{"database":"GEO","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Other":["ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE347nnn/GSE347079/"]},"type":"primary"},"statusCode":"OK","statusCodeValue":200}],"scores":null,"additional":{"omics_type":["Transcriptomics"],"species":["Mus musculus"],"gds_type":[" Non-coding RNA profiling by high throughput sequencing","Expression profiling by high throughput sequencing"],"full_dataset_link":["https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE347079"],"repository":["GEO"],"entry_type":["GSE"],"additional_accession":[]},"is_claimable":false,"name":"TGIRT-seq-based quantification of snoRNAs across 5 healthy mouse organs","description":"Small nucleolar RNAs (snoRNAs) are structured non-coding RNAs that are frequently under-quantified by standard RNA-seq protocols, since their compact secondary structure impedes efficient reverse transcription. TGIRT-seq relies on a thermostable group II intron reverse transcriptase with high fidelity, processivity, and structure-tolerant activity, making it particularly well suited to accurately capture and quantify these RNA species. This dataset presents a TGIRT-seq atlas generated from ribodepleted total RNA of five healthy mouse organs (bone, colon, gonad, liver and lung), enabling accurate snoRNA quantification alongside mRNAs, lncRNAs and other structured non-coding RNAs (e.g. tRNAs), using a genome annotation supplemented with an updated murine snoRNA annotation and tRNA gene annotations from GtRNAdb.","dates":{"publication":"2026/09/13"},"accession":"GSE347079","cross_references":{"GSM":["GSM10045622","GSM10045621","GSM10045624","GSM10045623","GSM10045620","GSM10045630","GSM10045619","GSM10045618","GSM10045629","GSM10045626","GSM10045625","GSM10045617","GSM10045628","GSM10045627","GSM10045616"],"GPL":["37305"],"GSE":["347079"],"taxon":["Mus musculus"]}}