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| Home > Publications database > REDInet: a temporal convolutional network-based classifier for A-to-I RNA editing detection harnessing million known events. |
| Home > Publications database > REDInet: a temporal convolutional network-based classifier for A-to-I RNA editing detection harnessing million known events. |
| Journal Article | DKFZ-2025-00640 |
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2025
Oxford University Press
Oxford [u.a.]
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Please use a persistent id in citations: doi:10.1093/bib/bbaf107
Abstract: A-to-I ribonucleic acid (RNA) editing detection is still a challenging task. Current bioinformatics tools rely on empirical filters and whole genome sequencing or whole exome sequencing data to remove background noise, sequencing errors, and artifacts. Sometimes they make use of cumbersome and time-consuming computational procedures. Here, we present REDInet, a temporal convolutional network-based deep learning algorithm, to profile RNA editing in human RNA sequencing (RNAseq) data. It has been trained on REDIportal RNA editing sites, the largest collection of human A-to-I changes from >8000 RNAseq data of the genotype-tissue expression project. REDInet can classify editing events with high accuracy harnessing RNAseq nucleotide frequencies of 101-base windows without the need for coupled genomic data.
Keyword(s): Humans (MeSH) ; RNA Editing (MeSH) ; Computational Biology: methods (MeSH) ; Deep Learning (MeSH) ; Algorithms (MeSH) ; Neural Networks, Computer (MeSH) ; Sequence Analysis, RNA: methods (MeSH) ; Software (MeSH) ; A-to-I RNA editing ; REDItools ; RNAseq ; temporal convolutional network
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