F24%3A00137071" target="_blank" >RIV/00216224:14740/24:00137071 - isvavai.cz</a>

Result on the web

<a href="https://academic.oup.com/nargab/article/6/3/lqae116/7744945" target="_blank" >https://academic.oup.com/nargab/article/6/3/lqae116/7744945</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1093/nargab/lqae116" target="_blank" >10.1093/nargab/lqae116</a>

Result language

angličtina

Original language name

Deep learning and direct sequencing of labeled RNA captures transcriptome dynamics

Original language description

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.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10608 - Biochemistry and molecular biology

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

NAR Genomics and Bioinformatics

ISSN

2631-9268

e-ISSN

2631-9268

Volume of the periodical

6

Issue of the periodical within the volume

3

Country of publishing house

GB - UNITED KINGDOM

Number of pages

9

Pages from-to

1-9

UT code for WoS article

001300170800001

EID of the result in the Scopus database

2-s2.0-85202977843