RdRpCATCH: a unified resource for RNA virus discovery using viral RNA-dependent RNA polymerase profile Hidden Markov models

TY - JOUR

T1 - RdRpCATCH

T2 - a unified resource for RNA virus discovery using viral RNA-dependent RNA polymerase profile Hidden Markov models

AU - Karapliafis, Dimitris

AU - Neri, Uri

AU - Olendraite, Ingrida

AU - Charon, Justine

AU - Sakaguchi, Shoichi

AU - Hou, Xin

AU - de Ridder, Dick

AU - Zwart, Mark P.

AU - Kupczok, Anne

N1 - Data archiving: no NIOO data

PY - 2026/9

Y1 - 2026/9

N2 - 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.

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.

U2 - 10.1093/nargab/lqag076

DO - 10.1093/nargab/lqag076

M3 - Article

AN - SCOPUS:105044069548

SN - 2631-9268

VL - 8

JO - NAR Genomics and Bioinformatics

JF - NAR Genomics and Bioinformatics

IS - 3

M1 - lqag076

ER -