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Current Opinion in Virology Jun 2022Despite the growing interest in the microbiome in recent years, the study of the virome, the major part of which is made up of bacteriophages, is relatively... (Review)
Review
Despite the growing interest in the microbiome in recent years, the study of the virome, the major part of which is made up of bacteriophages, is relatively underdeveloped compared with their bacterial counterparts. This is due in part to the lack of a universally conserved marker such as the 16S rRNA gene. For this reason, the development of metagenomic approaches was a major milestone in the study of the viruses in the microbiome or virome. However, it has become increasingly clear that these wet-lab methods have not yet been able to detect the full range of viruses present, and our understanding of the composition of the virome remains incomplete. In recent years, a range of new technologies has been developed to further our understanding. Direct RNA-Seq technologies bypass the need for cDNA synthesis, thus avoiding biases subjected to this step, which further expands our understanding of RNA viruses. The new generation of amplification methods could solve the low biomass issue relevant to most virome samples while reducing the error rate and biases caused by whole genome amplification. The application of long-read sequencing to virome samples can resolve the shortcomings of short-read sequencing in generating complete viral genomes and avoid the biases introduced by the assembly. Novel experimental methods developed to measure viruses' host range can help overcome the challenges of assigning hosts to many phages, specifically unculturable ones.
Topics: Bacteriophages; Metagenome; Metagenomics; RNA, Ribosomal, 16S; Virome; Viruses
PubMed: 35643020
DOI: 10.1016/j.coviro.2022.101231 -
STAR Protocols Sep 2023The analysis of metagenomic data obtained via high-throughput DNA sequencing is primarily carried out by a dedicated binning process involving clustering contigs,...
The analysis of metagenomic data obtained via high-throughput DNA sequencing is primarily carried out by a dedicated binning process involving clustering contigs, presumably belonging to the same species. Here, we present a protocol for improving the quality of binning using BinSPreader. We describe steps for typical metagenome assembly and binning workflow. We then detail binning refining, its variants, output, and possible caveats. This protocol optimizes the process of reconstructing more complete genomes of microorganisms that make up the metagenome. For complete details on the use and execution of this protocol, please refer to Tolstoganov et al..
Topics: Metagenome; Sequence Analysis, DNA; Metagenomics; Cluster Analysis; High-Throughput Nucleotide Sequencing
PubMed: 37405923
DOI: 10.1016/j.xpro.2023.102417 -
BMC Bioinformatics May 2020Microorganisms are important occupants of many different environments. Identifying the composition of microbes and estimating their abundance promote understanding of...
BACKGROUND
Microorganisms are important occupants of many different environments. Identifying the composition of microbes and estimating their abundance promote understanding of interactions of microbes in environmental samples. To understand their environments more deeply, the composition of microorganisms in environmental samples has been studied using metagenomes, which are the collections of genomes of the microorganisms. Although many tools have been developed for taxonomy analysis based on different algorithms, variability of analysis outputs of existing tools from the same input metagenome datasets is the main obstacle for many researchers in this field.
RESULTS
Here, we present a novel meta-analysis tool for metagenome taxonomy analysis, called TAMA, by intelligently integrating outputs from three different taxonomy analysis tools. Using an integrated reference database, TAMA performs taxonomy assignment for input metagenome reads based on a meta-score by integrating scores of taxonomy assignment from different taxonomy classification tools. TAMA outperformed existing tools when evaluated using various benchmark datasets. It was also successfully applied to obtain relative species abundance profiles and difference in composition of microorganisms in two types of cheese metagenome and human gut metagenome.
CONCLUSION
TAMA can be easily installed and used for metagenome read classification and the prediction of relative species abundance from multiple numbers and types of metagenome read samples. TAMA can be used to more accurately uncover the composition of microorganisms in metagenome samples collected from various environments, especially when the use of a single taxonomy analysis tool is unreliable. TAMA is an open source tool, and can be downloaded at https://github.com/jkimlab/TAMA.
Topics: Bacteria; Classification; Databases, Genetic; Datasets as Topic; High-Throughput Nucleotide Sequencing; Metagenome; Metagenomics; Models, Genetic; Phylogeny
PubMed: 32397982
DOI: 10.1186/s12859-020-3533-7 -
Pharmacological Research Mar 2013The microbes residing in and on the human body influence human physiology in many ways, particularly through their impact on the metabolism of xenobiotic compounds,... (Review)
Review
The microbes residing in and on the human body influence human physiology in many ways, particularly through their impact on the metabolism of xenobiotic compounds, including therapeutic drugs, antibiotics, and diet-derived bioactive compounds. Despite the importance of these interactions and the many possibilities for intervention, microbial xenobiotic metabolism remains a largely underexplored component of pharmacology. Here, we discuss the emerging evidence for both direct and indirect effects of the human gut microbiota on xenobiotic metabolism, and the initial links that have been made between specific compounds, diverse members of this complex community, and the microbial genes responsible. Furthermore, we highlight the many parallels to the now well-established field of environmental bioremediation, and the vast potential to leverage emerging metagenomic tools to shed new light on these important microbial biotransformations.
Topics: Animals; Biotransformation; Gastrointestinal Tract; Humans; Metagenome; Metagenomics; Xenobiotics
PubMed: 22902524
DOI: 10.1016/j.phrs.2012.07.009 -
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 -
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 -
GigaScience Jan 2024Linked-read sequencing technologies generate high-base quality short reads that contain extrapolative information on long-range DNA connectedness. These advantages of...
BACKGROUND
Linked-read sequencing technologies generate high-base quality short reads that contain extrapolative information on long-range DNA connectedness. These advantages of linked-read technologies are well known and have been demonstrated in many human genomic and metagenomic studies. However, existing linked-read analysis pipelines (e.g., Long Ranger) were primarily developed to process sequencing data from the human genome and are not suited for analyzing metagenomic sequencing data. Moreover, linked-read analysis pipelines are typically limited to 1 specific sequencing platform.
FINDINGS
To address these limitations, we present the Linked-Read ToolKit (LRTK), a unified and versatile toolkit for platform agnostic processing of linked-read sequencing data from both human genome and metagenome. LRTK provides functions to perform linked-read simulation, barcode sequencing error correction, barcode-aware read alignment and metagenome assembly, reconstruction of long DNA fragments, taxonomic classification and quantification, and barcode-assisted genomic variant calling and phasing. LRTK has the ability to process multiple samples automatically and provides users with the option to generate reproducible reports during processing of raw sequencing data and at multiple checkpoints throughout downstream analysis. We applied LRTK on linked reads from simulation, mock community, and real datasets for both human genome and metagenome. We showcased LRTK's ability to generate comparative performance results from preceding benchmark studies and to report these results in publication-ready HTML document plots.
CONCLUSIONS
LRTK provides comprehensive and flexible modules along with an easy-to-use Python-based workflow for processing linked-read sequencing datasets, thereby filling the current gap in the field caused by platform-centric genome-specific linked-read data analysis tools.
Topics: Humans; Genome, Human; Metagenome; Software; Metagenomics; Sequence Analysis, DNA; High-Throughput Nucleotide Sequencing; Computational Biology
PubMed: 38869148
DOI: 10.1093/gigascience/giae028 -
Bioinformatics (Oxford, England) Jan 2023The Metagenomic Intra-Species Diversity Analysis System (MIDAS) is a scalable metagenomic pipeline that identifies single nucleotide variants (SNVs) and gene copy number...
SUMMARY
The Metagenomic Intra-Species Diversity Analysis System (MIDAS) is a scalable metagenomic pipeline that identifies single nucleotide variants (SNVs) and gene copy number variants in microbial populations. Here, we present MIDAS2, which addresses the computational challenges presented by increasingly large reference genome databases, while adding functionality for building custom databases and leveraging paired-end reads to improve SNV accuracy. This fast and scalable reengineering of the MIDAS pipeline enables thousands of metagenomic samples to be efficiently genotyped.
AVAILABILITY AND IMPLEMENTATION
The source code is available at https://github.com/czbiohub/MIDAS2.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
Topics: Metagenome; Software; Metagenomics; Genotype; Databases, Factual
PubMed: 36321886
DOI: 10.1093/bioinformatics/btac713 -
Viruses Jun 2023Blood transfusion safety is an essential element of public health. Current blood screening strategies rely on targeted techniques that could miss unknown or unexpected...
Blood transfusion safety is an essential element of public health. Current blood screening strategies rely on targeted techniques that could miss unknown or unexpected pathogens. Recent studies have demonstrated the presence of a viral community (virobiota/virome) in the blood of healthy individuals. Here, we characterized the blood virome in patients frequently exposed to blood transfusion by using Illumina metagenomic sequencing. The virome of these patients was compared to viruses present in healthy blood donors. A total number of 155 beta-thalassemia, 149 hemodialysis, and 100 healthy blood donors were pooled with five samples per pool. Members of the and family were most frequently observed. Interestingly, samples of healthy blood donors harbored traces of potentially pathogenic viruses, including adeno-, rota-, and Merkel cell polyomavirus. Viruses of the family were most abundant in the blood of hemodialysis patients and displayed a higher anellovirus richness. Pegiviruses () were only observed in patient populations. An overall trend of higher eukaryotic read abundance in both patient groups was observed. This might be associated with increased exposure through blood transfusion. Overall, the findings in this study demonstrated the presence of various viruses in the blood of Iranian multiple-transfused patients and healthy blood donors.
Topics: Humans; Iran; Virome; Viruses; Anelloviridae; Metagenome; Metagenomics
PubMed: 37515113
DOI: 10.3390/v15071425 -
Current Issues in Molecular Biology 2017Surveys of environmental microbial communities using metagenomic approach produce vast volumes of multidimensional data regarding the phylogenetic and functional... (Review)
Review
Surveys of environmental microbial communities using metagenomic approach produce vast volumes of multidimensional data regarding the phylogenetic and functional composition of the microbiota. Faced with such complex data, a metagenomic researcher needs to select the means for data analysis properly. Data visualization became an indispensable part of the exploratory data analysis and serves a key to the discoveries. While the molecular-genetic analysis of even a single bacterium presents multiple layers of data to be properly displayed and perceived, the studies of microbiota are significantly more challenging. Here we present a review of the state-of-art methods for the visualization of metagenomic data in a multi-level manner: from the methods applicable to an in-depth analysis of a single metagenome to the techniques appropriate for large-scale studies containing hundreds of environmental samples.
Topics: Bacteria; Computer Graphics; Databases, Genetic; Metagenome; Metagenomics; Microbiota
PubMed: 28686567
DOI: 10.21775/cimb.024.037