Towards reproducible metabarcoding data: Lessons from an international cross-laboratory experiment

New insights into biologic interpretation of bioinformatic pipelines for fish eDNA metabarcoding: A case study in Pearl River estuary

Environmental DNA (eDNA) metabarcoding is an emerging tool for monitoring biological communities in aquatic ecosystems. The selection of bioinformatic pipelines significantly impacts the results of biodiversity assessments. However, there is currently no consensus on the appropriate bioinformatic pipelines for fish community analysis in eDNA metabarcoding. In this study, we compared three bioinformatic pipelines (Uparse, DADA2, and UNOISE3) using real and mock (constructed with 15/30 known fish) communities to investigate the differences in biological interpretation during the data analysis process in eDNA metabarcoding. Performance evaluation and diversity analyses revealed that the choice of bioinformatic pipeline could impact the biological results of metabarcoding experiments. Among the three pipelines, the operational taxonomic units (OTU)-based pipeline (Uparse) showed the best performance (sensitivity: 0.6250 ± 0.0166; compositional similarity: 0.4000 ± 0.0571), the highest richness (25-102) and minimal inter-group differences in alpha diversity. It suggested the OTU-based pipeline possessed superior capability in fish diversity monitoring compared to ASV/ZOTU-based pipeline. Additionally, the Bray-Curtis distance matrix achieved the highest discriminative effect in the PCoA (43.3%-53.89%) and inter-group analysis (P < 0.01), indicating it was better at distinguishing compositional differences or specific genera of fish community at different sampling sites than other distance matrices. These findings provide new insights into fish community monitoring through eDNA metabarcoding in estuarine environments.

Relating target fish DNA concentration to community composition analysis in freshwater fish via metabarcoding

Metabarcoding has been widely accepted as a useful tool for biodiversity assessment based on eDNA. The method allows for the detection of entire groups of organisms in a single sample, making it particularly applicable in aquatic habitats. The high sensitivity of the molecular approaches is especially beneficial in detecting elusive and rare fish species, improving biodiversity assessments. Numerous biotic and abiotic factors that affect the persistence and availability of fish DNA in surface waters and therefore affecting species detectability, have been identified. However, little is known about the relationship between the total fish DNA concentration and the detectability of differential abundant species. In this study three controlled mock-community DNA samples (56 individual samples) were analyzed by (i) metabarcoding (MiSeq) of 12S rDNA (175 bp) and by (ii) total freshwater fish DNA quantification (via qPCR of 12S rDNA). We show that the fish DNA quantity affects the relative abundance of species-specific sequences and the detectability of rare species. In particular we found that samples with a concentration between 1000 pg/μL down to 10 pg/μL of total fish DNA revealed a stable relative frequency of DNA sequences obtained for a specific fish species, as well as a low variability between replicates. Additionally, we observed that even in complex mock-community DNA samples, a total fish DNA concentration of 23 pg/μL was sufficient to reliably detect all species in every replicate, including three rare species with proportions of ≤0.5 %. We also found that the DNA barcode similarity between species can affect detectability, if evenness is low. Our data suggest that the total DNA concentration of fish is an important factor to consider when analyzing and interpreting relative sequence abundance data. Therefore, the workflow proposed here will contribute to an ecologically and economically efficient application of metabarcoding in fish biodiversity assessment.

Variability and bias in microbiome metagenomic sequencing: an interlaboratory study comparing experimental protocols

Several studies have documented the significant impact of methodological choices in microbiome analyses. The myriad of methodological options available complicate the replication of results and generally limit the comparability of findings between independent studies that use differing techniques and measurement pipelines. Here we describe the Mosaic Standards Challenge (MSC), an international interlaboratory study designed to assess the impact of methodological variables on the results. The MSC did not prescribe methods but rather asked participating labs to analyze 7 shared reference samples (5 × human stool samples and 2 × mock communities) using their standard laboratory methods. To capture the array of methodological variables, each participating lab completed a metadata reporting sheet that included 100 different questions regarding the details of their protocol. The goal of this study was to survey the methodological landscape for microbiome metagenomic sequencing (MGS) analyses and the impact of methodological decisions on metagenomic sequencing results. A total of 44 labs participated in the MSC by submitting results (16S or WGS) along with accompanying metadata; thirty 16S rRNA gene amplicon datasets and 14 WGS datasets were collected. The inclusion of two types of reference materials (human stool and mock communities) enabled analysis of both MGS measurement variability between different protocols using the biologically-relevant stool samples, and MGS bias with respect to ground truth values using the DNA mixtures. Owing to the compositional nature of MGS measurements, analyses were conducted on the ratio of Firmicutes: Bacteroidetes allowing us to directly apply common statistical methods. The resulting analysis demonstrated that protocol choices have significant effects, including both bias of the MGS measurement associated with a particular methodological choices, as well as effects on measurement robustness as observed through the spread of results between labs making similar methodological choices. In the analysis of the DNA mock communities, MGS measurement bias was observed even when there was general consensus among the participating laboratories. This study was the result of a collaborative effort that included academic, commercial, and government labs. In addition to highlighting the impact of different methodological decisions on MGS result comparability, this work also provides insights for consideration in future microbiome measurement study design.

Harnessing environmental DNA to reveal biogeographical patterns of non-indigenous species for improved co-governance of the marine environment in Aotearoa New Zealand

Aotearoa New Zealand's Northern region is a major gateway for the incursion and establishment of non-indigenous species (NIS) populations due to high numbers of recreational and commercial vessels. This region also holds a unique marine ecosystem, home to many taonga (treasured) species of cultural and economic importance. Regular surveillance, eradication plans and public information sharing are undertaken by local communities and governmental organizations to protect these ecosystems from the impact of NIS. Recently, considerable investments went into environmental DNA (eDNA) research, a promising approach for the early detection of NIS for complementing existing biosecurity systems. We applied eDNA metabarcoding for elucidating bioregional patterns of NIS distributions across a gradient from harbors (NIS hotspots) to open seas (spreading areas). Samples were collected during a research cruise sailing across three Aotearoa New Zealand harbors, Waitematā, Whangārei and Pēwhairangi (Bay of Islands), and their adjacent coastal waters. The small-ribosomal subunit (18S rRNA) and mitochondrial cytochrome c oxidase I (COI) genes were screened using the online Pest Alert Tool for automated detection of putative NIS sequences. Using a probabilistic modelling approach, location-dependent occupancies of NIS were investigated and related to the current information on species distribution from biosecurity surveillance programs. This study was collaboratively designed with Māori partners to initiate a model of co-governance within the existing science system.

THAPBI PICT-a fast, cautious, and accurate metabarcoding analysis pipeline

THAPBI PICT is an open source software pipeline for metabarcoding analysis of Illumina paired-end reads, including cases of multiplexing where more than one amplicon is amplified per DNA sample. Initially a Phytophthora ITS1 Classification Tool (PICT), we demonstrate using worked examples with our own and public data sets how, with appropriate primer settings and a custom database, it can be applied to other amplicons and organisms, and used for reanalysis of existing datasets. The core dataflow of the implementation is (i) data reduction to unique marker sequences, often called amplicon sequence variants (ASVs), (ii) dynamic thresholds for discarding low abundance sequences to remove noise and artifacts (rather than error correction by default), before (iii) classification using a curated reference database. The default classifier assigns a label to each query sequence based on a database match that is either perfect, or a single base pair edit away (substitution, deletion or insertion). Abundance thresholds for inclusion can be set by the user or automatically using per-batch negative or synthetic control samples. Output is designed for practical interpretation by non-specialists and includes a read report (ASVs with classification and counts per sample), sample report (samples with counts per species classification), and a topological graph of ASVs as nodes with short edit distances as edges. Source code available from https://github.com/peterjc/thapbi-pict/ with documentation including installation instructions.

Hurdles and opportunities in implementing marine biosecurity systems in data-poor regions

Managing marine nonindigenous species (mNIS) is challenging, because marine environments are highly connected, allowing the dispersal of species across large spatial scales, including geopolitical borders. Cross-border inconsistencies in biosecurity management can promote the spread of mNIS across geopolitical borders, and incursions often go unnoticed or unreported. Collaborative surveillance programs can enhance the early detection of mNIS, when response may still be possible, and can foster capacity building around a common threat. Regional or international databases curated for mNIS can inform local monitoring programs and can foster real-time information exchange on mNIS of concern. When combined, local species reference libraries, publicly available mNIS databases, and predictive modeling can facilitate the development of biosecurity programs in regions lacking baseline data. Biosecurity programs should be practical, feasible, cost-effective, mainly focused on prevention and early detection, and be built on the collaboration and coordination of government, nongovernment organizations, stakeholders, and local citizens for a rapid response.

Contaminants reach everywhere: Fish dietary samples should be surface decontaminated prior to molecular diet analysis

Knowledge of trophic interaction is necessary to understand the dynamics of ecosystems and develop ecosystem-based management. The key data to measure these interactions should come from large-scale diet analyses with good taxonomic resolution. To that end, molecular methods that analyze prey DNA from guts and feces provide high-resolution dietary taxonomic data. However, molecular diet analysis may also produce unreliable results if the samples are contaminated by external sources of DNA. Employing the freshwater European whitefish (Coregonus lavaretus) as a tracer for sample contamination, we studied the possible route of whitefish in beaked redfish (Sebastes mentella) guts sampled in the Barents Sea. We used whitefish-specific COI primers for diagnostic analysis, and fish-specific 12S and metazoa-specific COI primers for metabarcoding analyses of intestine and stomach contents of fish samples that were either not cleaned, water cleaned, or bleach cleaned after being in contact with whitefish. Both the diagnostic and COI metabarcoding revealed clear positive effects of cleaning samples as whitefish were detected in significantly higher numbers of uncleaned samples compared to water or bleach-cleaned samples. Stomachs were more susceptible to contamination than intestines and bleach cleaning reduced the frequency of whitefish contamination. Also, the metabarcoding approach detected significantly more reads of whitefish in the stomach than in intestine samples. The diagnostic analysis and COI metabarcoding detected contaminants in a higher and comparable number of gut samples than the 12S-based approach. Our study underlines thus the importance of surface decontamination of aquatic samples to obtain reliable diet information from molecular data.

Maximising signal-to-noise ratios in environmental DNA-based monitoring

Man's impacts on global ecosystems are increasing and there is a growing demand that these activities be appropriately monitored. Monitoring requires measurement of a response metric ('signal') that changes maximally and consistently in response to the monitored activity irrespective of other factors ('noise'), thus maximising the signal-to-noise ratio. Indices derived from time-consuming morphology-based taxonomic identification of organisms are a core part of many monitoring programmes. Metabarcoding is an alternative to morphology-based identification and involves the sequencing of short fragments of DNA ('markers') from multiple taxa simultaneously. DNA suitable for metabarcoding includes that extracted from environmental samples (eDNA). Metabarcoding outputs DNA sequences that can be identified (annotated) by matching them against archived annotated sequences. However, sequences from most organisms are not archived - preventing annotation and potentially limiting metabarcoding in monitoring applications. Consequently, there is growing interest in using unannotated sequences as response metrics in monitoring programmes. We compared the sequences from three commonly used markers (16S (V3/V4 regions), 18S (V1/V2 regions) and COI) and, sampling along steep impact gradients, showed that the 16S and COI sequences were associated with the largest and smallest signal-to-noise ratio respectively. We trialled four separate, intuitive, noise-reduction approaches and demonstrated that removing less frequent sequences improved the signal-to-noise ratio, partitioning an additional 25 % from noise to explanatory factors in non-parametric ANOVA (NPA) and reducing dispersion in the data. For the 16S marker, retaining only the most frequently observed sequence, per sample, resulting in nine sequences across 150 samples, generated a near-maximal signal-to-noise ratio (95 % of the variance explained in NPA). We recommend that NPA, combined with rigorous elimination of less frequent sequences, be used to pre-filter sequences/taxa being used in monitoring applications. Our approach will simplify downstream analysis, for example the identification of key taxa and functional associations.

A manager's guide to using eDNA metabarcoding in marine ecosystems

Environmental DNA (eDNA) metabarcoding is a powerful tool that can enhance marine ecosystem/biodiversity monitoring programs. Here we outline five important steps managers and researchers should consider when developing eDNA monitoring program: (1) select genes and primers to target taxa; (2) assemble or develop comprehensive barcode reference databases; (3) apply rigorous site occupancy based decontamination pipelines; (4) conduct pilot studies to define spatial and temporal variance of eDNA; and (5) archive samples, extracts, and raw sequence data. We demonstrate the importance of each of these considerations using a case study of eDNA metabarcoding in the Ports of Los Angeles and Long Beach. eDNA metabarcoding approaches detected 94.1% (16/17) of species observed in paired trawl surveys while identifying an additional 55 native fishes, providing more comprehensive biodiversity inventories. Rigorous benchmarking of eDNA metabarcoding results improved ecological interpretation and confidence in species detections while providing archived genetic resources for future analyses. Well designed and validated eDNA metabarcoding approaches are ideally suited for biomonitoring applications that rely on the detection of species, including mapping invasive species fronts and endangered species habitats as well as tracking range shifts in response to climate change. Incorporating these considerations will enhance the utility and efficacy of eDNA metabarcoding for routine biomonitoring applications.

Demystifying eDNA validation

As environmental DNA (eDNA) approaches gain momentum for biodiversity analysis, validation becomes a key consideration. I focus on four facets of eDNA validation. Validation through technical processes, legal use, official statements, and 'good enough' scenarios can advance the field to aid societal issues such as climate emergency and biodiversity crisis.

Toward global integration of biodiversity big data: a harmonized metabarcode data generation module for terrestrial arthropods

Metazoan metabarcoding is emerging as an essential strategy for inventorying biodiversity, with diverse projects currently generating massive quantities of community-level data. The potential for integrating across such data sets offers new opportunities to better understand biodiversity and how it might respond to global change. However, large-scale syntheses may be compromised if metabarcoding workflows differ from each other. There are ongoing efforts to improve standardization for the reporting of inventory data. However, harmonization at the stage of generating metabarcode data has yet to be addressed. A modular framework for harmonized data generation offers a pathway to navigate the complex structure of terrestrial metazoan biodiversity. Here, through our collective expertise as practitioners, method developers, and researchers leading metabarcoding initiatives to inventory terrestrial biodiversity, we seek to initiate a harmonized framework for metabarcode data generation, with a terrestrial arthropod module. We develop an initial set of submodules covering the 5 main steps of metabarcode data generation: (i) sample acquisition; (ii) sample processing; (iii) DNA extraction; (iv) polymerase chain reaction amplification, library preparation, and sequencing; and (v) DNA sequence and metadata deposition, providing a backbone for a terrestrial arthropod module. To achieve this, we (i) identified key points for harmonization, (ii) reviewed the current state of the art, and (iii) distilled existing knowledge within submodules, thus promoting best practice by providing guidelines and recommendations to reduce the universe of methodological options. We advocate the adoption and further development of the terrestrial arthropod module. We further encourage the development of modules for other biodiversity fractions as an essential step toward large-scale biodiversity synthesis through harmonization.

Emerging technologies revolutionise insect ecology and monitoring

Insects are the most diverse group of animals on Earth, but their small size and high diversity have always made them challenging to study. Recent technological advances have the potential to revolutionise insect ecology and monitoring. We describe the state of the art of four technologies (computer vision, acoustic monitoring, radar, and molecular methods), and assess their advantages, current limitations, and future potential. We discuss how these technologies can adhere to modern standards of data curation and transparency, their implications for citizen science, and their potential for integration among different monitoring programmes and technologies. We argue that they provide unprecedented possibilities for insect ecology and monitoring, but it will be important to foster international standards via collaboration.

Best practices in metabarcoding of fungi: From experimental design to results

The development of high-throughput sequencing (HTS) technologies has greatly improved our capacity to identify fungi and unveil their ecological roles across a variety of ecosystems. Here we provide an overview of current best practices in metabarcoding analysis of fungal communities, from experimental design through molecular and computational analyses. By reanalysing published data sets, we demonstrate that operational taxonomic units (OTUs) outperform amplified sequence variants (ASVs) in recovering fungal diversity, a finding that is particularly evident for long markers. Additionally, analysis of the full-length ITS region allows more accurate taxonomic placement of fungi and other eukaryotes compared to the ITS2 subregion. Finally, we show that specific methods for compositional data analyses provide more reliable estimates of shifts in community structure. We conclude that metabarcoding analyses of fungi are especially promising for integrating fungi into the full microbiome and broader ecosystem functioning context, recovery of novel fungal lineages and ancient organisms as well as barcoding of old specimens including type material.

Temporal and Spatial Variation of the Skin-Associated Bacteria from Healthy Participants and Atopic Dermatitis Patients

Several factors have been shown to influence the composition of the bacterial communities inhabiting healthy skin, with variation between different individuals, differing skin depths, and body locations (spatial-temporal variation). Atopic dermatitis (AD) is a chronic skin disease also affecting the skin-associated bacterial communities. While the effects of AD have been studied on these processes individually, few have considered how AD disrupts the spatial-temporal variation of the skin bacteria as a whole (i.e., considered these processes simultaneously). Here, we characterized the skin-associated bacterial communities of healthy volunteers and lesional and nonlesional skin of AD patients by metabarcoding the universal V3-V4 16S rRNA region from tape strip skin samples. We quantified the spatial-temporal variation (interindividual variation, differing skin depths, multiple time points) of the skin-associated bacteria within healthy controls and AD patients, including the relative change induced by AD in each. Interindividual variation correlated with the bacterial community far more strongly than any other factors followed by skin depth and then AD status. There was no significant temporal variation found within either AD patients or healthy controls. The bacterial community was found to vary markedly according to AD severity, and between patients without and with filaggrin mutations. Therefore, future studies may benefit from sampling subsurface epidermal communities and considering AD severity and the host genome in understanding the role of the skin bacterial community within AD pathogenesis rather than considering AD as a presence-absence disorder. IMPORTANCE The bacteria associated with human skin may influence skin barrier function and the immune response. Previous studies have attempted to understand the factors that regulate the skin bacteria, characterizing the spatial-temporal variation of the skin bacteria within unaffected skin. Here, we quantified the effect of AD on the skin bacteria on multiple spatial-temporal factors simultaneously. Although significant community variation between healthy controls and AD patients was observed, the effects of AD on the overall bacterial community were relatively low compared to other measured factors. Results here suggest that changes in specific taxa rather than wholesale changes in the skin bacteria are associated with mild to moderate AD. Further studies would benefit from incorporating the complexity of AD into models to better understand the condition, including AD severity and the host genome, alongside microbial composition.

Comparing PCR-generated artifacts of different polymerases for improved accuracy of DNA metabarcoding

Accuracy of PCR amplification is vital for obtaining reliable amplicon-sequencing results by metabarcoding. Here, we performed a comparative analysis of error profiles in the PCR products by 14 different PCR kits using a mock eukaryotic community DNA sample mimicking metabarcoding analysis. To prepare a mock eukaryotic community from the marine environment, equal amounts of plasmid DNA from 40 microalgal species were mixed and used for amplicon-sequencing by a high-throughput sequencing approach. To compare the differences in PCR kits used for this experiment, we focused on the following seven parameters: 1) Quality, 2) Chimera, 3) Blast top hit accuracy, 4) Deletion, 5) Insertion, 6) Base substitution and 7) Amplification bias amongst species. The results showed statistically significant differences (p &lt; 0.05) for all of the seven parameters depending on the PCR kits used. These differences may result from the different DNA polymerases included in each kit, although the result can also be influenced by PCR reaction conditions. Simultaneous analysis of several parameters suggested that kits containing KOD plus Neo (TOYOBO) and HotStart Taq DNA polymerase (BiONEER, CA, US) at the annealing temperature of 65 °C displayed better results in terms of parameters associated with chimeras, top hit similarity and deletions.