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  3. Viral Non-coding Rna Structure Annotation And Api-based Data Retrieval With Rfam And R2dt.
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  3. Viral Non-coding Rna Structure Annotation And Api-based Data Retrieval With Rfam And R2dt.

Related Experiment Video

Confocal Imaging of Double-Stranded RNA and Pattern Recognition Receptors in Negative-Sense RNA Virus Infection
06:44

Confocal Imaging of Double-Stranded RNA and Pattern Recognition Receptors in Negative-Sense RNA Virus Infection

Published on: January 26, 2019

Viral non-coding RNA structure annotation and API-based data retrieval with Rfam and R2DT.

Philippa Muston1, Sandra Triebel2, Eric Nawrocki3

  • 1European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton, United Kingdom.

Biorxiv : the Preprint Server for Biology
|May 25, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Rfam database now offers automated non-coding RNA (ncRNA) annotation for viral genomes and programmatic access via its RESTful API. This enhances RNA biology research by enabling custom bioinformatics pipelines and machine learning workflows.

Keywords:
APIInfernalR2DTRNA familiesRfamSecondary structureSequence databaseVirusesncRNA annotation

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • RNA Biology

Background:

  • Rfam is a critical database for non-coding RNA (ncRNA) families, offering alignments, structures, and models.
  • It aids in identifying structured ncRNAs and understanding RNA structure-function relationships.

Purpose of the Study:

  • To present computational protocols for automated ncRNA annotation of viral genomes.
  • To enable programmatic interaction with the Rfam database via its RESTful API.

Main Methods:

  • Developed computational protocols for automated ncRNA annotation of viral genomes.
  • Utilized Rfam's RESTful API for programmatic data retrieval and interaction.
  • Employed R2DT for genome-wide RNA structure visualization and 2D diagram generation.

Main Results:

  • Demonstrated automated ncRNA annotation for viral genomes.
  • Showcased genome-wide RNA structure visualization from sequence and alignments.
  • Provided practical examples for accessing Rfam metadata, alignments, and structures via API.

Conclusions:

  • The presented methods facilitate the integration of Rfam data into custom bioinformatics pipelines.
  • Researchers can now more easily perform comparative analyses and machine learning workflows involving ncRNAs.
  • Enhanced tools support virology and RNA biology research through improved data accessibility and analysis capabilities.