TY  - JOUR
AU  - Fonzino, Adriano
AU  - Mazzacuva, Pietro Luca
AU  - Handen, Adam
AU  - Silvestris, Domenico Alessandro
AU  - Arnold, Annette
AU  - Pecori, Riccardo
AU  - Pesole, Graziano
AU  - Picardi, Ernesto
TI  - REDInet: a temporal convolutional network-based classifier for A-to-I RNA editing detection harnessing million known events.
JO  - Briefings in bioinformatics
VL  - 26
IS  - 2
SN  - 1467-5463
CY  - Oxford [u.a.]
PB  - Oxford University Press
M1  - DKFZ-2025-00640
SP  - bbaf107
PY  - 2025
AB  - 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.
KW  - Humans
KW  - RNA Editing
KW  - Computational Biology: methods
KW  - Deep Learning
KW  - Algorithms
KW  - Neural Networks, Computer
KW  - Sequence Analysis, RNA: methods
KW  - Software
KW  - A-to-I RNA editing (Other)
KW  - REDItools (Other)
KW  - RNAseq (Other)
KW  - temporal convolutional network (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:40112338
C2  - pmc:PMC11924403
DO  - DOI:10.1093/bib/bbaf107
UR  - https://inrepo02.dkfz.de/record/300154
ER  -