Mock Community Research Articles

Factors impacting target-enriched long-read sequencing of resistomes and mobilomes.

We investigated the efficiency of target-enriched long-read sequencing (TELSeq) for detecting antimicrobial resistance genes (ARGs) and mobile genetic elements (MGEs) within complex matrices. We aimed to overcome limitations associated with traditional antimicrobial resistance (AMR) detection methods, including short-read shotgun metagenomics, which can lack sensitivity, specificity, and the ability to provide detailed genomic context. By combining biotinylated probe-based enrichment with long-read sequencing, we facilitated the amplification and sequencing of ARGs, eliminating the need for bioinformatic reconstruction. Our experimental design included replicates of human fecal microbiota transplant material, bovine feces, pristine prairie soil, and a mock human gut microbial community, allowing us to examine variables including genomic DNA input and probe set composition. Our findings demonstrated that TELSeq markedly improves the detection rates of ARGs and MGEs compared to traditional sequencing methods, underlining its potential for accurate AMR monitoring. A key insight from our research is the importance of incorporating mobilome profiles to better predict the transferability of ARGs within microbial communities, prompting a recommendation for the use of combined ARG-MGE probe sets for future studies. We also reveal limitations for ARG detection from low-input workflows, and describe the next steps for ongoing protocol refinement to minimize technical variability and expand utility in clinical and public health settings. This effort is part of our broader commitment to advancing methodologies that address the global challenge of AMR.

Distinct Escherichia coli transcriptional profiles in the guts of recurrent UTI sufferers revealed by pangenome hybrid selection

Low-abundance members of microbial communities are difficult to study in their native habitats, including Escherichia coli, a minor but common inhabitant of the gastrointestinal tract, and key opportunistic pathogen of the urinary tract. While multi-omic analyses have detailed interactions between uropathogenic Escherichia coli (UPEC) and the bladder mediating urinary tract infection (UTI), little is known about UPEC in its pre-infection reservoir, the gastrointestinal tract, partly due to its low relative abundance (<1%). To sensitively explore the genomes and transcriptomes of diverse gut E. coli, we develop E. coli PanSelect, which uses probes designed to specifically capture E. coli’s broad pangenome. We demonstrate its ability to enrich diverse E. coli by orders of magnitude, in a mock community and in human stool from a study investigating recurrent UTI (rUTI). Comparisons of transcriptomes between gut E. coli of women with and without history of rUTI suggest rUTI gut E. coli are responding to increased oxygen and nitrate, suggestive of mucosal inflammation, which may have implications for recurrent disease. E. coli PanSelect is well suited for investigations of in vivo E. coli biology in other low-abundance environments, and the framework described here has broad applicability to other diverse, low-abundance organisms.

High-throughput ecological interaction mapping of dairy microorganisms

To engineer efficient microbial management strategies in the food industry, a comprehensive understanding of microbial interactions is crucial. Microorganisms live in communities where they influence each other in several ways. Although much attention has been paid to the production of antagonistic metabolites in lactic acid bacteria (LAB), research that accounts for the complexity of their ecological interactions and their dynamics remains limited. This study explores binary interactions within a mock community of 94 strains, including 23 LAB from culture collections and 71 isolated from dairy products. Using a colony-picking robot and image analysis, bidirectional interactions were measured at high throughput on solid media, where one test strain was challenged against other mock community members as the target strains. Assays of 15 test strains (14 LAB and one yeast) yielded 1,142 bidirectionally mapped interactions, classified by ecological type over seven days. The results showed variation in the strength, directionality, and type of interactions depending on the origin of the target strains. Cooperation rates increased for strains isolated from raw milk to pasteurized milk and cheese, while exploitation was more common in raw milk strains. Cooperating strains also exhibited more similar ecological behaviors than competing strains, suggesting the presence of microbial cliques. Interestingly, Lactococcus cremoris ATCC 19257 developed pink-red pigmentation when co-cultured with Pseudomonas aeruginosa. Overall, these findings present an unprecedented exploratory data set that significantly improves our understanding of microbial interactions at the system level, with potential applications in strain selection for food processes such as fermentation, bioprotection, and probiotics.

Variation in the metagenomic analysis of fecal microbiome composition calls for a standardized operating approach.

The reproducibility in microbiome studies is limited due to the lack of one gold-standard operating procedure. The aim of this study was to examine the impact of protocol variations on microbiome composition using metagenomic data sets from a single center. We assessed the variation in a data set consisted of 2,722 subjects, including 9 subcohorts harboring healthy subjects and patients with various disorders, such as inflammatory bowel disease, colorectal cancer, and type 2 diabetes. Two different DNA extraction kits, with or without lyticase, and two sample storage methods were compared. Our results indicated that DNA extraction had the largest impact on gut microbiota diversity among all host factors and sample operating procedures. Healthy subjects matched by age, body mass index, and sample operating methods exhibited reduced, yet significant differences (PERMANOVA, P < 0.05) in gut microbiota composition across studies. The variations contributed by DNA extraction were primarily driven by different recovery efficiency of gram-positive bacteria, e.g., phyla Firmicutes and Actinobacteria. This was further confirmed by a parallel comparison of fecal samples from five healthy subjects and a standard mock community. In addition, the DNA extraction method influenced DNA biomass, quality, and the detection of specific lineage-associated diseases. Sample operating approach and batch effects should be considered for cohorts with large sample size or longitudinal cohorts to ensure that source data were appropriately generated and analyzed. Comparison between samples processed with inconsistent methods should be dealt with caution. This study will promote the establishment of a sample operating standard to enhance our understanding of microbiome and translating in clinical practice.IMPORTANCEThe reproducibility of human gut microbiome studies has been suboptimal across cohorts and study design choices. One possible reason for the disagreement is the introduction of systemic biases due to differences in methodologies. In our study, we utilized microbial metagenomic data sets from 2,722 fecal samples generated from a single research center to examine the extent to which sample storage and DNA extraction influence the quantification of microbial composition and compared this variable with other sources of technical and biological variation. Our research highlights the impact of DNA extraction methods when analyzing microbiome data and suggests that the microbiome profile may be influenced by differences in the extraction efficiency of bacterial species. With metagenomics sequencing being increasingly used in clinical biology, our findings provide insight into the challenges using metagenomics sequencing in clinical diagnostics, where the detection of certain species and its abundance relative to a "healthy reference" is key.

DeepPL: A deep-learning-based tool for the prediction of bacteriophage lifecycle.

Bacteriophages (phages) are viruses that infect bacteria and can be classified into two different lifecycles. Virulent phages (or lytic phages) have a lytic cycle that can lyse the bacteria host after their infection. Temperate phages (or lysogenic phages) can integrate their phage genomes into bacterial chromosomes and replicate with bacterial hosts via the lysogenic cycle. Identifying phage lifecycles is a crucial step in developing suitable applications for phages. Compared to the complicated traditional biological experiments, several tools have been designed for predicting phage lifecycle using different algorithms, such as random forest (RF), linear support-vector classifier (SVC), and convolutional neural network (CNN). In this study, we developed a natural language processing (NLP)-based tool-DeepPL-for predicting phage lifecycles via nucleotide sequences. The test results showed that our DeepPL had an accuracy of 94.65% with a sensitivity of 92.24% and a specificity of 95.91%. Moreover, DeepPL had 100% accuracy in lifecycle prediction on the phages we isolated and biologically verified previously in the lab. Additionally, a mock phage community metagenomic dataset was used to test the potential usage of DeepPL in viral metagenomic research. DeepPL displayed a 100% accuracy for individual phage complete genomes and high accuracies ranging from 71.14% to 100% on phage contigs produced by various next-generation sequencing technologies. Overall, our study indicates that DeepPL has a reliable performance on phage lifecycle prediction using the most fundamental nucleotide sequences and can be applied to future phage and metagenomic research.

Detection and characterization of Campylobacter in air samples from poultry houses using shot-gun metagenomics – a pilot study

BackgroundFoodborne pathogens such as Campylobacter jejuni are responsible for a large proportion of the gastrointestinal infections worldwide associated with poultry meat. Campylobacter spp. can be found in the chicken fecal microbiome and can contaminate poultry meat during the slaughter process. Commonly used sampling methods to detect Campylobacter spp. at poultry farms use fecal droppings or boot swabs in combination with conventional culture techniques or PCR. In this pilot study, we have used air filtering and filters spiked with mock communities in combination with shotgun metagenomics to detect Campylobacter and test the applicability of this approach for the detection and characterization of foodborne pathogens. To the best of our knowledge is this the first study that combines air filtering with shotgun metagenomic sequencing for detection and characterization of Campylobacter.ResultsAnalysis of air filters spiked with different levels of Campylobacter, into a background of mock or poultry house communities, indicated that we could detect as little as 200 colony forming units (CFU) Campylobacter per sample using our protocols. The results indicate that even with limited sequencing effort we could detect Campylobacter in the samples analysed in this study. We observed significant amounts of Campylobacter in real-life samples from poultry houses using both real-time PCR as well as shotgun metagenomics, suggesting that the flocks in both houses were infected with Campylobacter spp. Interestingly, in both houses we find diverse microbial communities present in the indoor air which reflect the fecal microbiome of poultry. Some of the identified genera such as Staphylococcus, Escherichia and Pseudomonas are known to contain opportunistic pathogenic species.ConclusionsThese results show that air sampling of poultry houses in combination with shotgun metagenomics can detect and identify Campylobacter spp. present at low levels. This is important since early detection of Campylobacter enables measures to be put in place to ensure the safety of broiler products, animal health and public health. This approach has the potential to detect any pathogen present in poultry house air.

Optimization of a DNA extraction protocol for improving bacterial and fungal classification based on Nanopore sequencing.

Ribosomal RNA gene amplicon sequencing is commonly used to evaluate microbiome profiles in health and disease and document the impact of interventional treatments. Nanopore sequencing is attractive since it can provide greater classification at the species level. However, optimized protocols to target marker genes for bacterial and fungal profiling are needed. To achieve an increased taxonomic resolution, we developed extraction and full-length amplicon PCR-based approaches using Nanopore sequencing. Three lysis conditions were applied to a mock microbial community, including known bacterial and fungal species: ZymoBIOMICS lysis buffer (ML) alone, incorporating bead-beating (MLB) or bead-beating plus MetaPolyzyme enzymatic treatment (MLBE). In profiling of bacteria in comparison to reference data, MLB had more statistically different bacterial phyla and genera than the other two conditions. In fungal profiling, MLB had a significant increase of Ascomycota and a decline of Basidiomycota, subsequently failing to detect Malassezia and Cryptococcus. Also, a principal coordinates analysis plot by the Bray-Curtis metric showed a significant difference among groups for bacterial (P=0.033) and fungal (P=0.012) profiles, highlighting the importance of understanding the biases present in pretreatment. Overall, microbial profiling and diversity analysis revealed that ML and MLBE are more similar than MLB for both bacteria and fungi; therefore, using this specific pipeline, bead-beating is not recommended for whole gene amplicon sequencing. However, ML alone was suggested as an optimal approach considering DNA yield, taxonomic classification, reagent cost and hands-on time. This could be an initial proof-of-concept study for simultaneous human bacterial and fungal microbiome studies.

Dammed coastal waterways are less diverse, more homogenous, and dominated by non-native species: Comprehensive insights from quantitative analysis of environmental DNA

Small dams are commonplace worldwide and impact local and regional aquatic diversity by altering habitats and disrupting dispersal networks. Quantifying the local and regional impacts of dams requires nearly comprehensive species occurrence data. We used environmental DNA (eDNA) metabarcoding to test theoretical predictions about the impacts of dams on local and regional bony fish diversity within the Chesapeake Bay Watershed, USA. We analyzed eDNA from 465 sampling points within 34 waterbodies documenting the distributions of 61 species. On average, dammed waterbodies had approximately half (48 %) as many species per site as undammed (lower alpha diversity) and more homogenous species composition (lower beta diversity). Native migratory species were less than one tenth (0.08) as likely to be detected at dammed sites than undammed sites, native resident (non-migratory) species were one third (0.34) as likely, whereas introduced species were 2.6 times more likely to be detected. Our sampling and bioinformatics methods were validated by a diverse mock community control. Our results suggest that dams in coastal waterways homogenize fish metacommunities, reduce local biodiversity through dispersal limitation and habitat alteration, and favor the dominance of lentic-adapted introduced species while potentially restricting the spread of introduced catfish. Decisions to construct or decommission dams should consider local and regional impacts on biodiversity.

RAPiD: a rapid and accurate plant pathogen identification pipeline for on-site nanopore sequencing.

Nanopore sequencing technology has enabled the rapid, on-site taxonomic identification of samples from anything and anywhere. However, sequencing errors, inadequate databases, as well as the need for bioinformatic expertise and powerful computing resources, have hampered the widespread use of the technology for pathogen identification in the agricultural sector. Here we present RAPiD, a lightweight and accurate real-time taxonomic profiling pipeline. Compared to other metagenomic profilers, RAPiD had a higher classification precision achieved through the use of a curated, non-redundant database of common agricultural pathogens and extensive quality filtering of alignments. On a fungal, bacterial and mixed mock community RAPiD was the only pipeline to detect all members of the communities. We also present a protocol for in-field sample processing enabling pathogen identification from plant sample to sequence within 3 h using low-cost equipment. With sequencing costs continuing to decrease and more high-quality reference genomes becoming available, nanopore sequencing provides a viable method for rapid and accurate pathogen identification in the field. A web implementation of the RAPiD pipeline for real-time analysis is available at https://agrifuture.senckenberg.de.

The Organelle in the Ointment: improved detection of cryptic mitochondrial reads resolves many unknown sequences in cross-species microbiome analyses

Abstract The genomes of mitochondria and chloroplasts contain ribosomal RNA (rRNA) genes, reflecting their ancestry as free-living bacteria. These organellar rRNAs are often amplified in microbiome studies of animals and plants. If identified, they can be discarded, merely reducing sequencing depth. However, we identify certain high-abundance organeller RNAs not identified by common pipelines, which may compromise statistical analysis of microbiome structure and diversity. We quantified this by reanalyzing 7459 samples from seven 16S rRNA studies, including microbiomes from 927 unique animal genera. We find that under-annotation of cryptic mitochondrial and chloroplast reads affects multiple of these large-scale cross-species microbiome comparisons, and varies between host species, biasing comparisons. We offer a straightforward solution: supplementing existing taxonomies with diverse organelle rRNA sequences. This resolves up to 97% of unique unclassified sequences in some entire studies as mitochondrial (14% averaged across all studies), without increasing false positive annotations in mitochondria-free mock communities. Improved annotation decreases the proportion of unknown sequences by ≥10-fold in 2262 of 7459 samples (30%), spanning 5 of 7 major studies examined. We recommend leveraging organelle sequence diversity to better identify organelle gene sequences in microbiome studies, and provide code, data resources and tutorials that implement this approach.

Data-Independent Acquisition Mass Spectrometry as a Tool for Metaproteomics: Interlaboratory Comparison Using a Model Microbiome.

Mass spectrometry (MS)-based metaproteomics is used to identify and quantify proteins in microbiome samples, with the frequently used methodology being Data-Dependent Acquisition mass spectrometry (DDA-MS). However, DDA-MS is limited in its ability to reproducibly identify and quantify lower abundant peptides and proteins. To address DDA-MS deficiencies, proteomics researchers have started using Data-Independent Acquisition Mass Spectrometry (DIA-MS) for reproducible detection and quantification of peptides and proteins. We sought to evaluate the reproducibility and accuracy of DIA-MS metaproteomic measurements relative to DDA-MS using a mock community of known taxonomic composition. Artificial microbial communities of known composition were analyzed independently in three laboratories using DDA- and DIA-MS acquisition methods. DIA-MS yielded more protein and peptide identifications than DDA-MS in each laboratory. In addition, the protein and peptide identifications were more reproducible in all laboratories and provided an accurate quantification of proteins and taxonomic groups in the samples. We also identified some limitations of current DIA tools when applied to metaproteomic data, highlighting specific needs to improve DIA tools enabling analysis of metaproteomic datasets from complex microbiomes. Ultimately, DIA-MS represents a promising strategy for MS-based metaproteomics due to its large number of detected proteins and peptides, reproducibility, deep sequencing capabilities, and accurate quantitation.

Evaluating metagenomics and targeted approaches for diagnosis and surveillance of viruses

BackgroundMetagenomics is a powerful approach for the detection of unknown and novel pathogens. Workflows based on Illumina short-read sequencing are becoming established in diagnostic laboratories. However, high sequencing depth requirements, long turnaround times, and limited sensitivity hinder broader adoption. We investigated whether we could overcome these limitations using protocols based on untargeted sequencing with Oxford Nanopore Technologies (ONT), which offers real-time data acquisition and analysis, or a targeted panel approach, which allows the selective sequencing of known pathogens and could improve sensitivity.MethodsWe evaluated detection of viruses with readily available untargeted metagenomic workflows using Illumina and ONT, and an Illumina-based enrichment approach using the Twist Bioscience Comprehensive Viral Research Panel (CVRP), which targets 3153 viruses. We tested samples consisting of a dilution series of a six-virus mock community in a human DNA/RNA background, designed to resemble clinical specimens with low microbial abundance and high host content. Protocols were designed to retain the host transcriptome, since this could help confirm the absence of infectious agents. We further compared the performance of commonly used taxonomic classifiers.ResultsCapture with the Twist CVRP increased sensitivity by at least 10–100-fold over untargeted sequencing, making it suitable for the detection of low viral loads (60 genome copies per ml (gc/ml)), but additional methods may be needed in a diagnostic setting to detect untargeted organisms. While untargeted ONT had good sensitivity at high viral loads (60,000 gc/ml), at lower viral loads (600–6000 gc/ml), longer and more costly sequencing runs would be required to achieve sensitivities comparable to the untargeted Illumina protocol. Untargeted ONT provided better specificity than untargeted Illumina sequencing. However, the application of robust thresholds standardized results between taxonomic classifiers. Host gene expression analysis is optimal with untargeted Illumina sequencing but possible with both the CVRP and ONT.ConclusionsMetagenomics has the potential to become standard-of-care in diagnostics and is a powerful tool for the discovery of emerging pathogens. Untargeted Illumina and ONT metagenomics and capture with the Twist CVRP have different advantages with respect to sensitivity, specificity, turnaround time and cost, and the optimal method will depend on the clinical context.

Evaluation of a Nanopore Sequencing Strategy on Bacterial Communities From Marine Sediments

ABSTRACTFollowing the development of high‐throughput DNA sequencers, environmental prokaryotic communities were usually described by metabarcoding on short markers of the 16S domain. Among third‐generation sequencers, that offered the possibility to sequence the full 16S domain, the portable MinION from Oxford Nanopore was undervalued for metabarcoding because of its relatively higher error rate per read. Here we illustrate the limits and benefits of Nanopore sequencing devices by comparing the prokaryotic community structure in a mock community and 52 sediment samples from mangrove sites, inferred from full‐length 16S long‐reads (16S‐FL, ca. 1.5 kbp) on a MinION device, with those inferred from partial 16S short‐reads (16S‐V4V5, ca. 0.4 kbp, 16S‐V4V5) on Illumina MiSeq. 16S‐V4V5 and 16S‐FL retrieved all the bacterial species from the mock, but Nanopore long‐reads overestimated their diversity (56 species vs. 15). Whether these supplementary OTUs were artefactual or not, they only accounted for ca. 10% of the reads. From the sediment samples, with a coverage‐based rarefaction of reads and after singletons filtering, Mantel and Procrustean tests of co‐inertia showed that bacterial community structures inferred from 16S‐V4V5 and 16S‐FL were significantly similar, showing both a comparable contrast between sites and a coherent sea‐land orientation within sites. In our dataset, 84.7% and 98.8% of the 16S‐V4V5 assigned reads were assigned strictly to the same species and genus, respectively, than those detected by 16S‐FL. 16S‐FL detected 92.2% of the 309 families and 87.7% of the 448 genera that were detected by the short 16S‐V4V5. 16S‐FL recorded 973 additional species and 392 genus not detected by 16S‐V4V5 (31.5% and 10.4% of the 16S‐FL reads, respectively, among which 67.8% and 79.3% were assigned), produced by both primer specificities and different error rates. Thus, our results concluded an overall similarity between 16S‐V4V5 and 16S‐FL sequencing strategies for this type of environmental samples.

What's on the menu? A novel molecular gut content analysis to investigate the feeding behavior of phytophagous insects.

The relationship between phytophagous insects and plants is a central aspect of food webs and ecosystem functioning. The introduction of new species into an environment can have significant impacts on the food web of a native ecosystem. In many cases, there is a lack of knowledge on the biology and feeding behavior of invasive species prior their introduction and in the invaded regions. Gut content analyses of insects have provided valuable information on the host spectrum of insects. However, current approaches are time-consuming and costly. Here, we describe a new molecular gut content analysis (GCA) approach using the Oxford Nanopore (ONT) Flongle sequencing platform to characterize the plant DNA present in the gut of the highly polyphagous insect species Halyomorpha halys. We demonstrate that this technique efficiently amplifies and correctly identifies plant DNA in a mock community. We performed a feeding experiment to determine the sensitivity of this approach and to assess how long the plant DNA can be detected. All plants used in the feeding experiment were correctly identified and detected after 56 days. Surprisingly, we also detected various plant genera that were not included in the feeding experiment and thus were likely ingested months before the experiment. Our study suggests that the GCA using the ONT Flongle sequencing platform represents a rapid and cost-efficient diagnosis of the dietary preferences, host range, and the diversity of consumed plant species of pest insects with high precision.

Prokaryotic-virus-encoded auxiliary metabolic genes throughout the global oceans

BackgroundProkaryotic microbes have impacted marine biogeochemical cycles for billions of years. Viruses also impact these cycles, through lysis, horizontal gene transfer, and encoding and expressing genes that contribute to metabolic reprogramming of prokaryotic cells. While this impact is difficult to quantify in nature, we hypothesized that it can be examined by surveying virus-encoded auxiliary metabolic genes (AMGs) and assessing their ecological context.ResultsWe systematically developed a global ocean AMG catalog by integrating previously described and newly identified AMGs and then placed this catalog into ecological and metabolic contexts relevant to ocean biogeochemistry. From 7.6 terabases of Tara Oceans paired prokaryote- and virus-enriched metagenomic sequence data, we increased known ocean virus populations to 579,904 (up 16%). From these virus populations, we then conservatively identified 86,913 AMGs that grouped into 22,779 sequence-based gene clusters, 7248 (~ 32%) of which were not previously reported. Using our catalog and modeled data from mock communities, we estimate that ~ 19% of ocean virus populations carry at least one AMG. To understand AMGs in their metabolic context, we identified 340 metabolic pathways encoded by ocean microbes and showed that AMGs map to 128 of them. Furthermore, we identified metabolic “hot spots” targeted by virus AMGs, including nine pathways where most steps (≥ 0.75) were AMG-targeted (involved in carbohydrate, amino acid, fatty acid, and nucleotide metabolism), as well as other pathways where virus-encoded AMGs outnumbered cellular homologs (involved in lipid A phosphates, phosphatidylethanolamine, creatine biosynthesis, phosphoribosylamine-glycine ligase, and carbamoyl-phosphate synthase pathways).ConclusionsTogether, this systematically curated, global ocean AMG catalog and analyses provide a valuable resource and foundational observations to understand the role of viruses in modulating global ocean metabolisms and their biogeochemical implications.7DrB1a6JQbvrw3a2P4Hm3dVideo

A rapid phylogeny-based method for accurate community profiling of large-scale metabarcoding datasets.

Environmental DNA (eDNA) is becoming an increasingly important tool in diverse scientific fields from ecological biomonitoring to wastewater surveillance of viruses. The fundamental challenge in eDNA analyses has been the bioinformatical assignment of reads to taxonomic groups. It has long been known that full probabilistic methods for phylogenetic assignment are preferable, but unfortunately, such methods are computationally intensive and are typically inapplicable to modern next-generation sequencing data. We present a fast approximate likelihood method for phylogenetic assignment of DNA sequences. Applying the new method to several mock communities and simulated datasets, we show that it identifies more reads at both high and low taxonomic levels more accurately than other leading methods. The advantage of the method is particularly apparent in the presence of polymorphisms and/or sequencing errors and when the true species is not represented in the reference database.

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.

Benchmarking bacterial taxonomic classification using nanopore metagenomics data of several mock communities

Taxonomic classification is crucial in identifying organisms within diverse microbial communities when using metagenomics shotgun sequencing. While second-generation Illumina sequencing still dominates, third-generation nanopore sequencing promises improved classification through longer reads. However, extensive benchmarking studies on nanopore data are lacking. We systematically evaluated performance of bacterial taxonomic classification for metagenomics nanopore sequencing data for several commonly used classifiers, using standardized reference sequence databases, on the largest collection of publicly available data for defined mock communities thus far (nine samples), representing different research domains and application scopes. Our results categorize classifiers into three categories: low precision/high recall; medium precision/medium recall, and high precision/medium recall. Most fall into the first group, although precision can be improved without excessively penalizing recall with suitable abundance filtering. No definitive ‘best’ classifier emerges, and classifier selection depends on application scope and practical requirements. Although few classifiers designed for long reads exist, they generally exhibit better performance. Our comprehensive benchmarking provides concrete recommendations, supported by publicly available code for reassessment and fine-tuning by other scientists.

Complete pipeline for Oxford Nanopore Technology amplicon sequencing (ONT-AmpSeq): from pre-processing to creating an operational taxonomic unit table.

Amplicon sequencing has long served as a robust method for characterising microbial communities, and despite inherent resolution limitations, it remains a preferred technique, offering cost- and time-effective insights into bacterial compositions. Here, we introduce ONT-AmpSeq, a user-friendly pipeline designed for processing amplicon sequencing data generated from Oxford Nanopore Technology (ONT) devices. Our pipeline enables efficient creation of taxonomically annotated operational taxonomic unit (OTU) tables from ONT sequencing data, with the flexibility to multiplex amplicons on the same barcode. The pipeline encompasses six main steps-statistics, quality filtering, alignment, clustering, polishing, and taxonomic classification-integrating various state-of-the-art software tools. We provide a detailed description of each step, along with performance tests and robustness evaluations using both test data and a ZymoBIOMICS® Microbial Community Standard mock community dataset. Our results demonstrate the ability of ONT-AmpSeq to effectively process ONT amplicon data, offering valuable insights into microbial community composition. Additionally, we discuss the influence of polishing tools on taxonomic insight and the impact of taxonomic annotation methods on the derived microbial composition. Overall, ONT-AmpSeq represents a comprehensive solution for analysing ONT amplicon sequencing data, facilitating streamlined and reliable microbial community analysis. The pipeline, along with test data, is freely available for public use.

The impact of extraction method and pollen concentration on community composition for pollen metabarcoding

AbstractPremisePlants and pollinators closely interact with each other to form complex networks of species interactions. Metabarcoding of pollen collections has recently been proposed as an advantageous method for the construction of such networks, but the extent to which diversity and community analyses depend on the extraction method and pollen concentration used remains unclear.MethodsIn this study, we used a dilution series of two pollen mixtures (a mock community and pooled natural pollen loads from bumblebees) to assess the effect of mechanical homogenization and two DNA extraction kits (spin column DNA extraction kit and magnetic bead DNA extraction kit) on the detected pollen richness and community composition.ResultsAll species were successfully detected using the three methods, even in the most dilute samples. However, the extraction method had a significant effect on the detected pollen richness and community composition, with simple mechanical homogenization introducing an extraction bias.DiscussionOur findings suggest that all three methods are effective for detecting plant species in the pollen loads on insects, even in cases of very low pollen loads. However, our results also indicate that extraction methods can have a profound impact on the ability to correctly assess the community composition of the pollen loads on insects. The choice of extraction methodology should therefore be carefully considered to ensure reliable and unbiased results in pollen diversity and community analyses.