Metagenomic Sequence Datasets Research Articles

Global meta‐analysis reveals the drivers of gut microbiome variation across vertebrates

AbstractShifts in gut microbial diversity and structure are one route by which vertebrate hosts adapt to local environmental conditions. However, recent studies have mostly been limited to a single species, small sample sizes, or restricted geographic ranges. Therefore, drawing a global picture of vertebrate gut microbiome diversity, community structure, and determinants for their adaptive shifts remains to be elucidated. We here collected 6508 samples from 113 vertebrate species covering diverse classes, feeding behaviors, and host habitats based on 16S rRNA gene sequencing. The results showed that host diet pattern had a significant impact on gut microbiome variation, which might drive taxonomic and functional contents of gut microbiome across vertebrates. Of note, the phylum Fusobacteria were enriched in carnivorous vertebrate gut while herbivorous vertebrate gut selectively increased the abundance of Verrucomicrobia. Also, climate factors were strongly associated with gut microbiome variation across vertebrates. Interestingly, we found that the abundance of microbiota belonging to Bacteroidetes increased gradually while the members from Proteobacteria showed a decreasing trend from high‐ to low‐latitude zones, potentially contributing to vertebrate adaptation to local climate condition. Additionally, we comprehensively deciphered the common antibiotic resistomes and their potential mobility between terrestrial vertebrate gut microbiome (n = 487) and their sympatric soil biological environment samples (n = 203) by integrating metagenomic sequencing datasets. Particularly, potential horizontal antibiotic resistance genes (e.g., bacA) transfers were detected between vertebrates gut microbiome and their sympatric soil biological environment. Together, our findings provide new evidence of how external environmental factors affect vertebrate gut microbiome variation.

Facilitating accessible, rapid, and appropriate processing of ancient metagenomic data with AMDirT.

Access to sample-level metadata is important when selecting public metagenomic sequencing datasets for reuse in new biological analyses. The Standards, Precautions, and Advances in Ancient Metagenomics community (SPAAM, https://spaam-community.org) has previously published AncientMetagenomeDir, a collection of curated and standardised sample metadata tables for metagenomic and microbial genome datasets generated from ancient samples. However, while sample-level information is useful for identifying relevant samples for inclusion in new projects, Next Generation Sequencing (NGS) library construction and sequencing metadata are also essential for appropriately reprocessing ancient metagenomic data. Currently, recovering information for downloading and preparing such data is difficult when laboratory and bioinformatic metadata is heterogeneously recorded in prose-based publications. Through a series of community-based hackathon events, AncientMetagenomeDir was updated to provide standardised library-level metadata of existing and new ancient metagenomic samples. In tandem, the companion tool 'AMDirT' was developed to facilitate rapid data filtering and downloading of ancient metagenomic data, as well as improving automated metadata curation and validation for AncientMetagenomeDir. AncientMetagenomeDir was extended to include standardised metadata of over 6000 ancient metagenomic libraries. The companion tool 'AMDirT' provides both graphical- and command-line interface based access to such metadata for users from a wide range of computational backgrounds. We also report on errors with metadata reporting that appear to commonly occur during data upload and provide suggestions on how to improve the quality of data sharing by the community. Together, both standardised metadata reporting and tooling will help towards easier incorporation and reuse of public ancient metagenomic datasets into future analyses.

Bacteroidales-Specific Antimicrobial Genes Can Influence the Selection of the Dominant Fecal Strain of Bacteroides vulgatus and Bacteroides uniformis from the Gastrointestinal Tract Microbial Community.

Bacteroides vulgatus and Bacteroides uniformis are known to be abundant in the human fecal microbial community. Although these strains typically remain stable over time in humans, disruption of this microbial community following antibiotics resulted in the transient change to new strains suggesting that a complex, dynamic strain community exists in humans. To further study the selection of dominant fecal microbial strains from the gastrointestinal tract (GIT) community, we analyzed three longitudinal metagenomic sequencing data sets using BLAST+ to identify genes encoding Bacteroidales-specific antimicrobial proteins (BSAP) that have known functions to restrict species-specific replication of B. uniformis (BSAP-2) or B. vulgatus (BSAP-3) and have been postulated to provide a competitive advantage in microbial communities. In the HMP (Human Microbiome Project) data set, we found fecal samples from individuals had B. vulgatus or B. uniformis with either complete or deleted BSAP genes that did not change over time. We also examined fecal samples from two separate longitudinal data sets of individuals who had been given either single or multiple antibiotics. The BSAP gene pattern from most individuals given either single or multiple antibiotics recovered to be the same as the pre-antibiotic strain. However, in a few individuals, we found incomplete BSAP-3 genes at early times during the recovery that were replaced by B. vulgatus with the complete BSAP-3 gene, consistent with the function of the BSAP to specifically restrict Bacteroides spp. The results of these studies provide insights into the fluxes that occur in the Bacteroides spp. GIT community following perturbation and the dynamics of the selection of a dominant fecal strain of Bacteroides spp.

Whole community shotgun metagenomes of two biological soil crust types from the Mojave Desert.

We present six whole community shotgun metagenomic sequencing data sets of two types of biological soil crusts sampled at the ecotone of the Mojave Desert and Colorado Desert in California. These data will help us understand the diversity and function of biocrust microbial communities, which are essential for desert ecosystems.

Eukaryotic genomes from a global metagenomic data set illuminate trophic modes and biogeography of ocean plankton.

Single-celled eukaryotes play ecologically significant roles in the marine environment, yet fundamental questions about their biodiversity, ecological function, and interactions remain. Environmental sequencing enables researchers to document naturally occurring protistan communities, without culturing bias, yet metagenomic and metatranscriptomic sequencing approaches cannot separate individual species from communities. To more completely capture the genomic content of mixed protistan populations, we can create bins of sequences that represent the same organism (metagenome-assembled genomes [MAGs]). We developed the EukHeist pipeline, which automates the binning of population-level eukaryotic and prokaryotic genomes from metagenomic reads. We show exciting insight into what protistan communities are present and their trophic roles in the ocean. Scalable computational tools, like EukHeist, may accelerate the identification of meaningful genetic signatures from large data sets and complement researchers' efforts to leverage MAG databases for addressing ecological questions, resolving evolutionary relationships, and discovering potentially novel biodiversity.

Facilitating accessible, rapid, and appropriate processing of ancient metagenomic data with AMDirT

Background: Access to sample-level metadata is important when selecting public metagenomic sequencing datasets for reuse in new biological analyses. The Standards, Precautions, and Advances in Ancient Metagenomics community (SPAAM, https://spaam-community.github.io) has previously published AncientMetagenomeDir, a collection of curated and standardised sample metadata tables for metagenomic and microbial genome datasets generated from ancient samples. However, while sample-level information is useful for identifying relevant samples for inclusion in new projects, Next Generation Sequencing (NGS) library construction and sequencing metadata are also essential for appropriately reprocessing ancient metagenomic data. Currently, recovering information for downloading and preparing such data is difficult when laboratory and bioinformatic metadata is heterogeneously recorded in prose-based publications. Methods: Through a series of community-based hackathon events, AncientMetagenomeDir was updated to provide standardised library-level metadata of existing and new ancient metagenomic samples. In tandem, the companion tool 'AMDirT' was developed to facilitate automated metadata curation and data validation, as well as rapid data filtering and downloading. Results: AncientMetagenomeDir was extended to include standardised metadata of over 5000 ancient metagenomic libraries. The companion tool 'AMDirT' provides both graphical- and command-line interface based access to such metadata for users from a wide range of computational backgrounds. We also report on errors with metadata reporting that appear to commonly occur during data upload and provide suggestions on how to improve the quality of data sharing by the community. Conclusions: Together, both standardised metadata and tooling will help towards easier incorporation and reuse of public ancient metagenomic datasets into future analyses.

Metagenomics reveals the habitat specificity of biosynthetic potential of secondary metabolites in global food fermentations

BackgroundFermented foods are considered to be beneficial for human health. Secondary metabolites determined by biosynthetic gene clusters (BGCs) are precious bioactive compounds with various biological activities. However, the diversity and distribution of the biosynthetic potential of secondary metabolites in global food fermentations remain largely unknown. In this study, we performed a large-scale and comprehensive investigation for the BGCs in global food fermentations by metagenomics analysis.ResultsWe recovered 653 bacterial metagenome-assembled genomes (MAGs) from 367 metagenomic sequencing datasets covering 15 general food fermentation types worldwide. In total, 2334 secondary metabolite BGCs, including 1003 novel BGCs, were identified in these MAGs. Bacillaceae, Streptococcaceae, Streptomycetaceae, Brevibacteriaceae and Lactobacillaceae contained high abundances of novel BGCs (≥ 60 novel BGCs). Among 2334 BGCs, 1655 were habitat-specific, originating from habitat-specific species (80.54%) and habitat-specific genotypes within multi-habitat species (19.46%) in different food fermentation types. Biological activity analysis suggested that 183 BGC-producing secondary metabolites exhibited high probabilities of antibacterial activity (> 80%). These 183 BGCs were distributed across all 15 food fermentation types, and cheese fermentation contained the most BGC number.ConclusionsThis study demonstrates that food fermentation systems are an untapped reservoir of BGCs and bioactive secondary metabolites, and it provides novel insights into the potential human health benefits of fermented foods.CjJWvg8VSHBrW91QwqULd1Video

Benchmarking genome assembly methods on metagenomic sequencing data.

Metagenome assembly is an efficient approach to reconstruct microbial genomes from metagenomic sequencing data. Although short-read sequencing has been widely used for metagenome assembly, linked- and long-read sequencing have shown their advancements in assembly by providing long-range DNA connectedness. Many metagenome assembly tools were developed to simplify the assembly graphs and resolve the repeats in microbial genomes. However, there remains no comprehensive evaluation of metagenomic sequencing technologies, and there is a lack of practical guidance on selecting the appropriate metagenome assembly tools. This paper presents a comprehensive benchmark of 19 commonly used assembly tools applied to metagenomic sequencing datasets obtained from simulation, mock communities or human gut microbiomes. These datasets were generated using mainstream sequencing platforms, such as Illumina and BGISEQ short-read sequencing, 10x Genomics linked-read sequencing, and PacBio and Oxford Nanopore long-read sequencing. The assembly tools were extensively evaluated against many criteria, which revealed that long-read assemblers generated high contig contiguity but failed to reveal some medium- and high-quality metagenome-assembled genomes (MAGs). Linked-read assemblers obtained the highest number of overall near-complete MAGs from the human gut microbiomes. Hybrid assemblers using both short- and long-read sequencing were promising methods to improve both total assembly length and the number of near-complete MAGs. This paper also discussed the running time and peak memory consumption of these assembly tools and provided practical guidance on selecting them.

GPMeta: a GPU-accelerated method for ultrarapid pathogen identification from metagenomic sequences.

Metagenomic sequencing (mNGS) is a powerful diagnostic tool to detect causative pathogens in clinical microbiological testing owing to its unbiasedness and substantially reduced costs. Rapid and accurate classification of metagenomic sequences is a critical procedure for pathogen identification in dry-lab step of mNGS test. However, clinical practices of the testing technology are hampered by the challenge of classifying sequences within a clinically relevant timeframe. Here, we present GPMeta, a novel GPU-accelerated approach to ultrarapid pathogen identification from complex mNGS data, allowing users to bypass this limitation. Using mock microbial community datasets and public real metagenomic sequencing datasets from clinical samples, we show that GPMeta has not only higher accuracy but also significantly higher speed than existing state-of-the-art tools such as Bowtie2, Bwa, Kraken2 and Centrifuge. Furthermore, GPMeta offers GPMetaC clustering algorithm, a statistical model for clustering and rescoring ambiguous alignments to improve the discrimination of highly homologous sequences from microbial genomes with average nucleotide identity >95%. GPMetaC exhibits higher precision and recall rate than others. GPMeta underlines its key role in the development of the mNGS test in infectious diseases that require rapid turnaround times. Further study will discern how to best and easily integrate GPMeta into routine clinical practices. GPMeta is freely accessible to non-commercial users at https://github.com/Bgi-LUSH/GPMeta.

Evaluation of taxonomic classification and profiling methods for long-read shotgun metagenomic sequencing datasets

BackgroundLong-read shotgun metagenomic sequencing is gaining in popularity and offers many advantages over short-read sequencing. The higher information content in long reads is useful for a variety of metagenomics analyses, including taxonomic classification and profiling. The development of long-read specific tools for taxonomic classification is accelerating, yet there is a lack of information regarding their relative performance. Here, we perform a critical benchmarking study using 11 methods, including five methods designed specifically for long reads. We applied these tools to several mock community datasets generated using Pacific Biosciences (PacBio) HiFi or Oxford Nanopore Technology sequencing, and evaluated their performance based on read utilization, detection metrics, and relative abundance estimates.ResultsOur results show that long-read classifiers generally performed best. Several short-read classification and profiling methods produced many false positives (particularly at lower abundances), required heavy filtering to achieve acceptable precision (at the cost of reduced recall), and produced inaccurate abundance estimates. By contrast, two long-read methods (BugSeq, MEGAN-LR & DIAMOND) and one generalized method (sourmash) displayed high precision and recall without any filtering required. Furthermore, in the PacBio HiFi datasets these methods detected all species down to the 0.1% abundance level with high precision. Some long-read methods, such as MetaMaps and MMseqs2, required moderate filtering to reduce false positives to resemble the precision and recall of the top-performing methods. We found read quality affected performance for methods relying on protein prediction or exact k-mer matching, and these methods performed better with PacBio HiFi datasets. We also found that long-read datasets with a large proportion of shorter reads (< 2 kb length) resulted in lower precision and worse abundance estimates, relative to length-filtered datasets. Finally, for classification methods, we found that the long-read datasets produced significantly better results than short-read datasets, demonstrating clear advantages for long-read metagenomic sequencing.ConclusionsOur critical assessment of available methods provides best-practice recommendations for current research using long reads and establishes a baseline for future benchmarking studies.

Captivity and geography influence the antibiotic resistome of non-human primates.

Antibiotic resistance poses a serious threat for animals and humans health worldwide. Yet a comprehensive exploration of the influence of captivity and geography on non-human primate (NPH) gut antibiotic resistance remains incomplete. In this study, 131 metagenomic sequencing datasets of five species of NHPs included different regions and lifestyles were selected to perform the antibiotic resistance analysis. Nineteen related resistance antibiotics and 325 antibiotic resistance genes (ARGs) were obtained. A significantly higher abundance and diversity index of ARGs in the captive NHPs than in the wild was found but not for all of the samples. The biomarker-tracking of ARGs analysis identified key ARGs related to aminoglycoside resistance genes and tetracycline resistance genes. These results suggest that captivity and geography changes associated with human activities can lead to marked changes in the ecology of the NHP gut flora ARGs.

Freshwater microbial metagenomes sampled across different water body characteristics, space and time in Israel

Freshwater bodies are critical components of terrestrial ecosystems. The microbial communities of freshwater ecosystems are intimately linked water quality. These microbes interact with, utilize and recycle inorganic elements and organic matter. Here, we present three metagenomic sequence datasets (total of 182.9 Gbp) from different freshwater environments in Israel. The first dataset is from diverse freshwater bodies intended for different usages – a nature reserve, irrigation and aquaculture facilities, a tertiary wastewater treatment plant and a desert rainfall reservoir. The second represents a two-year time-series, collected during 2013–2014 at roughly monthly intervals, from a water reservoir connected to an aquaculture facility. The third is from several time-points during the winter and spring of 2015 in Lake Kinneret, including a bloom of the cyanobacterium Microcystis sp. These datasets are accompanied by physical, chemical, and biological measurements at each sampling point. We expect that these metagenomes will facilitate a wide range of comparative studies that seek to illuminate new aspects of freshwater microbial ecosystems and inform future water quality management approaches.

Multi-angle meta-analysis of the gut microbiome in Autism Spectrum Disorder: a step toward understanding patient subgroups

Observational studies have shown that the composition of the human gut microbiome in children diagnosed with Autism Spectrum Disorder (ASD) differs significantly from that of their neurotypical (NT) counterparts. Thus far, reported ASD-specific microbiome signatures have been inconsistent. To uncover reproducible signatures, we compiled 10 publicly available raw amplicon and metagenomic sequencing datasets alongside new data generated from an internal cohort (the largest ASD cohort to date), unified them with standardized pre-processing methods, and conducted a comprehensive meta-analysis of all taxa and variables detected across multiple studies. By screening metadata to test associations between the microbiome and 52 variables in multiple patient subsets and across multiple datasets, we determined that differentially abundant taxa in ASD versus NT children were dependent upon age, sex, and bowel function, thus marking these variables as potential confounders in case–control ASD studies. Several taxa, including the strains Bacteroides stercoris t__190463 and Clostridium M bolteae t__180407, and the species Granulicatella elegans and Massilioclostridium coli, exhibited differential abundance in ASD compared to NT children only after subjects with bowel dysfunction were removed. Adjusting for age, sex and bowel function resulted in adding or removing significantly differentially abundant taxa in ASD-diagnosed individuals, emphasizing the importance of collecting and controlling for these metadata. We have performed the largest (n = 690) and most comprehensive systematic analysis of ASD gut microbiome data to date. Our study demonstrated the importance of accounting for confounding variables when designing statistical comparative analyses of ASD- and NT-associated gut bacterial profiles. Mitigating these confounders identified robust microbial signatures across cohorts, signifying the importance of accounting for these factors in comparative analyses of ASD and NT-associated gut profiles. Such studies will advance the understanding of different patient groups to deliver appropriate therapeutics by identifying microbiome traits germane to the specific ASD phenotype.

500 metagenome-assembled microbial genomes from 30 subtropical estuaries in South China

As a unique geographical transition zone, the estuary is considered as a model environment to decipher the diversity, functions and ecological processes of microbial communities, which play important roles in the global biogeochemical cycle. Here we used surface water metagenomic sequencing datasets to construct metagenome-assembled genomes (MAGs) from 30 subtropical estuaries at a large scale along South China. In total, 500 dereplicated MAGs with completeness ≥ 50% and contamination ≤ 10% were obtained, among which more than one-thirds (n = 207 MAGs) have a completeness ≥ 70%. These MAGs are dominated by taxa assigned to the phylum Proteobacteria (n = 182 MAGs), Bacteroidota (n = 110) and Actinobacteriota (n = 104). These draft genomes can be used to study the diversity, phylogenetic history and metabolic potential of microbiota in the estuary, which should help improve our understanding of the structure and function of these microorganisms and how they evolved and adapted to extreme conditions in the estuarine ecosystem.

Mucosa-associated specific bacterial species disrupt the intestinal epithelial barrier in the autism phenome

Gut-Brain Axis provides a bidirectional communicational route, an imbalance of which can have pathophysiological consequences. Differential gut microbiome studies have become a frontier in autism research, affecting 85% of autistic children. The present study aims to understand how gut microbiota of autism subjects differ from their neurotypical counterparts. This study would help to identify the abundance of bacterial signature species in autism and their associated metabolites.16S rRNA metagenomic sequence datasets of 30 out of 206 autism subjects were selected from the American Gut Project Archive. First, the taxonomic assignment was inferred by similarity-based methods using the Quantitative Insights into Microbial Ecology (QIIME) toolkit. Next, species abundance was characterized, and a co-occurrence network was built to infer species interaction using measures of diversity. Thirdly, statistical parameters were incorporated to validate the findings. Finally, the identification of metabolites associated with these bacterial signature species connects with biological processes in the host through pathway analysis.Gut microbiome data revealed Akkermansia sp. and Faecalibacterium prausnitzii to be statistically lower in abundance in autistic children than their neurotypical peers with a five and two-fold decrease, respectively. While Prevotella sp. and Sutterella sp. showed a five and a two-fold increase in cases, respectively. The constructed pathway revealed succinate and butyrate as the significant metabolites for the bacterial signature species identified.The present study throws light on the role of mucosa-associated bacterial species: Veillonella sp., Prevotella sp., Akkermansia sp., Sutterella sp., Faecalibacterium prausnitzii, Lactobacillus sp., which can act as diagnostic criteria for detection of gut dysbiosis in autism.

Adintoviruses: a proposed animal-tropic family of midsize eukaryotic linear dsDNA (MELD) viruses.

Polintons (also known as Mavericks) were initially identified as a widespread class of eukaryotic transposons named for their hallmark type B DNA polymerase and retrovirus-like integrase genes. It has since been recognized that many polintons encode possible capsid proteins and viral genome-packaging ATPases similar to those of a diverse range of double-stranded DNA viruses. This supports the inference that at least some polintons are actually viruses capable of cell-to-cell spread. At present, there are no polinton-associated capsid protein genes annotated in public sequence databases. To rectify this deficiency, we used a data-mining approach to investigate the distribution and gene content of polinton-like elements and related DNA viruses in animal genomic and metagenomic sequence datasets. The results define a discrete family-like clade of viruses with two genus-level divisions. We propose the family name Adintoviridae, connoting similarities to adenovirus virion proteins and the presence of a retrovirus-like integrase gene. Although adintovirus-class PolB sequences were detected in datasets for fungi and various unicellular eukaryotes, sequences resembling adintovirus virion proteins and accessory genes appear to be restricted to animals. Degraded adintovirus sequences are endogenized into the germlines of a wide range of animals, including humans.

Hierarchical Microbial Functions Prediction by Graph Aggregated Embedding.

Matching 16S rRNA gene sequencing data to a metabolic reference database is a meaningful way to predict the metabolic function of bacteria and archaea, bringing greater insight to the working of the microbial community. However, some operational taxonomy units (OTUs) cannot be functionally profiled, especially for microbial communities from non-human samples cultured in defective media. Therefore, we herein report the development of Hierarchical micrObial functions Prediction by graph aggregated Embedding (HOPE), which utilizes co-occurring patterns and nucleotide sequences to predict microbial functions. HOPE integrates topological structures of microbial co-occurrence networks with k-mer compositions of OTU sequences and embeds them into a lower-dimensional continuous latent space, while maximally preserving topological relationships among OTUs. The high imbalance among KEGG Orthology (KO) functions of microbes is recognized in our framework that usually yields poor performance. A hierarchical multitask learning module is used in HOPE to alleviate the challenge brought by the long-tailed distribution among classes. To test the performance of HOPE, we compare it with HOPE-one, HOPE-seq, and GraphSAGE, respectively, in three microbial metagenomic 16s rRNA sequencing datasets, including abalone gut, human gut, and gut of Penaeus monodon. Experiments demonstrate that HOPE outperforms baselines on almost all indexes in all experiments. Furthermore, HOPE reveals significant generalization ability. HOPE's basic idea is suitable for other related scenarios, such as the prediction of gene function based on gene co-expression networks. The source code of HOPE is freely available at https://github.com/adrift00/HOPE.

Culturing novel and abundant pelagiphages in the ocean.

Viruses play a key role in biogeochemical cycling and host mortality, metabolism, physiology and evolution in the ocean. Viruses that infect the globally abundant SAR11 bacteria (pelagiphages) were reported to be an important component of the marine viral communities. Our current knowledge of pelagiphages is based on a few studies and therefore is limited. In this study, 10 new pelagiphages were isolated and genomically characterized. These pelagiphages represent the first cultivated representatives of four viral lineages only found in metagenomic sequencing datasets previously. Many abundant environmental viral sequences, i.e., single-virus vSAG 37-F6 and several Global Ocean Viromes (GOV) viral populations, are now further confirmed with these pelagiphages. Viromic read mapping reveals that these new pelagiphages are globally distributed in the ocean and can be detected throughout the water column. Remarkably, isolation of these pelagiphages contributed up to 12% of all viromic reads annotated in the analysed viromes. Altogether, this study has greatly broadened our understanding of pelagiphages regarding their morphology, genetic diversity, infection strategies, and distribution pattern. The availability of these newly isolated pelagiphages and their genome sequences will allow us to further explore their infectivities and ecological strategies.

Comparative genome analysis proposes three new Aureobasidium species isolated from grape juice.

Aureobasidium pullulans is the most abundant and ubiquitous species within the genus and is also considered a core component of the grape juice microflora. So far, a small number of other Aureobasidium species have been reported, that in contrast to A. pullulans, appear far more constrained to specific habitats. It is unknown whether grape juice is a reservoir of novel Aureobasidium species, overlooked in the course of conventional morphological and meta-barcoding analyses. In this study, eight isolates from grape juice taxonomically classified as Aureobasidium through ITS sequencing were subjected to whole-genome phylogenetic, synteny and nucleotide identity analyses, which revealed three isolates to likely represent newly discovered Aureobasidium species. Analyses of ITS and metagenomic sequencing datasets show that these species can be present in grape juice samples from different locations and vintages. Functional annotation revealed the Aureobasidium isolates possess the genetic potential to support growth on the surface of plants and grapes. However, the loss of several genes associated with tolerance to diverse environmental stresses suggest a more constrained ecological range than A. pullulans.

Comparison of microbiome samples: methods and computational challenges.

The study of microbial communities crucially relies on the comparison of metagenomic next-generation sequencing data sets, for which several methods have been designed in recent years. Here, we review three key challenges in the comparison of such data sets: species identification and quantification, the efficient computation of distances between metagenomic samples and the identification of metagenomic features associated with a phenotype such as disease status. We present current solutions for such challenges, considering both reference-based methods relying on a database of reference genomes and reference-free methods working directly on all sequencing reads from the samples.