RdRpCATCH: a unified resource for RNA virus discovery using viral RNA-dependent RNA polymerase profile Hidden Markov models
Abstract
Recent advances in large-scale sequence mining have expanded our knowledge of RNA virus diversity. Most genome mining approaches for detecting RNA viruses rely on identifying the conserved RNA-dependent RNA polymerase (RdRp) by scanning sequencing datasets with specialized profile Hidden Markov Models (pHMMs). Recently, several new pHMM databases for RdRp detection have been released, each following distinct design principles. However, their relative performance remains unclear, and their accessibility to users without advanced computational expertise is limited. Here, we introduce the RdRp Collaborative Analysis Tool with Collections of pHMMs (RdRpCATCH: https://github.com/dimitris-karapliafis/RdRpCATCH), a platform that consolidates publicly available RdRp pHMM resources into a single, user-friendly framework. RdRpCATCH enables the scanning of (meta)transcriptomic assemblies to discover RNA viruses and provides subsequent taxonomic annotation of detected contigs. A comparative analysis of RdRp pHMM databases reveals that most are highly effective at detecting the known diversity of RNA viruses while minimizing false positives, supporting their joint use within RdRpCATCH. RdRpCATCH is distributed as both a conda package and a web server application (https://rdrpcatch.bioinformatics.nl), facilitating access for researchers with diverse levels of computational expertise. By integrating multiple pHMM resources, this unified framework addresses fragmentation in the field and reduces technical barriers, enabling comprehensive viral discovery.
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BibTeX
@article{karapliafis2026rdrpcatch,
title = {{RdRpCATCH: a unified resource for RNA virus discovery using viral RNA-dependent RNA polymerase profile Hidden Markov models}},
author = {Dimitris Karapliafis and Uri Neri and Ingrida Olendraite and Justine Charon and Shoichi Sakaguchi and Xin Hou and Dick de Ridder and Mark Zwart and Anne Kupczok},
journal = {NAR Genomics and Bioinformatics},
year = {2026},
month = oct,
doi = {10.1093/nargab/lqag076},
url = {https://hal.inrae.fr/hal-05793854v3},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - RdRpCATCH: a unified resource for RNA virus discovery using viral RNA-dependent RNA polymerase profile Hidden Markov models
AU - Dimitris Karapliafis
AU - Uri Neri
AU - Ingrida Olendraite
AU - Justine Charon
AU - Shoichi Sakaguchi
AU - Xin Hou
AU - Dick de Ridder
AU - Mark Zwart
AU - Anne Kupczok
PY - 2026
DA - 2026/10/12
JO - NAR Genomics and Bioinformatics
DO - 10.1093/nargab/lqag076
UR - https://hal.inrae.fr/hal-05793854v3
AB - Recent advances in large-scale sequence mining have expanded our knowledge of RNA virus diversity. Most genome mining approaches for detecting RNA viruses rely on identifying the conserved RNA-dependent RNA polymerase (RdRp) by scanning sequencing datasets with specialized profile Hidden Markov Models (pHMMs). Recently, several new pHMM databases for RdRp detection have been released, each following distinct design principles. However, their relative performance remains unclear, and their accessibility to users without advanced computational expertise is limited. Here, we introduce the RdRp Collaborative Analysis Tool with Collections of pHMMs (RdRpCATCH: https://github.com/dimitris-karapliafis/RdRpCATCH), a platform that consolidates publicly available RdRp pHMM resources into a single, user-friendly framework. RdRpCATCH enables the scanning of (meta)transcriptomic assemblies to discover RNA viruses and provides subsequent taxonomic annotation of detected contigs. A comparative analysis of RdRp pHMM databases reveals that most are highly effective at detecting the known diversity of RNA viruses while minimizing false positives, supporting their joint use within RdRpCATCH. RdRpCATCH is distributed as both a conda package and a web server application (https://rdrpcatch.bioinformatics.nl), facilitating access for researchers with diverse levels of computational expertise. By integrating multiple pHMM resources, this unified framework addresses fragmentation in the field and reduces technical barriers, enabling comprehensive viral discovery.
SN - 2631-9268
ER -