Benchmarking bioinformatic virus identification tools using real-world metagenomic data across biomes.

BACKGROUND: As most viruses remain uncultivated, metagenomics is currently the main method for virus discovery. Detecting viruses in metagenomic data is not trivial. In the past few years, many bioinformatic virus identification tools have been developed for this task, making it challenging to choose the right tools, parameters, and cutoffs. As all these tools measure different biological signals, and use different algorithms and training and reference databases, it is imperative to conduct an independent benchmarking to give users objective guidance. RESULTS: We compare the performance of nine state-of-the-art virus identification tools in thirteen modes on eight paired viral and microbial datasets from three distinct biomes, including a new complex dataset from Antarctic coastal waters. The tools have highly variable true positive rates (0-97%) and false positive rates (0-30%). PPR-Meta best distinguishes viral from microbial contigs, followed by DeepVirFinder, VirSorter2, and VIBRANT. Different tools identify different subsets of the benchmarking data and all tools, except for Sourmash, find unique viral contigs. Performance of tools improved with adjusted parameter cutoffs, indicating that adjustment of parameter cutoffs before usage should be considered. CONCLUSIONS: Together, our independent benchmarking facilitates selecting choices of bioinformatic virus identification tools and gives suggestions for parameter adjustments to viromics researchers.

Evaluating and improving the representation of bacterial contents in long-read metagenome assemblies.

BACKGROUND: In the metagenomic assembly of a microbial community, abundant species are often thought to assemble well given their deeper sequencing coverage. This conjuncture is rarely tested or evaluated in practice. We often do not know how many abundant species are missing and do not have an approach to recover them. RESULTS: Here, we propose k-mer based and 16S RNA based methods to measure the completeness of metagenome assembly. We show that even with PacBio high-fidelity (HiFi) reads, abundant species are often not assembled, as high strain diversity may lead to fragmented contigs. We develop a novel reference-free algorithm to recover abundant metagenome-assembled genomes (MAGs) by identifying circular assembly subgraphs. Complemented with a reference-free genome binning heuristics based on dimension reduction, the proposed method rescues many abundant species that would be missing with existing methods and produces competitive results compared to those state-of-the-art binners in terms of total number of near-complete genome bins. CONCLUSIONS: Our work emphasizes the importance of metagenome completeness, which has often been overlooked. Our algorithm generates more circular MAGs and moves a step closer to the complete representation of microbial communities.

Microbial communities, functional, and flavor differences among three different-colored high-temperature Daqu: A comprehensive metagenomic, physicochemical, and electronic sensory analysis.

High-temperature Daqu (HTD) is the starter for producing sauce-flavor Baijiu, with different-colored Daqu (white, yellow, and black) reflecting variations in fermentation chamber conditions, chemical reactions, and associated microbiota. Understanding the relationship between Daqu characteristics and flavor/taste is challenging yet vital for improving Baijiu fermentation. This study utilized metagenomic sequencing, physicochemical analysis, and electronic sensory evaluation to compare three different-colored HTD and their roles in fermentation. Fungi and bacteria dominated the HTD-associated microbiota, with fungi increasing as the fermentation temperature rose. The major fungal genera were Aspergillus (40.17%) and Kroppenstedtia (21.16%), with Aspergillus chevalieri (25.65%) and Kroppenstedtia eburnean (21.07%) as prevalent species. Microbial communities, functionality, and physicochemical properties, particularly taste and flavor, were color-specific in HTD. Interestingly, the microbial communities in different-colored HTDs demonstrated robust functional complementarity. White Daqu exhibited non-significantly higher α-diversity compared to the other two Daqu. It played a crucial role in breaking down substrates such as starch, proteins, hyaluronic acid, and glucan, contributing to flavor precursor synthesis. Yellow Daqu, which experienced intermediate temperature and humidity, demonstrated good esterification capacity and a milder taste profile. Black Daqu efficiently broke down raw materials, especially complex polysaccharides, but had inferior flavor and taste. Notably, large within-group variations in physicochemical quality and microbial composition were observed, highlighting limitations in color-based HTD quality assessment. Water content in HTD was associated with Daqu flavor, implicating its crucial role. This study revealed the complementary roles of the three HTD types in sauce-flavor Baijiu fermentation, providing valuable insights for product enhancement.

Enhanced crystalline cellulose degradation by a novel metagenome-derived cellulase enzyme.

Metagenomics has revolutionized access to genomic information of microorganisms inhabiting the gut of herbivorous animals, circumventing the need for their isolation and cultivation. Exploring these microorganisms for novel hydrolytic enzymes becomes unattainable without utilizing metagenome sequencing. In this study, we harnessed a suite of bioinformatic analyses to discover a novel cellulase-degrading enzyme from the camel rumen metagenome. Among the protein-coding sequences containing cellulase-encoding domains, we identified and subsequently cloned and purified a promising candidate cellulase enzyme, Celcm05-2, to a state of homogeneity. The enzyme belonged to GH5 subfamily 4 and exhibited robust enzymatic activity under acidic pH conditions. It maintained hydrolytic activity under various environmental conditions, including the presence of metal ions, non-ionic surfactant Triton X-100, organic solvents, and varying temperatures. With an optimal temperature of 40 °C, Celcm05-2 showcased remarkable efficiency when deployed on crystalline cellulose (> 3.6 IU/mL), specifically Avicel, thereby positioning it as an attractive candidate for a myriad of biotechnological applications spanning biofuel production, paper and pulp processing, and textile manufacturing. Efficient biodegradation of waste paper pulp residues and the evidence of biopolishing suggested that Celcm05-2 can be used in the bioprocessing of cellulosic craft fabrics in the textile industry. Our findings suggest that the camel rumen microbiome can be mined for novel cellulase enzymes that can find potential applications across diverse biotechnological processes.

Functional Redundant Microbiome Enhanced Anaerobic Digestion Efficiency under Ammonium Inhibition Conditions.

Revealing the role of functional redundancy is of great importance considering its key role in maintaining the stability of microbial ecosystems in response to various disturbances. However, experimental evidence on this point is still lacking due to the difficulty in "manipulating" and depicting the degree of redundancy. In this study, manipulative experiments of functional redundancy were conducted by adopting the mixed inoculation strategy to evaluate its role in engineered anaerobic digestion systems under ammonium inhibition conditions. The results indicated that the functional redundancy gradient was successfully constructed and confirmed by evidence from pathway levels. All mixed inoculation groups exhibited higher methane production regardless of the ammonium level, indicating that functional redundancy is crucial in maintaining the system's efficiency. Further analysis of the metagenome-assembled genomes within different functional guilds revealed that the extent of redundancy decreased along the direction of the anaerobic digestion flow, and the role of functional redundancy appeared to be related to the stress level. The study also found that microbial diversity of key functional populations might play a more important role than their abundance on the system's performance under stress. The findings provide direct evidence and highlight the critical role of functional redundancy in enhancing the efficiency and stability of anaerobic digestion.

Washed microbiota transplantation for Crohn's disease: A metagenomic, metatranscriptomic, and metabolomic-based study.

BACKGROUND: Fecal microbiota transplantation (FMT) is a promising therapeutic approach for treating Crohn's disease (CD). The new method of FMT, based on the automatic washing process, was named as washed microbiota transplantation (WMT). Most existing studies have focused on observing the clinical phenomena. However, the mechanism of action of FMT for the effective management of CD-particularly in-depth multi-omics analysis involving the metagenome, metatranscriptome, and metabolome-has not yet been reported. AIM: To assess the efficacy of WMT for CD and explore alterations in the microbiome and metabolome in response to WMT. METHODS: We conducted a prospective, open-label, single-center clinical study. Eleven CD patients underwent WMT. Their clinical responses (defined as a decrease in their CD Activity Index score of > 100 points) and their microbiome (metagenome, metatranscriptome) and metabolome profiles were evaluated three months after the procedure. RESULTS: Seven of the 11 patients (63.6%) showed an optimal clinical response three months post-WMT. Gut microbiome diversity significantly increased after WMT, consistent with improved clinical symptoms. Comparison of the metagenome and metatranscriptome analyses revealed consistent alterations in certain strains, such as Faecalibacterium prausnitzii, Roseburia intestinalis, and Escherichia coli. In addition, metabolomics analyses demonstrated that CD patients had elevated levels of various amino acids before treatment compared to the donors. However, levels of vital amino acids that may be associated with disease progression (e.g., L-glutamic acid, gamma-glutamyl-leucine, and prolyl-glutamine) were reduced after WMT. CONCLUSION: WMT demonstrated therapeutic efficacy in CD treatment, likely due to the effective reconstruction of the patient's microbiome. Multi-omics techniques can effectively help decipher the potential mechanisms of WMT in treating CD.

Hidden diversity and potential ecological function of phosphorus acquisition genes in widespread terrestrial bacteriophages.

Phosphorus (P) limitation of ecosystem processes is widespread in terrestrial habitats. While a few auxiliary metabolic genes (AMGs) in bacteriophages from aquatic habitats are reported to have the potential to enhance P-acquisition ability of their hosts, little is known about the diversity and potential ecological function of P-acquisition genes encoded by terrestrial bacteriophages. Here, we analyze 333 soil metagenomes from five terrestrial habitat types across China and identify 75 viral operational taxonomic units (vOTUs) that encode 105 P-acquisition AMGs. These AMGs span 17 distinct functional genes involved in four primary processes of microbial P-acquisition. Among them, over 60% (11/17) have not been reported previously. We experimentally verify in-vitro enzymatic activities of two pyrophosphatases and one alkaline phosphatase encoded by P-acquisition vOTUs. Thirty-six percent of the 75 P-acquisition vOTUs are detectable in a published global topsoil metagenome dataset. Further analyses reveal that, under certain circumstances, the identified P-acquisition AMGs have a greater influence on soil P availability and are more dominant in soil metatranscriptomes than their corresponding bacterial genes. Overall, our results reinforce the necessity of incorporating viral contributions into biogeochemical P cycling.

Biosynthetic potential of the sediment microbial subcommunities of an unexplored karst ecosystem and its ecological implications.

Microbial communities from various environments have been studied in the quest for new natural products with a broad range of applications in medicine and biotechnology. We employed an enrichment method and genome mining tools to examine the biosynthetic potential of microbial communities in the sediments of a coastal sinkhole within the karst ecosystem of the Yucatán Peninsula, Mexico. Our investigation led to the detection of 203 biosynthetic gene clusters (BGCs) and 55 secondary metabolites (SMs) within 35 high-quality metagenome-assembled genomes (MAGs) derived from these subcommunities. The most abundant types of BGCs were Terpene, Nonribosomal peptide-synthetase, and Type III polyketide synthase. Some of the in silico identified BGCs and SMs have been previously reported to exhibit biological activities against pathogenic bacteria and fungi. Others could play significant roles in the sinkhole ecosystem, such as iron solubilization and osmotic stress protection. Interestingly, 75% of the BGCs showed no sequence homology with bacterial BGCs previously reported in the MiBIG database. This suggests that the microbial communities in this environment could be an untapped source of genes encoding novel specialized compounds. The majority of the BGCs were identified in pathways found in the genus Virgibacillus, followed by Sporosarcina, Siminovitchia, Rhodococcus, and Halomonas. The latter, along with Paraclostridium and Lysinibacillus, had the highest number of identified BGC types. This study offers fresh insights into the potential ecological role of SMs from sediment microbial communities in an unexplored environment, underscoring their value as a source of novel natural products.

Pangenome databases improve host removal and mycobacteria classification from clinical metagenomic data.

BACKGROUND: Culture-free real-time sequencing of clinical metagenomic samples promises both rapid pathogen detection and antimicrobial resistance profiling. However, this approach introduces the risk of patient DNA leakage. To mitigate this risk, we need near-comprehensive removal of human DNA sequences at the point of sequencing, typically involving the use of resource-constrained devices. Existing benchmarks have largely focused on the use of standardized databases and largely ignored the computational requirements of depletion pipelines as well as the impact of human genome diversity. RESULTS: We benchmarked host removal pipelines on simulated and artificial real Illumina and Nanopore metagenomic samples. We found that construction of a custom kraken database containing diverse human genomes results in the best balance of accuracy and computational resource usage. In addition, we benchmarked pipelines using kraken and minimap2 for taxonomic classification of Mycobacterium reads using standard and custom databases. With a database representative of the Mycobacterium genus, both tools obtained improved specificity and sensitivity, compared to the standard databases for classification of Mycobacterium tuberculosis. Computational efficiency of these custom databases was superior to most standard approaches, allowing them to be executed on a laptop device. CONCLUSIONS: Customized pangenome databases provide the best balance of accuracy and computational efficiency when compared to standard databases for the task of human read removal and M. tuberculosis read classification from metagenomic samples. Such databases allow for execution on a laptop, without sacrificing accuracy, an especially important consideration in low-resource settings. We make all customized databases and pipelines freely available.

NURECON: A Novel Online System for Determining Nutrition Requirements Based on Microbial Composition.

Dietary habits have been proven to have an impact on the microbial composition and health of the human gut. Over the past decade, researchers have discovered that gut microbiota can use nutrients to produce metabolites that have major implications for human physiology. However, there is no comprehensive system that specifically focuses on identifying nutrient deficiencies based on gut microbiota, making it difficult to interpret and compare gut microbiome data in the literature. This study proposes an analytical platform, NURECON, that can predict nutrient deficiency information in individuals by comparing their metagenomic information to a reference baseline. NURECON integrates a next-generation bacterial 16S rRNA analytical pipeline (QIIME2), metabolic pathway prediction tools (PICRUSt2 and KEGG), and a food compound database (FooDB) to enable the identification of missing nutrients and provide personalized dietary suggestions. Metagenomic information from total number of 287 healthy subjects was used to establish baseline microbial composition and metabolic profiles. The uploaded data is analyzed and compared to the baseline for nutrient deficiency assessment. Visualization results include gut microbial composition, related enzymes, pathways, and nutrient abundance. NURECON is a user-friendly online platform that provides nutritional advice to support dietitians' research or menu design.

The microbiome of a Pacific moon jellyfish Aurelia coerulea.

The impact of microbiome in animal physiology is well appreciated, but characterization of animal-microbe symbiosis in marine environments remains a growing need. This study characterizes the microbial communities associated with the moon jellyfish Aurelia coerulea, first isolated from the East Pacific Ocean and has since been utilized as an experimental system. We find that the microbiome of this Pacific Aurelia culture is dominated by two taxa, a Mollicutes and Rickettsiales. The microbiome is stable across life stages, although composition varies. Mining the host sequencing data, we assembled the bacterial metagenome-assembled genomes (MAGs). The bacterial MAGs are highly reduced, and predict a high metabolic dependence on the host. Analysis using multiple metrics suggest that both bacteria are likely new species. We therefore propose the names Ca. Mariplasma lunae (Mollicutes) and Ca. Marinirickettsia aquamalans (Rickettsiales). Finally, comparison with studies of Aurelia from other geographical populations suggests the association with Ca. Mariplasma lunae occurs in Aurelia from multiple geographical locations. The low-diversity microbiome of Aurelia provides a relatively simple system to study host-microbe interactions.

Hi-C metagenomics facilitate comparative genome analysis of bacteria and yeast from spontaneous beer and cider.

Sequence-based analysis of fermented foods and beverages' microbiomes offers insights into their impact on taste and consumer health. High-throughput metagenomics provide detailed taxonomic and functional community profiling, but bacterial and yeast genome reconstruction and mobile genetic elements tracking are to be improved. We established a pipeline for exploring fermented foods microbiomes using metagenomics coupled with chromosome conformation capture (Hi-C metagenomics). The approach was applied to analyze a collection of spontaneously fermented beers and ciders (n = 12). The Hi-C reads were used to reconstruct the metagenome-assembled genomes (MAGs) of bacteria and yeasts facilitating subsequent comparative genomic analysis, assembly scaffolding and exploration of "plasmid-bacteria" links. For a subset of beverages, yeasts were isolated and characterized phenotypically. The reconstructed Hi-C MAGs primarily belonged to the Lactobacillaceae family in beers, along with Acetobacteraceae and Enterobacteriaceae in ciders, exhibiting improved quality compared to conventional metagenomic MAGs. Comparative genomic analysis of Lactobacillaceae Hi-C MAGs revealed clustering by niche and suggested genetic determinants of survival and probiotic potential. For Pediococcus damnosus, Hi-C-based networks of contigs enabled linking bacteria with plasmids. Analyzing phylogeny and accessory genes in the context of known reference genomes offered insights into the niche specialization of beer lactobacilli. The subspecies-level diversity of cider Tatumella spp. was disentangled using a Hi-C-based graph. We obtained highly complete yeast Hi-C MAGs primarily represented by Brettanomyces and Saccharomyces, with Hi-C-facilitated chromosome-level genome assembly for the former. Utilizing Hi-C metagenomics to unravel the genomic content of individual species can provide a deeper understanding of the ecological interactions within the food microbiome, aid in bioprospecting beneficial microorganisms, improving quality control and improving innovative fermented products.

Current progresses and challenges for microbiome research in human health: a perspective.

It is becoming increasingly clear that the human microbiota, also known as "the hidden organ", possesses a pivotal role in numerous processes involved in maintaining the physiological functions of the host, such as nutrient extraction, biosynthesis of bioactive molecules, interplay with the immune, endocrine, and nervous systems, as well as resistance to the colonization of potential invading pathogens. In the last decade, the development of metagenomic approaches based on the sequencing of the bacterial 16s rRNA gene via Next Generation Sequencing, followed by whole genome sequencing via third generation sequencing technologies, has been one of the great advances in molecular biology, allowing a better profiling of the human microbiota composition and, hence, a deeper understanding of the importance of microbiota in the etiopathogenesis of different pathologies. In this scenario, it is of the utmost importance to comprehensively characterize the human microbiota in relation to disease pathogenesis, in order to develop novel potential treatment or preventive strategies by manipulating the microbiota. Therefore, this perspective will focus on the progress, challenges, and promises of the current and future technological approaches for microbiome profiling and analysis.

MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes.

BACKGROUND: With the rapid increase in throughput of long-read sequencing technologies, recent studies have explored their potential for taxonomic classification by using alignment-based approaches to reduce the impact of higher sequencing error rates. While alignment-based methods are generally slower, k-mer-based taxonomic classifiers can overcome this limitation, potentially at the expense of lower sensitivity for strains and species that are not in the database. RESULTS: We present MetageNN, a memory-efficient long-read taxonomic classifier that is robust to sequencing errors and missing genomes. MetageNN is a neural network model that uses short k-mer profiles of sequences to reduce the impact of distribution shifts on error-prone long reads. Benchmarking MetageNN against other machine learning approaches for taxonomic classification (GeNet) showed substantial improvements with long-read data (20% improvement in F1 score). By utilizing nanopore sequencing data, MetageNN exhibits improved sensitivity in situations where the reference database is incomplete. It surpasses the alignment-based MetaMaps and MEGAN-LR, as well as the k-mer-based Kraken2 tools, with improvements of 100%, 36%, and 23% respectively at the read-level analysis. Notably, at the community level, MetageNN consistently demonstrated higher sensitivities than the previously mentioned tools. Furthermore, MetageNN requires < 1/4th of the database storage used by Kraken2, MEGAN-LR and MMseqs2 and is > 7× faster than MetaMaps and GeNet and > 2× faster than MEGAN-LR and MMseqs2. CONCLUSION: This proof of concept work demonstrates the utility of machine-learning-based methods for taxonomic classification using long reads. MetageNN can be used on sequences not classified by conventional methods and offers an alternative approach for memory-efficient classifiers that can be optimized further.

Deep learning methods in metagenomics: a review.

The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most prevalent applications of metagenomics is the study of microbial environments, such as the human gut. The gut microbiome plays a crucial role in human health, providing vital information for patient diagnosis and prognosis. However, analysing metagenomic data remains challenging due to several factors, including reference catalogues, sparsity and compositionality. Deep learning (DL) enables novel and promising approaches that complement state-of-the-art microbiome pipelines. DL-based methods can address almost all aspects of microbiome analysis, including novel pathogen detection, sequence classification, patient stratification and disease prediction. Beyond generating predictive models, a key aspect of these methods is also their interpretability. This article reviews DL approaches in metagenomics, including convolutional networks, autoencoders and attention-based models. These methods aggregate contextualized data and pave the way for improved patient care and a better understanding of the microbiome's key role in our health.

Integrative metagenomic analysis reveals distinct gut microbial signatures related to obesity.

Obesity is a metabolic disorder closely associated with profound alterations in gut microbial composition. However, the dynamics of species composition and functional changes in the gut microbiome in obesity remain to be comprehensively investigated. In this study, we conducted a meta-analysis of metagenomic sequencing data from both obese and non-obese individuals across multiple cohorts, totaling 1351 fecal metagenomes. Our results demonstrate a significant decrease in both the richness and diversity of the gut bacteriome and virome in obese patients. We identified 38 bacterial species including Eubacterium sp. CAG:274, Ruminococcus gnavus, Eubacterium eligens and Akkermansia muciniphila, and 1 archaeal species, Methanobrevibacter smithii, that were significantly altered in obesity. Additionally, we observed altered abundance of five viral families: Mesyanzhinovviridae, Chaseviridae, Salasmaviridae, Drexlerviridae, and Casjensviridae. Functional analysis of the gut microbiome indicated distinct signatures associated to obesity and identified Ruminococcus gnavus as the primary driver for function enrichment in obesity, and Methanobrevibacter smithii, Akkermansia muciniphila, Ruminococcus bicirculans, and Eubacterium siraeum as functional drivers in the healthy control group. Additionally, our results suggest that antibiotic resistance genes and bacterial virulence factors may influence the development of obesity. Finally, we demonstrated that gut vOTUs achieved a diagnostic accuracy with an optimal area under the curve of 0.766 for distinguishing obesity from healthy controls. Our findings offer comprehensive and generalizable insights into the gut bacteriome and virome features associated with obesity, with the potential to guide the development of microbiome-based diagnostics.

A generic approach to infer community-level fitness of microbial genes.

The gene content in a metagenomic pool defines the function potential of a microbial community. Natural selection, operating on the level of genomes or genes, shapes the evolution of community functions by enriching some genes while depriving the others. Despite the importance of microbiomes in the environment and health, a general metric to evaluate the community-wide fitness of microbial genes remains lacking. In this work, we adapt the classic neutral model of species and use it to predict how the abundances of different genes will be shaped by selection, regardless of at which level the selection acts. We establish a simple metric that quantitatively infers the average survival capability of each gene in a microbiome. We then experimentally validate the predictions using synthetic communities of barcoded Escherichia coli strains undergoing neutral assembly and competition. We further show that this approach can be applied to publicly available metagenomic datasets to gain insights into the environment-function interplay of natural microbiomes.

Integrating taxonomic signals from MAGs and contigs improves read annotation and taxonomic profiling of metagenomes.

Metagenomic analysis typically includes read-based taxonomic profiling, assembly, and binning of metagenome-assembled genomes (MAGs). Here we integrate these steps in Read Annotation Tool (RAT), which uses robust taxonomic signals from MAGs and contigs to enhance read annotation. RAT reconstructs taxonomic profiles with high precision and sensitivity, outperforming other state-of-the-art tools. In high-diversity groundwater samples, RAT annotates a large fraction of the metagenomic reads, calling novel taxa at the appropriate, sometimes high taxonomic ranks. Thus, RAT integrative profiling provides an accurate and comprehensive view of the microbiome from shotgun metagenomics data. The package of Contig Annotation Tool (CAT), Bin Annotation Tool (BAT), and RAT is available at https://github.com/MGXlab/CAT_pack (from CAT pack v6.0). The CAT pack now also supports Genome Taxonomy Database (GTDB) annotations.

An integrated metagenomic, metabolomic and transcriptomic survey of Populus across genotypes and environments.

Bridging molecular information to ecosystem-level processes would provide the capacity to understand system vulnerability and, potentially, a means for assessing ecosystem health. Here, we present an integrated dataset containing environmental and metagenomic information from plant-associated microbial communities, plant transcriptomics, plant and soil metabolomics, and soil chemistry and activity characterization measurements derived from the model tree species Populus trichocarpa. Soil, rhizosphere, root endosphere, and leaf samples were collected from 27 different P. trichocarpa genotypes grown in two different environments leading to an integrated dataset of 318 metagenomes, 98 plant transcriptomes, and 314 metabolomic profiles that are supported by diverse soil measurements. This expansive dataset will provide insights into causal linkages that relate genomic features and molecular level events to system-level properties and their environmental influences.

High-throughput DNA extraction and cost-effective miniaturized metagenome and amplicon library preparation of soil samples for DNA sequencing.

Reductions in sequencing costs have enabled widespread use of shotgun metagenomics and amplicon sequencing, which have drastically improved our understanding of the microbial world. However, large sequencing projects are now hampered by the cost of library preparation and low sample throughput, comparatively to the actual sequencing costs. Here, we benchmarked three high-throughput DNA extraction methods: ZymoBIOMICS™ 96 MagBead DNA Kit, MP BiomedicalsTM FastDNATM-96 Soil Microbe DNA Kit, and DNeasy® 96 PowerSoil® Pro QIAcube® HT Kit. The DNA extractions were evaluated based on length, quality, quantity, and the observed microbial community across five diverse soil types. DNA extraction of all soil types was successful for all kits, however DNeasy® 96 PowerSoil® Pro QIAcube® HT Kit excelled across all performance parameters. We further used the nanoliter dispensing system I.DOT One to miniaturize Illumina amplicon and metagenomic library preparation volumes by a factor of 5 and 10, respectively, with no significant impact on the observed microbial communities. With these protocols, DNA extraction, metagenomic, or amplicon library preparation for one 96-well plate are approx. 3, 5, and 6 hours, respectively. Furthermore, the miniaturization of amplicon and metagenome library preparation reduces the chemical and plastic costs from 5.0 to 3.6 and 59 to 7.3 USD pr. sample. This enhanced efficiency and cost-effectiveness will enable researchers to undertake studies with greater sample sizes and diversity, thereby providing a richer, more detailed view of microbial communities and their dynamics.