RNAcentral 2021: secondary structure integration, improved sequence search and new member databases

By B. Sweeney, Anton I. Petrov, C. E. Ribas, Robert D. Finn, Alex Bateman, M. Szymanski, W. Karłowski, S. Seemann, Jan Gorodkin, J. Cannone and 35 more. 2020

337 citations recorded by the source corpus.

Abstract RNAcentral is a comprehensive database of non-coding RNA (ncRNA) sequences that provides a single access point to 44 RNA resources and >18 million ncRNA sequences from a wide range of organisms and RNA types. RNAcentral now also includes secondary (2D) structure information for >13 million sequences, making RNAcentral the world’s largest RNA 2D structure database. The 2D diagrams are displayed using R2DT, a new 2D structure visualization method that uses consistent, reproducible and recognizable layouts for related RNAs. The sequence similarity search has been updated with a faster interface featuring facets for filtering search results by RNA type, organism, source database or any keyword. This sequence search tool is available as a reusable web component, and has been integrated into several RNAcentral member databases, including Rfam, miRBase and snoDB. To allow for a more fine-grained assignment of RNA types and subtypes, all RNAcentral sequences have been annotated with Sequence Ontology terms. The RNAcentral database continues to grow and provide a central data resource for the RNA community. RNAcentral is freely available at https://rnacentral.org.

Subfields: Molecular Biology

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Citations in the corpus

Advancing RNA 3D Structure Prediction: Exploring Hierarchical and Hybrid Approaches in CASP15 (2023, methodology): “24 To gather such information on the query sequence, we performed a sequence analysis based on the PDB, 23 Rfam, 25 and RNAcentral, 26 as well as relevant published experimental data.”
Accurate prediction of nucleic acid and protein-nucleic acid complexes using RoseTTAFoldNA (2022, methodology): “…(made more difficult by subsampling MSAs) should further increase model accuracy [10]; for this purpose there are databases of structured RNAs [15,16] and DNA binding profiles for thousands of proteins [17,18], and the latter should be useful for training a model fine-tuned for DNA…”
Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA (2023, background): “Use of high-confidence predicted structures as additional training examples (made more difficult by subsampling MSAs) should further increase model accuracy 10 ; for this purpose there are databases of structured RNAs 17,18 and DNA-binding profiles for thousands of proteins 19,20 , and the latter”