Rfam 14: expanded coverage of metagenomic, viral and microRNA families

By Ioanna Kalvari, Eric P. Nawrocki, Nancy Ontiveros-Palacios, Joanna Argasinska, Kevin Lamkiewicz, Manja Marz, Sam Griffiths-Jones, C. Toffano-Nioche, D. Gautheret, Zasha Weinberg and 5 more. Nucleic Acids Res., 2020

876 citations recorded by the source corpus.

Abstract Rfam is a database of RNA families where each of the 3444 families is represented by a multiple sequence alignment of known RNA sequences and a covariance model that can be used to search for additional members of the family. Recent developments have involved expert collaborations to improve the quality and coverage of Rfam data, focusing on microRNAs, viral and bacterial RNAs. We have completed the first phase of synchronising microRNA families in Rfam and miRBase, creating 356 new Rfam families and updating 40. We established a procedure for comprehensive annotation of viral RNA families starting with Flavivirus and Coronaviridae RNAs. We have also increased the coverage of bacterial and metagenome-based RNA families from the ZWD database. These developments have enabled a significant growth of the database, with the addition of 759 new families in Rfam 14. To facilitate further community contribution to Rfam, expert users are now able to build and submit new families using the newly developed Rfam Cloud family curation system. New Rfam website features include a new sequence similarity search powered by RNAcentral, as well as search and visualisation of families with pseudoknots. Rfam is freely available at https://rfam.org.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c260a02d3ffe645d99a4511f0870bd93e2877195

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

In Molecular Biology

Citations in the corpus

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 specificity…”
FlyBase: updates to the Drosophila genes and genomes database (2024, positive): “To increase the GO annotation coverage of ncRNAs, we have incorporated computationally derived annotations based on the RNAcentral's mapping of GO terms to Rfam entries (Kalvari et al .”
RNA tertiary structure prediction in CASP15 by the GeneSilico group: Folding simulations based on statistical potentials and spatial restraints (2023, methodology): “MeSSPredRNA also incorporates an Infernal scan of RNA families in the Rfam database, 5,6 and mapping of secondary structures of the homologous families on the query RNA sequence.”
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”
Advancing RNA 3D Structure Prediction: Exploring Hierarchical and Hybrid Approaches in CASP15 (2023, methodology): “25 Next, we retrieved the conserved 2D structure for this RNA family (mammalian CPEB3 ribozyme) provided by R-scape, 43 which revealed two important pseudoknots PK1 and PK2 shown in This result underscores the crucial role of the folding constraints and highlights the efficacy of Vfold2D-MC in…”
MODOMICS: a database of RNA modifications and related information. 2023 update (2023, methodology): “‘Unmodified’ versions of sequences from MODOMICS were aligned to covariance models from Rfam ( 23 ) using Infernal ( 24 ).”
Bakta: rapid and standardized annotation of bacterial genomes via alignment-free sequence identification (2021, methodology): “For the annotation of non-coding features, bacterial covariance models for rRNA genes, ncRNA genes and ncRNA regulatory regions are downloaded and extracted from Rfam [21] via custom MySQL scripts, and filtered by manually curated blocklists.”
RNA tertiary structure prediction using RNAComposer in CASP15 (2023, methodology): “Among others, we found adequate secondary structures in Rfam 22 and in the literature.”