Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/117221
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.date.accessioned | 2024-01-12T17:44:56Z | - |
dc.date.available | 2024-01-12T17:44:56Z | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | Martinek, V., Martin, J., Belair, C., Payea, M. J., Malla, S., Alexiou, P., & Maragkakis, M. (2023). Deep learning and direct sequencing of labeled RNA captures transcriptome dynamics. bioRxiv, 2023-11. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/117221 | - |
dc.description.abstract | 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. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Cold Spring Harbor Laboratory | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | RNA-protein interactions | en_GB |
dc.subject | Deep learning (Machine learning) | en_GB |
dc.subject | Transfer learning (Machine learning) | en_GB |
dc.title | Deep learning and direct sequencing of labeled RNA captures transcriptome dynamics | en_GB |
dc.type | article | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder | en_GB |
dc.description.reviewed | non peer-reviewed | en_GB |
dc.identifier.doi | 10.1101/2023.11.17.567581 | - |
dc.publication.title | bioRxiv | en_GB |
dc.contributor.creator | Martinek, Vlastimil | - |
dc.contributor.creator | Martin, Jessica | - |
dc.contributor.creator | Belair, Cedric | - |
dc.contributor.creator | Payea, Matthew John | - |
dc.contributor.creator | Malla, Sulochan | - |
dc.contributor.creator | Alexiou, Panagiotis | - |
dc.contributor.creator | Maragkakis, Manolis | - |
Appears in Collections: | Scholarly Works - FacHScABS |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Deep_learning_and_direct_sequencing_of_labeled_RNA_captures_transcriptome_dynamics.pdf Restricted Access | 1.99 MB | Adobe PDF | View/Open Request a copy |
Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.