REDInet: a temporal convolutional network-based classifier for A-to-I RNA editing detection harnessing million known events

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.

Authors

Institutions

Publication Details

Journal
Briefings in Bioinformatics
Published
2025-03-01
DOI
https://doi.org/10.1093/bib/bbaf107
Citations
3
Primary Topic
RNA regulation and disease
Type
article
Field-Weighted Citation Impact
2.42

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

REDInet: a temporal convolutional network-based classifier for A-to-I RNA editing detection harnessing million known events

Domenico Alessandro Silvestris, Graziano Pesole, Adriano Fonzino, Riccardo Pecori et al.
3 citations
Briefings in Bioinformatics
RNA regulation and disease
2.42
article

REDInet: a temporal convolutional network-based classifier for A-to-I RNA editing detection harnessing million known events

Domenico Alessandro Silvestris, Graziano Pesole, Adriano Fonzino, Riccardo Pecori, Pietro Luca Mazzacuva, Adam Handen, Ernesto Picardi, Annette Arnold
article en
3 citations

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.

Briefings in BioinformaticsVol. 26(2)
Università Campus Bio-Medico (IT), German Cancer Research Center (DE), Johannes Gutenberg University Mainz (DE), National Academies of Sciences, Engineering, and Medicine (US), Heidelberg University (DE), University of Chicago (US), Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies (IT), University of Bari Aldo Moro (IT)
Regione Puglia, National Institutes of Health, National Heart, Lung, and Blood Institute, National Institute of Mental Health, National Institute on Drug Abuse, National Human Genome Research Institute, NIH Office of the Director, National Cancer Institute, National Institute of Neurological Disorders and Stroke, Common Fund
Openalex Percentile: Top 12%
RNA regulation and disease
2.42
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.