SemiBin2: self-supervised contrastive learning leads to better MAGs for short- and long-read sequencing

CAMI Benchmarking Portal: online evaluation and ranking of metagenomic software

Finding appropriate software and parameter settings to process shotgun metagenome data is essential for meaningful metagenomic analyses. To enable objective and comprehensive benchmarking of metagenomic software, the community-led initiative for the Critical Assessment of Metagenome Interpretation (CAMI) promotes standards and best practices. Since 2015, CAMI has provided comprehensive datasets, benchmarking guidelines, and challenges. However, benchmarking had to be conducted offline, requiring substantial time and technical expertise and leading to gaps in results between challenges. We introduce the CAMI Benchmarking Portal-a central repository of CAMI resources and web server for the evaluation and ranking of metagenome assembly, binning, and taxonomic profiling software. The portal simplifies evaluation, enabling users to easily compare their results with previous and other users' submissions through a variety of metrics and visualizations. As a demonstration, we benchmark software performance on the marine dataset of the CAMI II challenge. The portal currently hosts 28 675 results and is freely available at https://cami-challenge.org/.

Faecal metagenomes of great tits and blue tits provide insights into host, diet, pathogens and microbial biodiversity

Background. The vertebrate gut microbiome plays crucial roles in host health and disease. However, there is limited information on the microbiomes of wild birds, most of which is restricted to barcode sequences. We therefore explored the use of shotgun metagenomics on the faecal microbiomes of two wild bird species widely used as model organisms in ecological studies: the great tit (Parus major) and the Eurasian blue tit (Cyanistes caeruleus). Results. Short-read sequencing of five faecal samples generated a metagenomic dataset, revealing substantial variation in composition between samples. Reference-based profiling with Kraken2 identified key differences in the ratios of reads assigned to host, diet and microbes. Some samples showed high abundance of potential pathogens, including siadenoviruses, coccidian parasites and the antimicrobial-resistant bacterial species Serratia fonticola. From metagenome assemblies, we obtained complete mitochondrial genomes from the host species and from Isospora spp., while metagenome-assembled genomes documented new prokaryotic species. Conclusions. Here, we have shown the utility of shotgun metagenomics in uncovering microbial diversity beyond what is possible with 16S rRNA gene sequencing. These findings provide a foundation for future hypothesis testing and microbiome manipulation to improve fitness in wild bird populations. The study also highlights the potential role of wild birds in the dissemination of antimicrobial resistance.

Metagenomic and metatranscriptomic analyses reveal uncharted microbial constituents responsible for polyhydroxybutyrate biodegradation in coastal waters

Polyhydroxybutyrate (PHB) has attracted attention as a representative polymer for biodegradable plastics produced by microorganisms. Since information regarding the fate of PHB released into the environment is limited, it is necessary to identify them based on metagenomic information. We estimated the PHB biodegradability in coastal water samples collected from 15 near shore sites around Japan using oxygen consumption as an indicator in laboratory-scale incubation experiments and conducted 16S rRNA gene-based microbial community profiling. The PHB-biodegradation-rate was significantly positively correlated with the diversity indices of the microbial community in seawater prior to incubation, indicating that seawater with higher diversity is more advantageous for biodegradation. We identified 41 operational taxonomic units exhibiting a significant positive correlation between their abundance and PHB-degradation-rates; these included several microorganisms with hitherto unreported PHB-degrading ability. Next, we analyzed gene expression patterns over incubation time using seawater samples employing metagenomic and metatranscriptomic approaches. Fifty-seven putative extracellular PHB/PHA depolymerase genes were found in 38 metagenomic bins and their expression changed with increasing biodegradation rates, indicating that PHB released into the marine environment is subject to degradation by phylogenetically diverse PHB-depolymerase-producing bacteria. These findings should contribute to expanding the knowledge on degradation of biodegradable plastics by complex marine microbial ecosystems.

A prebiotic dietary pilot intervention restores faecal metabolites and may be neuroprotective in Parkinson's Disease

Current treatment of Parkinson's Disease (PD) remains symptomatic, and disease-modifying approaches are urgently required. A promising approach is to modify intestinal microbiota and key metabolites of bacterial fermentation: short-chain fatty acids (SCFA), which are decreased in PD. A prospective, controlled pilot study (DRKS00034528) was conducted on 11 couples (PD patient plus healthy spouse as control (CO)). Participants followed a 4-week diet rich in dietary fibre, including intake of the prebiotic Lactulose. Gut metagenomes, faecal and urinary metabolites, and clinical characteristics were assessed. The dietary intervention significantly augmented faecal SCFA and increased Bifidobacteria spp., reducing PD-related gastrointestinal symptoms. The pre-existing bacterial dysbiosis in PD (depletion of Blautia, Dorea, Erysipelatoclostridium) persisted. Bacterial metabolite composition in faeces and urine positively changed with the intervention: Brain-relevant gut metabolic functions involved in neuroprotective and antioxidant pathways, including S-adenosyl methionine, glutathione, and inositol, improved in PD. These promising results warrant further investigation in larger cohorts.

Genome-centric metagenomics reveals uncharacterised microbiomes in Angus cattle

Understanding the intricate nexus between cattle health and microbiome dynamics holds profound implications for enhancing animal productivity and welfare. However, our understanding of the role of these microbial communities is limited in beef cattle, especially in understudied body sites such as the oral and nasal microbiome. Here, using a genome-centric metagenomics approach, we recovered substantial metagenome-assembled genomes (MAGs) from the faecal, oral and nasal microbiome of Australian Angus cattle from different herds and life stages. The MAGs recovered from faecal samples were dominated by Bacillota and Bacteroidota, while the MAGs from saliva and nasal mucus samples were mainly associated with Pseudomonadota, Actinomycetota and Bacteroidota. Functional annotation of the MAGs revealed enriched pathways involved in the production of some amino acids, nucleic acids and short chain fatty acids (SCFA). The metabolic capacities of the MAGs were correlated with their taxonomy, notably at the phylum level. Overall, this study provides a comprehensive catalogue of MAGs to further our understanding of their role in the health and fitness of beef cattle.

CoverM: read alignment statistics for metagenomics

SUMMARY

Genome-centric analysis of metagenomic samples is a powerful method for understanding the function of microbial communities. Calculating read coverage is a central part of analysis, enabling differential coverage binning for recovery of genomes and estimation of microbial community composition. Coverage is determined by processing read alignments to reference sequences of either contigs or genomes. Per-reference coverage is typically calculated in an ad-hoc manner, with each software package providing its own implementation and specific definition of coverage. Here we present a unified software package CoverM which calculates several coverage statistics for contigs and genomes in an ergonomic and flexible manner. It uses "Mosdepth arrays" for computational efficiency and avoids unnecessary I/O overhead by calculating coverage statistics from streamed read alignment results.

AVAILABILITY AND IMPLEMENTATION

CoverM is free software available at https://github.com/wwood/coverm. CoverM is implemented in Rust, with Python (https://github.com/apcamargo/pycoverm) and Julia (https://github.com/JuliaBinaryWrappers/CoverM_jll.jl) interfaces.

Benchmarking metagenomic binning tools on real datasets across sequencing platforms and binning modes

Metagenomic binning is a culture-free approach that facilitates the recovery of metagenome-assembled genomes by grouping genomic fragments. However, there remains a lack of a comprehensive benchmark to evaluate the performance of metagenomic binning tools across various combinations of data types and binning modes. In this study, we benchmark 13 metagenomic binning tools using short-read, long-read, and hybrid data under co-assembly, single-sample, and multi-sample binning, respectively. The benchmark results demonstrate that multi-sample binning exhibits optimal performance across short-read, long-read, and hybrid data. Moreover, multi-sample binning outperforms other binning modes in identifying potential antibiotic resistance gene hosts and near-complete strains containing potential biosynthetic gene clusters across diverse data types. This study also recommends three efficient binners across all data-binning combinations, as well as high-performance binners for each combination.

A review of neural networks for metagenomic binning

One of the main goals of metagenomic studies is to describe the taxonomic diversity of microbial communities. A crucial step in metagenomic analysis is metagenomic binning, which involves the (supervised) classification or (unsupervised) clustering of metagenomic sequences. Various machine learning models have been applied to address this task. In this review, the contributions of artificial neural networks (ANN) in the context of metagenomic binning are detailed, addressing both supervised, unsupervised, and semi-supervised approaches. 34 ANN-based binning tools are systematically compared, detailing their architectures, input features, datasets, advantages, disadvantages, and other relevant aspects. The findings reveal that deep learning approaches, such as convolutional neural networks and autoencoders, achieve higher accuracy and scalability than traditional methods. Gaps in benchmarking practices are highlighted, and future directions are proposed, including standardized datasets and optimization of architectures, for third-generation sequencing. This review provides support to researchers in identifying trends and selecting suitable tools for the metagenomic binning problem.

Species-resolved profiling of antibiotic resistance genes in complex metagenomes through long-read overlapping with Argo

Environmental surveillance of antibiotic resistance genes (ARGs) is critical for understanding and mitigating the spread of antimicrobial resistance. Current short-read-based ARG profiling methods are limited in their ability to provide detailed host information, which is indispensable for tracking the transmission and assessing the risk of ARGs. Here, we present Argo, a novel approach that leverages long-read overlapping to rapidly identify and quantify ARGs in complex environmental metagenomes at the species level. Argo significantly enhances the resolution of ARG detection by assigning taxonomic labels collectively to clusters of reads, rather than to individual reads. By benchmarking the performance in host identification using simulation, we confirm the advantage of long-read overlapping over existing metagenomic profiling strategies in terms of accuracy. Using sequenced mock communities with varying quality scores and read lengths, along with a global fecal dataset comprising 329 human and non-human primate samples, we demonstrate Argo's capability to deliver comprehensive and species-resolved ARG profiles in real settings.

PhyloFunc: phylogeny-informed functional distance as a new ecological metric for metaproteomic data analysis

BACKGROUND

Beta-diversity is a fundamental ecological metric for exploring dissimilarities between microbial communities. On the functional dimension, metaproteomics data can be used to quantify beta-diversity to understand how microbial community functional profiles vary under different environmental conditions. Conventional approaches to metaproteomic functional beta-diversity often treat protein functions as independent features, ignoring the evolutionary relationships among microbial taxa from which different proteins originate. A more informative functional distance metric that incorporates evolutionary relatedness is needed to better understand microbiome functional dissimilarities.

RESULTS

Here, we introduce PhyloFunc, a novel functional beta-diversity metric that incorporates microbiome phylogeny to inform on metaproteomic functional distance. Leveraging the phylogenetic framework of weighted UniFrac distance, PhyloFunc innovatively utilizes branch lengths to weigh between-sample functional distances for each taxon, rather than differences in taxonomic abundance as in weighted UniFrac. Proof of concept using a simulated toy dataset and a real dataset from mouse inoculated with a synthetic gut microbiome and fed different diets show that PhyloFunc successfully captured functional compensatory effects between phylogenetically related taxa. We further tested a third dataset of complex human gut microbiomes treated with five different drugs to compare PhyloFunc's performance with other traditional distance methods. PCoA and machine learning-based classification algorithms revealed higher sensitivity of PhyloFunc in microbiome responses to paracetamol. We provide PhyloFunc as an open-source Python package (available at https://pypi.org/project/phylofunc/ ), enabling efficient calculation of functional beta-diversity distances between a pair of samples or the generation of a distance matrix for all samples within a dataset.

CONCLUSIONS

Unlike traditional approaches that consider metaproteomics features as independent and unrelated, PhyloFunc acknowledges the role of phylogenetic context in shaping the functional landscape in metaproteomes. In particular, we report that PhyloFunc accounts for the functional compensatory effect of taxonomically related species. Its effectiveness, ecological relevance, and enhanced sensitivity in distinguishing group variations are demonstrated through the specific applications presented in this study. Video Abstract.

Minisyncoccus archaeiphilus gen. nov., sp. nov., a mesophilic, obligate parasitic bacterium and proposal of Minisyncoccaceae fam. nov., Minisyncoccales ord. nov., Minisyncoccia class. nov. and Minisyncoccota phyl. nov. formerly referred to as Candidatus Patescibacteria or candidate phyla radiation

In the domain Bacteria, one of the largest, most diverse and environmentally ubiquitous phylogenetic groups, Candidatus Patescibacteria (also known as candidate phyla radiation/CPR), remains poorly characterized, leaving a major knowledge gap in microbial ecology. We recently discovered a novel cross-domain symbiosis between Ca. Patescibacteria and Archaea in highly purified enrichment cultures and proposed Candidatus taxa for the characterized species, including Ca. Minisyncoccus archaeophilus and the corresponding family Ca. Minisyncoccaceae. In this study, we report the isolation of this bacterium, designated strain PMX.108T, in a two-strain co-culture with a host archaeon, Methanospirillum hungatei strain DSM 864T (JF-1T), and hereby describe it as the first representative species of Ca. Patescibacteria. Strain PMX.108T was isolated from mesophilic methanogenic sludge in an anaerobic laboratory-scale bioreactor treating synthetic purified terephthalate- and dimethyl terephthalate-manufacturing wastewater. The strain could not grow axenically and is obligately anaerobic and parasitic, strictly depending on M. hungatei as a host. The genome was comparatively large (1.54 Mbp) compared to other members of the clade, lacked some genes involved in the biosynthesis pathway and encoded type IV pili-related genes associated with the parasitic lifestyle of ultrasmall microbes. The G+C content of the genomic DNA was 36.6 mol%. Here, we report the phenotypic and genomic properties of strain PMX.108T; we propose Minisyncoccus archaeiphilus gen. nov., sp. nov. to accommodate this strain. The type strain of the species is PMX.108T (=JCM 39522T). We also propose the associated family, order, class and phylum as Minisyncoccaceae fam. nov. Minisyncoccales nov., Minisyncoccia class. nov. and Minisyncoccota phyl. nov. within the bacterial kingdom Bacillati.

Metagenomic insights into the development of microbial communities of straw and leaf composts

Introduction

Soil microbiome is a major source of physiologically active microorganisms, which can be potentially mobilized by adding various nutrients. To study this process, a long-term experiment was conducted on the decomposition of oat straw and leaf litter using soil as a microbial inoculum.

Methods

Combined analyses of enzymatic activity and NGS data for 16S rRNA gene amplicon and full metagenome sequencing were applied to study taxonomic, carbohydrate-active enzyme (CAZy), and polysaccharide utilization loci (PULs) composition of microbial communities at different stages of decomposition between substrates.

Results

In straw degradation, the microbial community demonstrated higher amylase, protease, catalase, and cellulase activities, while peroxidase, invertase, and polyphenol oxidase were more active in leaf litter. Consistent with this, the metagenome analysis showed that the microbiome of straw compost was enriched in genes for metabolic pathways of simpler compounds. At the same time, there were more genes for aromatic compound degradation pathways in leaf litter compost. We identified nine metagenome-assembled genomes (MAGs) as the most promising prokaryotic decomposers due to their abnormally high quantity of PULs for their genome sizes, which were confirmed by 16S rRNA gene amplicon sequencing to constitute the bulk of the community at all stages of substrate degradation. MAGs from Bacteroidota (Chitinophaga and Ohtaekwangia) and Actinomycetota (Streptomyces) were found in both composts, while those from Bacillota (Pristimantibacillus) were specific for leaf litter. The most frequently identified PULs were specialized on xylans and pectins, but not cellulose, suggesting that PUL databases may be underrepresented in clusters for complex substrates.

Discussion

Our study explores microbial communities from natural ecosystems, such as soil and lignocellulosic waste, which are capable of decomposing lignocellulosic substrates. Using a comprehensive approach with chemical analyses of the substrates, amplicon, and full metagenome sequencing data, we have shown that such communities may be a source of identifying the highly effective decomposing species with novel PULs.

Deep learning in microbiome analysis: a comprehensive review of neural network models

Microbiome research, the study of microbial communities in diverse environments, has seen significant advances due to the integration of deep learning (DL) methods. These computational techniques have become essential for addressing the inherent complexity and high-dimensionality of microbiome data, which consist of different types of omics datasets. Deep learning algorithms have shown remarkable capabilities in pattern recognition, feature extraction, and predictive modeling, enabling researchers to uncover hidden relationships within microbial ecosystems. By automating the detection of functional genes, microbial interactions, and host-microbiome dynamics, DL methods offer unprecedented precision in understanding microbiome composition and its impact on health, disease, and the environment. However, despite their potential, deep learning approaches face significant challenges in microbiome research. Additionally, the biological variability in microbiome datasets requires tailored approaches to ensure robust and generalizable outcomes. As microbiome research continues to generate vast and complex datasets, addressing these challenges will be crucial for advancing microbiological insights and translating them into practical applications with DL. This review provides an overview of different deep learning models in microbiome research, discussing their strengths, practical uses, and implications for future studies. We examine how these models are being applied to solve key problems and highlight potential pathways to overcome current limitations, emphasizing the transformative impact DL could have on the field moving forward.

Ecological insights into the microbiology of food using metagenomics and its potential surveillance applications

A diverse array of micro-organisms can be found on food, including those that are pathogenic or resistant to antimicrobial drugs. Metagenomics involves extracting and sequencing the DNA of all micro-organisms on a sample, and here, we used a combination of culture and culture-independent approaches to investigate the microbial ecology of food to assess the potential application of metagenomics for the microbial surveillance of food. We cultured common foodborne pathogens and other organisms including Escherichia coli, Klebsiella/Raoultella spp., Salmonella spp. and Vibrio spp. from five different food commodities and compared their genomes to the microbial communities obtained by metagenomic sequencing following host (food) DNA depletion. The microbial populations of retail food were found to be predominated by psychrotrophic bacteria, driven by the cool temperatures in which the food products are stored. Pathogens accounted for a small percentage of the food metagenome compared to the psychrotrophic bacteria, and cultured pathogens were inconsistently identified in the metagenome data. The microbial composition of food varied amongst different commodities, and metagenomics was able to classify the taxonomic origin of 59% of antimicrobial resistance genes (ARGs) found on food to the genus level, but it was unclear what percentage of ARGs were associated with mobile genetic elements and thus transferable to other bacteria. Metagenomics may be used to survey the ARG burden, composition and carriage on foods to which consumers are exposed. However, food metagenomics, even after depleting host DNA, inconsistently identifies pathogens without enrichment or further bait capture.

Life on the edge: Microbial diversity, resistome, and virulome in soils from the union glacier cold desert

The high-latitude regions of Antarctica remain among the most remote, extreme, and least explored areas on Earth. Still, microbial life has been reported in these environments, with limited information on their genetic properties and functional capabilities. Although diverse autochthonous multidrug-resistant bacteria were found in Antarctic Peninsula soils, posing whether these soils could act as a source of resistance determinants that could emerge among pathogens, we still lack information regarding the resistome of areas closer to the South Pole. Moreover, no previous studies have evaluated the pathogenic potential of microbes inhabiting Antarctic soils. In this work, we combined metagenomic and culture-dependent approaches to investigate the microbial diversity, resistome, virulome, and mobile genetic elements (MGEs) in soils from Union Glacier, a cold desert in West Antarctica. Despite the extreme conditions, several bacterial phyla were found, predominating Actinomycetota and Pseudomonadota, with limited archaeal and fungal taxa. Contrastive with Ecology Glacier soils from King George Island, the Union Glacier soil bacterial community is significantly less diverse, mainly attributed to scarce moisture. We recovered >80 species-level representative genomes (SRGs) of predominant bacteria and an ammonia-oxidating nitrogen- and carbon-fixing archaeon from a novel species of Nitrosocosmicus. Several resistance and virulence genes were found in Union Glacier soils, similar to those in other Antarctic cold desert areas but significantly distinct from those observed in maritime Antarctica and other non-cryosphere biomes. Furthermore, we characterized bacterial isolates resistant to up to 24 clinical antibiotics, mainly Pseudomonas, Arthrobacter, Plantibacter, and Flavobacterium. Moreover, some isolates produced putative virulence factors, including siderophores, pyocyanins, and exoenzymes with hemolytic, lecithinase, protease, and DNAse activity. This evidence uncovers a largely unexplored resistome and virulome hosted by deep Antarctica's soil microbial communities and the presence of bacteria with pathogenic potential, highlighting the relevance of One Health approaches for environmental surveillance in this continent.

One Year of Gluten-Free Diet Impacts Gut Function and Microbiome in Celiac Disease

BACKGROUND & AIMS

Currently, the main treatment for celiac disease (CD) is the gluten-free diet (GFD). This observational cohort study investigated the impact of CD and 1 year of GFD on gut function and microbiome.

METHODS

A total of 36 newly diagnosed patients and 36 healthy volunteers (HVs) were studied at baseline and at 12-month follow-up. Small bowel water content (SBWC), whole gut transit time (WGTT), and colon volumes were measured by magnetic resonance imaging. Stool sample DNA was subjected to shotgun metagenomic sequencing. Species-level abundances and gene functions, including CAZymes (carbohydrate active enzymes) were determined.

RESULTS

SBWC was significantly higher in people with CD (157 ± 15 mL) vs (HVs 100 ± 12 mL) (P = .003). WGTT was delayed in people with CD (68 ± 8 hours) vs HVs (41 ± 5 hours) (P = .002). The differences reduced after 12 months of GFD but not significantly. Well-being in the CD group significantly improved after GFD but did not recover to control values. CD fecal microbiota showed a high abundance of proteolytic gene functions, associated with Escherichia coli, Enterobacter, and Peptostreptococcus. GFD significantly reduced Bifidobacteria and increased Blautia wexlerae. Microbiome composition correlated positively with WGTT, colonic volume, and Akkermansia municphilia but negatively with B wexerelae. Following GFD, the reduction in WGTT and colonic volume was significantly associated with increased abundance of B wexlerae. There were also significant alterations in CAZyme profiles, specifically starch- and arabinoxylan-degrading families.

CONCLUSIONS

CD impacted gut function and microbiota. GFD ameliorated but did not reverse these effects, significantly reducing Bifidobacteria associated with reduced intake of resistant starch and arabinoxylan from wheat.

CLINICALTRIALS

gov, number: NCT02551289.

Feedstock-dependent antibiotic resistance gene patterns and expression profiles in industrial scale biogas plants revealed by meta-omics technology

This study investigated antimicrobial resistance in the anaerobic digesters of two industrial-scale biogas plants processing agricultural biomass and municipal wastewater sludge. A combination of deep sequencing and genome-centric workflow was implemented for metagenomic and metatranscriptomics data analysis to comprehensively examine potential antimicrobial resistance in microbial communities. Anaerobic microbes were found to harbour numerous antibiotic resistance genes (ARGs), with 58.85% of the metagenome-assembled genomes (MAGs) harbouring antibiotic resistance. A moderately positive correlation was observed between the abundance and expression of ARGs. ARGs were located primarily on bacterial chromosomes. A higher expression of resistance genes was observed on plasmids than on chromosomes. Risk index assessment suggests that most ARGs identified posed a significant risk to human health. However, potentially pathogenic bacteria showed lower ARG expression than non-pathogenic ones, indicating that anaerobic treatment is effective against pathogenic microbes. Resistomes at the gene category level were associated with various antibiotic resistance categories, including multidrug resistance, beta-lactams, glycopeptides, peptides, and macrolide-lincosamide-streptogramin (MLS). Differential expression analysis revealed specific genes associated with potential pathogenicity, emphasizing the importance of active gene expression in assessing the risks associated with ARGs.

Metagenome quality metrics and taxonomical annotation visualization through the integration of MAGFlow and BIgMAG

Background

Building Metagenome-Assembled Genomes (MAGs) from highly complex metagenomics datasets encompasses a series of steps covering from cleaning the sequences, assembling them to finally group them into bins. Along the process, multiple tools aimed to assess the quality and integrity of each MAG are implemented. Nonetheless, even when incorporated within end-to-end pipelines, the outputs of these pieces of software must be visualized and analyzed manually lacking integration in a complete framework.

Methods

We developed a Nextflow pipeline (MAGFlow) for estimating the quality of MAGs through a wide variety of approaches (BUSCO, CheckM2, GUNC and QUAST), as well as for annotating taxonomically the metagenomes using GTDB-Tk2. MAGFlow is coupled to a Python-Dash application (BIgMAG) that displays the concatenated outcomes from the tools included by MAGFlow, highlighting the most important metrics in a single interactive environment along with a comparison/clustering of the input data.

Results

By using MAGFlow/BIgMAG, the user will be able to benchmark the MAGs obtained through different workflows or establish the quality of the MAGs belonging to different samples following the divide and rule methodology.

Conclusions

MAGFlow/BIgMAG represents a unique tool that integrates state-of-the-art tools to study different quality metrics and extract visually as much information as possible from a wide range of genome features.

Fairy: fast approximate coverage for multi-sample metagenomic binning

BACKGROUND

Metagenomic binning, the clustering of assembled contigs that belong to the same genome, is a crucial step for recovering metagenome-assembled genomes (MAGs). Contigs are linked by exploiting consistent signatures along a genome, such as read coverage patterns. Using coverage from multiple samples leads to higher-quality MAGs; however, standard pipelines require all-to-all read alignments for multiple samples to compute coverage, becoming a key computational bottleneck.

RESULTS

We present fairy ( https://github.com/bluenote-1577/fairy ), an approximate coverage calculation method for metagenomic binning. Fairy is a fast k-mer-based alignment-free method. For multi-sample binning, fairy can be > 250 × faster than read alignment and accurate enough for binning. Fairy is compatible with several existing binners on host and non-host-associated datasets. Using MetaBAT2, fairy recovers 98.5 % of MAGs with > 50 % completeness and < 5 % contamination relative to alignment with BWA. Notably, multi-sample binning with fairy is always better than single-sample binning using BWA ( > 1.5 × more > 50 % complete MAGs on average) while still being faster. For a public sediment metagenome project, we demonstrate that multi-sample binning recovers higher quality Asgard archaea MAGs than single-sample binning and that fairy's results are indistinguishable from read alignment.

CONCLUSIONS

Fairy is a new tool for approximately and quickly calculating multi-sample coverage for binning, resolving a computational bottleneck for metagenomics. Video Abstract.

Solving genomic puzzles: computational methods for metagenomic binning

Metagenomics involves the study of genetic material obtained directly from communities of microorganisms living in natural environments. The field of metagenomics has provided valuable insights into the structure, diversity and ecology of microbial communities. Once an environmental sample is sequenced and processed, metagenomic binning clusters the sequences into bins representing different taxonomic groups such as species, genera, or higher levels. Several computational tools have been developed to automate the process of metagenomic binning. These tools have enabled the recovery of novel draft genomes of microorganisms allowing us to study their behaviors and functions within microbial communities. This review classifies and analyzes different approaches of metagenomic binning and different refinement, visualization, and evaluation techniques used by these methods. Furthermore, the review highlights the current challenges and areas of improvement present within the field of research.

Circular metagenome-assembled genome of Candidatus Cloacimonadota recovered from anaerobic digestion sludge

This study reports a circular metagenome-assembled genome (cMAG) of Candidatus Cloacimonadota recovered from a mesophilic full-scale food waste treatment plant. The cMAG spans 2,298,113 bp, with 980× coverage and 1 contig.

Genome-resolved metagenomics: a game changer for microbiome medicine

Recent substantial evidence implicating commensal bacteria in human diseases has given rise to a new domain in biomedical research: microbiome medicine. This emerging field aims to understand and leverage the human microbiota and derivative molecules for disease prevention and treatment. Despite the complex and hierarchical organization of this ecosystem, most research over the years has relied on 16S amplicon sequencing, a legacy of bacterial phylogeny and taxonomy. Although advanced sequencing technologies have enabled cost-effective analysis of entire microbiota, translating the relatively short nucleotide information into the functional and taxonomic organization of the microbiome has posed challenges until recently. In the last decade, genome-resolved metagenomics, which aims to reconstruct microbial genomes directly from whole-metagenome sequencing data, has made significant strides and continues to unveil the mysteries of various human-associated microbial communities. There has been a rapid increase in the volume of whole metagenome sequencing data and in the compilation of novel metagenome-assembled genomes and protein sequences in public depositories. This review provides an overview of the capabilities and methods of genome-resolved metagenomics for studying the human microbiome, with a focus on investigating the prokaryotic microbiota of the human gut. Just as decoding the human genome and its variations marked the beginning of the genomic medicine era, unraveling the genomes of commensal microbes and their sequence variations is ushering us into the era of microbiome medicine. Genome-resolved metagenomics stands as a pivotal tool in this transition and can accelerate our journey toward achieving these scientific and medical milestones.

Circular metagenome-assembled genome of Candidatus Patescibacteria recovered from anaerobic digestion sludge

A single-contig, circular metagenome-assembled genome (cMAG) of Candidatus (Ca.) Patescibacteria was reconstructed from a mesophilic full-scale food waste treatment plant in Japan. The genome is of small size and lacks fundamental biosynthetic pathways. Taxonomic analysis using the Genome Taxonomy Database revealed that this cMAG belonged to the genus JAEZRQ01 (Ca. Parcubacteria).

A comparison of short-read, HiFi long-read, and hybrid strategies for genome-resolved metagenomics

Shotgun metagenomics enables the reconstruction of complex microbial communities at a high level of detail. Such an approach can be conducted using both short-read and long-read sequencing data, as well as a combination of both. To assess the pros and cons of these different approaches, we used 22 fecal DNA extracts collected weekly for 11 weeks from two respective lab mice to study seven performance metrics over four combinations of sequencing depth and technology: (i) 20 Gbp of Illumina short-read data, (ii) 40 Gbp of short-read data, (iii) 20 Gbp of PacBio HiFi long-read data, and (iv) 40 Gbp of hybrid (20 Gbp of short-read +20 Gbp of long-read) data. No strategy was best for all metrics; instead, each one excelled across different metrics. The long-read approach yielded the best assembly statistics, with the highest N50 and lowest number of contigs. The 40 Gbp short-read approach yielded the highest number of refined bins. Finally, the hybrid approach yielded the longest assemblies and the highest mapping rate to the bacterial genomes. Our results suggest that while long-read sequencing significantly improves the quality of reconstructed bacterial genomes, it is more expensive and requires deeper sequencing than short-read approaches to recover a comparable amount of reconstructed genomes. The most optimal strategy is study-specific and depends on how researchers assess the trade-off between the quantity and quality of recovered genomes.IMPORTANCEMice are an important model organism for understanding the gut microbiome. When studying these gut microbiomes using DNA techniques, researchers can choose from technologies that use short or long DNA reads. In this study, we perform an extensive benchmark between short- and long-read DNA sequencing for studying mice gut microbiomes. We find that no one approach was best for all metrics and provide information that can help guide researchers in planning their experiments.

Metagenomes and metagenome-assembled genomes from ex vivo fecal incubations of six unique donors

We present a donor-specific collection of 78 metagenomes (13/donor) and 143 metagenome-assembled genomes (MAGs), representing the gut microbiomes of six healthy adult human donors. In addition to adding to the catalog of publicly available human gut MAGs, this resource permits a genome-resolved look into microbial co-occurrence across six individuals.

Effective binning of metagenomic contigs using contrastive multi-view representation learning

Contig binning plays a crucial role in metagenomic data analysis by grouping contigs from the same or closely related genomes. However, existing binning methods face challenges in practical applications due to the diversity of data types and the difficulties in efficiently integrating heterogeneous information. Here, we introduce COMEBin, a binning method based on contrastive multi-view representation learning. COMEBin utilizes data augmentation to generate multiple fragments (views) of each contig and obtains high-quality embeddings of heterogeneous features (sequence coverage and k-mer distribution) through contrastive learning. Experimental results on multiple simulated and real datasets demonstrate that COMEBin outperforms state-of-the-art binning methods, particularly in recovering near-complete genomes from real environmental samples. COMEBin outperforms other binning methods remarkably when integrated into metagenomic analysis pipelines, including the recovery of potentially pathogenic antibiotic-resistant bacteria (PARB) and moderate or higher quality bins containing potential biosynthetic gene clusters (BGCs).

Community-scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing

Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome-scale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species. Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta-)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models. We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third-generation sequencing, and we discuss the opportunities of long-read sequencing, strain-level characterisation and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling.

Illuminating the oral microbiome and its host interactions: recent advancements in omics and bioinformatics technologies in the context of oral microbiome research

The oral microbiota has an enormous impact on human health, with oral dysbiosis now linked to many oral and systemic diseases. Recent advancements in sequencing, mass spectrometry, bioinformatics, computational biology, and machine learning are revolutionizing oral microbiome research, enabling analysis at an unprecedented scale and level of resolution using omics approaches. This review contains a comprehensive perspective of the current state-of-the-art tools available to perform genomics, metagenomics, phylogenomics, pangenomics, transcriptomics, proteomics, metabolomics, lipidomics, and multi-omics analysis on (all) microbiomes, and then provides examples of how the techniques have been applied to research of the oral microbiome, specifically. Key findings of these studies and remaining challenges for the field are highlighted. Although the methods discussed here are placed in the context of their contributions to oral microbiome research specifically, they are pertinent to the study of any microbiome, and the intended audience of this includes researchers would simply like to get an introduction to microbial omics and/or an update on the latest omics methods. Continued research of the oral microbiota using omics approaches is crucial and will lead to dramatic improvements in human health, longevity, and quality of life.