ArticleBriefings in bioinformatics2025
REDInet: a temporal convolutional network-based classifier for A-to-I RNA editing detection harnessing million known events.
Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- AGAP1 and noncanonical GDP binding regulate triple-negative breast cancer development.Cellular and molecular life sciences : CMLS · 2026Article
- Structure-aware graph learning predicts RNA editability across tissues and species.Research square · 2026Article
- Precise, minimally evolved adenine base editors generated through mutation reversion analysis.Nature biotechnology · 2026Article
- Ab initio detection of multiple epitranscriptomic modifications from Oxford nanopore technology direct RNA sequencing data.Briefings in bioinformatics · 2026Article
- REDIportal: toward an integrated view of the A-to-I editing.Nucleic acids research · 2025Article
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8 authors.
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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.
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