MetaBinner: a high-performance and stand-alone ensemble binning method to recover individual genomes from complex microbial communities

Metagenomic source tracking after microbiota transplant therapy

Reliable engraftment assessment of donor microbial communities and individual strains is an essential component of characterizing the pharmacokinetics of microbiota transplant therapies (MTTs). Recent methods for measuring donor engraftment use whole-genome sequencing and reference databases or metagenome-assembled genomes (MAGs) to track individual bacterial strains but lack the ability to disambiguate DNA that matches both donor and patient microbiota. Here, we describe a new, cost-efficient analytic pipeline, MAGEnTa, which compares post-MTT samples to a database comprised MAGs derived directly from donor and pre-treatment metagenomic data, without relying on an external database. The pipeline uses Bayesian statistics to determine the likely sources of ambiguous reads that align with both the donor and pre-treatment samples. MAGEnTa recovers engrafted strains with minimal type II error in a simulated dataset and is robust to shallow sequencing depths in a downsampled dataset. Applying MAGEnTa to a dataset from a recent MTT clinical trial for ulcerative colitis, we found the results to be consistent with 16S rRNA gene SourceTracker analysis but with added MAG-level specificity. MAGEnTa is a powerful tool to study community and strain engraftment dynamics in the development of MTT-based treatments that can be integrated into frameworks for functional and taxonomic analysis.

Genome-resolved metagenomics from short-read sequencing data in the era of artificial intelligence

Genome-resolved metagenomics is a computational method that enables researchers to reconstruct microbial genomes from a given sample directly. This process involves three major steps, i.e. (i) preprocessing of the reads (ii) metagenome assembly, and (iii) genome binning, with (iv) taxonomic classification, and (v) functional annotation as additional steps. Despite the availability of multiple bioinformatics approaches, metagenomic data analysis encounters various challenges due to high dimensionality, data sparseness, and complexity. Meanwhile, integrating artificial intelligence (AI) at different stages of data analysis has transformed genome-resolved metagenomics. Though the application of machine learning and deep learning in metagenomic annotation started earlier, the emergence of better sequencing technologies, improved throughput, and reduced processing time have rendered the initial models less efficient. Consequently, the number of AI-based metagenomics tools is continuously increasing. The recent AI-based tools demonstrate superior performance in handling complex and multi-dimensional metagenomics data, offering improved accuracy, scalability, and efficiency compared to traditional models. In this paper, we reviewed recent AI-based tools specifically developed for short-read metagenomic data, and their underlying models for genome-resolved metagenomics. It also discusses the performance of these tools and overviews their usability in metagenomics research. We believe this study will provide researchers with insights into the strengths and limitations of current AI-based approaches, serving as a valuable resource for selecting appropriate tools and guiding future advancements in genome-resolved metagenomics.

Algae biofilm produces less microbe-derived dissolved organic nitrogen under higher C/N ratio conditions

The increased release of microbe-derived dissolved organic nitrogen (mDON) during biological nutrient removal (BNR) processes, particularly under carbon dosing conditions, has emerged as a primary cause to eutrophication. Although algae biofilm (AB) has potential in mitigating mDON discharge, the influence of wastewater carbon-to-nitrogen (C/N) ratios on mDON formation remains poorly understood. Here, we investigated AB's mDON formation and utilization performance, molecular characteristics, and metabolic traits under C/N ratios ranging from 1 to 8. All AB reactors reached mDON concentrations <1.3 mg/L, presenting a trend of first rising and then falling as C/N ratios rose. At the highest C/N ratio, AB effectively reduced mDON concentrations to 0.88 ± 0.08 mg/L, representing a reduction greater than 50 % compared to conventional BNR processes, and achieved a total nitrogen removal efficiency of 97.19 %. Redundancy and network analysis revealed that dominant algae (Chlorophyta and Cyanobacteria) and bacteria (Bacteroidota and Proteobacteria) exhibited distinct mDON production and utilization patterns across different C/N ratios. Algae proliferated under higher C/N ratios promoted the synergistic algal-bacteria interactions, enabling labile DON recycling and reducing its chemodiversity. This was also supported by the increased genetic investments in DON metabolism under higher C/N ratios. Conversely, bacterial activity, responsible for diversifying mDON pools via cross-module transformation reactions, was inhibited under elevated C/N ratios. Overall, AB is demonstrated robust for DON-related eutrophication control, even under high C/N ratios. This study first investigates the effects of C/N ratios on the mDON fates within algae biofilm systems and reveals the taxon-specific formation and utilization patterns.

First Animal Source Metagenome Assembly of Lawsonella clevelandensis from Canine External Otitis

External otitis is one of the most common conditions in dogs to be presented to the veterinarian. Moreover, the disorder is often challenging to manage. The range and role of microorganisms involved in the pathogenesis are currently not fully understood. Therefore, the condition has been studied using third-generation sequencing (Oxford Nanopore Technology) to gain a more complete picture of the pathogens involved. Throughout the metagenome assembly of a sample from the ear canal of an 11-year-old female Yorkshire terrier suffering from chronic external otitis, a genome of Lawsonella clevelandensis was compiled. To our knowledge, this result is the first of its type of animal origin. The outcome of the assembly is a single circular chromosome with a length of 1,909,339 bp and 1727 predicted genes. One open reading frame associated with antimicrobial resistance could have been identified. Comparing all available genomes, the species can be associated with three main genome clusters. The finding contributes to the extending knowledge bank about this often-overlooked pathogen and raises attention to the role of nanopore sequencing by the identification and characterization of microorganisms that are difficult to culture.

CAMI Benchmarking Portal: online evaluation and ranking of metagenomic software

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

ProteoSeeker: A Feature-Rich Metagenomic Analysis Tool for Accessible and Comprehensive Metagenomic Exploration

The vast majority of microbial diversity remains unculturable, limiting access to novel biotechnological resources. Advances in metagenomics have expanded the understanding of microbial communities, yet targeted protein discovery remains challenging. This study introduces ProteoSeeker, a command-line tool for streamlined metagenomic protein identification and annotation. ProteoSeeker operates in two primary modes: i) Seek mode, which screens the proteins according to user-defined protein families, and ii) Taxonomy mode, which uncovers the taxonomy of the host organisms. By automating key steps, ProteoSeeker reduces computational complexity, enabling time-efficient and comprehensive metagenomic analysis for both specialized and nonspecialized users. The efficiency of ProteoSeeker to achieve targeted enzyme discovery is demonstrated by identifying extremophilic enzymes with desired biochemical features, such as amylases for starch hydrolysis and carbonic anhydrases for CO₂ capture applications. By democratizing functional metagenomics, ProteoSeeker is anticipated to accelerate biotechnology, synthetic biology, and biomedical research and innovation.

Alanine Derived from Ruminococcus_E bovis Alleviates Energy Metabolic Disorders during the Peripartum Period by Providing Glucogenic Precursors

Peripartum dairy cows commonly experience energy metabolism disorders, which lead to passive culling of postpartum cows and a decrease in milk quality. By using ketosis peripartum dairy cows as a model, this study aims to elucidate the metabolic mechanism of peripartum cows and provide a novel way for managing energy metabolic disorders. From a cohort of 211 cows, we integrated multi-omics data (metagenomics, metabolomics, and transcriptomics) to identify key microbes and then utilized an in vitro rumen fermentation simulation system and ketogenic hepatic cells to validate the potential mechanisms and the effects of postbiotics derived from key microbes. Postpartum cows with metabolic disorders compensate for glucose deficiency through mobilizing muscle proteins, which leads to marked decreases in milk protein content. Concurrently, these cows experience rumen microbiota disturbance, with marked decreases in the concentrations of volatile fatty acids and microbial protein, and the deficiency of alanine (Ala) in microbial protein is correlated with the metabolic disorder phenotype. Metagenomic binning and in vitro fermentation assays reveal that Ruminococcus_E bovis (MAG 189) is enriched in amino acid biosynthesis functions and responsible for Ala synthesis. Furthermore, transcriptomic and metabolomic analyses of the liver in metabolic disorder cows also show impaired amino acid metabolism. Supplementation with Ala can alleviate ketogenesis in liver cell models by activating the gluconeogenesis pathway. This study reveals that Ruminococcus_E bovis is associated with host energy metabolism homeostasis by supplying glucogenic precursors to the liver and suggests the use of Ala as a method for the treatment of energy metabolism disorders in peripartum cows.

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

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

A review of neural networks for metagenomic binning

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

Bioinformatic approaches to blood and tissue microbiome analyses: challenges and perspectives

Advances in next-generation sequencing have resulted in a growing understanding of the microbiome and its role in human health. Unlike traditional microbiome analysis, blood and tissue microbiome analyses focus on the detection and characterization of microbial DNA in blood and tissue, previously considered a sterile environment. In this review, we discuss the challenges and methodologies associated with analyzing these samples, particularly emphasizing blood and tissue microbiome research. Key preprocessing steps-including the removal of ribosomal RNA, host DNA, and other contaminants-are critical to reducing noise and accurately capturing microbial evidence. We also explore how taxonomic profiling tools, machine learning, and advanced normalization techniques address contamination and low microbial biomass, thereby improving reliability. While it offers the potential for identifying microbial involvement in systemic diseases previously undetectable by traditional methods, this methodology also carries risks and lacks universal acceptance due to concerns over reliability and interpretation errors. This paper critically reviews these factors, highlighting both the promise and pitfalls of using blood and tissue microbiome analyses as a tool for biomarker discovery.

Deciphering the biosynthetic landscape of biofilms in glacier-fed streams

Glacier-fed streams are permanently cold, ultra-oligotrophic, and physically unstable environments, yet microbial life thrives in benthic biofilm communities. Within biofilms, microorganisms rely on secondary metabolites for communication and competition. However, the diversity and genetic potential of secondary metabolites in glacier-fed stream biofilms remain poorly understood. In this study, we present the first large-scale exploration of biosynthetic gene clusters (BGCs) from benthic glacier-fed stream biofilms sampled by the Vanishing Glaciers project from the world's major mountain ranges. We found a remarkable diversity of BGCs, with more than 8,000 of them identified within 2,868 prokaryotic metagenome-assembled genomes, some of them potentially conferring ecological advantages, such as UV protection and quorum sensing. The BGCs were distinct from those sourced from other aquatic microbiomes, with over 40% of them being novel. The glacier-fed stream BGCs exhibited the highest similarity to BGCs from glacier microbiomes. BGC composition displayed geographic patterns and correlated with prokaryotic alpha diversity. We also found that BGC diversity was positively associated with benthic chlorophyll a and prokaryotic diversity, indicative of more biotic interactions in more extensive biofilms. Our study provides new insights into a hitherto poorly explored microbial ecosystem, which is now changing at a rapid pace as glaciers are shrinking due to climate change.

IMPORTANCE

Glacier-fed streams are characterized by low temperatures, high turbidity, and high flow. They host a unique microbiome within biofilms, which form the foundation of the food web and contribute significantly to biogeochemical cycles. Our investigation into secondary metabolites, which likely play an important role in these complex ecosystems, found a unique genetic potential distinct from other aquatic environments. We found the potential to synthesize several secondary metabolites, which may confer ecological advantages, such as UV protection and quorum sensing. This biosynthetic diversity was positively associated with the abundance and complexity of the microbial community, as well as concentrations of chlorophyll a. In the face of climate change, our study offers new insights into a vanishing ecosystem.

Predicting climate-change impacts on the global glacier-fed stream microbiome

The shrinkage of glaciers and the vanishing of glacier-fed streams (GFSs) are emblematic of climate change. However, forecasts of how GFS microbiome structure and function will change under projected climate change scenarios are lacking. Combining 2,333 prokaryotic metagenome-assembled genomes with climatic, glaciological, and environmental data collected by the Vanishing Glaciers project from 164 GFSs draining Earth's major mountain ranges, we here predict the future of the GFS microbiome until the end of the century under various climate change scenarios. Our model framework is rooted in a space-for-time substitution design and leverages statistical learning approaches. We predict that declining environmental selection promotes primary production in GFSs, stimulating both bacterial biomass and biodiversity. Concomitantly, predictions suggest that the phylogenetic structure of the GFS microbiome will change and entire bacterial clades are at risk. Furthermore, genomic projections reveal that microbiome functions will shift, with intensified solar energy acquisition pathways, heterotrophy and algal-bacterial interactions. Altogether, we project a 'greener' future of the world's GFSs accompanied by a loss of clades that have adapted to environmental harshness, with consequences for ecosystem functioning.

Mapping the metagenomic diversity of the multi-kingdom glacier-fed stream microbiome

Glacier-fed streams (GFS) feature among Earth's most extreme aquatic ecosystems marked by pronounced oligotrophy and environmental fluctuations. Microorganisms mainly organize in biofilms within them, but how they cope with such conditions is unknown. Here, leveraging 156 metagenomes from the Vanishing Glaciers project obtained from sediment samples in GFS from 9 mountains ranges, we report thousands of metagenome-assembled genomes (MAGs) encompassing prokaryotes, algae, fungi and viruses, that shed light on biotic interactions within glacier-fed stream biofilms. A total of 2,855 bacterial MAGs were characterized by diverse strategies to exploit inorganic and organic energy sources, in part via functional redundancy and mixotrophy. We show that biofilms probably become more complex and switch from chemoautotrophy to heterotrophy as algal biomass increases in GFS owing to glacier shrinkage. Our MAG compendium sheds light on the success of microbial life in GFS and provides a resource for future research on a microbiome potentially impacted by climate change.

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

Background

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

Methods

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

Results

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

Conclusions

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

Weak magnetic field promotes denitrification by stimulating ferromagnetic ion-containing metalloprotein expression

Weak magnetic field (WMF) has been recognized to promote biological denitrification processes; however, the underlying mechanisms remain largely unexplored, hindering the optimization of its effectiveness. Here, we systematically investigated the effects of WMF on denitrification performance, enzyme activity, microbial community, and metaproteome in packed bed bioreactors treating high nitrate wastewater under different WMF intensities and C:N ratios. Results showed that WMFs significantly promoted denitrification by consistently stimulating the activities of denitrifying reductases and NAD+/NADH biosynthesis across decreasing C:N ratios. Reductases and electron transfer enzymes involved in denitrification were overproduced due to the significantly enriched overexpression of ferromagnetic ion-containing (FIC) metalloproteins. We also observed WMFs' intensity-dependent selective pressure on microbial community structures despite the effects being limited compared to those caused by changing C:N ratios. By coupling genome-centric metaproteomics and structure prediction, we found the dominant denitrifier, Halomonas, was outcompeted by Pseudomonas and Azoarcus under WMFs, likely due to its structural deficiencies in iron uptake, suggesting that advantageous ferromagnetic ion acquisition capacity was necessary to satisfy the substrate demand for FIC metalloprotein overproduction. This study advances our understanding of the biomagnetic effects in the context of complex communities and highlights WMF's potential for manipulating FIC protein-associated metabolism and fine-tuning community structure.

Insights into Prime Editing Technology: A Deep Dive into Fundamentals, Potentials, and Challenges

As the most versatile and precise gene editing technology, prime editing (PE) can establish a durable cure for most human genetic disorders. Several generations of PE have been developed based on an editor machine or prime editing guide RNA (pegRNA) to achieve any kind of genetic correction. However, due to the early stage of development, PE complex elements need to be optimized for more efficient editing. Smart optimization of editor proteins as well as pegRNA has been contemplated by many researchers, but the universal PE machine's current shortcomings remain to be solved. The modification of PE elements, fine-tuning of the host genes, manipulation of epigenetics, and blockage of immune responses could be used to reach more efficient PE. Moreover, the host factors involved in the PE process, such as repair and innate immune system genes, have not been determined, and PE cell context dependency is still poorly understood. Regarding the large size of the PE elements, delivery is a significant challenge and the development of a universal viral or nonviral platform is still far from complete. PE versions with shortened variants of reverse transcriptase are still too large to fit in common viral vectors. Overall, PE faces challenges in optimization for efficiency, high context dependency during the cell cycling, and delivery due to the large size of elements. In addition, immune responses, unpredictability of outcomes, and off-target effects further limit its application, making it essential to address these issues for broader use in nonpersonalized gene editing. Besides, due to the limited number of suitable animal models and computational modeling, the prediction of the PE process remains challenging. In this review, the fundamentals of PE, including generations, potential, optimization, delivery, in vivo barriers, and the future landscape of the technology are discussed.

Triumphs of genomic-assisted breeding in crop improvement

Conventional breeding approaches have played a significant role in meeting the food demand remarkably well until now. However, the increasing population, yield plateaus in certain crops, and limited recombination necessitate using genomic resources for genomics-assisted crop improvement programs. As a result of advancements in the next-generation sequence technology, GABs have developed dramatically to characterize allelic variants and facilitate their rapid and efficient incorporation in crop improvement programs. Genomics-assisted breeding (GAB) has played an important role in harnessing the potential of modern genomic tools, exploiting allelic variation from genetic resources and developing cultivars over the past decade. The availability of pangenomes for major crops has been a significant development, albeit with varying degrees of completeness. Even though adopting these technologies is essentially determined on economic grounds and cost-effective assays, which create a wealth of information that can be successfully used to exploit the latent potential of crops. GAB has been instrumental in harnessing the potential of modern genomic resources and exploiting allelic variation for genetic enhancement and cultivar development. GAB strategies will be indispensable for designing future crops and are expected to play a crucial role in breeding climate-smart crop cultivars with higher nutritional value.

Fairy: fast approximate coverage for multi-sample metagenomic binning

BACKGROUND

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

RESULTS

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

CONCLUSIONS

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

Gene regulatory networks reveal sex difference in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) has been observed to have significant sex differences in incidence, prognosis, and response to therapy. However, the molecular mechanisms responsible for these disparities have not been investigated extensively.

METHODS

Sample-specific gene regulatory network methods were used to analyze RNA sequencing data from non-cancerous human lung samples from The Genotype Tissue Expression Project (GTEx) and lung adenocarcinoma primary tumor samples from The Cancer Genome Atlas (TCGA); results were validated on independent data.

RESULTS

We found that genes associated with key biological pathways including cell proliferation, immune response and drug metabolism are differentially regulated between males and females in both healthy lung tissue and tumor, and that these regulatory differences are further perturbed by tobacco smoking. We also discovered significant sex bias in transcription factor targeting patterns of clinically actionable oncogenes and tumor suppressor genes, including AKT2 and KRAS. Using differentially regulated genes between healthy and tumor samples in conjunction with a drug repurposing tool, we identified several small-molecule drugs that might have sex-biased efficacy as cancer therapeutics and further validated this observation using an independent cell line database.

CONCLUSIONS

These findings underscore the importance of including sex as a biological variable and considering gene regulatory processes in developing strategies for disease prevention and management.

Solving genomic puzzles: computational methods for metagenomic binning

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

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

BACKGROUND

Using 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.

RESULTS

In 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.

CONCLUSION

The 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.

AVAILABILITY

A python package is available at: https://github.com/SayehSobhani/AFITBin .

Step-by-Step Metagenomics for Food Microbiome Analysis: A Detailed Review

This review article offers a comprehensive overview of the current understanding of using metagenomic tools in food microbiome research. It covers the scientific foundation and practical application of genetic analysis techniques for microbial material from food, including bioinformatic analysis and data interpretation. The method discussed in the article for analyzing microorganisms in food without traditional culture methods is known as food metagenomics. This approach, along with other omics technologies such as nutrigenomics, proteomics, metabolomics, and transcriptomics, collectively forms the field of foodomics. Food metagenomics allows swift and thorough examination of bacteria and potential metabolic pathways by utilizing foodomic databases. Despite its established scientific basis and available bioinformatics resources, the research approach of food metagenomics outlined in the article is not yet widely implemented in industry. The authors believe that the integration of next-generation sequencing (NGS) with rapidly advancing digital technologies such as artificial intelligence (AI), the Internet of Things (IoT), and big data will facilitate the widespread adoption of this research strategy in microbial analysis for the food industry. This adoption is expected to enhance food safety and product quality in the near future.

Generating barcodes for nanopore sequencing data with PRO

DNA barcodes, short and unique DNA sequences, play a crucial role in sample identification when processing many samples simultaneously, which helps reduce experimental costs. Nevertheless, the low quality of long-read sequencing makes it difficult to identify barcodes accurately, which poses significant challenges for the design of barcodes for large numbers of samples in a single sequencing run. Here, we present a comprehensive study of the generation of barcodes and develop a tool, PRO, that can be used for selecting optimal barcode sets and demultiplexing. We formulate the barcode design problem as a combinatorial problem and prove that finding the optimal largest barcode set in a given DNA sequence space in which all sequences have the same length is theoretically NP-complete. For practical applications, we developed the novel method PRO by introducing the probability divergence between two DNA sequences to expand the capacity of barcode kits while ensuring demultiplexing accuracy. Specifically, the maximum size of the barcode kits designed by PRO is 2,292, which keeps the length of barcodes the same as that of the official ones used by Oxford Nanopore Technologies (ONT). We validated the performance of PRO on a simulated nanopore dataset with high error rates. The demultiplexing accuracy of PRO reached 98.29% for a barcode kit of size 2,922, 4.31% higher than that of Guppy, the official demultiplexing tool. When the size of the barcode kit generated by PRO is the same as the official size provided by ONT, both tools show superior and comparable demultiplexing accuracy.

Genome-resolved metagenomics: a game changer for microbiome medicine

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

Advances in Single-Cell Transcriptome Sequencing and Spatial Transcriptome Sequencing in Plants

"Omics" typically involves exploration of the structure and function of the entire composition of a biological system at a specific level using high-throughput analytical methods to probe and analyze large amounts of data, including genomics, transcriptomics, proteomics, and metabolomics, among other types. Genomics characterizes and quantifies all genes of an organism collectively, studying their interrelationships and their impacts on the organism. However, conventional transcriptomic sequencing techniques target population cells, and their results only reflect the average expression levels of genes in population cells, as they are unable to reveal the gene expression heterogeneity and spatial heterogeneity among individual cells, thus masking the expression specificity between different cells. Single-cell transcriptomic sequencing and spatial transcriptomic sequencing techniques analyze the transcriptome of individual cells in plant or animal tissues, enabling the understanding of each cell's metabolites and expressed genes. Consequently, statistical analysis of the corresponding tissues can be performed, with the purpose of achieving cell classification, evolutionary growth, and physiological and pathological analyses. This article provides an overview of the research progress in plant single-cell and spatial transcriptomics, as well as their applications and challenges in plants. Furthermore, prospects for the development of single-cell and spatial transcriptomics are proposed.

A genomic compendium of cultivated human gut fungi characterizes the gut mycobiome and its relevance to common diseases

The gut fungal community represents an essential element of human health, yet its functional and metabolic potential remains insufficiently elucidated, largely due to the limited availability of reference genomes. To address this gap, we presented the cultivated gut fungi (CGF) catalog, encompassing 760 fungal genomes derived from the feces of healthy individuals. This catalog comprises 206 species spanning 48 families, including 69 species previously unidentified. We explored the functional and metabolic attributes of the CGF species and utilized this catalog to construct a phylogenetic representation of the gut mycobiome by analyzing over 11,000 fecal metagenomes from Chinese and non-Chinese populations. Moreover, we identified significant common disease-related variations in gut mycobiome composition and corroborated the associations between fungal signatures and inflammatory bowel disease (IBD) through animal experimentation. These resources and findings substantially enrich our understanding of the biological diversity and disease relevance of the human gut mycobiome.

Pangenome analysis reveals transposon-driven genome evolution in cotton

BACKGROUND

Transposable elements (TEs) have a profound influence on the trajectory of plant evolution, driving genome expansion and catalyzing phenotypic diversification. The pangenome, a comprehensive genetic pool encompassing all variations within a species, serves as an invaluable tool, unaffected by the confounding factors of intraspecific diversity. This allows for a more nuanced exploration of plant TE evolution.

RESULTS

Here, we constructed a pangenome for diploid A-genome cotton using 344 accessions from representative geographical regions, including 223 from China as the main component. We found 511 Mb of non-reference sequences (NRSs) and revealed the presence of 5479 previously undiscovered protein-coding genes. Our comprehensive approach enabled us to decipher the genetic underpinnings of the distinct geographic distributions of cotton. Notably, we identified 3301 presence-absence variations (PAVs) that are closely tied to gene expression patterns within the pangenome, among which 2342 novel expression quantitative trait loci (eQTLs) were found residing in NRSs. Our investigation also unveiled contrasting patterns of transposon proliferation between diploid and tetraploid cotton, with long terminal repeat (LTR) retrotransposons exhibiting a synchronized surge in polyploids. Furthermore, the invasion of LTR retrotransposons from the A subgenome to the D subgenome triggered a substantial expansion of the latter following polyploidization. In addition, we found that TE insertions were responsible for the loss of 36.2% of species-specific genes, as well as the generation of entirely new species-specific genes.

CONCLUSIONS

Our pangenome analyses provide new insights into cotton genomics and subgenome dynamics after polyploidization and demonstrate the power of pangenome approaches for elucidating transposon impacts and genome evolution.

Adverse Environmental Perturbations May Threaten Kelp Farming Sustainability by Exacerbating Enterobacterales Diseases

Globally kelp farming is gaining attention to mitigate land-use pressures and achieve carbon neutrality. However, the influence of environmental perturbations on kelp farming remains largely unknown. Recently, a severe disease outbreak caused extensive kelp mortality in Sanggou Bay, China, one of the world's largest high-density kelp farming areas. Here, through in situ investigations and simulation experiments, we find indications that an anomalously dramatic increase in elevated coastal seawater light penetration may have contributed to dysbiosis in the kelp Saccharina japonica's microbiome. This dysbiosis promoted the proliferation of opportunistic pathogenic Enterobacterales, mainly including the genera Colwellia and Pseudoalteromonas. Using transcriptomic analyses, we revealed that high-light conditions likely induced oxidative stress in kelp, potentially facilitating opportunistic bacterial Enterobacterales attack that activates a terrestrial plant-like pattern recognition receptor system in kelp. Furthermore, we uncover crucial genotypic determinants of Enterobacterales dominance and pathogenicity within kelp tissue, including pathogen-associated molecular patterns, potential membrane-damaging toxins, and alginate and mannitol lysis capability. Finally, through analysis of kelp-associated microbiome data sets under the influence of ocean warming and acidification, we conclude that such Enterobacterales favoring microbiome shifts are likely to become more prevalent in future environmental conditions. Our study highlights the need for understanding complex environmental influences on kelp health and associated microbiomes for the sustainable development of seaweed farming.

Genome-resolved metagenomics identifies novel active microbes in biogeochemical cycling within methanol-enriched soil

Metagenome assembled genomes (MAGs), generated from sequenced 13C-labelled DNA from 13C-methanol enriched soils, were binned using an ensemble approach. This method produced a significantly larger number of higher-quality MAGs compared to direct binning approaches. These MAGs represent both the primary methanol utilizers and the secondary utilizers labelled via cross-feeding and predation on the labelled methylotrophs, including numerous uncultivated taxa. Analysis of these MAGs enabled the identification of multiple metabolic pathways within these active taxa that have climatic relevance relating to nitrogen, sulfur and trace gas metabolism. This includes denitrification, dissimilatory nitrate reduction to ammonium, ammonia oxidation and metabolism of organic sulfur species. The binning of viral sequence data also yielded extensive viral MAGs, identifying active viral replication by both lytic and lysogenic phages within the methanol-enriched soils. These MAGs represent a valuable resource for characterizing biogeochemical cycling within terrestrial environments.

Recovery of 1887 metagenome-assembled genomes from the South China Sea

The South China Sea (SCS) is a marginal sea characterized by strong land-sea biogeochemical interactions. SCS has a distinctive landscape with a multitude of seamounts in its basin. Seamounts create "seamount effects" that influence the diversity and distribution of planktonic microorganisms in the surrounding oligotrophic waters. Although the vertical distribution and community structure of marine microorganisms have been explored in certain regions of the global ocean, there is a lack of comprehensive microbial genomic surveys for uncultured microorganisms in SCS, particularly in the seamount regions. Here, we employed a metagenomic approach to study the uncultured microbial communities sampled from the Xianbei seamount region to the North Coast waters of SCS. A total of 1887 non-redundant prokaryotic metagenome-assembled genomes (MAGs) were reconstructed, of which, 153 MAGs were classified as high-quality MAGs based on the MIMAG standards. The community structure and genomic information provided by this dataset could be used to analyze microbial distribution and metabolism in the SCS.

Duck pan-genome reveals two transposon insertions caused bodyweight enlarging and white plumage phenotype formation during evolution

Structural variations (SVs) are a major source of domestication and improvement traits. We present the first duck pan-genome constructed using five genome assemblies capturing ∼40.98 Mb new sequences. This pan-genome together with high-depth sequencing data (∼46.5×) identified 101,041 SVs, of which substantial proportions were derived from transposable element (TE) activity. Many TE-derived SVs anchoring in a gene body or regulatory region are linked to duck's domestication and improvement. By combining quantitative genetics with molecular experiments, we, for the first time, unraveled a 6945 bp Gypsy insertion as a functional mutation of the major gene IGF2BP1 associated with duck bodyweight. This Gypsy insertion, to our knowledge, explains the largest effect on bodyweight among avian species (27.61% of phenotypic variation). In addition, we also examined another 6634 bp Gypsy insertion in MITF intron, which triggers a novel transcript of MITF, thereby contributing to the development of white plumage. Our findings highlight the importance of using a pan-genome as a reference in genomics studies and illuminate the impact of transposons in trait formation and livestock breeding.

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

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

Alignment-based Protein Mutational Landscape Prediction: Doing More with Less

The wealth of genomic data has boosted the development of computational methods predicting the phenotypic outcomes of missense variants. The most accurate ones exploit multiple sequence alignments, which can be costly to generate. Recent efforts for democratizing protein structure prediction have overcome this bottleneck by leveraging the fast homology search of MMseqs2. Here, we show the usefulness of this strategy for mutational outcome prediction through a large-scale assessment of 1.5M missense variants across 72 protein families. Our study demonstrates the feasibility of producing alignment-based mutational landscape predictions that are both high-quality and compute-efficient for entire proteomes. We provide the community with the whole human proteome mutational landscape and simplified access to our predictive pipeline.

Ultrasensitive proteomics depicted an in-depth landscape for the very early stage of mouse maternal-to-zygotic transition

Single-cell or low-input multi-omics techniques have revolutionized the study of pre-implantation embryo development. However, the single-cell or low-input proteomic research in this field is relatively underdeveloped because of the higher threshold of the starting material for mammalian embryo samples and the lack of hypersensitive proteome technology. In this study, a comprehensive solution of ultrasensitive proteome technology (CS-UPT) was developed for single-cell or low-input mouse oocyte/embryo samples. The deep coverage and high-throughput routes significantly reduced the starting material and were selected by investigators based on their demands. Using the deep coverage route, we provided the first large-scale snapshot of the very early stage of mouse maternal-to-zygotic transition, including almost 5,500 protein groups from 20 mouse oocytes or zygotes for each sample. Moreover, significant protein regulatory networks centered on transcription factors and kinases between the MII oocyte and 1-cell embryo provided rich insights into minor zygotic genome activation.

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

MOTIVATION

Metagenomic binning methods to reconstruct metagenome-assembled genomes (MAGs) from environmental samples have been widely used in large-scale metagenomic studies. The recently proposed semi-supervised binning method, SemiBin, achieved state-of-the-art binning results in several environments. However, this required annotating contigs, a computationally costly and potentially biased process.

RESULTS

We propose SemiBin2, which uses self-supervised learning to learn feature embeddings from the contigs. In simulated and real datasets, we show that self-supervised learning achieves better results than the semi-supervised learning used in SemiBin1 and that SemiBin2 outperforms other state-of-the-art binners. Compared to SemiBin1, SemiBin2 can reconstruct 8.3-21.5% more high-quality bins and requires only 25% of the running time and 11% of peak memory usage in real short-read sequencing samples. To extend SemiBin2 to long-read data, we also propose ensemble-based DBSCAN clustering algorithm, resulting in 13.1-26.3% more high-quality genomes than the second best binner for long-read data.

AVAILABILITY AND IMPLEMENTATION

SemiBin2 is available as open source software at https://github.com/BigDataBiology/SemiBin/ and the analysis scripts used in the study can be found at https://github.com/BigDataBiology/SemiBin2_benchmark.

A survey on computational strategies for genome-resolved gut metagenomics

Recovering high-quality metagenome-assembled genomes (HQ-MAGs) is critical for exploring microbial compositions and microbe-phenotype associations. However, multiple sequencing platforms and computational tools for this purpose may confuse researchers and thus call for extensive evaluation. Here, we systematically evaluated a total of 40 combinations of popular computational tools and sequencing platforms (i.e. strategies), involving eight assemblers, eight metagenomic binners and four sequencing technologies, including short-, long-read and metaHiC sequencing. We identified the best tools for the individual tasks (e.g. the assembly and binning) and combinations (e.g. generating more HQ-MAGs) depending on the availability of the sequencing data. We found that the combination of the hybrid assemblies and metaHiC-based binning performed best, followed by the hybrid and long-read assemblies. More importantly, both long-read and metaHiC sequencings link more mobile elements and antibiotic resistance genes to bacterial hosts and improve the quality of public human gut reference genomes with 32% (34/105) HQ-MAGs that were either of better quality than those in the Unified Human Gastrointestinal Genome catalog version 2 or novel.

Closed genomes uncover a saltwater species of Candidatus Electronema and shed new light on the boundary between marine and freshwater cable bacteria

Cable bacteria of the Desulfobulbaceae family are centimeter-long filamentous bacteria, which are capable of conducting long-distance electron transfer. Currently, all cable bacteria are classified into two candidate genera: Candidatus Electronema, typically found in freshwater environments, and Candidatus Electrothrix, typically found in saltwater environments. This taxonomic framework is based on both 16S rRNA gene sequences and metagenome-assembled genome (MAG) phylogenies. However, most of the currently available MAGs are highly fragmented, incomplete, and thus likely miss key genes essential for deciphering the physiology of cable bacteria. Also, a closed, circular genome of cable bacteria has not been published yet. To address this, we performed Nanopore long-read and Illumina short-read shotgun sequencing of selected environmental samples and a single-strain enrichment of Ca. Electronema aureum. We recovered multiple cable bacteria MAGs, including two circular and one single-contig. Phylogenomic analysis, also confirmed by 16S rRNA gene-based phylogeny, classified one circular MAG and the single-contig MAG as novel species of cable bacteria, which we propose to name Ca. Electronema halotolerans and Ca. Electrothrix laxa, respectively. The Ca. Electronema halotolerans, despite belonging to the previously recognized freshwater genus of cable bacteria, was retrieved from brackish-water sediment. Metabolic predictions showed several adaptations to a high salinity environment, similar to the "saltwater" Ca. Electrothrix species, indicating how Ca. Electronema halotolerans may be the evolutionary link between marine and freshwater cable bacteria lineages.