a bioinformatics pipeline for analysing chemical high-throughput RNA structure-probing data

RNAcentral 27 is now available, featuring circular RNAs, AI-ready data exports, a new MCP server, and over 55 million ncRNA sequences.

About the Team Rfam and RNAcentral are key resources for RNA biology, serving tens of thousands of users every year and widely cited in the scientific literature. We are recruiting a Bioinformatics Da...

Accessing RNA data programmatically with the Rfam and RNAcentral APIs -

Chatbot for access to cross-EBI documentation

The Nobel laureate and CU Boulder professor, recently ranked #1 globally for RNA research, will speak at the World Economic Forum annual meeting in Davos,

Abstract. RNAcentral was founded in 2014 to serve as a comprehensive database of non-coding RNA sequences. It began by providing a single unified interface

Predicting the folded structures of RNA molecules poses greater challenges than proteins, but steady progress continues.

Abstract. Expression Atlas (https://www.ebi.ac.uk/gxa/home) is EMBL-EBI’s comprehensive knowledgebase for gene and protein expression across tissues, cell

RNAcentral - Exploring non-coding RNAs

The human RNome, the complete set of RNA molecules in human cells, arises through complex processing and includes diverse molecular species. While research traditionally focuses on four canonical nucl...

Structural RNAs exhibit a vast array of recurrent short three-dimensional (3D) elements found in loop regions involving non-Watson–Crick interactions that help arrange canonical double helices into tertiary structures. Here we present CaCoFold-R3D, a probabilistic grammar that predicts these RNA 3D motifs (also termed modules) jointly with RNA secondary structure over a sequence or alignment. CaCoFold-R3D uses evolutionary information present in an RNA alignment to reliably identify canonical helices (including pseudoknots) by covariation. Here we further introduce the R3D grammars, which also exploit helix covariation that constrains the positioning of the mostly noncovarying RNA 3D motifs. Our method runs predictions over an almost-exhaustive list of over 50 known RNA motifs (‘everything’). Motifs can appear in any nonhelical loop region (including three-way, four-way and higher junctions) (‘everywhere’). All structural motifs as well as the canonical helices are arranged into one single structure predicted by one single joint probabilistic grammar (‘all-at-once’). Our results demonstrate that CaCoFold-R3D is a valid alternative for predicting the all-residue interactions present in a RNA 3D structure. CaCoFold-R3D is fast and easily customizable for novel motif discovery and shows promising value both as a strong input for deep learning approaches to all-atom structure prediction as well as toward guiding RNA design as drug targets for therapeutic small molecules.