ParAquaSeq, a Database of Ecologically Annotated rRNA Sequences Covering Zoosporic Parasites Infecting Aquatic Primary Producers in Natural and Industrial Systems

Abstract

Amplicon sequencing tools such as metabarcoding are commonly used for thorough characterisation of microbial diversity in natural samples. They mostly rely on the amplification of conserved universal markers, mainly ribosomal genes, allowing the taxonomic assignment of barcodes. However, linking taxonomic classification with functional traits is not straightforward and requires knowledge of each taxonomic group to confidently assign taxa to a given functional trait. Zoosporic parasites are highly diverse and yet understudied, with many undescribed species and host associations. However, they can have important impacts on host populations in natural ecosystems (e.g., controlling harmful algal blooms), as well as on industrial-scale algae production, e.g. aquaculture, causing their collapse or economic losses. Here, we present ParAquaSeq, a curated database of available molecular ribosomal sequences belonging to zoosporic parasites infecting aquatic vascular plants, macroalgae and photosynthetic microorganisms, i.e. microalgae and cyanobacteria. These sequences are aligned with ancillary data and other information currently available, including details on their hosts, occurrence, culture availability and associated bibliography. The database includes 1131 curated sequences from marine, freshwater and industrial or artificial environments, and belonging to 13 different taxonomic groups, including Chytridiomycota, Oomycota, Phytomyxea, and Syndiniophyceae. The curated database will allow a comprehensive analysis of zoosporic parasites in molecular datasets to answer questions related to their occurrence and distribution in natural communities. Especially through meta-analysis, the database serves as a valuable tool for developing effective mitigation and sustainable management strategies in the algae biomass industry, but it will also help to identify knowledge gaps for future research.