Metagenomic Samples Research Articles

CAIM: coverage-based analysis for identification of microbiome.

Accurate taxonomic profiling of microbial taxa in a metagenomic sample is vital to gain insights into microbial ecology. Recent advancements in sequencing technologies have contributed tremendously toward understanding these microbes at species resolution through a whole shotgun metagenomic approach. In this study, we developed a new bioinformatics tool, coverage-based analysis for identification of microbiome (CAIM), for accurate taxonomic classification and quantification within both long- and short-read metagenomic samples using an alignment-based method. CAIM depends on two different containment techniques to identify species in metagenomic samples using their genome coverage information to filter out false positives rather than the traditional approach of relative abundance. In addition, we propose a nucleotide-count-based abundance estimation, which yield lesser root mean square error than the traditional read-count approach. We evaluated the performance of CAIM on 28 metagenomic mock communities and 2 synthetic datasets by comparing it with other top-performing tools. CAIM maintained a consistently good performance across datasets in identifying microbial taxa and in estimating relative abundances than other tools. CAIM was then applied to a real dataset sequenced on both Nanopore (with and without amplification) and Illumina sequencing platforms and found high similarity of taxonomic profiles between the sequencing platforms. Lastly, CAIM was applied to fecal shotgun metagenomic datasets of 232 colorectal cancer patients and 229 controls obtained from 4 different countries and 44 primary liver cancer patients and 76 controls. The predictive performance of models using the genome-coverage cutoff was better than those using the relative-abundance cutoffs in discriminating colorectal cancer and primary liver cancer patients from healthy controls with a highly confident species markers.

Time-series metagenomics reveals changing protistan ecology of a temperate dimictic lake

BackgroundProtists, single-celled eukaryotic organisms, are critical to food web ecology, contributing to primary productivity and connecting small bacteria and archaea to higher trophic levels. Lake Mendota is a large, eutrophic natural lake that is a Long-Term Ecological Research site and among the world’s best-studied freshwater systems. Metagenomic samples have been collected and shotgun sequenced from Lake Mendota for the last 20 years. Here, we analyze this comprehensive time series to infer changes to the structure and function of the protistan community and to hypothesize about their interactions with bacteria.ResultsBased on small subunit rRNA genes extracted from the metagenomes and metagenome-assembled genomes of microeukaryotes, we identify shifts in the eukaryotic phytoplankton community over time, which we predict to be a consequence of reduced zooplankton grazing pressures after the invasion of a invasive predator (the spiny water flea) to the lake. The metagenomic data also reveal the presence of the spiny water flea and the zebra mussel, a second invasive species to Lake Mendota, prior to their visual identification during routine monitoring. Furthermore, we use species co-occurrence and co-abundance analysis to connect the protistan community with bacterial taxa. Correlation analysis suggests that protists and bacteria may interact or respond similarly to environmental conditions. Cryptophytes declined in the second decade of the timeseries, while many alveolate groups (e.g., ciliates and dinoflagellates) and diatoms increased in abundance, changes that have implications for food web efficiency in Lake Mendota.ConclusionsWe demonstrate that metagenomic sequence-based community analysis can complement existing efforts to monitor protists in Lake Mendota based on microscopy-based count surveys. We observed patterns of seasonal abundance in microeukaryotes in Lake Mendota that corroborated expectations from other systems, including high abundance of cryptophytes in winter and diatoms in fall and spring, but with much higher resolution than previous surveys. Our study identified long-term changes in the abundance of eukaryotic microbes and provided context for the known establishment of an invasive species that catalyzes a trophic cascade involving protists. Our findings are important for decoding potential long-term consequences of human interventions, including invasive species introduction.BaNPxYigHXA7H5uqkH8sryVideo

AFITbin: a metagenomic contig binning method using aggregate l-mer frequency based on initial and terminal nucleotides

BackgroundUsing next-generation sequencing technologies, scientists can sequence complex microbial communities directly from the environment. Significant insights into the structure, diversity, and ecology of microbial communities have resulted from the study of metagenomics. The assembly of reads into longer contigs, which are then binned into groups of contigs that correspond to different species in the metagenomic sample, is a crucial step in the analysis of metagenomics. It is necessary to organize these contigs into operational taxonomic units (OTUs) for further taxonomic profiling and functional analysis. For binning, which is synonymous with the clustering of OTUs, the tetra-nucleotide frequency (TNF) is typically utilized as a compositional feature for each OTU.ResultsIn this paper, we present AFIT, a new l-mer statistic vector for each contig, and AFITBin, a novel method for metagenomic binning based on AFIT and a matrix factorization method. To evaluate the performance of the AFIT vector, the t-SNE algorithm is used to compare species clustering based on AFIT and TNF information. In addition, the efficacy of AFITBin is demonstrated on both simulated and real datasets in comparison to state-of-the-art binning methods such as MetaBAT 2, MaxBin 2.0, CONCOT, MetaCon, SolidBin, BusyBee Web, and MetaBinner. To further analyze the performance of the purposed AFIT vector, we compare the barcodes of the AFIT vector and the TNF vector.ConclusionThe results demonstrate that AFITBin shows superior performance in taxonomic identification compared to existing methods, leveraging the AFIT vector for improved results in metagenomic binning. This approach holds promise for advancing the analysis of metagenomic data, providing more reliable insights into microbial community composition and function.AvailabilityA python package is available at: https://github.com/SayehSobhani/AFITBin.

Comparison of Culture-Dependent and Culture-Independent Methods for Routine Identification of Airborne Microorganisms in Speleotherapeutic Caves.

The effective identification of bacterial and fungal isolates is essential for microbiological monitoring in environments like speleotherapeutic caves. This study compares MALDI-TOF MS and the OmniLog ID System, two high-throughput culture-based identification methods. MALDI-TOF MS identified 80.0% of bacterial isolates to the species level, while the OmniLog ID System identified 92.9%. However, species-level matches between the methods were only 48.8%, revealing considerable discrepancies. For discrepant results, MALDI-TOF MS matched molecular identification at the genus level in 90.5% of cases, while the OmniLog ID System matched only in 28.6%, demonstrating MALDI-TOF MS's superiority. The OmniLog ID System had difficulties identifying genera from the order Micrococcales. Fungal identification success with MALDI-TOF MS was 30.6% at the species level, potentially improvable with a customised spectral library, compared to the OmniLog ID System's 16.7%. Metagenomic approaches detected around 100 times more microbial taxa than culture-based methods, highlighting human-associated microorganisms, especially Staphylococcus spp. In addition to Staphylococcus spp. and Micrococcus spp. as indicators of cave anthropisation, metagenomics revealed another indicator, Cutibacterium acnes. This study advocates a multi-method approach combining MALDI-TOF MS, the OmniLog ID System, culture-based, and metagenomic analyses for comprehensive microbial identification. Metagenomic sampling on nitrocellulose filters provided superior read quality and microbial representation over liquid sampling, making it preferable for cave air sample collection.

Metapresence: a tool for accurate species detection in metagenomics based on the genome-wide distribution of mapping reads.

Shotgun metagenomics allows comprehensive sampling of the genomic information of microbes in a given environment and is a tool of choice for studying complex microbial systems. Mapping sequencing reads against a set of reference or metagenome-assembled genomes is in principle a simple and powerful approach to define the species-level composition of the microbial community under investigation. However, despite the widespread use of this approach, there is no established way to properly interpret the alignment results, with arbitrary relative abundance thresholds being routinely used to discriminate between present and absent species. Such an approach can be affected by significant biases, especially in the identification of rare species. Therefore, it is important to develop new metrics to overcome these biases. Here, we present Metapresence, a new tool to perform reliable identification of the species in metagenomic samples based on the distribution of mapped reads on the reference genomes. The analysis is based on two metrics describing the breadth of coverage and the genomic distance between consecutive reads. We demonstrate the high precision and wide applicability of the tool using data from various synthetic communities, a real mock community, and the gut microbiome of healthy individuals and antibiotic-associated-diarrhea patients. Overall, our results suggest that the proposed approach has a robust performance in hard-to-analyze microbial communities containing contaminated or closely related genomes in low abundance.IMPORTANCEDespite the prevalent use of genome-centric alignment-based methods to characterize microbial community composition, there lacks a standardized approach for accurately identifying the species within a sample. Currently, arbitrary relative abundance thresholds are commonly employed for this purpose. However, due to the inherent complexity of genome structure and biases associated with genome-centric approaches, this practice tends to be imprecise. Notably, it introduces significant biases, particularly in the identification of rare species. The method presented here addresses these limitations and contributes significantly to overcoming inaccuracies in precisely defining community composition, especially when dealing with rare members.

Unveiling genomic features linked to traits of plant growth-promoting bacterial communities from sugarcane

Microorganisms are ubiquitous, and those inhabiting plants have been the subject of several studies. Plant-associated bacteria exhibit various biological mechanisms that enable them to colonize host plants and, in some cases, enhance their fitness. In this study, we describe the genomic features predicted to be associated with plant growth-promoting traits in six bacterial communities isolated from sugarcane. The use of highly accurate single-molecule real-time sequencing technology for metagenomic samples from these bacterial communities allowed us to recover 17 genomes. The taxonomic assignments for the binned genomes were performed, revealing taxa distributed across three main phyla: Bacillota, Bacteroidota, and Pseudomonadota, with the latter being the most representative. Subsequently, we functionally annotated the metagenome-assembled genomes (MAGs) to characterize their metabolic pathways related to plant growth-promoting traits. Our study successfully identified the enrichment of important functions related to phosphate and potassium acquisition, modulation of phytohormones, and mechanisms for coping with abiotic stress. These findings could be linked to the robust colonization of these sugarcane endophytes.

Reference-free structural variant detection in microbiomes via long-read co-assembly graphs.

The study of bacterial genome dynamics is vital for understanding the mechanisms underlying microbial adaptation, growth, and their impact on host phenotype. Structural variants (SVs), genomic alterations of 50 base pairs or more, play a pivotal role in driving evolutionary processes and maintaining genomic heterogeneity within bacterial populations. While SV detection in isolate genomes is relatively straightforward, metagenomes present broader challenges due to the absence of clear reference genomes and the presence of mixed strains. In response, our proposed method rhea, forgoes reference genomes and metagenome-assembled genomes (MAGs) by encompassing all metagenomic samples in a series (time or other metric) into a single co-assembly graph. The log fold change in graph coverage between successive samples is then calculated to call SVs that are thriving or declining. We show rhea to outperform existing methods for SV and horizontal gene transfer (HGT) detection in two simulated mock metagenomes, particularly as the simulated reads diverge from reference genomes and an increase in strain diversity is incorporated. We additionally demonstrate use cases for rhea on series metagenomic data of environmental and fermented food microbiomes to detect specific sequence alterations between successive time and temperature samples, suggesting host advantage. Our approach leverages previous work in assembly graph structural and coverage patterns to provide versatility in studying SVs across diverse and poorly characterized microbial communities for more comprehensive insights into microbial gene flux. rhea is open source and available at: https://github.com/treangenlab/rhea.

Quest for the Nitrogen-Metabolic Versatility of Microorganisms in Soil and Marine Ecosystems.

Whether nitrogen (N)-metabolic versatility is a common trait of N-transforming microbes or if it only occurs in a few species is still unknown. We collected 83 soil samples from six soil types across China, retrieved 19 publicly available metagenomic marine sample data, and analyzed the functional traits of N-transforming microorganisms using metagenomic sequencing. More than 38% and 35% of N-transforming species in soil and marine ecosystems, respectively, encoded two or more N-pathways, although N-transforming species differed greatly between them. Furthermore, in both soil and marine ecosystems, more than 80% of nitrifying and N-fixing microorganisms at the species level were N-metabolic versatile. This study reveals that N-metabolic versatility is a common trait of N-transforming microbes, which could expand our understanding of the functional traits of drivers of nitrogen biogeochemistry.

Thanos: An R Package for the Gene-Centric Analysis of Functional Potential in Metagenomic Samples.

As the amount of metagenomic sequencing continues to increase, there is a growing need for tools that help biologists make sense of the data. Specifically, researchers are often interested in the potential of a microbial community to carry out a metabolic reaction, but this analysis requires knitting together multiple software tools into a complex pipeline. Thanos offers a user-friendly R package designed for the pathway-centric analysis and visualization of the functions encoded within metagenomic samples. It allows researchers to go beyond taxonomic profiles and find out, quantitatively, which pathways are prevalent in an environment, as well as comparing different environments in terms of their functional potential. The analysis is based on the sequencing depth of the genes of interest, either in the metagenome-assembled genomes (MAGs) or in the assembled reads (contigs), using a normalization strategy that enables comparison across samples. The package can import the data from multiple formats and offers functions for the visualization of the results as bar plots of the functional profile, box plots of compare functions across samples, and annotated pathway graphs. By streamlining the analysis of the functional potential encoded in microbial communities, Thanos can enable impactful discoveries in all the fields touched by metagenomics, from human health to the environmental sciences.

Novel metagenomics analysis of stony coral tissue loss disease.

Stony coral tissue loss disease (SCTLD) has devastated coral reefs off the coast of Florida and continues to spread throughout the Caribbean. Although a number of bacterial taxa have consistently been associated with SCTLD, no pathogen has been definitively implicated in the etiology of SCTLD. Previous studies have predominantly focused on the prokaryotic community through 16S rRNA sequencing of healthy and affected tissues. Here, we provide a different analytical approach by applying a bioinformatics pipeline to publicly available metagenomic sequencing samples of SCTLD lesions and healthy tissues from four stony coral species. To compensate for the lack of coral reference genomes, we used data from apparently healthy coral samples to approximate a host genome and healthy microbiome reference. These reads were then used as a reference to which we matched and removed reads from diseased lesion tissue samples, and the remaining reads associated only with disease lesions were taxonomically classified at the DNA and protein levels. For DNA classifications, we used a pathogen identification protocol originally designed to identify pathogens in human tissue samples, and for protein classifications, we used a fast protein sequence aligner. To assess the utility of our pipeline, a species-level analysis of a candidate genus, Vibrio, was used to demonstrate the pipeline's effectiveness. Our approach revealed both complementary and unique coral microbiome members compared to a prior metagenome analysis of the same dataset.

Utilizing co-abundances of antimicrobial resistance genes to identify potential co-selection in the resistome.

Understanding the collateral effects happening in a resistome can reveal previously unknown links between antimicrobial resistance genes (ARGs). Through the analysis of pairwise ARG abundances in 214K metagenomic samples, we observed that the co-abundance is highly dependent on the environmental context and argue that these correlations can be used to show the risk of co-selection occurring in different settings.

Divergent age-associated and metabolism-associated gut microbiome signatures modulate cardiovascular disease risk.

Insight into associations between the gut microbiome with metabolism and aging is crucial for tailoring interventions to promote healthy longevity. In a discovery cohort of 10,207 individuals aged 40-93 years, we used 21 metabolic parameters to classify individuals into five clusters, termed metabolic multimorbidity clusters (MCs), that represent different metabolic subphenotypes. Compared to the cluster classified as metabolically healthy (MC1), clusters classified as 'obesity-related mixed' (MC4) and 'hyperglycemia' (MC5) exhibited an increased 11.1-year cardiovascular disease (CVD) risk by 75% (multivariable-adjusted hazard ratio (HR): 1.75, 95% confidence interval (CI): 1.43-2.14) and by 117% (2.17, 1.72-2.74), respectively. These associations were replicated in a second cohort of 9,061 individuals with a 10.0-year follow-up. Based on analysis of 4,491 shotgun fecal metagenomes from the discovery cohort, we found that gut microbial composition was associated with both MCs and age. Next, using 55 age-specific microbial species to capture biological age, we developed a gut microbial age (MA) metric, which was validated in four external cohorts comprising 4,425 metagenomic samples. Among individuals aged 60 years or older, the increased CVD risk associated with MC4 or MC5, as compared to MC1, MC2 or MC3, was exacerbated in individuals with high MA but diminished in individuals with low MA, independent of age, sex and other lifestyle and dietary factors. This pattern, in which younger MA appears to counteract the CVD risk attributable to metabolic dysfunction, implies a modulating role of MA in cardiovascular health for metabolically unhealthy older people.

GWPD: a multifunctional platform to unravel biological risk factors in global engineered water systems

Engineered water systems such as wastewater treatment plants (WWTPs) are potential reservoirs of various biological risk factors (BRFs), including pathogens, antibiotic resistance genes (ARGs), and virulence factors (VFs). Currently, a BRF database relevant to engineered water systems on a global geographic scale is lacking. Here, we present the Global Wastewater Pathogen Database (GWPD, http://gwpd.hitsz.edu.cn), an online database that provides information on the diversity, abundance, and distribution of BRFs from 1302 metagenome samples obtained from 186 cities, 68 countries, and six continents. We sorted these samples into six types: sewer networks, influent, anoxic activated sludge, oxic activated sludge, effluent, and receiving/natural waters. In total, 476 pathogens, 442 ARGs, and 246 VFs were identified. As a multifunctional database, GWPD provides an interactive visualization of these BRFs in a world map, an information retrieval interface, and an online one-click service for BRF annotation from metagenome sequencing data. GWPD is built on a web service framework, which can be readily extended to future versions of GWPD by adding more functional modules and connecting to other data sources, such as epidemic databases, to support risk assessment and control in the context of “One Health.”

Global compositional and functional states of the human gut microbiome in health and disease.

The human gut microbiota is of increasing interest, with metagenomics a key tool for analyzing bacterial diversity and functionality in health and disease. Despite increasing efforts to expand microbial gene catalogs and an increasing number of metagenome-assembled genomes, there have been few pan-metagenomic association studies and in-depth functional analyses across different geographies and diseases. Here, we explored 6014 human gut metagenome samples across 19 countries and 23 diseases by performing compositional, functional cluster, and integrative analyses. Using interpreted machine learning classification models and statistical methods, we identified Fusobacterium nucleatum and Anaerostipes hadrus with the highest frequencies, enriched and depleted, respectively, across different disease cohorts. Distinct functional distributions were observed in the gut microbiomes of both westernized and nonwesternized populations. These compositional and functional analyses are presented in the open-access Human Gut Microbiome Atlas, allowing for the exploration of the richness, disease, and regional signatures of the gut microbiota across different cohorts.

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.

Deciphering Microbial Shifts in the Gut and Lung Microbiomes of COVID-19 Patients.

COVID-19, caused by SARS-CoV-2, results in respiratory and cardiopulmonary infections. There is an urgent need to understand not just the pathogenic mechanisms of this disease but also its impact on the physiology of different organs and microbiomes. Multiple studies have reported the effects of COVID-19 on the gastrointestinal microbiota, such as promoting dysbiosis (imbalances in the microbiome) following the disease's progression. Deconstructing the dynamic changes in microbiome composition that are specifically correlated with COVID-19 patients remains a challenge. Motivated by this problem, we implemented a biomarker discovery pipeline to identify candidate microbes specific to COVID-19. This involved a meta-analysis of large-scale COVID-19 metagenomic data to decipher the impact of COVID-19 on the human gut and respiratory microbiomes. Metagenomic studies of the gut and respiratory microbiomes of COVID-19 patients and of microbiomes from other respiratory diseases with symptoms similar to or overlapping with COVID-19 revealed 1169 and 131 differentially abundant microbes in the human gut and respiratory microbiomes, respectively, that uniquely associate with COVID-19. Furthermore, by utilizing machine learning models (LASSO and XGBoost), we demonstrated the power of microbial features in separating COVID-19 samples from metagenomic samples representing other respiratory diseases and controls (healthy individuals), achieving an overall accuracy of over 80%. Overall, our study provides insights into the microbiome shifts occurring in COVID-19 patients, shining a new light on the compositional changes.

Global soil metagenomics reveals distribution and predominance of Deltaproteobacteria in nitrogen-fixing microbiome

BackgroundBiological nitrogen fixation is a fundamental process sustaining all life on earth. While distribution and diversity of N2-fixing soil microbes have been investigated by numerous PCR amplicon sequencing of nitrogenase genes, their comprehensive understanding has been hindered by lack of de facto standard protocols for amplicon surveys and possible PCR biases. Here, by fully leveraging the planetary collections of soil shotgun metagenomes along with recently expanded culture collections, we evaluated the global distribution and diversity of terrestrial diazotrophic microbiome.ResultsAfter the extensive analysis of 1,451 soil metagenomic samples, we revealed that the Anaeromyxobacteraceae and Geobacteraceae within Deltaproteobacteria are ubiquitous groups of diazotrophic microbiome in the soils with different geographic origins and land usage types, with particular predominance in anaerobic soils (paddy soils and sediments).ConclusionOur results indicate that Deltaproteobacteria is a core bacterial taxon in the potential soil nitrogen fixation population, especially in anaerobic environments, which encourages a careful consideration on deltaproteobacterial diazotrophs in understanding terrestrial nitrogen cycling.D657cgwaNSF-wLxbnJEePrVideo

PGPg_finder: A comprehensive and user-friendly pipeline for identifying plant growth-promoting genes in genomic and metagenomic data

Identifying and comparing plant growth-promoting traits (PGPT) within whole-genome and metagenomic sequencing data can significantly advance agricultural research and promote sustainable crop production. This study introduces PGPg_finder, a comprehensive pipeline designed to annotate and compare PGPT from both whole-genome and metagenome sequencing datasets. This pipeline utilizes direct sequence annotation alongside de novo assembly methods to accurately detect PGPT. By cross-referencing sequences from the PLaBAse database, it identifies and quantifies the presence of these genes within the original datasets, facilitating an intuitive comparison of the abundance and distribution of PGPT across various samples. We evaluated the performance of PGPg_finder by analyzing genomes from five rhizobacterial strains: Paenibacillus vini, Paenibacillus polymyxa, Fictibacillus sp., Brevibacillus agri, and Bacillus cereus, and also metagenomic samples from bulk soils subjected to forest-to-pasture conversion in the Amazon rainforest. The genomic workflow revealed several genes associated with substrate utilization, abiotic stress neutralization, phosphate solubilization, and iron acquisition. It also identified genes unique to specific lineages, including those associated with colonization and plant-derived substrate usage in P. polymyxa, quorum sensing response and biofilm formation in P. vini, heavy metal detoxification and nitrogen acquisition in B. agri, and spore production and neutralizing biotic stress in B. cereus. The strain Fictibacillus sp. presented several unique genes related to surface attachment, stress response, xenobiotic degradation, phosphate solubilization, and phytohormone production. The use of PGPg_finder highlights its potential to uncover novel inoculants and strains. The metagenomic workflow distinguished plant-growth promotion gene profiles between soils from the Amazon rainforest and pasture, with the latter showing a profile more aligned with simple carbohydrate consumption, abiotic stress tolerance, motility and chemotaxis, and phosphorus mineralization. Native forests exhibited a profile associated with the degradation of complex organic matter, oxidative stress tolerance, xenobiotic degradation, bactericidal activity, iron acquisition, and volatile pathways. These findings underscore the effectiveness and sensitivity of PGPg_finder in accurately identifying and comparing PGPT genes, highlighting both commonalities and variations across samples. The application of this pipeline has the potential to significantly facilitate the identification of plant growth-promoting microbes.

Interpretable metric learning in comparative metagenomics: The adaptive Haar-like distance.

Random forests have emerged as a promising tool in comparative metagenomics because they can predict environmental characteristics based on microbial composition in datasets where β-diversity metrics fall short of revealing meaningful relationships between samples. Nevertheless, despite this efficacy, they lack biological insight in tandem with their predictions, potentially hindering scientific advancement. To overcome this limitation, we leverage a geometric characterization of random forests to introduce a data-driven phylogenetic β-diversity metric, the adaptive Haar-like distance. This new metric assigns a weight to each internal node (i.e., split or bifurcation) of a reference phylogeny, indicating the relative importance of that node in discerning environmental samples based on their microbial composition. Alongside this, a weighted nearest-neighbors classifier, constructed using the adaptive metric, can be used as a proxy for the random forest while maintaining accuracy on par with that of the original forest and another state-of-the-art classifier, CoDaCoRe. As shown in datasets from diverse microbial environments, however, the new metric and classifier significantly enhance the biological interpretability and visualization of high-dimensional metagenomic samples.

MASH-Ocean 1.0: Interactive platform for investigating microbial diversity, function, and biogeography with marine metagenomic data.

A large number of oceanic metagenomic data and environmental metadata have been published. However, most studies focused on limited ecosystems using different analysis tools, making it challenging to integrate these data into robust results and comprehensive global understanding of marine microbiome. Here, we constructed a systematic and quantitative analysis platform, the Microbiome Atlas/Sino-Hydrosphere for Ocean Ecosystem (MASH-Ocean: https://www.biosino.org/mash-ocean/), by integrating global marine metagenomic data and a unified data processing flow. MASH-Ocean 1.0 comprises 2147 metagenomic samples with five analysis modules: sample view, diversity, function, biogeography, and interaction network. This platform provides convenient and stable support for researchers in microbiology, environmental science, and biogeochemistry, to ensure the integration of omics data generated from hydrosphere ecosystems, to bridge the gap between elusive omics data and biological, ecological, and geological discovery, ultimately to foster the formation of a comprehensive atlas for aquatic environments.