dataesr/scientific-paragraphs-categorization huggingface dataset:
2006). Such bias is magnified with presence-absence data, commonly used for metazoan metabarcoding  (Ji et al. 2013 ). However, as intraspecific polymorphism and interspecific divergence are phylum-specific, imposing a universal clustering threshold on metabarcoding datasets is also introducing bias, penalizing groups with lower polymorphism or divergence levels, while overestimating species diversity in groups with higher interspecific divergence. Universal clustering thresholds can be avoided with tools such as swarm v2, a single-linkage clustering algorithm  (Mahé et al. 2015) , implemented in recent bioinformatic pipelines, such as FROGS  (Escudié et al. 2018)  or SLIM  (Dufresne et al. 2019). Based on network theory, swarm v2 aggregates sequences iteratively and locally around seed sequences, based on d, the number of nucleotide differences, to determine coherent groups of sequences, independent of amplicon input order, allowing highly scalable and fine-scale clustering.
Finally, it is widely recognized that homogeneous entities sharing a set of evolutionary and ecological properties, i. e. species  (Mayr 1942; Queiroz, de 2005) , sometimes referred to "ecotypes" for prokaryotes  (Cohan 2001; Gevers et al. 2005) , represent a fundamental category of biological organization that is the cornerstone of most ecological and evolutionary theories and empirical studies. Maintaining ASV information for feeding databases and cross-comparing studies is not incompatible with their clustering into OTUs, and this choice depends on the purpose of the study, i. e. providing a census of the extent and distribution of genetic polymorphism for a given gene, or a census of biodiversity to be used and manipulated in ecological or evolutionary studies. Here we evaluate DADA2 and LULU, using them alone and in combination with swarm v2, to assess the performance of these new tools for metabarcoding of metazoan communities.
Accepted Article This article is protected by copyright. All rights reserved  (Sinniger et al. 2016) , we evaluated the need for clustering and the effectiveness of LULU curation to select pipeline parameters delivering the most accurate resolution of two deep-sea mock communities. We then test the different bioinformatic tools on a deep-sea sediment dataset in order to select an optimal trade-off between inflating biodiversity estimates and loosing rare biodiversity. As a baseline for comparison, and in the perspective of the joint study of metazoan and microbial taxa, we also analysed the 16S V4-V5 rRNA barcode  (Parada et al. 2016 ) on these environmental samples. Our objectives were to (1) discuss the use of ASV vs OTU-centred datasets depending on taxonomic compartment and study objectives, and (2) determine the most adequate swarm-clustering and LULU curation thresholds that avoid inflating biodiversity estimates while retaining rare biodiversity.
5 μM concentration of each primer, 1X Phusion Master Mix, and an additional 1 mM MgCl 2 for COI. PCR amplifications (98 °C for 30 s; 40 cycles of 10 s at 98 °C, 45 s at 48 °C (COI) or 57 °C (18S), 30 s at 72 °C; and 72 °C for 5 min) were cleaned up with ExoSAP (Thermo Fisher Scientific, Waltham, MA, USA) and sent to Eurofins (Eurofins Scientific, Luxembourg) for Sanger sequencing. The barcode sequences obtained for all mock specimens were added to the databases used for taxonomic assignments of.
Sequencing procedures Amplicon libraries are characterized by low diversity sequences at the beginning of the reads due to the presence of the primer sequence. Low-diversity libraries can interfere in correct cluster identification, resulting in a drastic loss of data output. Therefore, loading concentrations of libraries were decreased to 8-9 pM (instead of 12-14 pM for standard libraries) and PhiX DNA spike-in was set to 20% in order to minimize impacts on run quality. Libraries were sequenced on HiSeq2500 (System User Guide Part # 15035786) instruments (Illumina, San Diego, CA, USA) in a 250 bp paired-end mode.
Read correction, amplicon cluster generation and taxonomic assignment Pairs of Illumina reads were corrected with DADA2 following the online tutorial for pairedend HiSeq data (https://benjjneb. github. io/dada2/bigdata_paired. html). Reads containing ambiguous bases removed and trimming lengths were adjusted based on sequence quality profiles, so that Qscores remained above 30 (truncLen at 220 for 18S and 16S, 200 for COI, maxEE at 2, truncQ at 11, maxN at 0). Error model calculation (for R1F/R2R read pairs and then R2F/R1R read pairs), read.
Accepted Article This article is protected by copyright. All rights reserved correction, and read merging was performed at default settings. Amplicons were filtered by size, with size ranges set to 330-390 bp for the 18S SSU rRNA marker gene, 300-326 bp for the COI marker gene, and 350-390 bp for the 16S rRNA marker gene, based on raw size distributions observed. Chimera removal and taxonomic assignment were performed with default methods implemented in DADA2. A second taxonomic assignment method was optionally implemented in the pipeline, allowing with more abundant ("parent") clusters. LULU was applied on the full dataset (mock and environmental samples) with a minimum relative co-occurrence of 0. 95 (default), using a minimum similarity threshold (minimum match) at 84% (default) and slightly higher at 90%, following recommendations of the authors for less variable loci than ITS.
Accepted Article This article is protected by copyright. All rights reserved levels can be efficiently removed with swarm clustering at low d-values. Although LULU curation may still be useful to merge redundant phylotypes in specific cases such as haplotype network analyses, this was not tested in this study. Indeed, parallelization not being currently available for LULU curation, the richness of prokaryote communities implied an unrealistic calculation time, even on a powerful cluster (e. g. LULU curation was at 20 -40% after 4 days of calculation on our cluster). In order to have reliable BLAST phylum assignments for pipeline comparison, final datasets were taxonomically filtered by retaining only clusters having a minimum hit identity of 86% for rRNA loci and 80% for COI. These values were chosen as they represent approximate minimum identities for reliable phylum assignment  (Stefanni et al.
Accepted Article This article is protected by copyright. All rights reserved permuting 999 times. Beta-diversity patterns were visualised via non-metric multidimensional scaling (NMDS) using the same dissimilarities stated above. Finally, BLAST and RDP taxonomic assignments were compared at the most adequate pipeline settings for each locus. BLAST and RDP datasets were compared on ASV-level for prokaryotes, and OTU-level for metazoans (swarm d=1, LULU with minimum match at 84% and minimum ratio at 1 for COI, and 90% and 100 respectively for 18S). As trials on MIDORI-UNIQUE resulted in very poor performance of RDP for COI (assignments belonging mostly to Insecta), the comparison was performed with MIDORI-UNIQUE subsampled to marine taxa only. For the global dataset, full ranges of BLAST hit identities and phylum-level bootstraps were plotted and numbers of clusters left after phylum-level and genus-level quality filtering were calculated, while for evaluation on the mock samples, rarefied data was subsampled to reliable phylum-level assignments (i.
Alpha diversity in mock communities A total of 1. 5 million (COI) and 2 million (18S) raw reads were obtained from the two mock communities (Table  S4 ). After refining (decontamination, renormalisation, removal of non-target taxa, and clusters unassigned at phylum-level or with unreliable phylum-level assignments), these numbers were decreased to 0. 7 million for COI and 1. 3 million for 18S. All ten mock species were detected in the COI dataset (Table  1 ), even with minimum relative DNA abundance levels as low as 0. 7% (Mock 5). With 18S, seven species were recovered and the three bivalve species remained unresolved. Taxonomic assignments were correct at the genus-level for six species with COI and three species for 18S, but all mock species produced ASVs/OTUs correctly assigned up to family or class level.
Dominant species generally produced more reads in both the clustered and non-clustered datasets, with the notable exception of the gastropod Paralepetopsis sp, which was poorly detected with 18S (Table  S5 ). When ASVs were clustered with swarm v2, this generally led to a reduction in taxonomic recovery: the two bivalves P. kilmeri and C. regab were taxonomically misidentified with COI at d ≥.
Accepted Article This article is protected by copyright. Curating ASVs/OTUs with LULU allowed reducing the number of clusters produced per species for both loci, and optimal results were obtained in datasets clustered at d ≥ 1 for COI and d = 1 for 18S. The number of unexpected clusters ("Others") was hardly affected by LULU curation (Table  1 ). In the COI dataset, curating with LULU at 84% or 90% minimum match resulted in similar OTU numbers, although 84% performed slightly better in Mock 3 (Table  1 ). Increasing the minimum ratio parameter to 100 or 1000 resulted in the retention of more error OTUs and thus higher OTU numbers in each mock species (data not shown). For 18S, both LULU minimum match and minimum ratio affected species recovery. LULU curation with minimum ratio = 1 led to the loss of the shrimp Chorocaris sp.
at both minimum match levels and the gastropod Paralepetopsis sp. at 84% minimum match (Table  S6 ). With minimum ratio at 100, Chorocaris sp. was retained in the dataset at both minimum match levels and Paralepetopsis sp. with minimum match at 90% (Table  1 ). With minimum ratio at 1000, both species were retained at both minimum match levels but more OTUs were retained for another species (Munidopsis sp. , Table  S6 ). As LULU curation with higher minimum ratio levels resulted in more accurate species compositions in the mock samples with 18S, we only present LULU curation with minimum ratio = 100 for the environmental samples.
High-throughput sequencing results A total of 44 million (18S), 33 million (COI) and 16 million (16S) reads were obtained from 42 sediment samples, 4 field controls, 2 extraction blanks, and 4 (16S), 8 (18S), and 10 (COI) PCR blanks (Table  S4 ). The final datasets contained ~5 million (COI) to ~8 million (18S) marine metazoan target reads and ~7 million prokaryotic 16S reads (Table  S4 ). COI reads produced 13,397 ASVs, 3,518 -5,563 OTUs after swarm clustering (d = 1-13), and 1,758 -10,028 OTUs after LULU curation (Table  S7 ). Final 18S reads comprised 8,280 ASVs, 1,869 -6,015 OTUs after swarm.
Patterns of beta-diversity between pipelines PERMANOVAs confirmed that sites differed significantly in terms of community structure, accounting from 46% to 89% of variation in data. Evaluating the effect of LULU curation for metazoans showed that LULU-curated data resolved similar community compositions than noncurated data, accounting for < 1% of variation in data (Fig. 2 ). Although ASV and OTU datasets detected similar amounts of variation due to sites in PERMANOVAs, clustering levels affected the ecological patterns resolved by ordinations in rRNA loci (Fig  2 ). Metazoan 18S ASVs showed strong segregation by ocean basin, with samples grouped by depth within each basin, and prokaryote ASVs showed both strong segregation by ocean basin and depth (Fig. 2 ). Clustering at d-values > 1 decreased differences among deep sites (> 1,000 m) across ocean basins, emphasizing the depth effect over the basin effect.
Taxonomic assignment quality Assigning with BLAST resulted in mock community assignments comparable to described above. With COI, eight of the ten species produced one single OTU, with six correctly assigned at genus-level, and two species were taxonomically correctly assigned only to class-level and produced 2-3 OTUs (Fig  S3 ). With 18S, seven species were recovered (4 correctly assigned at genus-level), with two producing more than one OTU, and the three vesicomyid bivalve species were taxonomically unresolved and assigned up to family-level while generating 2 OTUs. Assigning the COI dataset with RDP using the MIDORI-UNIQUE database resulted in assignments of the mock samples that did not match the expected taxa and were mostly belonging to arthropods, a problem not observed with BLAST (data not shown).
Accepted Article This article is protected by copyright. All rights reserved BLAST and RDP assigned similar amounts of OTUs in the prokaryote dataset, but BLAST assigned 20% (18S) and 70% (COI) less OTUs at phylum-level than RDP in the metazoan datasets, even at minimum hit identity of 70% (Table  S8 ). BLAST hit identities of the overall datasets varied strongly depending on phyla and marker gene (Fig. 3 ). For 18S, 90% of metazoan OTUs had assignment identities ≥ 86%, corresponding roughly to accurate phylum-level  (Stefanni et al. 2018; Edgar 2017 ). Only 34% had reliable genus-level assignments, i. e. with > 95% similarity (Table  S8 ). For COI, only 30% of metazoan assignments were reliable at phylum-level (≥ 80%), and only 1% at genus-level (> 93%). BLAST hit identity was much higher for prokaryotes, with 98% of ASVs assigned with ≥ 86% similarity to sequences in databases, and 65% had reliable genus-level assignments (> 95% similarity).
The results on the mock samples showed that even single individuals produced very different numbers of ASVs, suggesting that ASV-centred datasets do not accurately reflect species composition in metazoans. Intragenomic and intraspecific polymorphism are highly variable across taxa  (Plouviez et al. 2009; Teixeira et al. 2013) , as confirmed by the very variable decrease in cluster numbers observed with clustering in this study for different phyla (Fig. S1 ). The taxonomic compositions of samples based on ASVs may thus reflect genetic rather than species diversity. This distinction is important to keep in mind, as the species, i. e. "a lineage or group of connected lineages with a distinct role"  (Freudenstein et al. 2017) , constitutes the core of biodiversity inventories for biological and ecological studies.
2 ). This can be explained by the fact that clustering gives more weight to large distinct OTUs compared to many small (i. e. with low read numbers) ASVs. The deep Atlantic and Mediterranean sites, segregating at the ASV-level (possibly due to vicariance by distance), thus appeared more similar at high d-values, revealing the occurrence of distinct ASVs belonging to many shared OTUs and thus suggesting an ecological signal in fine-scale sequence variants. This is in accordance with other studies reporting differences in beta diversity patterns in ASV vs OTU datasets for ribosomal loci, when large divergence thresholds were used for clustering (Xiong and Zhan 2018;  Bokulich et al. 2013). This also reveals the interdependence of alpha and beta diversity components, so that clustering ASVs into OTUs and thereby reducing alpha diversity, leaves more space for beta diversity to be expressed, as observed in both population genetics  (Jost 2008; Beaumont and Nichols 1996)  and community analysis  (Jost 2007).
Importance of parameter adjustment for LULU curation LULU curation proved effective in limiting the number of multiple clusters produced by single individuals in the mock samples, confirming its efficiency to correct for intragenomic diversity (Table  1 ). Moreover, the fact that the number of unexpected clusters ("Others", Table  1 ) was not affected by LULU curation also shows that LULU specifically removes spurious OTUs and not true species diversity. However, careful adjustment of LULU parameters was needed, particularly for the minimum ratio, as at default level (1) it led to the loss of up to two mock species with 18S. This need for relaxed minimum ratio values can be explained by the non-ideal design of the mock samples. Indeed, LULU should be applied on datasets containing as many samples as possible, which should have compositional similarities (i.
However, as all mock species were retained at both minimum match levels with minimum ratio at 1000, the loss of that species at 84% may also just reflect the non-ideal mock design (Paralepetopsis sp. being very poorly amplified by 18S, it got merged to a bivalve OTU as their similarity was greater than 84%). Given the fact that 18S is evolving much slower than COI, this marker is taxonomically much less resolutive and phylum-level similarity is at ~86%  (Stefanni et al. 2018). As error OTUs are produced within each individual, it is reasonable to think that their similarity to their parent OTUs will be greater than phylum-level similarity, justifying the use of 90% minimum match. This increased minimum match also has the added benefit to decrease calculation time on large datasets. For COI,.
Accepted Article This article is protected by copyright. All rights reserved although results in the mock samples showed the best performance at minimum ratio of 1 and little effect of the minimum match parameter (90% vs 84%), both minimum match levels resulted in significantly different OTU numbers in the environmental samples (Table  2 , Fig. 1 ). This was not the case for 18S, where both 84% and 90% minimum match resulted in similar numbers of OTUs in the environmental samples (minimum ratio at 100). Thus, increasing the minimum ratio parameter is essential for not losing species in sample-poor datasets, and will be more correct than adjusting the minimum match. The mock communities used in this study, apart from being taxonomically limited to just 10 species, did unfortunately not contain several haplotypes of the same species (intraspecific variation).
Taxonomic resolution and assignment quality The COI locus allowed the detection of all ten species present in the mock samples, compared to seven in the 18S dataset (Table  1 ). This locus also provided much more accurate assignments, most of them correct at the genus (and species) level, confirming that COI uncovers more metazoan species and offers a better taxonomic resolution than 18S  (Tang et al. 2012; Clarke et al. 2017; Andújar et al. 2018b). Our results also support approaches combining nuclear and mitochondrial markers to achieve.
Accepted Article This article is protected by copyright. All rights reserved more comprehensive biodiversity inventories  (Cowart et al. 2015; Drummond et al. 2015; Zhan et al. 2014). Indeed, strong differences exist in amplification success among taxa  (Bhadury et al. 2006; Carugati et al. 2015) , exemplified by nematodes, which are well detected with 18S but not with COI  (Bucklin et al. 2011 ). The 18S barcode marker performed better in the detection of nematodes, annelids, platyhelminths, and xenacoelomorphs while COI mostly detected cnidarians, molluscs, and poriferans (Fig. 3 , Fig. S1 ), highlighting the complementarity of these two loci. This high complementarity of COI and 18S in terms of targeted taxa also supports the approach taken by  Stefanni et al. (2018) , indeed subsampling each gene dataset for its "best targeted phyla" and subsequently combining both seems to be a very efficient way to produce comprehensive and nonredundant biodiversity inventories.
Finally, compared to BLAST assignments, similar taxonomic assignments were observed using the RDP Bayesian Classifier on the mock samples for 18S and for COI when using the MIDORI-UNIQUE marine-only database (Fig. S3 ). Poor performance of RDP using the full MIDORI database is likely due to the size of the database, and to its low coverage of deep-sea species. Indeed, small databases, taxonomically similar to the targeted communities, and with sequences of the same length as the DNA fragment of interest, are known to maximise accurate identification  (Macheriotou et al. 2019; Ritari et al. 2015). The problem of underrepresentation of deep-sea taxa is especially apparent with the BLAST assignments, which generally displayed low identities to sequences in databases, especially for COI (Fig. 3 ).
Minimum similarities of 80% for COI and 86% for 18S as cutoff values for metazoans have been used to improve the taxonomic quality of metazoan metabarcoding datasets  (Stefanni et al. 2018). However, phylogenies of marine invertebrates are characterised by high levels of species divergence (up to ~30%), even within genera  (Zanol et al. 2010 ). Studies on deep-sea taxa have found that some invertebrate species had COI sequences diverging more than 20% from any other species present in molecular databases  (Shank et al. 1999; Herrera et al. 2015). At present, it thus seems difficult to work at taxonomic levels beyond phylumlevel with deep-sea metabarcoding data when using large public databases. When using the reduced marine-only COI database, RDP provided the most accurate assignments in the mock samples (Fig.
Accepted Article This article is protected by copyright. All rights reserved RDP training sets, without overrepresentation of terrestrial species, is therefore needed for this Bayesian assignment method to be effective on deep-sea datasets. With reduced and more specific databases, removing clusters with phylum-level bootstraps < 80 should be an efficient way to increase taxonomic quality of deep-sea metabarcoding datasets. At present, if accurate taxonomic assignments are sought while using universal primers, we advocate assigning taxonomy in two steps: first, using BLAST and a large database including all phyla amplifiable by the primer set as BLAST performs better than RDP in terms of speed. The clusters belonging to the groups of interest can then be extracted and re-assigned using RDP and a smaller, taxon-specific database.
CONCLUSIONS AND PERSPECTIVES Using mock communities and environmental samples, we evaluate several recent algorithms and assess their capacity to improve the quality of molecular biodiversity inventories of metazoans and prokaryotes. Our results support the fact that ASV data should be produced and communicated for reusability and reproducibility following the recommendations of  Callahan et al. (2017). This is especially useful in large projects spanning wide geographic zones and time scales, as different ASV datasets can easily be merged a posteriori, and clustered if necessary afterwards. However, our results confirm that both ASVs and OTUs describe relevant, yet different levels of biotic diversity. ASVs comprehensively describe genetic diversity (incl. intraspecies) while OTUs more accurately reflect interspecies diversity.
Considering 16S polymorphism observed in prokaryotic species  (Acinas et al. 2004 ) and the possible geographic segregation of their populations, using OTUs may also be suitable in prokaryotic datasets, for example in studies screening for species associations, as symbionts may be prone to differential fixation through enhanced drift  (Shapiro et al. 2016 ). This study emphasized that swarm clustering needs to be adapted to each genetic marker and taxonomic compartment, in order to identify an optimal balance between the correction for spurious clusters and the loss of species. Specifically, d-values > 1 appeared to be less appropriate with 18S for metazoans. Our results also demonstrated that LULU effectively curates metazoan biodiversity inventories obtained through metabarcoding. They underline the need to adapt parameters for LULU curation, in particular the minimum ratio level in the case of sample-poor datasets, where cooccurrence and abundance patterns may be distorted.