From short to long reads: enhanced protist diversity profiling via Nanopore metabarcoding

421 From short to long reads: enhanced protist diversity profiling via Nanopore metabarcoding Małgorzata Chwalińska1, Michał Karlicki1, Sarah Romac2, Fabrice Not2, Anna Karnkowska1 1 InstituteofEvolutionaryBiology,FacultyofBiology,UniversityofWarsaw,ul.ŻwirkiiWigury101,02-089Warsaw,Poland 2 CNRS,UMR7144AdaptationandDiversityinMarineEnvironment(AD2M)Laboratory,EcologyofMarinePlanktonteam,SorbonneUniversité,Station BiologiquedeRoscoff,PlaceGeorgesTeissier,Roscoff,France Correspondingauthor:AnnaKarnkowska([email protected]) Copyright: © Małgorzata Chwalińska et al. This is an open access article distributed under terms of the Creative Commons Attribution License (Attribution 4.0 International – CC BY 4.0). Research Article Abstract 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 rDNA OTUs from Nanopore long reads that are directly comparable to short-read V4-18S rDNA ASVs, 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. Key words: Amplicon sequencing, freshwater, marine, microbial eukaryotes, 18S rDNA, V4 region Introduction Microbial eukaryotes (i.e. protists) are very diverse and represent a significant part of microbial communities in the environments. Yet, their small size and limited culturing success have hampered efforts to fully explore their diversity so far. The advent of High-Throughput Sequencing (HTS) technologies applied to environmental DNA (eDNA) has revolutionised protist diversity studies for both ecological and Academic editor: Thorsten Stoeck Received: 4 July 2025 Accepted: 26 August 2025 Published: 8 October 2025 Citation: Chwalińska M, Karlicki M, Romac S, Not F, Karnkowska A (2025) From short to long reads: enhanced protist diversity profiling via Nanopore metabarcoding. Metabarcoding and Metagenomics 9: e163750. https:// doi.org/10.3897/mbmg.9.163750 Metabarcoding and Metagenomics 9: 421–447 (2025) DOI: 10.3897/mbmg.9.163750 422 Metabarcoding and Metagenomics 9: 421–447 (2025), DOI: 10.3897/mbmg.9.163750 Małgorzata Chwalińska et al.: Protist diversity profiling via Nanopore long reads evolutionary research (Burki et al. 2021). Metabarcoding, the most widely used approach to study protist diversity in the environment, typically targets the variable regions V4 and V9 of the 18S ribosomal RNA gene (rDNA), providing insights into the taxonomic composition and dynamics of protists over time and space (De Vargas et al. 2015; Mahé et al. 2017; Karlicki et al. 2024). The V9 region was initially preferred for protist diversity studies because it is shorter and therefore easier to sequence (Amaral-Zettler et al. 2009). However, with the development of the Illumina sequencing technology, the V4 region which offers a longer sequence with a greater variability and, thus, taxonomic resolution, has become the region of choice and is currently better represented in reference databases (Vaulot et al. 2022). The recent advent of long-read sequencing technologies, such as PacBio or Oxford Nanopore Technology (ONT) provides longer amplicon (i.e. metabarcodes) and are being proposed as a more effective approach for microbial diversity studies (Jamy et al. 2020; Bludau et al. 2025). For prokaryotes, the use of fulllength 16S rDNA reference sequences and even whole rDNA operons is becoming standard practice (Callahan et al. 2019; Olivier et al. 2023; Szoboszlay et al. 2023; Lemoinne et al. 2024). However, only a handful of studies have applied such long read approach to sequence the rDNA operon of protists from environmental samples (Jamy et al. 2020; Overgaard et al. 2024; Bludau et al. 2025). Long amplicons share certain limitations with short ones, such as primer bias (Vaulot et al. 2022). Moreover, as fragment length increases, amplification efficiency often declines, making the selection of primers for long-read metabarcoding difficult (Latz et al. 2022; Sandin et al. 2022). Sequencing technology-specific limitations further complicate long-read applications: PacBio sequencing, while highly accurate, remains costly and is typically limited to specialised facilities, while Nanopore is more affordable and widely accessible, but has a higher error rate. More specifically, Nanopore sequencing results in a high number of indels, which impacts the generation of high-quality Molecular Operational Taxonomic Units (MOTUs). Depending on the bioinformatic processing approach applied to high-throughput sequencing (HTS) data, two main types of MOTUs can be produced: Operational Taxonomic Units (OTUs) (Edgar 2017) and Amplicon Sequence Variants (ASVs) (Callahan et al. 2017). OTUs are constructed by clustering reads using similarity thresholds and ASVs are created in a process in which biological sequences are differentiated from sequencing errors (Callahan et al. 2017). For short-read sequencing, denoising approaches are commonly applied to generate ASVs, which provide a high taxonomic resolution of the MOTUs present in a sample. Similar denoising strategies have also been adapted for PacBio amplicons, where low error rates allow for reliable error correction and ASV inference (Callahan et al. 2019). However, this approach is not adapted for Nanopore data due to its higher error rates and less predictable error profiles which hinder accurate error modelling. As a result, clustering reads into OTUs, based on sequence similarity, appears as a more appropriate strategy for MOTUs generation from Nanopore long reads (Santos et al. 2020). To ensure the reliability and reproducibility of Nanopore-based metabarcoding, there is a clear need for a standardised pipeline to process Nanopore long reads. Efforts in this direction have already been made, particularly for bacterial communities. Pipelines, such as the EPI2ME platform (Oxford Nanopore Technologies), Emu (Curry et al. 2022) and MeTaPONT (Ammer-Herrmenau et al. 2021), have been developed to generate high-quality amplicons from 16S rDNA long reads. However, these 423 Metabarcoding and Metagenomics 9: 421–447 (2025), DOI: 10.3897/mbmg.9.163750 Małgorzata Chwalińska et al.: Protist diversity profiling via Nanopore long reads approaches rely on high quality reference-based alignments which demand more comprehensive databases than those currently available for protists. In addition, pipelines, based on clustering approaches, are being developed for analysing bacterial 16S rDNA long-read sequences (Rodríguez-Pérez et al. 2021; Dubois et al. 2024; Lemoinne et al. 2024; Schacksen et al. 2024). Most studies applying Nanopore metabarcoding to protists have either lacked specific pipelines for clustering reads into OTUs (Sandin et al. 2022; Hooper et al. 2023; Gaonkar and Campbell 2024) or have focused on pipelines developed for samples with low taxonomic complexity, such as clinical samples (Ohta et al. 2023; Huggins et al. 2024). NanoClust (Rodríguez-Pérez et al. 2021) has been shown to be effective for low-complexity protist communities (Huggins et al. 2024), but so far, only Natrix2 (Deep et al. 2023) has been shown to properly process long-read metabarcoding data for protists (Bludau et al. 2025). In addition to developing robust methods for generating OTUs from Nanopore long-read amplicons (Overgaard et al. 2024), a major challenge is to accurately estimate OTUs abundances. Longer meta-barcodes offer significant advantages as they provide higher taxonomic resolution and, therefore, allow a more detailed understanding of protist communities, from detailed biogeography to evolutionary studies (Jamy et al. 2020, 2022; Gaonkar and Campbell 2024). However, the extensive datasets generated up to now from short amplicons remain an invaluable resource, especially for large-scale (e.g. De Vargas et al. (2015)) or long-term studies (e.g. Yeh and Fuhrman (2022)). It is therefore important to evaluate how these approaches complement each other and can eventually be combined. While some attempts have been made for groups of organisms such as zooplankton (Chang et al. 2024), little is known about the impacts on ecological analyses or the feasibility of integrating short and long amplicons in comparative studies for protists. A recent comparison of Illumina V9-18S rDNA and Nanopore 18S rDNA protist metabarcodes from sediment samples (Bludau et al. 2025) demonstrated that Nanopore metabarcodes provide higher taxonomic resolution for protists, while both methods revealed similar diversity and basic community patterns. Here, we further addressed these critical challenges by analysing both Illumina and Nanopore metabarcoding data from a protist mock community, as well as from distinct environmental sample sets representing marine and freshwater ecosystems. To generate high-quality OTUs from Nanopore data, we introduce the BaNaNA (Barcoding Nanopore Neat Annotator) pipeline, primarily designed for metabarcoding analysis of microbial eukaryotes, but suitable for other taxa as well. We evaluated the effectiveness of Nanopore longread 18S rDNA and Illumina short-read V4-18S rDNA metabarcoding, providing a detailed assessment of how each method influences biodiversity and ecological interpretations, ultimately emphasising the advantages of Nanopore-based metabarcoding and the potential for combining both approaches. Materials and methods Samples collection Freshwater samples (Suppl. material 2: table S1) were collected at the end of July to the beginning of August in year 2020 from five lakes in the Great Masurian Lakeland District in north-eastern Poland. Samples were collected 424 Metabarcoding and Metagenomics 9: 421–447 (2025), DOI: 10.3897/mbmg.9.163750 Małgorzata Chwalińska et al.: Protist diversity profiling via Nanopore long reads with a modified Bernatowicz sampler from photic and aphotic zones of each lake, pre-filtered through 150 µm mesh-size net to remove zooplankton and larger particles and then filtered under pressure through 0.2 µm membrane Nucleopore filters (Whatman, Maidstone, UK). Filters were frozen at -20 °C and kept in a -80 °C freezer for long-time storage. Marine samples (Suppl. material 2: table S2) were collected during one of the annual MOOSE-GE campaigns (https://campagnes.flotteoceanographique.fr/campagnes/17001500/) (Coppola et al. 2019) from 31 August to 23 September 2017. A volume of 20 litres of seawater from 2 × 12 litre bottles was taken at the surface, DCM and deep waters (ca. 2000 m depth). The water was pre-filtered through 180 µm and filtered through 0.2 and 3 μm, 47 mm Nucleopore polycarbonate filters (Whatman, Maidstone, UK). After filtration, filters from both size fractions were flash-frozen in liquid nitrogen and stored independently at -80 °C until DNA extraction. Mock community preparation The mock community was composed of seven species from culture collections (Suppl. material 2: table S3). Species represent six groups of protists: Haptophyta, Euglenozoa, Chlorophyta, Ciliophora, Dinoflagellata and Cryptophyta (2 species). Amongst these, Prymnesium parvum (Haptophyta), Euglena gracilis (Euglenozoa) and Chlorella variabilis (Chlorophyta) were highly abundant, while Paramecium bursaria (Ciliophora), Gymnodinium fuscum (Dinoflagellata), Cryptomonas paramecium (Cryptophyta) and Cryptomonas gyropyrenoidosa (Cryptophyta) were added at low concentration (Suppl. material 2: table S3). In addition to the taxonomic differences, the species within the mock community differed considerably in cell size, morphology and cell number designed to mimic natural samples. Cells abundances were manually calculated using a Fuchs-Rosenthal chamber, all species being combined and filtered under pressure through 0.2 μm membrane Nucleopore filters and frozen at -80 °C. For Cryptomonas gyropyrenoidosa which did not have the 18S rDNA reference sequence available, we isolated DNA from the culture using NucleoSpin Tissue XS kit and amplified 18S rRNA gene using SA (5’ AACCTGGTTGATCCTGCCAGT 3’) (Medlin et al. 1988) and EukB (5’ TGATCCTTCTGCAGGTTCACCTAC 3’) (Medlin et al. 1988) primers (Suppl. materials 1, 2), purified the DNA using PCR Mini Kit (Syngen) and sequenced using Sanger with additional primer Euk528F (5’ CGGTAATTCCAGCTCC 3’) (Edgcomb et al. 2011). Fragments were then assembled using Lasergene Seqman Pro. DNA isolation and amplification DNA from freshwater samples and mock community was extracted from one quarter of the filter using the GeneMATRIX Soil DNA Purification Kit (EURx), its concentration being measured using NanoDrop (Thermo Scientific) and frozen at -80 °C. DNA from marine samples were extracted using a modified protocol from the NucleoSpin Plant II Mini or Midi kits (Macherey-Nagel), depending on the planktonic size-fraction. The detailed DNA extraction protocol is available on the online protocol repository protocols.io: dx.doi.org/10.17504/protocols.io.kxygxy5xdl8j/v1. DNA extracts were diluted to 5 ng/µl and amplified in three replicates for both sequencing methodologies using Phusion High-Fidelity DNA 425 Metabarcoding and Metagenomics 9: 421–447 (2025), DOI: 10.3897/mbmg.9.163750 Małgorzata Chwalińska et al.: Protist diversity profiling via Nanopore long reads polymerase (Finnzymes; ThermoFisher). For Illumina strategy, the V4 region of the 18S rRNA gene (∼ 380 bp) was targeted using the primers TAReuk454FWD1 (5’ CCAGCASCYGCGGTAATTCC 3’) and TAReukREV3 (5’ ACTTTCGTTCTTGATYRA 3’) (Stoeck et al. 2010). Amplification for marine samples is detailed on protocols.io: dx.doi.org/10.17504/protocols. io.bzucp6sw, while the freshwater and mock community amplification protocol can be found in the Suppl. materials 1, 2 of this article. For Nanopore sequencing, a fragment from the beginning of 18S all the way to the D2 region of 28S rDNA (amplicon size 3200 bp) was targeted using the SA (5’ TTTCTGTTGGTGCTGATATTGCAACCTGGTTGATCCTGCCAGT 3’) (Medlin et al. 1988) and D2C-R (5’ ACTTGCCTGTCGCTCTATCTTCCCTTGGTCCGTGTTTCAAGA 3’) (Scholin et al. 1994) primers extended by Nanopore adapters. The detailed protocol and the sequence of Nanopore adapters are to be found in the Suppl. materials 1, 2. After PCRs, the replicates were merged and purified together using a PCR Mini Kit (Syngen). Illumina library preparation and sequencing The library for freshwater samples and mock community was prepared and sequenced in the Genomics Core Facility at the Centre of New Technologies (University of Warsaw, Poland) using the Illumina MiSeq platform with 2 × 250 bp. Regarding marine samples, library adapter ligation and sequencing were performed in the same conditions by Fasteris (www.fasteris.com, Planles-Ouates, Switzerland) on a 2 × 250 bp MiSeq Illumina. Nanopore library preparation and sequencing Nanopore libraries were prepared using PCR Barcoding Expansion 1-12 (EXPPBC001) and Ligation Sequencing Kit (SQK-LSK114). Samples were sequenced on MinION Mk1B device using R10.4.1 flow cells. Illumina data analysis The quality of raw sequences was checked using FastQC v.0.11.5 (Andrews 2010). Primers were removed using the Cutadapt plugin for QIIME2 2023.9.1 environment (https://github.com/qiime2/q2-cutadapt) (Bolyen et al. 2019). Representative sequences (ASVs – Amplicon Sequence Variants) were created using DADA2 (Callahan et al. 2016) in the QIIME2 environment using the DADA2 denoise-paired function (https://github.com/qiime2/ q2-dada2). Final ASVs had their taxonomy assigned using global alignment method of VSEARCH v.2.7.1 (Rognes et al. 2016) to PR2 database v.5.0.0 (Guillou et al. 2012) with minimum threshold of 70% identity and minimum query coverage of 90%. Nanopore data analysis To overcome the high error rate associated with Nanopore sequencing technology, we have developed the BaNaNA (Barcoding Nanopore Neat Annotator) (https://github.com/ibe-uw/BaNaNA) - a Snakemake (Mölder et al. 2021) 426 Metabarcoding and Metagenomics 9: 421–447 (2025), DOI: 10.3897/mbmg.9.163750 Małgorzata Chwalińska et al.: Protist diversity profiling via Nanopore long reads pipeline to generate high-quality representative sequences, also known as Operational Taxonomic Units (OTUs), from long-reads amplicons (Fig. 1). The pipeline includes multiple steps, which are briefly outlined here. For a detailed description, visit the Wiki page on GitHub (https://github.com/ibeuw/BaNaNA/wiki). First, raw reads were basecalled and demultiplexed using Dorado v.0.5.1+a7fb3e3 (Oxford Nanopore Technologies https://github.com/ nanoporetech/dorado). For basecalling, the duplex option combined with the super-accurate model was used. We then filtered the reads for length and quality using Filtlong v.0.2.1 (https://github.com/rrwick/Filtlong) and extracted rDNA fragments using Barrnap 0.9 (https://github.com/tseemann/barrnap). An additional quality check of the rDNA fragments was performed using the custom Python 3.9.18 script and Biopython tool (Cock et al. 2009) (https://github. com/ibe-uw/BaNaNA/blob/main/scripts/extracting_rrna.py), which checked the presence of the correct rDNA structure (18S, 5.8S, 28S) and the length of each region. For further analysis, we focused exclusively on the 18S rDNA gene, although all fragments of the operon could potentially be used. We then calculated the average read quality with NanoPlot 1.42.0 (De Coster and Rademakers 2023) and FASTQ reads according to Filtlong and used this value as the threshold for the VSEARCH v.2.7.1 (Rognes et al. 2016) clustering step. To obtain consensus sequences, we used the custom script (https://github.com/ibe-uw/ BaNaNA/blob/main/scripts/mafft_consensus.py) that filters out clusters containing fewer than four sequences and then uses MAFFT v.7.310 (Katoh and Standley 2013) to create an alignment within clusters and compares each position to create final sequences. In the next step, we used Minimap2 2.24-r1122 (Li 2018) and Racon 1.5.0 (Vaser et al. 2017) to polish the sequences. Next, we added the names of the samples to the sequence header to make them easier to identify. After merging all processed samples, we used VSEARCH to detect and remove chimeric sequences, applying two approaches: (1) reference-based chimera detection using the PR2 5.0.0 database (Guillou et al. 2012) as a reference and (2) de novo chimera detection using the uchime2_denovo algorithm. Final clustering at 99% identity with VSEARCH allowed us to remove duplicate sequences and create final Operational Taxonomic Units (OTUs). Due to different lengths of sequences within clusters during consensus building and polishing, some OTUs accumulated a large number of ambiguous bases represented as Ns, which reduces their quality; the custom script (https://github.com/ibeuw/BaNaNA/blob/main/scripts/remove_Nseqs.py) was used to remove those sequences. Subsequently, the obtained OTUs were taxonomically annotated against the PR2 database using the global alignment method implemented Figure 1. Overview of the BaNaNA pipeline for obtaining OTUs from Nanopore reads. The diagram illustrates the sequential steps of the BaNaNA workflow, along with intergrated tools and custom scripts used at each stage of the analysis (created with Biorender.com). 427 Metabarcoding and Metagenomics 9: 421–447 (2025), DOI: 10.3897/mbmg.9.163750 Małgorzata Chwalińska et al.: Protist diversity profiling via Nanopore long reads in VSEARCH, using a minimum identity of 70% and a minimum query coverage of 90% as parameters. The abundances were calculated by counting the number of sequences in the clusters from the first clustering. After calculating the abundances, the OTU table is created with all samples. Additionally, the BaNaNA pipeline allows the use of various databases and modifications with respect to different primers and different fragments of the rDNA. Extraction of V4-tags from Nanopore sequences To assess the impact of sequence length on taxonomic annotation and to investigate the possibility of integrating Nanopore and Illumina data, we extracted V4-tags from the Nanopore OTUs. For this, we used the same primer sequences as for Illumina sequencing with three mismatches allowed as well as customised R and Python scripts with the help of the Biostrings package (Pagès et al. 2021) and the Biopython tool (Cock et al. 2009), respectively. The extracted V4 tags were then taxonomically assigned against the PR2 database independently of the Nanopore OTUs using VSEARCH v.2.7.1 (Rognes et al. 2016). Species-level classification and false positives assessment in the mock community To assess the representation of our mock community species in the PR2 database 5.0.0 (Guillou et al. 2012), we performed a global alignment of the sequences of the reference strains (Suppl. material 2: table S3) with the sequences of the database using VSEARCH v.2.7.1 (Rognes et al. 2016). A sequence was classified to species level if the best hit had at least 99% similarity with the reference strain sequence or if the best hit was already classified as the same species. If the best hit was only classified to genus level and had less than 99% similarity to the strain reference, the sequence was assigned to genus level. All other hits, which were classified to different genera were classified as false positives. Phylogenetic tree construction from Nanopore OTUs To construct the phylogenetic tree, we extracted freshwater and marine OTUs whose percent identity with the closest PR2 5.0.0 (Guillou et al. 2012) reference was between 80% and 97% and, therefore, likely represent taxa not represented in the database. Sequences were aligned using MAFFT v.7.310 (Katoh and Standley 2013) and the resulting alignment was trimmed with trimAl v.1.4.rev15 (Capella-Gutiérrez et al. 2009) using the -automated1 option. Phylogenetic inference was performed using IQ-TREE 2.0.6 (Minh et al. 2020) with the -m MFP option to determine the optimal substitution model. After constructing the tree, we manually inspected the alignment and tree to identify OTUs whose taxonomy was inconsistent with the clade in which they were placed. OTUs that appeared on unusually long branches were removed. The remaining inconsistent OTUs were further examined using BLAST searches (Altschul et al. 1990) against the NCBI nt database (Wheeler et al. 2007) to refine their taxonomy (3 OTUs); those that could not be reliably reclassified were considered dubious and removed, resulting in a total of 26 OTUs being excluded from the dataset. The sequences were then realigned, trimmed and the tree was recalculated using the same 428 Metabarcoding and Metagenomics 9: 421–447 (2025), DOI: 10.3897/mbmg.9.163750 Małgorzata Chwalińska et al.: Protist diversity profiling via Nanopore long reads methods as described above. The TIM2+F+R10 model was selected as the best fitting model for the final tree. The resulting tree was visualised using RStudio (RStudio Team 2020) with the ggtree (Yu et al. 2017), treeio (Wang et al. 2020), dplyr (Wickham et al. 2022) and readxl (Wickham and Bryan 2019) packages. Statistical analyses Further analyses were performed in RStudio (RStudio Team 2020) using libraries: phyloseq (McMurdie and Holmes 2013), vegan (Oksanen et al. 2022), tidyverse (Wickham et al. 2019), reshape2 (Wickham 2007), readxl (Wickham and Bryan 2019) and ggplot2 (Wickham 2016). Sequences with low abundance were removed from all datasets prior to further analyses, using different thresholds depending on the dataset. For environmental samples, sequences occurring fewer than five times in the entire dataset were excluded, whereas for the low-complexity mock community, sequences with a relative abundance below 0.01% were discarded. We calculated rarefaction curves by inspecting how number of species changes depending on the subsampling depth. For beta-diversity, we aggregated ASVs/OTUs at the genus level and calculated the Bray-Curtis dissimilarity index combined with the NMDS ordination method. Results Comparison of Illumina and Nanopore metabarcoding using a mock community We first analysed a mock community of seven species representing major protist lineages mixed at a defined concentration (Suppl. material 2: table S3). Illumina V4-18S rRNA gene sequencing yielded 622,100 raw reads which, after processing, generated a total of 268 ASVs with an average length of 379 bp (Suppl. material 2: table S4). Nanopore sequencing yielded 594,076 raw reads encompassing the full 18S rRNA gene ITS1, 5.8S rRNA gene, ITS2 and part of 28S rRNA gene. After in silico extraction of the 18S rRNA gene fragments and OTUs clustering using the BaNaNA pipeline, we identified 147 OTUs with an average sequence length of 1,843 bp (Suppl. material 2: table S5). Further extraction of the V4 region of 18S rRNA gene (V4-tags) from the OTUs yielded 145 sequences with an average length of 416.1 bp. Illumina V4-18S rDNA ASVs revealed a taxonomic composition that differed substantially from the original community structure of the mock community (Fig. 2A). Dinoflagellates were the most abundant group, followed by haptophytes, ciliates and cryptophytes, while euglenozoans were nearly absent from the dataset. In addition, a considerable number of Illumina ASVs were classified as “other taxa”, representing organisms that were not intentionally included in the mock community. The full-length 18S rDNA OTUs from Nanopore sequencing more closely mirrored the expected community composition, with haptophytes being the most abundant, followed by euglenozoans. However, ciliates replaced chlorophytes as the third most abundant group. No significant differences were observed between the V4-tags and the fulllength 18S rDNA OTUs. Nanopore datasets also contained additional taxa that 429 Metabarcoding and Metagenomics 9: 421–447 (2025), DOI: 10.3897/mbmg.9.163750 Małgorzata Chwalińska et al.: Protist diversity profiling via Nanopore long reads were absent from the original mock community, but their number and diversity appeared much larger in the Illumina data (Suppl. material 1: fig. S1). A detailed analysis of these additional taxa (mock_community_illumina_taxonomy_table. xlsx and mock_community_nanopore_taxonomy_table.xlsx; Zenodo repository DOI: 10.5281/zenodo.15673958) suggests that, although many of them likely represent biases introduced during ASVs or OTU generation, some might correspond to genuine occurrences of organisms or minor contaminations rather than sequencing artefacts (Suppl. material 1: fig. S2). To assess the accuracy of the taxonomic assignments across different approaches, comparisons were made at the species level, as all the mock community species are represented in the reference database. Ultimately, neither sequencing technology was able to accurately identify all seven taxa in the mock community at the species level or quantify them correctly (Table 1). Illumina sequencing failed to detect Paramecium bursaria at the species level, while Nanopore did not identify Cryptomonas gyropyrenoidosa and Chlorella variabilis. The Illumina data exhibited significant biases in taxon relative abundances, with Euglena gracilis and Chlorella variabilis being substantially underestimated by three and two orders of magnitude, respectively, while Gymnodinium fuscum and both Cryptomonas species were overestimated by three and two orders of magnitude, respectively. Both Nanopore-based barcodes (i.e. full-length 18S rDNA OTUs and V4-tags) were mostly in agreement and accurately estimated the relative abundance of Euglena gracilis and Cryptomonas paramecium and overestimated Gymnodinium fuscum by two orders of magnitude (Table 1). However, for Paramecium bursaria, the two Nanopore approaches yielded different results: the full-length 18S rDNA OTUs overestimated abundance by three orders of magnitude, whereas the V4-tags provided an estimate much closer to the actual cell abundance. Prymnesium parvum was the only taxon for which relative abundance was consistently estimated to the same level of magnitude across sequencing technologies and matched its proportion in the mock community, based on cell counts. The presence of additional taxa and the poor relative abundance obtained at the species level prompted us to investigate the impact of sequencing technology on the accuracy of taxonomic annotation. The V4-18S rDNA ASVs from Illumina sequencing had the fewest representative sequences classified to species-level Figure 2. A. Relative abundance of species in the mock community at the subdivision level, as determined by cell counts and all sequencing approaches. Taxa that were not intentionally included in the mock community are grouped as ‘Other’; B. Accuracy of MOTUs identification in the mock community. Bar plots display the accuracy of taxonomic assignment for ASVs and OTUs derived from the mock community, based on comparisons with known species composition. 436 Metabarcoding and Metagenomics 9: 421–447 (2025), DOI: 10.3897/mbmg.9.163750 Małgorzata Chwalińska et al.: Protist diversity profiling via Nanopore long reads gyropyrenoidosa in the Nanopore mock community dataset (Table 1) are most likely due to the low DNA content per cell and the low number of cells present. This is especially likely given that the species had their representatives present in the reference database and both groups (Chlorophyta and Cryptophyta) were well represented in the environmental data (Fig. 4A). Both Illumina and Nanopore sequencing revealed taxa that were not originally present in the mock community (Fig. 2A, Suppl. material 1: fig. S2), which we believe is due to the actual presence of organisms and contaminants, but also to artefacts in amplicon generation. This effect is particularly pronounced in the Illumina ASVs generated with DADA2, which yielded significantly more taxa than the OTUs derived from Nanopore data using the BaNaNA pipeline (Suppl. material 1: fig. S1, Fig. 2B). The high number of false positives associated with DADA2 was also observed in previous studies (e.g. Overgaard et al. (2024)). This is also reflected in the almost exponential increase of taxa at lower taxonomic levels, which is present only in the Illumina dataset, indicating a high level of background noise (Suppl. material 1: fig. S1), possibly leading to inflated diversity estimates in environmental samples (Fig. 3). In contrast, the Nanopore-based OTUs showed fewer false positives, suggesting a more accurate representation of actual protist diversity. Some non-mock taxa were shared across both platforms and may result from incidental presence of these taxa in cultures, for example, Perkinsea, a known parasite of dinoflagellates and Cryptomonas paramecium (= Chilomonas paramecium) (Brugerolle 2002; Itoïz et al. 2022). Others could be caused by minor contamination during laboratory processing or by cross-contamination such as “tag-jumping” (Schnell et al. 2015). These are known sources of errors (Santoferrara 2019), which however, had no significant impact on the ecological interpretation of obtained results (Fig. 4A, B, D). Reconstructing ecological patterns from Nanopore and Illuminabased metabarcoding Both sequencing technologies applied to environmental samples yielded similar taxonomic compositions (Fig. 4A, B) and successfully reconstructed ecological patterns (Fig. 4D). Such agreement has been previously demonstrated for longread PacBio metabarcoding studies (Burki et al. 2021; Jamy et al. 2022) and Nanopore metabarcoding on protist communities (Bludau et al. 2025). Samples as expected were first differentiated, based on the salinity of the environment dividing them into marine and freshwater ones (Fig. 4D). Furthermore, deepsea samples were clearly separated from the ones collected at the surface and DCM layers of the water column. Sunlit communities were characterised with photosynthetic taxa such as chlorophytes and haptophytes, whereas deep-sea samples were dominated by heterotrophic radiolarians and diplonemids (Fig. 4B). The survey of freshwater dimictic lakes from summer revealed an expected pattern with substantial presence of dinoflagellates and cryptophytes in oxygenated layers (Debroas et al. 2017; Karlicki et al. 2024) (Fig. 4A). The unclear distinction between the photic and aphotic zones could be due to the strong influence of sinking dead cells, as previously demonstrated (Karlicki et al. 2024). That is furthermore supported by higher relative abundance of parasitic Perkinsea in deeper fractions which has been noticed by other metabarcoding and microscopic surveys (Mangot et al. 2009; Karlicki et al. 2024). 437 Metabarcoding and Metagenomics 9: 421–447 (2025), DOI: 10.3897/mbmg.9.163750 Małgorzata Chwalińska et al.: Protist diversity profiling via Nanopore long reads The importance of reference database in metabarcoding studies The comprehensive nature of reference sequence database is crucial to properly describe protist diversity (Tragin et al. 2018). However, existing protist databases cover less diversity compared to the bacterial ones, thus making the taxonomic annotation less accurate. Additionally, it appears that the number of available protist reference sequences is disproportionate for marine and freshwater environments. Protists in marine ecosystems are better studied than freshwater ones with molecular methods and currently have much better representation of species in the databases. This disparity is well visible when looking at the percentage of identities of ASVs and OTUs to the closest reference (Fig. 4C) where generally lower identities for freshwater environment are caused by a missing close reference in the PR2 database. We also obtained twice as many novel fulllength 18S rDNA OTUs from freshwater data than from marine (Fig. 5). Our novel full-length 18S rDNA OTUs represent many groups of protists (Fig. 5) clearly showing systematic gaps in the reference databases, but also showing the potential to fill this gap and improve future classifications with long Nanopore meta-barcodes. The proper classification of long reads requires also databases with longer sequences; the primers tested here resulted in the full 18S rDNA; however, Nanopore sequencing allows us to generate amplicons covering the whole rDNA operon, though such databases allowing appropriate classification of the full operon are still very limited (Tedersoo et al. 2024; Krabberød et al. 2025). Longer is better: the benefits of Nanopore amplicons The short length of Illumina amplicons prevents accurate taxonomy resolution beyond the genus level, limiting evolutionary and fine scale ecological studies (Hugerth et al. 2014; Szoboszlay et al. 2023). Nanopore technology allows us to sequence at once the whole 18S rRNA gene and more, providing access to more information and, therefore, a much better species-level resolution of taxonomic annotation (Latz et al. 2022; Ohta et al. 2023; Petrone et al. 2023; Szoboszlay et al. 2023; Zhang et al. 2023; Pascoal et al. 2024; Bludau et al. 2025). We have focused here exclusively on the 18S rDNA, as more comprehensive databases are currently available only for this fragment. However, longer fragments spanning the entire operon are expected to provide even higher resolution once sufficient reference sequences are available — a process that is already underway (Krabberød et al. 2025). The decrease in annotation accuracy of shorter fragments is particularly evident when comparing full-length 18S rDNA OTUs with V4-tags. Overall, the relative abundance of full-length 18S rDNA OTUs, classified as Paramecium bursaria at the species level, was greatly overestimated and a similar case was expected for V4-tags (Table 1). However, due to the lower accuracy of annotation for the V4-tag, many sequences were assigned only to the genus level (Fig. 2B), resulting in much lower relative abundances calculated at the species level (Table 1). In addition, the relative abundance values in Table 1 are influenced not only by the precision of taxonomic annotation, but also by factors such as the amount of DNA isolated from each species (with larger species generally yielding more DNA) and the copy number of rDNA. On the other hand, shorter sequences are more likely to match with 100% identity to a reference than longer sequences, as seen with V4-tags, which generally 438 Metabarcoding and Metagenomics 9: 421–447 (2025), DOI: 10.3897/mbmg.9.163750 Małgorzata Chwalińska et al.: Protist diversity profiling via Nanopore long reads exhibit higher identity percentages with the same reference database compared to full-length 18S rDNA OTUs (Fig. 4C), which may result in overestimation of taxa. In addition, unlike short ASVs, long OTUs are also suitable for phylogenetic reconstruction (Fig. 5) (Overgaard et al. 2024), which allows their taxonomy to be curated and potentially to discover new lineages (Lara et al. 2009). Illumina amplicon sequencing is currently the gold standard for metabarcoding of protist communities (De Vargas et al. 2015; Mahé et al. 2017; Piredda et al. 2017; Ibarbalz et al. 2023; Karlicki et al. 2024) and vast amounts of data have been collected and deposited in public databases up till now. Still, the data are heterogeneous as they were often generated using different primers or covering different fragments of the 18S rRNA gene and, thus, preventing a proper datasets integration or comparison. Sequencing the whole 18S rRNA gene by Nanopore offers the possibility to use any primer pair to extract the needed fragment of the gene and combine it with Illumina datasets, as demonstrated by our V4-tags analysis. Taxonomic assignment of the extracted fragment gives similar results, yet with a lower accuracy compared to the full-length 18S rDNA OTUs (Figs 2, 4A, B). Therefore, we recommend to retain the taxonomic assignment from the whole full-length 18S rDNA to fully use the possibilities of long Nanopore OTUs before extracting any specific region of interest. Conclusions This study shows the effectiveness of long-read Nanopore sequencing for protist biodiversity, ecology and evolution research, particularly in combination with our newly-developed BaNaNA pipeline. Our results, based on high-quality OTUs, confirmed that Nanopore sequencing is a powerful and reliable tool for diversity studies, providing comparable results to Illumina while reducing the noise often associated with short-read technologies and improving taxonomic resolution. The ability to use those sequences for phylogenetic reconstructions and higher taxonomic resolution further highlights the advantages of longer amplicon data for ecological analyses. We have also demonstrated that large amounts of Illumina-based metabarcoding data can be effectively combined with Nanopore meta-barcodes. The V4-tags extracted from Nanopore full-length 18S rDNA OTUs and Illumina V418S rDNA ASVs are highly comparable. This compatibility enables the integration of data generated with different sequencing technologies and primer pairs and facilitates long-term studies by incorporating existing data. Finally, our analysis shows that marine samples with more complete reference databases benefit from the higher resolution of long-read sequencing. However, challenges in taxonomic annotation remain due to incomplete reference databases for freshwater and other environments, emphasising the need to further improve protist reference databases to fully exploit longreads metabarcoding potential. Acknowledgements Freshwater sampling was conducted using the facilities of the KUMAK Masurian Centre for Biodiversity and Education in Urwitałt, Faculty of Biology, University of Warsaw. We would like to thank all the MicroDivEr team members 439 Metabarcoding and Metagenomics 9: 421–447 (2025), DOI: 10.3897/mbmg.9.163750 Małgorzata Chwalińska et al.: Protist diversity profiling via Nanopore long reads who helped us with the sampling. We acknowledge the MOOSE programme (Mediterranean Ocean Observing System for the Environment) coordinated by CNRS-INSU and the Research Infrastructure ILICO (CNRS-IFREMER). Additional information Conflict of interest The authors have declared that no competing interests exist. Ethical statement No ethical statement was reported. Use of AI No use of AI was reported. Funding This work was supported by the National Science Centre, Poland (OPUS grant 2020/37/B/NZ8/01456 to A.K.). The author(s) declared financial support for research and publication of this article from the MOOSE programme (Mediterranean Ocean Observing System for the Environment) supported coordinated by CNRS-INSU and the Research Infrastructure ILICO (CNRS-IFREMER) and the French Oceanographic Fleet infrastructure (IFREMER). Author contributions Conceptualization: AK. Data curation: MC. Formal analysis: MC. Funding acquisition: AK. Investigation: MK, SR, AK, MC. Methodology: MC, SR, MK. Project administration: FN, AK. Resources: AK. Software: MC, MK. Supervision: AK, FN. Validation: MC. Visualization: MC. Writing - original draft: MC. Writing - review and editing: MC, MK, FN, AK, SR. 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