iMeta

Accurate species identification and abundance estimation are critical for the interpretation of whole metagenome shotgun sequencing (WMS) data. Numerous computational methods, broadly referred to as metagenomic profilers, have been developed to identify species in microbiome samples by classification of sequencing reads and quantification of their relative abundances. Yet, existing metagenomic profilers typically suffer from false positive identifications and consequently biased relative abundance estimation (as false positives can be accounted for more than 90% of total identified species). Here, we present a new metagenomic profiler MAP2B (MetAgenomic Profiler based on type IIB restriction site) to resolve those issues. We first illustrate the pitfalls of using relative abundance as the only feature in determining false positives. We then propose a feature set to distinguish false positives from true positives. By benchmarking the performance in metagenomic profiling using data from CAMI2 (Critical Assessment of Metagenome Interpretation: second round of challenge), we illustrate the superior performance of MAP2B (F1 score ~ 0.93) over existing metagenomic profilers (F1 score ranges from 0.18 to 0.58). We further tested the performance of MAP2B using real WMS data from an ATCC mock community, confirming its superior performance and robustness against sequencing depth. In addition, by leveraging WMS data from an IBD cohort, we demonstrate the taxonomic features obtained by MAP2B can better discriminate disease status and predict metabolomic profiles.

ObjectiveKeystone species are required for the integrity and stability of an ecological community, and therefore, are potential intervention targets for microbiome related diseases. DesignHere we describe an algorithm for the identification of keystone species from cross-sectional microbiome data of non-alcoholic fatty liver disease (NAFLD) based on causal inference theories and dynamic intervention modeling (DIM). ResultsEight keystone species in the gut of NAFLD, represented by P. loveana, A. indistinctus and D. pneumosintes, were identified by our algorithm, which could efficiently restore the microbial composition of the NAFLD toward a normal gut microbiome with 92.3% recovery. These keystone species regulate intestinal amino acids metabolism and acid-base environment to promote the growth of the butyrate-producing Lachnospiraceae and Ruminococcaceae species. ConclusionOur method may benefit microbiome studies in the broad fields of medicine, environmental science and microbiology. SUMMARYWhat is already known about this subject? O_LINon-alcoholic fatty liver disease (NAFLD) is a complex multifactorial disease whose pathogenesis remains unclear. C_LIO_LIDysbiosis in the gut microbiota affects the initiation and development of NAFLD, but the mechanisms is yet to be established. C_LIO_LIKeystone species represent excellent candidate targets for gut microbiome-based interventions, as they are defined as the species required for the integrity and stability of the ecological system. C_LI What are the new findings? O_LINAFLD showed significant dysbiosis in butyrate-producing Lachnospiraceae and Ruminococcaceae species. C_LIO_LIMicrobial interaction networks were constructed by the novel algorithm with causal inference. C_LIO_LIKeystone species were identified form microbial interaction networks through dynamic intervention modeling based on generalized Lotka-Volterra model. C_LIO_LIEight keystone species of NAFLD with the highest potential for restoring the microbial composition were identified. C_LI How might it impact on clinical practice in the foreseeable future? O_LIAn algorithm for the identification of keystone species from cross-sectional microbiome data based on causal inference theories and dynamic intervention modeling. C_LIO_LIEight keystone species in the gut of NAFLD, represented by P. loveana, A. indistinctus and D. pneumosintes, which could efficiently restore the microbial composition of the NAFLD toward a normal gut microbiome. C_LIO_LIOur method may benefit microbiome studies in the broad fields of medicine, environmental science and microbiology. C_LI

BackgroundFecal microbiota transplantation (FMT) may treat microbiome-associated diseases effectively. However, the mechanism and pattern of the FMT process require expositions. Previous studies indicated the necessity to track the FMT process at the microbial strain level. At this moment, shotgun metagenomic sequencing enables us to study strain variations during the FMT. ResultWe implemented a software package PStrain-tracer to study microbial strain variations during FMT from the shotgun metagenomic sequencing data. The package visualizes the strain alteration and traces the microbial engraftment during the FMT process. We applied the package to two typical FMT datasets: one ulcerative colitis (UC) dataset and one Clostridium difficile infection (CDI) dataset. We observed that when the engrafted species has more than one strain in the source sample, 99.3% of the engrafted species will engraft only a subset of strains. We further confirmed that the all-or-nothing manner unsuited the engraftment of species with multiple strains by heterozygous single-nucleotide polymorphisms (SNPs) count, revealing that strains prefer to engraft independently. Furthermore, we discovered a primary determinant of strain engrafted success is their proportion in species, as the engrafted strains from the donor and the pre-FMT recipient with proportions 33.10 % (p-value = 6e - 06) and 37.08 % (p-value = 9e - 05) significantly higher than ungrafted strains on average, respectively. All the datasets indicated that the diversity of strains bursts after FMT and decreases to one after eight weeks for twelve species. Previous studies neglected strains with their corresponding species showing insignificant differences between different samples. With the package, from the UC dataset, we successfully determined the strain variations of the species Roseburia intestinalis, a beneficial species reducing intestinal inflammation, colonized in the cured UC patient being engrafted from the donor, even if the patient hosted the same species yet before treatment. We found seven strains in donors from the CDI dataset and one strain in pre-FMT recipients from eight species that associated CDI FMT failure. ConclusionPStrain-tracer is the first framework that tracks strain alterations in metagenomic sequencing data of FMT. PStrain-tracer implemented several methods specialized for FMT experiment samples, such as visualization of strains abundance alteration in the FMT experiment and determinant strains detection in FMT failure. We applied PStrain-tracer on two published datasets, uncovered novel strains related to FMT failure, and demonstrated the necessity of analyzing the whole-genome shotgun metagenomic data of FMT at the strain level. We also developed an online visualizer of PStrain-tracer for the users to adjust their visualized results online. The package is available at https://github.com/deepomicslab/PStrain-tracer.

BackgroundThe human gut microbiome is associated with numerous human diseases. The whole-genome shotgun metagenomics sequencing helps accumulate a massive amount of gut microbiome data. However, few curated integrated platforms are available to explore the vast dataset. Advances in data generation pose new challenges to researchers attempting to analyze, visualize, and reuse published data. ResultGutMeta (human GUT whole-genome shotgun METAgenomics data analysis platform) is a one-stop online human gut metagenomic research platform that integrates a curated database, analyses, and visualizations. First, we built the Human Gut Metagenomics Database (HGMD), which contained taxonomy profiling and metadata of the metagenomics. HGMD collected the published human gut microbiome samples with whole metagenome shotgun (WMGS) sequencing data and consistently performed taxonomy classification using MetaPhlan3 for each sample. The various related metadata information was curated, and phenotypes were according to the MeSH ID. At this moment, HGMD contains 20,898 samples from 91 projects related to 65 diseases. Embedded tools could help users to explore the samples by keywords. Second, GutMeta provides researchers with user-friendly metagenomics analysis modules, including community diversity calculation, differential testing, dimension reduction, disease classifier construction, etc. Then, GutMeta provides corresponding interactive visualizations which can download as Scalable Vector Graphics (SVG), providing high-quality images. Further, GutMeta supplies two additional visualizations for the multi-level taxonomy overview for advanced investigations. GutMeta also supports online editing, including attribute adjustment, recoloring, reordering, and drag-and-drop. Third, GutMeta supports users in building their metagenomics analysis workspaces, including standard profiles uploading and built-in HGMD data import for online customized analyses and visualization. ConclusionGutMeta offers a solution to improve reproducibility in metagenomic research, with the standardized procedure from input data to downstream analysis and visualization. GutMeta is a free access analysis platform that integrates human gut WMGS sequencing data, nine online bioinformatics analysis and data visualization modules/pipelines, and a customized workspace. GutMeta is avaiable at https://GutMeta.deepomics.org.

The human gut microbiome data has been proven to be a powerful tool to understand the human body in both health and disease conditions. However, understanding their complex interactions and impact on the human body remains a challenging task. Unravelling the species-level interactions could allow us to study the causality of the microbiome. Moreover, it could lead us to better understand the underlying mechanisms of complex diseases and, subsequently, the discovery of new therapeutic targets. Given these challenges and benefits, it has become evident that a freely accessible and centralized platform for presenting gut microbiome interaction is essential to untangle the complexity and open multiple new paths and opportunities in disease- and drug-related research. Here, we present GutMicroNet, an interactive visualization platform of human gut microbiome interaction networks. We generated 45 gut microbiome co-abundance networks from various geographical origins, gender, and diseases based on the data presented in the Human Gut Microbiome Atlas. This interactive platform includes more than 1900 gut microbiome species and allows users to query multiple species at the same time based on their interests and adjust it based on the statistical properties. Moreover, users can download publication-ready figures or network information for further analysis. The platform can be accessed freely on https://gutmicro.net without any login requirements or limitations, including access to the full networks data.

Metagenomic profiling has advanced understanding of microbe-host interactions. However, widely used read-based approaches are limited by incomplete reference databases and the inability to resolve strain-level variation. Here, we present a scalable, genome-resolved framework that integrates population-specific metagenome assembled genomes (MAGs) to discover novel species, sub-species diversity, and disease associations. From 1,878 deeply sequenced samples in the Estonian microbiome cohort (EstMB-deep), we reconstructed 84,762 MAGs representing 2,257 species, including 353 (15.6%) previously uncharacterized species reaching up to 30% relative abundances in some individuals. We integrated these MAGs with the Unified Human Gastrointestinal Genome (UHGG) collection to create an expanded reference (GUTrep), enabling profiling of 2,509 EstMB individuals and testing associations with 33 prevalent diseases. Of 25 diseases with significant associations, 8 involved newly identified species, underscoring the value of population-specific MAGs. To quantify within-species diversity, we developed the Genome Unit Number (GUN), a novel MAG-based metric that informed sub-species analyses. Based on normalized GUN (nGUN), we prioritized Odoribacter splanchnicus, a prevalent species with the lowest sub-species heterogeneity, yielding sufficient power for sub-species association study. We identified two dominant genome units, GU-N1 and GU-N2, with distinct gene repertoires and divergent disease associations. Notably, GU-N1 was negatively associated with gastritis and duodenitis and hypertensive heart disease, associations undetected at the species level. Our study expands the human gut reference landscape, demonstrates the importance of population-specific MAGs for uncovering novel microbial diversity, and reveals new disease associations on sub-species level obscured at higher taxonomic levels, highlighting the need for genome-resolved approaches in microbiome research. IMPORTANCEMicrobiome studies increasingly recognize that species-level profiles can mask critical sub-species differences relevant to health and disease. However, our work shows that within-species diversity varies drastically across gut microbes, with some species exhibiting almost as many distinct sub-species clusters as recovered genomes, making association studies at the sub-species level essentially intractable. To address this, we introduce the Genome Unit Number (GUN), a scalable metric for quantifying sub-species structure. Using GUN, we demonstrate that only species with limited within-species diversity, such as Odoribacter splanchnicus, currently allow for robust sub-species association testing. These findings emphasize the need to systematically evaluate species structure across the gut microbiome and call for the development of new computational and statistical approaches to enable meaningful sub-species analyses in highly diverse species.

The prevailing taxonomic profiling methods for an environmental sample rely heavily on PCR amplification of SSU ribosomal RNA (rRNA) genes and genome-based reference databases. Identification and extraction of Illumina metagenomics sequencing data are PCR independent but technically challenging in recognition of the SSU rRNA fragments. Here we present Mitag4taxa, a computational pipeline designed for taxonomic profiling of microbial communities from metagenomic Illumina sequencing reads containing rRNA tags (mitag). A Hidden Markov Model (HMM) of SSU rRNA genes and those for the V4 region of 16S rRNA and the V9 region of 18S rRNA genes were created, respectively, using the representative sequences of different families and corresponding hypervariable regions in the SILVA database. The pipeline identifies and extracts 16S and 18S rRNA gene fragments along with the quality score from metagenomic or metatranscriptomic datasets using HMM search integrated with the models. The hypervariable regions, including the V4 region of 16S rRNA and the V9 region of 18S rRNA genes, can be further scanned and recruited for taxonomic classification and biodiversity estimate. To demonstrate its high reliability, the performance of Mitag4taxa was evaluated using both real and simulated datasets. In human gut metagenomic assessments, taxonomic profiles derived from Mitag4taxa showed high consistency with those based on conventional 16S rRNA gene amplicons, identifying dominant families such as Bacteroidaceae and Prevotellaceae with similar relative abundances. Statistical analyses confirmed highly significant positive correlations between Mitag4taxa and amplicon-based community structures. The 18S V9 module was further validated using shotgun metagenomic data from deep-sea sediment cores, successfully recovering key eukaryotic taxa such as Collodaria and Leotiomycetes. Furthermore, benchmarking against the RiboTagger software using CAMI marine simulated datasets revealed that Mitag4taxa achieved a higher average F1 score and lower error metrics. Overall, Mitag4taxa provides a complementary rRNA gene amplicon- and genome-independent strategy for microbial community profiling, enabling improved detection of both prokaryotic and eukaryotic taxa from metagenomic and metatranscriptomic sequencing data.

Microbiome samples are accumulating at a fast speed, leading to millions of accessible microbiome samples in the public databases. However, due to the lack of strict meta-data standard for data submission and other reasons, there is currently a non-neglectable proportion of microbiome samples in the public database that have no annotations about where these samples were collected, how they were processed and sequenced, etc., among which the missing information about collection niches (biome) is one of the most prominent. The lack of sample biome information has created a bottleneck for mining of the microbiome data, making it difficult in applications such as sample source tracking and biomarker discovery. Here we have designed Meta-Sorter, a neural network and transfer learning enabled AI method for improving the biome labeling of thousands of microbial community samples without detailed biome information. Results have shown that out of 16,507 samples that have no detailed biome annotations, 96.65% could be correctly classified, largely solving the missing biome labeling problem. Interestingly, we succeeded in classify 250 samples, which were sampled from benthic and water column but vaguely labeled as "Marine" in MGnify, in more details and with high fidelity. Whats more, many of successfully predicted sample labels were from studies that involved human-environment interactions, for which we could also clearly differentiated samples from environment or human. Taken together, we have improved the completeness of biome label information for thousands of microbial community samples, facilitating sample classification and knowledge discovery from millions of microbiome samples.

With the development of sequencing technologies and computational analysis in metagenomics, the genetic diversity of non-conserved regions has been receiving intensive attention to unravel the human gut microbial community. However, it remains a challenge to obtain enough microbial draft genomes at a high resolution from a single sample. In this work, we presented MetaTrass with a strategy of binning first and assembling later to assemble high-quality draft genomes based on metagenomics co-barcoding reads and the public reference genomes. We applied the tool to the single tube long fragment reads datasets for four human faecal samples, and generated more high-quality draft genomes with longer contiguity and higher resolution than the common combination strategies of genome assembling and binning. A total of 178 high-quality genomes was successfully assembled by MetaTrass, but the maximum of 58 was generated by the optimal common combination strategy in our tests. These high-quality genomes paved the way for genetic diversity and lineage analysis among different samples. With the high capability of assembling high-quality genomes of metagenomics datasets, MetaTrass will facilitate the study of spatial characters and dynamics of complex microbial communities at high resolution. The open-source code of MetaTrass is available at https://github.com/BGI-Qingdao/MetaTrass.

Viruses of prokaryotes are extremely abundant and diverse. Culture-independent approaches have recently shed light on the biodiversity these biological entities1,2. One fundamental question when trying to understand their ecological roles is: which host do they infect? To tackle this issue we developed a machine-learning approach named Random Forest Assignment of Hosts (RaFAH), based on the analysis of nearly 200,000 viral genomes. RaFAH outperformed other methods for virus-host prediction (F1-score = 0.97 at the level of phylum). RaFAH was applied to diverse datasets encompassing genomes of uncultured viruses derived from eight different biomes of medical, biotechnological, and environmental relevance, and was capable of accurately describing these viromes. This led to the discovery of 537 genomic sequences of archaeal viruses. These viruses represent previously unknown lineages and their genomes encode novel auxiliary metabolic genes, which shed light on how these viruses interfere with the host molecular machinery. RaFAH is available at https://sourceforge.net/projects/rafah/.

Metagenome sequencing has revolutionized functional microbiome analysis across diverse ecosystems, but is fraught with technical hurdles. We introduce MOSHPIT (https://moshpit.readthedocs.io), software built on the QIIME 2 framework (Q2F) that integrates best-in-class CAMI2-validated metagenome tools with robust provenance tracking and multiple user interfaces, enabling streamlined, reproducible metagenome analysis for all expertise levels. By building on Q2F, MOSHPIT enhances scalability, interoperability, and reproducibility in complex workflows, democratizing and accelerating discovery at the frontiers of metagenomics.

Specific roles of gut microbes in COVID-19 progression are critical. However, the circumstantial mechanism remains elusive. In this study, shotgun metagenomic or metatranscriptomic sequencing were performed on fecal samples collected from 13 COVID-19 patients and controls. We analyzed the structure of gut microbiota, identified the characteristic bacteria and selected biomarkers. Further, GO, KEGG and eggNOG annotation were employed to correlate the taxon alteration and corresponding functions. The gut microbiota of COVID-19 patients was characterized by the enrichment of opportunistic pathogens and depletion of commensals. The abundance of Bacteroides spp. displayed an inverse relationship to COVID-19 severity, whereas Actinomyces oris, Escherichia coli, and Gemmiger formicilis were positively correlated with disease severity. The genes encoding oxidoreductase were significantly enriched in SARS-CoV-2 infection. KEGG annotation indicated that the expression of ABC transporter was up regulated, while the synthesis pathway of butyrate was aberrantly reduced. Furthermore, increased metabolism of lipopolysaccharide, polyketide sugar, sphingolipids and neutral amino acids was found. These results suggested the gut microbiome of COVID-19 patients was correlated with disease severity and in a state of excessive inflammatory response. Healthy gut microbiota may enhance antiviral defenses via butyrate metabolism, whereas the accumulation of opportunistic and inflammatory bacteria may exacerbate the disease progression.

Comprehensive metagenomic sequence classification of diverse environmental samples faces significant computing memory challenges due to exponentially expanding genome databases. Here, we present Kun-peng, featuring a unique ordered 4GB block database design for ultra-efficient resource management, faster processing, and higher accuracy. When benchmarked on mock communities (Amos HiLo, Mixed, and NIST) against Kraken2, Centrifuge, and Sylph. Kun-peng matched Sylph, achieving the highest precision and lowest false-positive rates while demonstrating superior time and memory efficiency among all tested tools. Furthermore, Kun-pengs efficient database architecture enables the practical utilization of large-scale reference databases that were previously computationally prohibitive. In comprehensive testing across 586 air, water, soil, and human metagenomic samples using an expansive pan-domain database (204,477 genomes, 4.3TB), Kun-peng classified 69.78-94.29% of reads, achieving 38-43% higher classification rates than Kraken2 with the standard database. Unexpectedly, Sylph failed to classify any reads in air samples and left > 99.85% of reads unclassified in water and soil samples. In terms of computational efficiency, Kun-peng processed each sample in 0.2[~]11.2 minutes using only 4.0[~]35.4GB peak memory. Remarkably, these processing times were comparable to Kraken2 using the standard database (81GB, 5% of the pan-domain database). Memory-wise, Kun-peng required only 35.4GB peak memory with the pan-domain database, representing a 473-fold reduction compared to Kraken2. When compared to Sylph, Kun-peng processes samples up to 46.3 times faster while using up to 20.6 times less memory. Overall, Kun-peng offers an ultra-memory-efficient, fast, and accurate solution for pan-domain metagenomic classifications.

Microbial source tracking quantifies the potential origin of microbial communities, facilitates better understanding of how the taxonomic structure and community functions were formed and maintained. However, previous methods involve a tradeoff between speed and accuracy, and have encountered difficulty in source tracking under many context-dependent settings. Here, we present EXPERT for context-aware microbial source tracking, in which we adopted a Transfer Learning approach to profoundly elevate and expand the applicability of source tracking, enabling biologically informed novel microbial knowledge discovery. We demonstrate that EXPERT can predict microbial sources with performance superior to other methods in efficiency and accuracy. More importantly, we demonstrate EXPERTs context-aware ability on several applications, including tracking the progression of infant gut microbiome development and monitoring the changes of gut microbiome for colorectal cancer patients. Broadly, transfer learning enables accurate and context-aware microbial source tracking and has the potential for novel microbial knowledge discovery.

Invertible promoters (invertons) are regulatory elements found in bacteria, with inverted repeat sequences at both ends, leading to alternating changes in the expression of the regulated genes. Since invertons were present in more than 20% of bacterial genomes, while they regulated more than 5% of genes in these genomes, they are of pivotal importance for microbial functional dynamics especially when under stress. However, the prevalence of invertons, as well as the full spectrum of gene functions regulated by them, remain poorly understood. In this study, we developed DeepInverton, a deep learning model capable of accurately identifying novel inverton sequences without sequencing reads, which could profile inverton sequences from large genomic and metagenomic datasets. We conducted a pan-genomic and pan-metagenomic analysis of invertons on 68,969 bacterial genomes and 8,516 metagenome samples, resulting in a comprehensive overview of more than 200,000 nonredundant invertons and their regulated gene functional patterns. This result suggests that invertons, as a key player for bacterial adaptation to environmental stresses, are prevalent in bacterial genomes. Among the genomes analyzed, we observed a profound enrichment of invertons in pathogen such as Bordetella pertussis, and discovered a significant increase of inverton enrichment rates in strains associated with recent pertussis outbreaks, as well as novel evolving strains, unveiling a hidden link between the evolution of Bordetella pertussis and its inverton enrichment. We also utilized DeepInverton to explore inverton profiles mong human and marine metagenomes. Results revealed an unprecedented diversity of functional genes regulated by invertons, including antimicrobial resistance, biofilm formation and flagella, indicating their potential role in facilitating environmental adaptation. The in vitro experiments have confirmed the functions of tens of novel invertons that we have identified. Overall, we developed the DeepInverton model for exploration of invertons at unprecedented scale, which enabled our comprehensive profiling of invertons and their regulated genes. The comprehensive inverton profiles have deepen our understanding of invertons at pan-genome and metagenome scale, and could enabled a broad spectrum of inverton-related applications in microbial ecology and synthetic biology.

Microbial communities exert a substantial influence on human health and have been unequivocally associated with a spectrum of human maladies, encompassing conditions such as anxiety1, depression2, hypertension3, cardiovascular diseases4, obesity4,5, diabetes6, inflammatory bowel disease7, and cancer8,9. This intricate interplay between microbiota community structures and host pathophysiology has kindled substantial interest and spurred active research endeavors across various scientific domains. Despite significant strides in sequencing technologies, which have unveiled the vast diversity of microbial populations across diverse ecosystems, the analysis of microbiome data remains a formidable challenge. The complexity inherent in such data, compounded by the absence of standardized data processing and analysis workflows, continues to pose substantial hurdles. The tidyverse paradigm, comprised of a suite of R packages meticulously crafted to facilitate efficient data manipulation and visualization, has garnered considerable acclaim within the data science community10. Its appeal stems from its innate simplicity and efficacy in organizing and processing data10. In recent times, a plethora of tools have been devised to address distinct omics data processing and analysis needs, including notable initiatives such as the tidymass project11, tidyomics project12, tidymicro13, and MicrobiotaProcess13,14. However, a conspicuous gap persists in the form of a standardized, tidyverse-based package for seamless and rigorous microbiome data processing and analysis. To address this burgeoning demand for standardized and reproducible microbiome data analysis, we introduce microbiomedataset, an R package that embraces the tidyverse ethos to furnish a structured framework for the organization and processing of microbiome data. Microbiomedataset offers a comprehensive, customizable solution for the management, structuring, and processing of microbiome data. Importantly, this package seamlessly integrates with established bioinformatics tools, facilitating its incorporation into existing analytical pipelines11,13,14,15. Within this manuscript, we proffer an in-depth overview of the microbiomedataset package, elucidating its multifarious functionalities. Moreover, we substantiate its utility through illustrative case studies employing a publicly available microbiome dataset. It is imperative to underscore that microbiomedataset constitutes an integral component of the larger tidymicrobiome project, accessible via www.tidymicrobiome.org. Tidymicrobiome epitomizes an ecosystem of R packages that share a coherent design philosophy, grammar, and data structure, collectively engendering a robust, reproducible, and object-oriented computational framework. This project's development has been guided by several key tenets: (1) Cross-platform compatibility, (2) Uniformity, shareability, traceability, and reproducibility, and (3) Flexibility and extensibility. We further expound upon the advantages inherent in adopting a tidyverse-style framework for microbiome data analysis, underscoring the pronounced benefits in terms of standardization and reproducibility that microbiomedataset offers. In sum, microbiomedataset furnishes an accessible and efficient avenue for microbiome data analysis, catering to both neophyte and seasoned R users alike.

MotivationMicrobial community samples and sequencing data have been accumulated at a speed faster than ever, with tens of thousands of samples been sequenced each year. Mining such a huge amount of multi-source heterogeneous data is becoming more and more difficult. Among several sample mining bottlenecks, efficient and accurate search of samples is one of the most prominent: Faced with millions of samples in the data repository, traditional sample comparison and search approaches fall short in speed and accuracy. ResultsHere we proposed Meta-Prism 2.0, a microbial community sample search method based on smart pair-wise sample comparison, which pushed the time and memory efficiency to a new limit, without the compromise of accuracy. Based on memory-saving data structure, time-saving instruction pipeline, and boost scheme optimization, Meta-Prism 2.0 has enabled ultra-fast, accurate and memory-efficient search among millions of samples. Meta-Prism 2.0 has been put to test on several datasets, with largest containing one million samples. Results have shown that firstly, as a distance-based method, Meta-Prism 2.0 is not only faster than other distance-based methods, but also faster than unsupervised methods. Its 0.00001s per sample pair search speed, as well as 8GB memory needs for searching against one million samples, have enabled it to be the most efficient method for sample comparison. Additionally, Meta-Prism 2.0 could achieve the comparison accuracy and search precision that are comparable or better than other contemporary methods. Thirdly, Meta-Prism 2.0 can precisely identify the original biome for samples, thus enabling sample source tracking. ConclusionIn summary, Meta-Prism 2.0 can perform accurate searches among millions of samples with very low memory cost and fast speed, enabling knowledge discovery from samples at a massive scale. It has changed the traditional resource-intensive sample comparison and search scheme to a cheap and effective procedure, which could be conducted by researchers everyday even on a laptop, for insightful sample search and knowledge discovery. Meta-Prism 2.0 could be accessed at: https://github.com/HUST-NingKang-Lab/Meta-Prism-2.0.

Information extracted from microbiome sequences through deep-learning techniques can significantly improve protein structure and function modeling. However, the model training and metagenome search were largely blind with low efficiency. Built on 4.25 billion microbiome sequences from four major biomes (Gut, Lake, Soil and Fermentor), we proposed a MetaSource model to decode the inherent link of microbial niches with protein homologous families. Large-scale protein family folding experiments showed that a targeted approach using predicted biomes significantly outperform combined metagenome datasets in both speed of MSA collection and accuracy of deep-learning structure assembly. These results revealed the important link of biomes with protein families and provided a useful bluebook to guide future microbiome sequence database and modeling development for protein structure and function prediction.

Bacterial pathogens spread in clinical and environmental settings, and mobile genetic elements (MGEs), such as plasmids and phages, mediate the transfer of virulence factor genes (VFGs) and antimicrobial resistance genes (ARGs) among bacterial communities. Metagenomic analysis of environmental and wastewater samples using highly accurate long-read sequencing technologies, such as PacBio HiFi sequencing, provides valuable insights into monitoring the regional spread of VFGs and ARGs, including dissemination mediated by MGEs. No visualization tool is currently available for the comprehensive display of numerous resulting circular metagenome-assembled genomes (cMAGs) with functional gene annotations. Here, we developed VicMAG, a visualization tool for highly complex cMAGs derived from long-read metagenome assemblies annotated using updated databases of VFGs, ARGs, and MGEs. Using 353 cMAGs from PacBio HiFi sequencing of a wastewater sample, we demonstrated the utility of VicMAG for metagenome visualization. VicMAG provides comprehensive, size-aware visualization of cMAGs representing bacterial chromosomes and plasmids, annotated with VFGs, ARGs, and phages. By simultaneously visualizing all cMAGs in a framework, VicMAG facilitates a holistic understanding of the distribution and genomic context of VFGs and ARGs across complex microbial communities. This tool supports integrated surveillance of bacteria associated with virulence and antimicrobial resistance across clinical, environmental, and One Health contexts.

Functional redundancy is a key property of ecosystems and represents the fact that phylogenetically unrelated taxa can play similar functional roles within an ecosystem. The redundancy of potential functions of human microbiome has been recently quantified using metagenomics data. Yet, the redundancy of functions which are actually expressed within the human microbiome remains largely unexplored. Here, we quantify the protein-level functional redundancy in the human gut microbiome using metaproteomics and network approaches. In particular, our ultra-deep metaproteomics approach revealed high protein-level functional redundancy and high nestedness in proteomic content networks - bipartite graphs that connect taxa with their expressed functions. We further examined multiple metaproteomics datasets and showed that various environmental factors, including individuality, biogeography, xenobiotics, and disease, significantly altered the protein-level functional redundancy. Finally, by projecting the bipartite proteomic content networks into unipartite weighted genus networks, functional hub genera across individual microbiomes were discovered, suggesting that there may be a universal principle of functional organization in microbiome assembly. HighlightsO_LIUltra-deep metaproteomics reveals high protein-level functional redundancy in the human gut microbiome C_LIO_LIWithin-sample proteomic content networks display universal topology C_LIO_LIVarious environmental factors influence the redundancy of expressed functions C_LIO_LIFunctional hub genera are present across different datasets C_LI