Assessing the performance of different approaches for functional and taxonomic annotation of metagenomes

Unveiling the hidden viromes across the animal tree of life: insights from a taxonomic classification pipeline applied to invertebrates of 31 metazoan phyla

Invertebrates constitute the majority of animal species on Earth, including most disease-causing agents or vectors, with more diverse viromes when compared to vertebrates. Recent advancements in high-throughput sequencing have significantly expanded our understanding of invertebrate viruses, yet this knowledge remains biased toward a few well-studied animal lineages. In this study, we analyze invertebrate DNA and RNA viromes for 31 phyla using 417 publicly available RNA-Seq data sets from diverse environments in the marine-terrestrial and marine-freshwater gradients. This study aims to (i) estimate virome compositions at the family level for the first time across the animal tree of life, including the first exploration of the virome in several phyla, (ii) quantify the diversity of invertebrate viromes and characterize the structure of invertebrate-virus infection networks, and (iii) investigate host phylum and habitat influence on virome differences. Results showed that a set of few viral families of eukaryotes, comprising Retroviridae, Flaviviridae, and several families of giant DNA viruses, were ubiquitous and highly abundant. Nevertheless, some differences emerged between phyla, revealing for instance a less diverse virome in Ctenophora compared to the other animal phyla. Compositional analysis of the viromes showed that the host phylum explained over five times more variance in composition than its habitat. Moreover, significant similarities were observed between the viromes of some phylogenetically related phyla, which could highlight the influence of co-evolution in shaping invertebrate viromes.IMPORTANCEThis study significantly enhances our understanding of the global animal virome by characterizing the viromes of previously unexamined invertebrate lineages from a large number of animal phyla. It showcases the great diversity of viromes within each phylum and investigates the role of habitat shaping animal viral communities. Furthermore, our research identifies dominant virus families in invertebrates and distinguishes phyla with analogous viromes. This study sets the road toward a deeper understanding of the virome across the animal tree of life.

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 in-depth evaluation of metagenomic classifiers for soil microbiomes

BACKGROUND

Recent endeavours in metagenomics, exemplified by projects such as the human microbiome project and TARA Oceans, have illuminated the complexities of microbial biomes. A robust bioinformatic pipeline and meticulous evaluation of their methodology have contributed to the success of these projects. The soil environment, however, with its unique challenges, requires a specialized methodological exploration to maximize microbial insights. A notable limitation in soil microbiome studies is the dearth of soil-specific reference databases available to classifiers that emulate the complexity of soil communities. There is also a lack of in-vitro mock communities derived from soil strains that can be assessed for taxonomic classification accuracy.

RESULTS

In this study, we generated a custom in-silico mock community containing microbial genomes commonly observed in the soil microbiome. Using this mock community, we simulated shotgun sequencing data to evaluate the performance of three leading metagenomic classifiers: Kraken2 (supplemented with Bracken, using a custom database derived from GTDB-TK genomes along with its own default database), Kaiju, and MetaPhlAn, utilizing their respective default databases for a robust analysis. Our results highlight the importance of optimizing taxonomic classification parameters, database selection, as well as analysing trimmed reads and contigs. Our study showed that classifiers tailored to the specific taxa present in our samples led to fewer errors compared to broader databases including microbial eukaryotes, protozoa, or human genomes, highlighting the effectiveness of targeted taxonomic classification. Notably, an optimal classifier performance was achieved when applying a relative abundance threshold of 0.001% or 0.005%. The Kraken2 supplemented with bracken, with a custom database demonstrated superior precision, sensitivity, F1 score, and overall sequence classification. Using a custom database, this classifier classified 99% of in-silico reads and 58% of real-world soil shotgun reads, with the latter identifying previously overlooked phyla using a custom database.

CONCLUSION

This study underscores the potential advantages of in-silico methodological optimization in metagenomic analyses, especially when deciphering the complexities of soil microbiomes. We demonstrate that the choice of classifier and database significantly impacts microbial taxonomic profiling. Our findings suggest that employing Kraken2 with Bracken, coupled with a custom database of GTDB-TK genomes and fungal genomes at a relative abundance threshold of 0.001% provides optimal accuracy in soil shotgun metagenome analysis.

Impact of Anthropogenic Activities on Microbial Community Structure in Riverbed Sediments of East Kazakhstan

Heavy metal (HMe) pollution in regions with mining and metallurgy activities is known to be a serious environmental problem worldwide. Hydrological processes contribute to the dissemination of HMes (drainage, precipitation, flow rate). The aim of the present study is to investigate the microbial community structure in ten river sediments sampled in different regions of East Kazakhstan, which are contaminated with HMes. The overall degree of sediment contamination with HMes (Cr, Cu, Zn, Pb, and Cd) was assessed using the pollution index Zc, which ranged from 0.43 to 21.6, with the highest in Ridder City (Zc = 21.6) and Ust-Kamenogorsk City, 0.8 km below the dam of the hydroelectric power station (Zc = 19.6). The tested samples considerably differed in organic matter, total carbon, nitrogen, and phosphorus content, as well as in the abundance of HMe-related functional gene families and antibiotic resistance genes. Metagenomic analysis of benthic microorganisms showed the prevalence of Proteobacteria (88.84-97.61%) and Actinobacteria (1.21-5.98%) at the phylum level in all samples. At the class level, Actinobacteria (21.68-57.48%), Betaproteobacteria (19.38-41.17%), and Alphaproteobacteria (10.0-39.78%) were the most common among the classified reads. To the best of our knowledge, this is the first study on the metagenomic characteristics of benthic microbial communities exposed to chronic HMe pressure in different regions of East Kazakhstan.

Mock community taxonomic classification performance of publicly available shotgun metagenomics pipelines

Shotgun metagenomic sequencing comprehensively samples the DNA of a microbial sample. Choosing the best bioinformatics processing package can be daunting due to the wide variety of tools available. Here, we assessed publicly available shotgun metagenomics processing packages/pipelines including bioBakery, Just a Microbiology System (JAMS), Whole metaGenome Sequence Assembly V2 (WGSA2), and Woltka using 19 publicly available mock community samples and a set of five constructed pathogenic gut microbiome samples. Also included is a workflow for labelling bacterial scientific names with NCBI taxonomy identifiers for better resolution in assessing results. The Aitchison distance, a sensitivity metric, and total False Positive Relative Abundance were used for accuracy assessments for all pipelines and mock samples. Overall, bioBakery4 performed the best with most of the accuracy metrics, while JAMS and WGSA2, had the highest sensitivities. Furthermore, bioBakery is commonly used and only requires a basic knowledge of command line usage. This work provides an unbiased assessment of shotgun metagenomics packages and presents results assessing the performance of the packages using mock community sequence data.

Long-term mitigation of drought changes the functional potential and life-strategies of the forest soil microbiome involved in organic matter decomposition

Climate change can alter the flow of nutrients and energy through terrestrial ecosystems. Using an inverse climate change field experiment in the central European Alps, we explored how long-term irrigation of a naturally drought-stressed pine forest altered the metabolic potential of the soil microbiome and its ability to decompose lignocellulolytic compounds as a critical ecosystem function. Drought mitigation by a decade of irrigation stimulated profound changes in the functional capacity encoded in the soil microbiome, revealing alterations in carbon and nitrogen metabolism as well as regulatory processes protecting microorganisms from starvation and desiccation. Despite the structural and functional shifts from oligotrophic to copiotrophic microbial lifestyles under irrigation and the observation that different microbial taxa were involved in the degradation of cellulose and lignin as determined by a time-series stable-isotope probing incubation experiment with 13C-labeled substrates, degradation rates of these compounds were not affected by different water availabilities. These findings provide new insights into the impact of precipitation changes on the soil microbiome and associated ecosystem functioning in a drought-prone pine forest and will help to improve our understanding of alterations in biogeochemical cycling under a changing climate.

A Transiently Hypersaline Microbial Mat Harbors a Diverse and Stable Archaeal Community in the Cuatro Cienegas Basin, Mexico

Microbial mats are biologically diverse communities that are analogs to some of the earliest ecosystems on Earth. In this study, we describe a unique transiently hypersaline microbial mat uncovered in a shallow pond within the Cuatro Cienegas Basin (CCB) in northern México. The CCB is an endemism-rich site that harbors living stromatolites that have been studied to understand the conditions of the Precambrian Earth. These microbial mats form elastic domes filled with biogenic gas, and the mats have a relatively large and stable subpopulation of archaea. For this reason, this site has been termed archaean domes (AD). The AD microbial community was analyzed by metagenomics over three seasons. The mat exhibited a highly diverse prokaryotic community dominated by bacteria. Bacterial sequences are represented in 37 phyla, mainly Proteobacteria, Firmicutes, and Actinobacteria, that together comprised >50% of the sequences from the mat. Archaea represented up to 5% of the retrieved sequences, with up to 230 different archaeal species that belong to 5 phyla (Euryarchaeota, Crenarchaeota, Thaumarchaeota, Korarchaeota, and Nanoarchaeota). The archaeal taxa showed low variation despite fluctuations in water and nutrient availability. In addition, predicted functions highlight stress responses to extreme conditions present in the AD, including salinity, pH, and water/drought fluctuation. The observed complexity of the AD mat thriving in high pH and fluctuating water and salt conditions within the CCB provides an extant model of great value for evolutionary studies, as well as a suitable analog to the early Earth and Mars.

Metagenomic Analyses Reveal the Influence of Depth Layers on Marine Biodiversity on Tropical and Subtropical Regions

The emergence of open ocean global-scale studies provided important information about the genomics of oceanic microbial communities. Metagenomic analyses shed light on the structure of marine habitats, unraveling the biodiversity of different water masses. Many biological and environmental factors can contribute to marine organism composition, such as depth. However, much remains unknown about microbial communities' taxonomic and functional features in different water layer depths. Here, we performed a metagenomic analysis of 76 publicly available samples from the Tara Ocean Project, distributed in 8 collection stations located in tropical or subtropical regions, and sampled from three layers of depth (surface water layer-SRF, deep chlorophyll maximum layer-DCM, and mesopelagic zone-MES). The SRF and DCM depth layers are similar in abundance and diversity, while the MES layer presents greater diversity than the other layers. Diversity clustering analysis shows differences regarding the taxonomic content of samples. At the domain level, bacteria prevail in most samples, and the MES layer presents the highest proportion of archaea among all samples. Taken together, our results indicate that the depth layer influences microbial sample composition and diversity.

From defaults to databases: parameter and database choice dramatically impact the performance of metagenomic taxonomic classification tools

In metagenomic analyses of microbiomes, one of the first steps is usually the taxonomic classification of reads by comparison to a database of previously taxonomically classified genomes. While different studies comparing metagenomic taxonomic classification methods have determined that different tools are 'best', there are two tools that have been used the most to-date: Kraken (k-mer-based classification against a user-constructed database) and MetaPhlAn (classification by alignment to clade-specific marker genes), the latest versions of which are Kraken2 and MetaPhlAn 3, respectively. We found large discrepancies in both the proportion of reads that were classified as well as the number of species that were identified when we used both Kraken2 and MetaPhlAn 3 to classify reads within metagenomes from human-associated or environmental datasets. We then investigated which of these tools would give classifications closest to the real composition of metagenomic samples using a range of simulated and mock samples and examined the combined impact of tool-parameter-database choice on the taxonomic classifications given. This revealed that there may not be a one-size-fits-all 'best' choice. While Kraken2 can achieve better overall performance, with higher precision, recall and F1 scores, as well as alpha- and beta-diversity measures closer to the known composition than MetaPhlAn 3, the computational resources required for this may be prohibitive for many researchers, and the default database and parameters should not be used. We therefore conclude that the best tool-parameter-database choice for a particular application depends on the scientific question of interest, which performance metric is most important for this question and the limit of available computational resources.

metaGOflow: a workflow for the analysis of marine Genomic Observatories shotgun metagenomics data

BACKGROUND

Genomic Observatories (GOs) are sites of long-term scientific study that undertake regular assessments of the genomic biodiversity. The European Marine Omics Biodiversity Observation Network (EMO BON) is a network of GOs that conduct regular biological community samplings to generate environmental and metagenomic data of microbial communities from designated marine stations around Europe. The development of an effective workflow is essential for the analysis of the EMO BON metagenomic data in a timely and reproducible manner.

FINDINGS

Based on the established MGnify resource, we developed metaGOflow. metaGOflow supports the fast inference of taxonomic profiles from GO-derived data based on ribosomal RNA genes and their functional annotation using the raw reads. Thanks to the Research Object Crate packaging, relevant metadata about the sample under study, and the details of the bioinformatics analysis it has been subjected to, are inherited to the data product while its modular implementation allows running the workflow partially. The analysis of 2 EMO BON samples and 1 Tara Oceans sample was performed as a use case.

CONCLUSIONS

metaGOflow is an efficient and robust workflow that scales to the needs of projects producing big metagenomic data such as EMO BON. It highlights how containerization technologies along with modern workflow languages and metadata package approaches can support the needs of researchers when dealing with ever-increasing volumes of biological data. Despite being initially oriented to address the needs of EMO BON, metaGOflow is a flexible and easy-to-use workflow that can be broadly used for one-sample-at-a-time analysis of shotgun metagenomics data.

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

BACKGROUND

Long-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.

RESULTS

Our 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.

CONCLUSIONS

Our critical assessment of available methods provides best-practice recommendations for current research using long reads and establishes a baseline for future benchmarking studies.

Metagenomic views on taxonomic and functional profiles of the Himalayan Tsomgo cold lake and unveiling its deterzome potential

Cold habitat is considered a potential source for detergent industry enzymes. This study aims at the metagenomic investigation of Tsomgo lake for taxonomic and functional annotation, unveiling the deterzome potential of the residing microbiota at this site. The present investigation revealed molecular profiling of microbial community structure and functional potential of the high-altitude Tsomgo lake samples of two different temperatures, harvested during March and August. Bacteria were found to be the most dominant phyla, with traces of genomic pieces of evidence belonging to archaea, viruses, and eukaryotes. Proteobacteria and Actinobacteria were noted to be the most abundant bacterial phyla in the cold lake. In-depth metagenomic investigation of the cold aquatic habitat revealed novel genes encoding detergent enzymes, amylase, protease, and lipase. Further, metagenome-assembled genomes (MAGs) belonging to the psychrophilic bacterium, Arthrobacter alpinus, were constructed from the metagenomic data. The annotation depicted the presence of detergent enzymes and genes for low-temperature adaptation in Arthrobacter alpinus. Psychrophilic microbial isolates were screened for lipase, protease, and amylase activities to further strengthen the metagenomic findings. A novel strain of Acinetobacter sp. was identified with the dual enzymatic activity of protease and amylase. The bacterial isolates exhibited hydrolyzing activity at low temperatures. This metagenomic study divulged novel genomic resources for detergent industry enzymes, and the bacterial isolates secreting cold-active amylase, lipase, and protease enzymes. The findings manifest that Tsomgo lake is a potential bioresource of cold-active enzymes, vital for various industrial applications.

Bacteroides abundance drives birth mode dependent infant gut microbiota developmental trajectories

Background and aims

Birth mode and other early life factors affect a newborn's microbial colonization with potential long-term health effects. Individual variations in early life gut microbiota development, especially their effects on the functional repertoire of microbiota, are still poorly characterized. This study aims to provide new insights into the gut microbiome developmental trajectories during the first year of life.

Methods

Our study comprised 78 term infants sampled at 3 weeks, 3 months, 6 months, and 12 months (n = 280 total samples), and their mothers were sampled in late pregnancy (n = 50). Fecal DNA was subjected to shotgun metagenomic sequencing. Infant samples were studied for taxonomic and functional maturation, and maternal microbiota was used as a reference. Hierarchical clustering on taxonomic profiles was used to identify the main microbiota developmental trajectories in the infants, and their associations with perinatal and postnatal factors were assessed.

Results

In line with previous studies, infant microbiota composition showed increased alpha diversity and decreased beta diversity by age, converging toward an adult-like profile. However, we did not observe an increase in functional alpha diversity, which was stable and comparable with the mother samples throughout all the sampling points. Using a de novo clustering approach, two main infant microbiota clusters driven by Bacteroidaceae and Clostridiaceae emerged at each time point. The clusters were associated with birth mode and their functions differed mainly in terms of biosynthetic and carbohydrate degradation pathways, some of which consistently differed between the clusters for all the time points. The longitudinal analysis indicated three main microbiota developmental trajectories, with the majority of the infants retaining their characteristic cluster until 1 year. As many as 40% of vaginally delivered infants were grouped with infants delivered by C-section due to their clear and persistent depletion in Bacteroides. Intrapartum antibiotics, any perinatal or postnatal factors, maternal microbiota composition, or other maternal factors did not explain the depletion in Bacteroides in the subset of vaginally born infants.

Conclusion

Our study provides an enhanced understanding of the compositional and functional early life gut microbiota trajectories, opening avenues for investigating elusive causes that influence non-typical microbiota development.

Functional characterization of prokaryotic dark matter: the road so far and what lies ahead

Eight-hundred thousand to one trillion prokaryotic species may inhabit our planet. Yet, fewer than two-hundred thousand prokaryotic species have been described. This uncharted fraction of microbial diversity, and its undisclosed coding potential, is known as the "microbial dark matter" (MDM). Next-generation sequencing has allowed to collect a massive amount of genome sequence data, leading to unprecedented advances in the field of genomics. Still, harnessing new functional information from the genomes of uncultured prokaryotes is often limited by standard classification methods. These methods often rely on sequence similarity searches against reference genomes from cultured species. This hinders the discovery of unique genetic elements that are missing from the cultivated realm. It also contributes to the accumulation of prokaryotic gene products of unknown function among public sequence data repositories, highlighting the need for new approaches for sequencing data analysis and classification. Increasing evidence indicates that these proteins of unknown function might be a treasure trove of biotechnological potential. Here, we outline the challenges, opportunities, and the potential hidden within the functional dark matter (FDM) of prokaryotes. We also discuss the pitfalls surrounding molecular and computational approaches currently used to probe these uncharted waters, and discuss future opportunities for research and applications.

The Core Human Microbiome: Does It Exist and How Can We Find It? A Critical Review of the Concept

The core microbiome, which refers to a set of consistent microbial features across populations, is of major interest in microbiome research and has been addressed by numerous studies. Understanding the core microbiome can help identify elements that lead to dysbiosis, and lead to treatments for microbiome-related health states. However, defining the core microbiome is a complex task at several levels. In this review, we consider the current state of core human microbiome research. We consider the knowledge that has been gained, the factors limiting our ability to achieve a reliable description of the core human microbiome, and the fields most likely to improve that ability. DNA sequencing technologies and the methods for analyzing metagenomics and amplicon data will most likely facilitate higher accuracy and resolution in describing the microbiome. However, more effort should be invested in characterizing the microbiome’s interactions with its human host, including the immune system and nutrition. Other components of this holobiontic system should also be emphasized, such as fungi, protists, lower eukaryotes, viruses, and phages. Most importantly, a collaborative effort of experts in microbiology, nutrition, immunology, medicine, systems biology, bioinformatics, and machine learning is probably required to identify the traits of the core human microbiome.

Deep learning of a bacterial and archaeal universal language of life enables transfer learning and illuminates microbial dark matter

The majority of microbial genomes have yet to be cultured, and most proteins identified in microbial genomes or environmental sequences cannot be functionally annotated. As a result, current computational approaches to describe microbial systems rely on incomplete reference databases that cannot adequately capture the functional diversity of the microbial tree of life, limiting our ability to model high-level features of biological sequences. Here we present LookingGlass, a deep learning model encoding contextually-aware, functionally and evolutionarily relevant representations of short DNA reads, that distinguishes reads of disparate function, homology, and environmental origin. We demonstrate the ability of LookingGlass to be fine-tuned via transfer learning to perform a range of diverse tasks: to identify novel oxidoreductases, to predict enzyme optimal temperature, and to recognize the reading frames of DNA sequence fragments. LookingGlass enables functionally relevant representations of otherwise unknown and unannotated sequences, shedding light on the microbial dark matter that dominates life on Earth.

Computational methods to analyse microbial systems rely on reference databases which do not capture their full functional diversity. Here the authors develop a deep learning model and apply it using transfer learning, creating biologically useful models for multiple different tasks.

Inferring Species Compositions of Complex Fungal Communities from Long- and Short-Read Sequence Data

Our study is unique in that it provides an in-depth comparative study of a real-life complex fungal community analyzed with multiple long- and short-read sequencing approaches. These technologies and their application are currently of great interest to diverse biologists as they seek to characterize the community compositions of microbiomes.

Shotgun Metagenomics of Deep Forest Soil Layers Show Evidence of Altered Microbial Genetic Potential for Biogeochemical Cycling

Soil microorganisms such as Bacteria and Archaea play important roles in the biogeochemical cycling of soil nutrients, because they act as decomposers or are mutualistic or antagonistic symbionts, thereby influencing plant growth and health. In the present study, we investigated the vertical distribution of soil metagenomes to a depth of 1.5 m in Swiss forests of European beech and oak species on calcareous bedrock. We explored the functional genetic potential of soil microorganisms with the aim to disentangle the effects of tree genus and soil depth on the genetic repertoire, and to gain insight into the microbial C and N cycling. The relative abundance of reads assigned to taxa at the domain level indicated a 5–10 times greater abundance of Archaea in the deep soil, while Bacteria showed no change with soil depth. In the deep soil there was an overrepresentation of genes for carbohydrate-active enzymes, which are involved in the catalyzation of the transfer of oligosaccharides, as well as in the binding of carbohydrates such as chitin or cellulose. In addition, N-cycling genes (NCyc) involved in the degradation and synthesis of N compounds, in nitrification and denitrification, and in nitrate reduction were overrepresented in the deep soil. Consequently, our results indicate that N-transformation in the deep soil is affected by soil depth and that N is used not only for assimilation but also for energy conservation, thus indicating conditions of low oxygen in the deep soil. Using shotgun metagenomics, our study provides initial findings on soil microorganisms and their functional genetic potential, and how this may change depending on soil properties, which shift with increasing soil depth. Thus, our data provide novel, deeper insight into the “dark matter” of the soil.

Increasing prediction performance of colorectal cancer disease status using random forests classification based on metagenomic shotgun sequencing data

Dysfunction of microbial communities in various human body sites has been shown to be associated with a variety of diseases raising the possibility of predicting diseases based on metagenomic samples. Although many studies have investigated this problem, there are no consensus on the optimal approaches for predicting disease status based on metagenomic samples. Using six human gut metagenomic datasets consisting of large numbers of colorectal cancer patients and healthy controls from different countries, we investigated different software packages for extracting relative abundances of known microbial genomes and for integrating mapping and assembly approaches to obtain the relative abundance profiles of both known and novel genomes. The random forests (RF) classification algorithm was then used to predict colorectal cancer status based on the microbial relative abundance profiles. Based on within data cross-validation and cross-dataset prediction, we show that the RF prediction performance using the microbial relative abundance profiles estimated by Centrifuge is generally higher than that using the microbial relative abundance profiles estimated by MetaPhlAn2 and Bracken. We also develop a novel method to integrate the relative abundance profiles of both known and novel microbial organisms to further increase the prediction performance for colorectal cancer from metagenomes.

Small investments with big returns: environmental genomic bioprospecting of microbial life

Microorganisms and their natural products are major drivers of ecological processes and industrial applications. Microbial bioprospecting has been critical for the advancement in various fields such as pharmaceuticals, sustainable industries, food security and bioremediation. Next generation sequencing has been paramount in the exploration of diverse environmental microbiomes. It presents a culture-independent approach to investigating hitherto uncultured taxa, resulting in the creation of massive sequence databases, which are available in the public domain. Genome mining searches available (meta)genomic data for target biosynthetic genes, and combined with the large-scale public data, this in-silico bioprospecting method presents an efficient and extensive way to uncover microbial bioproducts. Bioinformatic tools have progressed to a stage where we can recover genomes from the environment; these metagenome-assembled genomes present a way to understand the metabolic capacity of microorganisms in a physiological and ecological context. Environmental sampling been extensive across various ecological settings, including microbiomes with unique physicochemical properties that could influence the discovery of novel functions and metabolic pathways. Although in-silico methods cannot completely substitute in-vitro studies, the contextual information it provides is invaluable for understanding the ecological and taxonomic distribution of microbial genotypes and to form effective strategies for future microbial bioprospecting efforts.

Bacterial low-abundant taxa are key determinants of a healthy airway metagenome in the early years of human life

The default removal of low-abundance (rare) taxa from microbial community analyses may lead to an incomplete picture of the taxonomic and functional microbial potential within the human habitat. Publicly available shotgun metagenomics data of healthy children and children with cystic fibrosis (CF) were reanalysed to study the development of the rare species biosphere, which was here defined by either the 15th, 25th or 35th species abundance percentile. We found that healthy children contained an age-independent network of abundant (core) and rare species with both entities being essential in maintaining the network structure. The protein sequence usage for more than 100 bacterial metabolic pathways differed between the core and rare species biosphere. In CF children, the background structure was underdeveloped and random forest bootstrapping based on all constituents of the early airway metagenome and host-associated factors indicated that rare taxa were the most important variables in deciding whether a child was healthy or suffered from the life-limiting CF disease. Attempts failed to make the age-independent CF network as robust as the healthy structure when an increasing number of bacterial taxa from the healthy network was incorporated into the CF structure by computer-based model simulations. However, the transfer of a key combination of taxa from the healthy to the CF network structure with high species diversity and low species dominance, correlated with a more robust CF network and a topological approximation of CF and healthy graph structures. Rothia mucilaginosa, Streptococci and rare species were essential in improving the underdeveloped CF network.

Metagenomics reveals contrasting energy utilization efficiencies of captive and wild camels (Camelus ferus)

Captive conditions can affect the symbiotic microbiome of animals. In this study, we compared the structural and functional differences of the gastrointestinal microbiomes of wild Bactrian camels (Camelus ferus) between wild and captive populations, as well as their different host energy utilization performances through metagenomics. The results showed that wild-living camels harbored more microbial taxa related to the production of volatile fatty acids, fewer methanogens, and fewer genes encoding enzymes involved in methanogenesis, leading to higher energy utilization efficiency compared to that of captive-living camels. These findings suggest that the wild-living camel fecal microbiome demonstrates a series of adaptive characteristics that enable the host to adjust to a relatively barren field environment. Our study provides novel insights into the mechanisms of wildlife adaptations to habitats from the perspective of the microbiome.

Diverse methanogens, bacteria and tannase genes in the feces of the endangered volcano rabbit (Romerolagus diazi)

Background

The volcano rabbit is the smallest lagomorph in Mexico, it is monotypic and endemic to the Trans-Mexican Volcanic Belt. It is classified as endangered by Mexican legislation and as critically endangered by the IUCN, in the Red List. Romerolagus diazi consumes large amounts of grasses, seedlings, shrubs, and trees. Pines and oaks contain tannins that can be toxic to the organisms which consume them. The volcano rabbit microbiota may be rich in bacteria capable of degrading fiber and phenolic compounds.

Methods

We obtained the fecal microbiome of three adults and one young rabbit collected in Coajomulco, Morelos, Mexico. Taxonomic assignments and gene annotation revealed the possible roles of different bacteria in the rabbit gut. We searched for sequences encoding tannase enzymes and enzymes associated with digestion of plant fibers such as cellulose and hemicellulose.

Results

The most representative phyla within the Bacteria domain were: Proteobacteria, Firmicutes and Actinobacteria for the young rabbit sample (S1) and adult rabbit sample (S2), which was the only sample not confirmed by sequencing to correspond to the volcano rabbit. Firmicutes, Actinobacteria and Cyanobacteria were found in adult rabbit samples S3 and S4. The most abundant phylum within the Archaea domain was Euryarchaeota. The most abundant genera of the Bacteria domain were Lachnoclostridium (Firmicutes) and Acinetobacter (Proteobacteria), while Methanosarcina predominated from the Archaea. In addition, the potential functions of metagenomic sequences were identified, which include carbohydrate and amino acid metabolism. We obtained genes encoding enzymes for plant fiber degradation such as endo 1,4 β-xylanases, arabinofuranosidases, endoglucanases and β-glucosidases. We also found 18 bacterial tannase sequences.

Seasonal and annual changes in the microbial communities of Ofunato Bay, Japan, based on metagenomics

Five years of datasets from 2015 to 2019 of whole genome shotgun sequencing for cells trapped on 0.2-µm filters of seawater collected monthly from Ofunato Bay, an enclosed bay in Japan, were analysed, which included the 2015 data that we had reported previously. Nucleotide sequences were determined for extracted DNA from three locations for both the upper (1 m) and deeper (8 or 10 m) depths. The biotic communities analysed at the domain level comprised bacteria, eukaryotes, archaea and viruses. The relative abundance of bacteria was over 60% in most months for the five years. The relative abundance of the SAR86 cluster was highest in the bacterial group, followed by Candidatus Pelagibacter and Planktomarina. The relative abundance of Ca. Pelagibacter showed no relationship with environmental factors, and those of SAR86 and Planktomarina showed positive correlations with salinity and dissolved oxygen, respectively. The bacterial community diversity showed seasonal changes, with high diversity around September and low diversity around January for all five years. Nonmetric multidimensional scaling analysis also revealed that the bacterial communities in the bay were grouped in a season-dependent manner and linked with environmental variables such as seawater temperature, salinity and dissolved oxygen.

Landfill microbiome harbour plastic degrading genes: A metagenomic study of solid waste dumping site of Gujarat, India

Globally, environmental pollution by plastic waste has become a severe ecological and social problem worldwide. The present study aimed to analyse the bacterial community structure and functional potential of the landfill site using high throughput shotgun metagenomic approach to understand plastic degrading capabilities present in the municipal solid waste (MSW) dumping site. In this study, soil, leachate and compost samples were collected from various locations (height and depth) of the Pirana landfill site in Ahmedabad city Gujarat, India. In total 30 phyla, 58 class, 125 order, 278 families, 793 genera, and 2468 species were predicted. The most dominant phyla detected were Proteobacteria, Bacteroidetes, Firmicutes, and Actinobacteria in the soil and compost samples. Whereas, in leachate samples, the predominant phyla belonged to Firmicutes (54.24%) followed by Actinobacteria (43.67%) and Proteobacteria (1.02%). The functional profiling revealed the presence of enzymatic groups and pathways involved in biodegradation of xenobiotics. The results also demonstrated the presence of potential genes that is associated with the biodegradation of different types of plastics such as polyethylene (PE), polyethylene terephthalate (PET), and polystyrene (PS). Present study extablishes the relationship between microbial community structure and rich sources of gene pool, which are actively involved in biodegradation of plastic waste in landfill sites.

Using RNA-Sequencing Data to Examine Tissue-Specific Garlic Microbiomes

Garlic (Allium sativum) is a perennial bulbous plant. Due to its clonal propagation, various diseases threaten the yield and quality of garlic. In this study, we conducted in silico analysis to identify microorganisms, bacteria, fungi, and viruses in six different tissues using garlic RNA-sequencing data. The number of identified microbial species was the highest in inflorescences, followed by flowers and bulb cloves. With the Kraken2 tool, 57% of identified microbial reads were assigned to bacteria and 41% were assigned to viruses. Fungi only made up 1% of microbial reads. At the species level, Streptomyces lividans was the most dominant bacteria while Fusarium pseudograminearum was the most abundant fungi. Several allexiviruses were identified. Of them, the most abundant virus was garlic virus C followed by shallot virus X. We obtained a total of 14 viral genome sequences for four allexiviruses. As we expected, the microbial community varied depending on the tissue types, although there was a dominant microorganism in each tissue. In addition, we found that Kraken2 was a very powerful and efficient tool for the bacteria using RNA-sequencing data with some limitations for virome study.

Tamock: simulation of habitat-specific benchmark data in metagenomics

BACKGROUND

Simulated metagenomic reads are widely used to benchmark software and workflows for metagenome interpretation. The results of metagenomic benchmarks depend on the assumptions about their underlying ecosystems. Conclusions from benchmark studies are therefore limited to the ecosystems they mimic. Ideally, simulations are therefore based on genomes, which resemble particular metagenomic communities realistically.

RESULTS

We developed Tamock to facilitate the realistic simulation of metagenomic reads according to a metagenomic community, based on real sequence data. Benchmarks samples can be created from all genomes and taxonomic domains present in NCBI RefSeq. Tamock automatically determines taxonomic profiles from shotgun sequence data, selects reference genomes accordingly and uses them to simulate metagenomic reads. We present an example use case for Tamock by assessing assembly and binning method performance for selected microbiomes.

CONCLUSIONS

Tamock facilitates automated simulation of habitat-specific benchmark metagenomic data based on real sequence data and is implemented as a user-friendly command-line application, providing extensive additional information along with the simulated benchmark data. Resulting benchmarks enable an assessment of computational methods, workflows, and parameters specifically for a metagenomic habitat or ecosystem of a metagenomic study.

AVAILABILITY

Source code, documentation and install instructions are freely available at GitHub ( https://github.com/gerners/tamock ).

Identification of core and rare species in metagenome samples based on shotgun metagenomic sequencing, Fourier transforms and spectral comparisons

In shotgun metagenomic sequencing applications, low signal-to-noise ratios may complicate species-level differentiation of genetically similar core species and impede high-confidence detection of rare species. However, core and rare species can take pivotal roles in their habitats and should hence be studied as one entity to gain insights into the total potential of microbial communities in terms of taxonomy and functionality. Here, we offer a solution towards increased species-level specificity, decreased false discovery and omission rates of core and rare species in complex metagenomic samples by introducing the rare species identifier (raspir) tool. The python software is based on discrete Fourier transforms and spectral comparisons of biological and reference frequency signals obtained from real and ideal distributions of short DNA reads mapping towards circular reference genomes. Simulation-based testing of raspir enabled the detection of rare species with genome coverages of less than 0.2%. Species-level differentiation of rare Escherichia coli and Shigella spp., as well as the clear delineation between human Streptococcus spp. was feasible with low false discovery (1.3%) and omission rates (13%). Publicly available human placenta sequencing data were reanalysed with raspir. Raspir was unable to identify placental microbial communities, reinforcing the sterile womb paradigm.

Toward Accurate and Robust Environmental Surveillance Using Metagenomics

Environmental surveillance is a critical tool for combatting public health threats represented by the global COVID-19 pandemic and the continuous increase of antibiotic resistance in pathogens. With its power to detect entire microbial communities, metagenomics-based methods stand out in addressing the need. However, several hurdles remain to be overcome in order to generate actionable interpretations from metagenomic sequencing data for infection prevention. Conceptually and technically, we focus on viability assessment, taxonomic resolution, and quantitative metagenomics, and discuss their current advancements, necessary precautions and directions to further development. We highlight the importance of building solid conceptual frameworks and identifying rational limits to facilitate the application of techniques. We also propose the usage of internal standards as a promising approach to overcome analytical bottlenecks introduced by low biomass samples and the inherent lack of quantitation in metagenomics. Taken together, we hope this perspective will contribute to bringing accurate and consistent metagenomics-based environmental surveillance to the ground.

Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.

Metagenomic Approaches to Analyze Antimicrobial Resistance: An Overview

Antimicrobial resistance is a major global public health problem, which develops when pathogens acquire antimicrobial resistance genes (ARGs), primarily through genetic recombination between commensal and pathogenic microbes. The resistome is a collection of all ARGs. In microorganisms, the primary method of ARG acquisition is horizontal gene transfer (HGT). Thus, understanding and identifying HGTs, can provide insight into the mechanisms of antimicrobial resistance transmission and dissemination. The use of high-throughput sequencing technologies has made the analysis of ARG sequences feasible and accessible. In particular, the metagenomic approach has facilitated the identification of community-based antimicrobial resistance. This approach is useful, as it allows access to the genomic data in an environmental sample without the need to isolate and culture microorganisms prior to analysis. Here, we aimed to reflect on the challenges of analyzing metagenomic data in the three main approaches for studying antimicrobial resistance: (i) analysis of microbial diversity, (ii) functional gene analysis, and (iii) searching the most complete and pertinent resistome databases.

The human respiratory tract microbial community structures in healthy and cystic fibrosis infants

The metagenome development of the human respiratory tract was investigated by shotgun metagenome metagenomic sequencing of cough swabs from healthy children and children with cystic fibrosis (CF) between 3 weeks and 6 years of age. A healthy microbial community signature was associated with increased absolute abundances in terms of bacterial–human cell ratios of core and rare species across all age groups, with a higher diversity of rare species and a tightly interconnected species co-occurrence network, in which individual members were found in close proximity to each other and negative correlations were absent. Even without typical CF pathogens, the CF infant co-occurrence network was found to be less stable and prone to fragmentation due to fewer connections between species, a higher number of bridging species and the presence of negative species correlations. Detection of low-abundant DNA of the CF hallmark pathogen Pseudomonas aeruginosa was neither disease- nor age-associated in our cohort. Healthy and CF children come into contact with P. aeruginosa on a regular basis and from early on.