{"database":"GEO","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Other":["ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE305nnn/GSE305317/"]},"type":"primary"},"statusCode":"OK","statusCodeValue":200}],"scores":null,"additional":{"omics_type":["Transcriptomics"],"species":["Homo sapiens"],"gds_type":["Expression profiling by high throughput sequencing"],"full_dataset_link":["https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE305317"],"repository":["GEO"],"entry_type":["GSE"],"additional_accession":[]},"is_claimable":false,"name":"Deciphering the regulatory code of RNA inosine through enzymatic precision mapping and explainable deep learning model","description":"Adenosine-to-inosine (A-to-I) RNA editing is one of the most abundant RNA modifications, participating in multiple critical biological processes. We previously reported Slic-seq, its unique terminal-blocking strategy, significantly enhances inosine detection accuracy. However, by capturing only inosine-modified RNA fragments, it lacks complete gene expression profiling and quantitative capability at editing sites. These limitations hinder its broad application in transcriptome analysis. Here, we present REX-seq (RNA Endonuclease and XRN1 sequencing), a highly sensitive RNA editing detection technology based on the combined action of ENDOV enzyme-specific cleavage and XRN1 exonuclease. its unique design allows for comprehensive A-to-I editome characterization without compromising the accuracy of gene expression quantification.","dates":{"publication":"2026/08/25"},"accession":"GSE305317","cross_references":{"GSM":["GSM9168674","GSM9168675","GSM9168676","GSM9168673"],"GPL":["24676"],"GSE":["305317"],"taxon":["Homo sapiens"]}}