FAIR RNA Data: Open Resources for Transcriptomics, Structure, and Regulatory Network Research

This Research Topic is part of our FAIR series. For other Research Topics in the series, please see below:FAIR Metabolomics: Open Datasets, Shared Standards, and the Road to AI-Ready DataFAIR Cell Biology Data: From Image Archives to Interoperable Signalling DatasetsFAIR Genomics: Standards, Repositories, and Reproducibility in Chromatin and Genome ResearchFAIR Mitochondrial Data: Open Resources for Integrative Research in Bioenergetics and DiseaseFAIR Structural Data in the Age of AI: From Deposition to DiscoveryMaking Glycan Data FAIR: Addressing the Interoperability Gap in GlycoscienceRNA biology has undergone a transformation in scope and scale, encompassing coding transcripts, regulatory non-coding RNAs, RNA modifications, RNA-protein interactions, and the structural dynamics of RNA molecules in living cells. This breadth has generated a rich ecosystem of databases (RNAcentral, miRBase, ENCODE, ArrayExpress) and data types, but also significant fragmentation: datasets are often deposited in incompatible formats, RNA modification data lacks standardised reporting, and connections between sequence, structure, and regulatory function are rarely made explicit.This Research Topic invites contributions that advance FAIR data practices across RNA biology, including metadata standards for RNA sequencing experiments, interoperable formats for RNA structure and modification data, and ontologies linking RNA entities across databases and species. As single-cell and spatial transcriptomics become standard, the need for FAIR-compliant deposition in resources such as CELLxGENE is increasingly urgent. We also encourage contributions addressing the FAIR sharing of RNA modification datasets (MODOMICS, RMBase), RNA-protein interaction data from CLIP-seq experiments, and long-read transcriptomics data. Contributions tackling FAIR practices for long-read transcriptomics data, where raw data deposition is essential for isoform-level reanalysis, are of particular interest.Studies demonstrating how FAIR-compliant RNA datasets have enabled cross-study integration or powered machine learning approaches to RNA function prediction are especially welcome. FAIR² Data Articles presenting curated, analysis-ready transcriptomics or RNA interaction datasets are an ideal submission type.We welcome contributions addressing, but not limited to:- Metadata standards for bulk, single-cell, and spatial RNA sequencing experiments- FAIR deposition of RNA modification (epitranscriptomic) data (MODOMICS, RMBase, m6A databases)- Interoperability between RNA structure databases (RNAcentral, Rfam, RNA Structure Atlas)- FAIR-compliant sharing of RNA-protein interaction data (CLIPdb, ENCORI, CLIP-seq)- Non-coding RNA-target interaction data deposition (miRBase, LNCipedia, circRNA databases)- Alternative splicing event datasets and cross-study integration workflows- Single-cell and spatial transcriptomics data in standardised, open repositories (CELLxGENE)- Machine learning applications enabled by FAIR RNA datasets Topic editors: Tapan Kumar Mohanta Source: https://www.frontiersin.org/research-topics/83006/fair-rna-data-open-resources-for-transcriptomics-structure-and-regulatory-network-research