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RNA viruses are categorized into positive-strand, negative-strand, or double-stranded groups based on their genomic structure and replication mechanisms. This classification dictates how they exploit host cellular machinery for protein synthesis and replication. Some RNA viruses also utilize reverse transcription as part of their life cycle, further diversifying their replication strategies.Positive-Strand RNA VirusesPositive-strand RNA viruses have genomes that function directly as messenger...
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Confocal Imaging of Double-Stranded RNA and Pattern Recognition Receptors in Negative-Sense RNA Virus Infection
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RdRpCATCH: a unified resource for RNA virus discovery using viral RNA-dependent RNA polymerase profile Hidden Markov

Dimitris Karapliafis1, Uri Neri2, Ingrida Olendraite3

  • 1Bioinformatics Group, Wageningen University, Droevendaalsesteeg 1, 6708PB Wageningen, The Netherlands.

NAR Genomics and Bioinformatics
|July 10, 2026
PubMed
Summary

RdRpCATCH integrates RNA virus detection tools, making it easier to find new RNA viruses in sequence data. This user-friendly platform combines multiple profile Hidden Markov Models (pHMMs) for comprehensive viral discovery.

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Area of Science:

  • Virology
  • Bioinformatics
  • Computational Biology

Background:

  • Large-scale sequence mining has revealed significant RNA virus diversity.
  • Detecting RNA viruses often involves identifying the RNA-dependent RNA polymerase (RdRp) using profile Hidden Markov Models (pHMMs).
  • Existing RdRp pHMM databases vary in design, with unclear performance comparisons and limited accessibility for non-experts.

Purpose of the Study:

  • To develop a unified, user-friendly platform for RNA virus discovery by consolidating existing RdRp pHMM resources.
  • To enable comprehensive scanning of (meta)transcriptomic data for RNA viruses and provide taxonomic annotation.
  • To compare the performance of different RdRp pHMM databases and assess their suitability for integrated use.

Main Methods:

  • Development of the RdRp Collaborative Analysis Tool with Collections of pHMMs (RdRpCATCH).
  • Integration of multiple publicly available RdRp pHMM databases into a single framework.
  • Comparative analysis of the performance of various RdRp pHMM databases in detecting known RNA viruses and minimizing false positives.
  • Distribution of RdRpCATCH as a conda package and a web server application.

Main Results:

  • RdRpCATCH successfully consolidates diverse RdRp pHMM resources into an accessible platform.
  • Comparative analysis confirmed that most RdRp pHMM databases are effective in detecting known RNA viruses with low false positive rates.
  • The integrated approach within RdRpCATCH supports comprehensive viral discovery.

Conclusions:

  • RdRpCATCH addresses fragmentation and technical barriers in RNA virus discovery by unifying multiple pHMM resources.
  • The platform enhances accessibility for researchers with varying computational expertise.
  • RdRpCATCH facilitates more comprehensive and efficient discovery of RNA viruses.