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In the last decades, environmental metabarcoding has revolutionised biodiversity research, particularly for microbial organisms such as protists, enabling large-scale assessments of diversity and ecological patterns across time and space. With the advent of long-read sequencing, Nanopore-based metabarcoding represents a promising alternative to short-read approaches. Due to the limited number of available studies, the effectiveness of Nanopore sequencing - alone or in combination with short-read data - for assessing the biodiversity and ecological patterns of protists in different ecosystems is not yet sufficiently explored. Here, we present BaNaNA (Barcoding Nanopore Neat Annotator), a pipeline designed to generate high-quality OTUs and abundance estimates from Nanopore sequencing data. The performance of the pipeline was evaluated using a mock community as well as on marine and freshwater environmental samples to demonstrate its relevance for protist biodiversity and ecological studies. Our results show that BaNaNA generates high-quality full-length 18S rDNAOTUs from Nanopore long reads that are directly comparable to short-read V4-18S rDNAASVs, supporting their synergistic use in long-term biodiversity studies. While both approaches reveal similar overall community diversity, long-read OTUs provide greater taxonomic resolution, richer phylogenetic information enabling the discovery of new clades and yield fewer false positives. These advantages make long-read Nanopore metabarcoding not only a powerful cost effective complement, but also a reliable replacement to short-read methods. By providing a pipeline for processing Nanopore data, BaNaNA paves the way for a broader application of long-read Nanopore sequencing in protist ecology and biodiversity research.

Microbial communities, which include prokaryotes and protists, play an important role in aquatic ecosystems and influence ecological processes. To understand these communities, metabarcoding provides a powerful tool to assess their taxonomic composition and track spatio-temporal dynamics in both marine and freshwater environments. While marine ecosystems have been extensively studied, there is a notable research gap in understanding eukaryotic microbial communities in temperate lakes. Our study addresses this gap by investigating the free-living bacteria and small protist communities in Lake Roś (Poland), a dimictic temperate lake. Metabarcoding analysis revealed that both the bacterial and protist communities exhibit distinct seasonal patterns that are not necessarily shaped by dominant taxa. Furthermore, machine learning and statistical methods identified crucial amplicon sequence variants (ASVs) specific to each season. In addition, we identified a distinct community in the anoxic hypolimnion. We have also shown that the key factors shaping the composition of analysed community are temperature, oxygen, and silicon concentration. Understanding these community structures and the underlying factors is important in the context of climate change potentially impacting mixing patterns and leading to prolonged stratification.

Biofilms are key components of freshwater microbial communities, contributing critically to ecosystem functioning. Composed of both bacteria and protists, biofilm communities are shaped by environmental conditions and the lifestyle traits of their constituent organisms. Although biofilm and planktonic communities may share some taxa, the extent of the overlap and the factors driving community differentiation remain poorly understood. Here, we used high-throughput metabarcoding of 16S and 18S rRNA genes to investigate bacterial and protist communities associated with Phragmites australis stem biofilms and planktonic fractions across five lakes spanning an eutrophication gradient in the Masurian Lake District, northeastern Poland. The biofilm communities on reed stems were highly distinct from planktonic ones, sharing only a small core microbiome. The biofilm core not only exceeded that of planktonic communities but also contained characteristic taxa, identified via machine learning, that were distinct from planktonic assemblages, including several with potential roles in bioremediation and as bioindicators. Planktonic communities were strongly influenced by environmental factors, whereas Phragmites australis-associated epiphytic biofilm communities appeared more uniform across lakes, consistent with host-associated selection and potentially with lifestyle-related buffering. Moreover, biofilm suspensions exhibited higher apparent physiological activity per inoculum volume and broader substrate use under our assay conditions than the free-living communities. Altogether, our findings demonstrate the ecological significance of biofilms in freshwater systems and underscore the need to integrate both biofilm and planktonic fractions of prokaryotic and eukaryotic organisms to fully capture microbial diversity, function, and ecosystem resilience.

Microbial communities, consisting of prokaryotes and protists, play a central role in ecological processes in aquatic environments. To understand these communities, metabarcoding provides a powerful tool to assess their taxonomic composition and to track spatio-temporal dynamics in both marine and freshwater environments. While previous research has primarily focused on marine ecosystems, it is important to study microbial communities in freshwater environments, which are characterised by high diversity and susceptibility to rapid environmental change. In temperate lakes, despite extensive research on temporal changes in physico-chemical factors and microscopic studies of plankton, there is a notable research gap regarding their eukaryotic microbial communities. Our study fills this gap by investigating the diversity and seasonal changes of prokaryotic and eukaryotic communities in Lake Roś (Poland), a representative temperate lake characterised by two mixing episodes in spring and autumn and pronounced stratification in summer. Our metabarcoding analysis revealed that both the bacterial and protist communities exhibit distinct seasonal patterns that are not necessarily shaped by dominant taxa. To decipher the drivers of the seasonal communities, we used machine learning and statistical methods and identified crucial amplicon sequence variants (ASVs) specific to each season. In addition, we identified a distinct community in the anoxic hypolimnion. We have also shown that the key factors shaping the community composition in Lake Roś are temperature, oxygen and silicon concentration. Understanding these community structures and the underlying factors is crucial in the context of climate change, which might affect mixing patterns and lead to prolonged stratification. Given the pronounced seasonal shifts observed in these communities, we can anticipate that climate change will profoundly impact the functioning of temperate dimictic lakes.

Abstract Motivation With a large number of metagenomic datasets becoming available, the eukaryotic metagenomics emerged as a new challenge. The proper classification of eukaryotic nuclear and organellar genomes is an essential step towards the better understanding of eukaryotic diversity. Results We developed Tiara, a deep-learning-based approach for identification of eukaryotic sequences in the metagenomic data sets. Its two-step classification process enables the classification of nuclear and organellar eukaryotic fractions and subsequently divides organellar sequences to plastidial and mitochondrial. Using test dataset, we have shown that Tiara performs similarly to EukRep for prokaryotes classification and outperformed it for eukaryotes classification with lower calculation time. Tiara is also the only available tool correctly classifying organellar sequences. Availability and implementation Tiara is implemented in python 3.8, available at https://github.com/ibe-uw/tiara and tested on Unix-based systems. It is released under an open-source MIT license and documentation is available at https://ibe-uw.github.io/tiara. Version 1.0.1 of Tiara has been used for all benchmarks. Competing Interest Statement The authors have declared no competing interest.