ArticleGenome biology2024
DEMINING: A deep learning model embedded framework to distinguish RNA editing from DNA mutations in RNA sequencing data.
Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Spatial transcriptomics reveals laminar and cell-type-specific A-to-I RNA editing signatures in the macaque cortex.Nature communications · 2026Article
- Structure-aware graph learning predicts RNA editability across tissues and species.Research square · 2026Article
- Specific and efficient RNA A-to-I editing through cleavage of an ADAR inhibitor.Nature biotechnology · 2026Article
- TEITbase: a database for transposable element (TE)-initiated transcripts in human cancers.Database : the journal of biological databases and curation · 2026Article
- A novel sequence-based transformer model architecture for integrating multi-omics data in preterm birth risk prediction.NPJ digital medicine · 2025Article
- DEMINING: A deep learning model embedded framework to distinguish RNA editing from DNA mutations in RNA sequencing data.Genome biology · 2024Article
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Authors and funding
5 authors.
Funding
Abstract
Precise calling of promiscuous adenosine-to-inosine RNA editing sites from transcriptomic datasets is hindered by DNA mutations and sequencing/mapping errors. Here, we present a stepwise computational framework, called DEMINING, to distinguish RNA editing and DNA mutations directly from RNA sequencing datasets, with an embedded deep learning model named DeepDDR. After transfer learning, DEMINING can also classify RNA editing sites and DNA mutations from non-primate sequencing samples. When applied in samples from acute myeloid leukemia patients, DEMINING uncovers previously underappreciated DNA mutation and RNA editing sites; some associated with the upregulated expression of host genes or the production of neoantigens.
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Registered trials
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