High-throughput sequencing (HTS), more specifically RNA-seq of plant tissue, has become an indispensable tool for plant virologists to detect and identify plant viruses. During the data analysis step, plant virologists typically compare the obtained sequences to reference virus databases only, which lead to our hypothesis that they might be missing possible traces of other pathogens in the data. In this study, we set up a community effort to re-analyze existing RNA-seq datasets used for virus detection to check for the potential presence of non-viral pathogens or pests. In total 101 datasets from 15 participants derived from 51 different plant species were re-analyzed, of which 37 were selected for subsequent in-depth analyses. In 29 of the 37 selected samples (78%), we found convincing traces of non-viral plant pathogens or pests (&gt;100 reads per million). The most observed organism categories were fungi (15/37 datasets), insects (13/37) and mites (9/37). Nematodes were not observed and only a few samples showed the presence of plant pathogenic phytoplasmas (1/37), bacteria (3/37) and oomycetes (4/37). In conclusion, we were able to show that it is possible to detect non-viral pathogens or pests in these metatranscriptomics datasets, in this case primarily fungi, insects and mites. With this study, we hope to raise awareness among plant virologists that their data might be useful for fellow plant pathologists in other disciplines (bacteriology, mycology, entomology) as well.< Réduire