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Microbial Genomics Apr 2024The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most... (Review)
Review
The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most prevalent applications of metagenomics is the study of microbial environments, such as the human gut. The gut microbiome plays a crucial role in human health, providing vital information for patient diagnosis and prognosis. However, analysing metagenomic data remains challenging due to several factors, including reference catalogues, sparsity and compositionality. Deep learning (DL) enables novel and promising approaches that complement state-of-the-art microbiome pipelines. DL-based methods can address almost all aspects of microbiome analysis, including novel pathogen detection, sequence classification, patient stratification and disease prediction. Beyond generating predictive models, a key aspect of these methods is also their interpretability. This article reviews DL approaches in metagenomics, including convolutional networks, autoencoders and attention-based models. These methods aggregate contextualized data and pave the way for improved patient care and a better understanding of the microbiome's key role in our health.
Topics: Humans; Deep Learning; Microbiota; Metagenome; Gastrointestinal Microbiome; Metagenomics
PubMed: 38630611
DOI: 10.1099/mgen.0.001231 -
Journal of Biomolecular Techniques : JBT Apr 2017
Topics: DNA; High-Throughput Nucleotide Sequencing; Humans; Metagenome; Metagenomics; RNA
PubMed: 28400709
DOI: 10.7171/jbt.17-2801-010 -
Microbiological Research Dec 2022Microbial cells attached to inert or living surfaces adopt biofilm mode with self-produced exopolysaccharide matrix containing polysaccharides, proteins, and... (Review)
Review
Microbial cells attached to inert or living surfaces adopt biofilm mode with self-produced exopolysaccharide matrix containing polysaccharides, proteins, and extracellular DNA, for protection from adverse external stimuli. Biofilms in hospitals and industries serve as a breeding ground for drug-resistant pathogens and ARG enrichment that are linked to pathogenicity and also impede industrial production process. Biofilm formation, including virulence and pathogenicity, is regulated through quorum sensing (QS), a means of bacterial cell to cell communication for cooperative physiological processes. Hence, QS inhibition through quorum quenching (QQ) is a feasible approach to inhibit biofilm formation. In contrast, biofilms have beneficial roles in promoting plant growth, biocontrol, and wastewater treatment. Furthermore, polymicrobial biofilms can harbour novel compounds and species of industrial and pharmaceutical interest. Hence, surveillance of biofilm microbiome structure and functional attributes is crucial to determine the extent of the risk it poses and to harness its bioactive potential. One of the most preferred approaches to delineate the microbiome is culture-independent metagenomics. In this context, this review article explores the biofilm microbiome in built and natural settings such as agriculture, household appliances, wastewater treatment plants, hospitals, microplastics, and dental biofilm. We have also discussed the recent reports on discoveries of novel QS and biofilm inhibitors through conventional, metagenomics, and machine learning approaches. Finally, we present biofilm-derived novel metagenome-assembled genomes (MAGs), genomes, and taxa of medical and industrial interest.
Topics: Biofilms; Metagenome; Metagenomics; Microplastics; Pharmaceutical Preparations; Plastics; Quorum Sensing
PubMed: 36194989
DOI: 10.1016/j.micres.2022.127207 -
Genomics, Proteomics & Bioinformatics Dec 2018Metagenomes from uncultured microorganisms are rich resources for novel enzyme genes. The methods used to screen the metagenomic libraries fall into two categories,... (Review)
Review
Metagenomes from uncultured microorganisms are rich resources for novel enzyme genes. The methods used to screen the metagenomic libraries fall into two categories, which are based on sequence or function of the enzymes. The sequence-based approaches rely on the known sequences of the target gene families. In contrast, the function-based approaches do not involve the incorporation of metagenomic sequencing data and, therefore, may lead to the discovery of novel gene sequences with desired functions. In this review, we discuss the function-based screening strategies that have been used in the identification of enzymes from metagenomes. Because of its simplicity, agar plate screening is most commonly used in the identification of novel enzymes with diverse functions. Other screening methods with higher sensitivity are also employed, such as microtiter plate screening. Furthermore, several ultra-high-throughput methods were developed to deal with large metagenomic libraries. Among these are the FACS-based screening, droplet-based screening, and the in vivo reporter-based screening methods. The application of these novel screening strategies has increased the chance for the discovery of novel enzyme genes.
Topics: Animals; Bacteria; Enzymes; Gene Library; High-Throughput Screening Assays; Metagenome; Metagenomics; Plants
PubMed: 30597257
DOI: 10.1016/j.gpb.2018.01.002 -
Applied and Environmental Microbiology Oct 2020Viruses are ubiquitous particles comprising genetic material that can infect bacteria, archaea, and fungi, as well as human and other animal cells. Given that... (Review)
Review
Viruses are ubiquitous particles comprising genetic material that can infect bacteria, archaea, and fungi, as well as human and other animal cells. Given that determining virus composition and function in association with states of human health and disease is of increasing interest, we anticipate that the field of viral metagenomics will continue to expand and be applied in a variety of areas ranging from surveillance to discovery and will rely heavily upon the continued development of reference materials and databases. Information regarding viral composition and function readily translates into biological and clinical applications, including the rapid sequence identification of pathogenic viruses in various sample types. However, viral metagenomic approaches often lack appropriate standards and reference materials to enable cross-study comparisons and assess potential biases which can be introduced at the various stages of collection, storage, processing, and sequence analysis. In addition, implementation of appropriate viral reference materials can aid in the benchmarking of current and development of novel assays for virus identification, discovery, and surveillance. As the field of viral metagenomics expands and standardizes, results will continue to translate into diverse applications.
Topics: Genome, Viral; Humans; Metagenome; Metagenomics; Viruses
PubMed: 32917759
DOI: 10.1128/AEM.01794-20 -
Communications Biology Oct 2023Assembly of reads from metagenomic samples is a hard problem, often resulting in highly fragmented genome assemblies. Metagenomic binning allows us to reconstruct...
Assembly of reads from metagenomic samples is a hard problem, often resulting in highly fragmented genome assemblies. Metagenomic binning allows us to reconstruct genomes by re-grouping the sequences by their organism of origin, thus representing a crucial processing step when exploring the biological diversity of metagenomic samples. Here we present Adversarial Autoencoders for Metagenomics Binning (AAMB), an ensemble deep learning approach that integrates sequence co-abundances and tetranucleotide frequencies into a common denoised space that enables precise clustering of sequences into microbial genomes. When benchmarked, AAMB presented similar or better results compared with the state-of-the-art reference-free binner VAMB, reconstructing ~7% more near-complete (NC) genomes across simulated and real data. In addition, genomes reconstructed using AAMB had higher completeness and greater taxonomic diversity compared with VAMB. Finally, we implemented a pipeline Integrating VAMB and AAMB that enabled improved binning, recovering 20% and 29% more simulated and real NC genomes, respectively, compared to VAMB, with moderate additional runtime.
Topics: Metagenome; Genome, Microbial; Metagenomics; Cluster Analysis; Benchmarking
PubMed: 37865678
DOI: 10.1038/s42003-023-05452-3 -
Current Issues in Molecular Biology 2019Methanotrophic microorganisms utilize methane as an electron donor and a carbon source. To date, the capacity to oxidize methane is restricted to microorganisms from... (Review)
Review
Methanotrophic microorganisms utilize methane as an electron donor and a carbon source. To date, the capacity to oxidize methane is restricted to microorganisms from three bacterial and one archaeal phyla. Most of our knowledge of methanotrophic metabolism has been obtained using highly enriched or pure cultures grown in the laboratory. However, many methanotrophs currently evade cultivation, thus metagenomics provides a complementary approach for gaining insight into currently unisolated microorganisms. Here we synthesize the studies using metagenomics to glean information about methanotrophs. We complement this summary with an analysis of methanotroph marker genes from 235 publically available metagenomic datasets. We analyze the phylogenetic and environmental distribution of methanotrophs sampled by metagenomics. We also highlight metabolic insights that methanotroph genomes assembled from metagenomes are illuminating. In summary, metagenomics has increased methanotrophic foliage within the tree of life, as well as provided new insights into methanotroph metabolism, which collectively can guide new cultivation efforts. Lastly, given the importance of methanotrophs for biotechnological applications and their capacity to filter greenhouse gases from a variety of ecosystems, metagenomics will continue to be an important component in the arsenal of tools needed for understanding methanotroph diversity and metabolism in both engineered and natural systems.
Topics: Archaea; Biodiversity; Energy Metabolism; Metagenome; Metagenomics; Methane; Methanobacteriales; Microbiota; Phylogeny; Soil Microbiology
PubMed: 31166185
DOI: 10.21775/cimb.033.057 -
Microbiome Mar 2016Assembly of metagenomic sequence data into microbial genomes is of fundamental value to improving our understanding of microbial ecology and metabolism by elucidating... (Review)
Review
Assembly of metagenomic sequence data into microbial genomes is of fundamental value to improving our understanding of microbial ecology and metabolism by elucidating the functional potential of hard-to-culture microorganisms. Here, we provide a synthesis of available methods to bin metagenomic contigs into species-level groups and highlight how genetic diversity, sequencing depth, and coverage influence binning success. Despite the computational cost on application to deeply sequenced complex metagenomes (e.g., soil), covarying patterns of contig coverage across multiple datasets significantly improves the binning process. We also discuss and compare current genome validation methods and reveal how these methods tackle the problem of chimeric genome bins i.e., sequences from multiple species. Finally, we explore how population genome assembly can be used to uncover biogeographic trends and to characterize the effect of in situ functional constraints on the genome-wide evolution.
Topics: Contig Mapping; Datasets as Topic; Genome, Microbial; Metagenome; Metagenomics; Sequence Analysis, DNA
PubMed: 26951112
DOI: 10.1186/s40168-016-0154-5 -
Scientific Data Feb 2023Common culturing techniques and priorities bias our discovery towards specific traits that may not be representative of microbial diversity in nature. So far, these...
Common culturing techniques and priorities bias our discovery towards specific traits that may not be representative of microbial diversity in nature. So far, these biases have not been systematically examined. To address this gap, here we use 116,884 publicly available metagenome-assembled genomes (MAGs, completeness ≥80%) from 203 surveys worldwide as a culture-independent sample of bacterial and archaeal diversity, and compare these MAGs to the popular RefSeq genome database, which heavily relies on cultures. We compare the distribution of 12,454 KEGG gene orthologs (used as trait proxies) in the MAGs and RefSeq genomes, while controlling for environment type (ocean, soil, lake, bioreactor, human, and other animals). Using statistical modeling, we then determine the conditional probabilities that a species is represented in RefSeq depending on its genetic repertoire. We find that the majority of examined genes are significantly biased for or against in RefSeq. Our systematic estimates of gene prevalences across bacteria and archaea in nature and gene-specific biases in reference genomes constitutes a resource for addressing these issues in the future.
Topics: Animals; Archaea; Bacteria; Genome, Microbial; Metagenome; Metagenomics
PubMed: 36759614
DOI: 10.1038/s41597-023-01994-7 -
Nature Biotechnology Nov 2023Metagenomic assembly enables new organism discovery from microbial communities, but it can only capture few abundant organisms from most metagenomes. Here we present...
Metagenomic assembly enables new organism discovery from microbial communities, but it can only capture few abundant organisms from most metagenomes. Here we present MetaPhlAn 4, which integrates information from metagenome assemblies and microbial isolate genomes for more comprehensive metagenomic taxonomic profiling. From a curated collection of 1.01 M prokaryotic reference and metagenome-assembled genomes, we define unique marker genes for 26,970 species-level genome bins, 4,992 of them taxonomically unidentified at the species level. MetaPhlAn 4 explains ~20% more reads in most international human gut microbiomes and >40% in less-characterized environments such as the rumen microbiome and proves more accurate than available alternatives on synthetic evaluations while also reliably quantifying organisms with no cultured isolates. Application of the method to >24,500 metagenomes highlights previously undetected species to be strong biomarkers for host conditions and lifestyles in human and mouse microbiomes and shows that even previously uncharacterized species can be genetically profiled at the resolution of single microbial strains.
Topics: Humans; Animals; Mice; Metagenome; Microbiota; Gastrointestinal Microbiome; Metagenomics; Phylogeny
PubMed: 36823356
DOI: 10.1038/s41587-023-01688-w