Journal articles on the topic 'Human metagenomic'
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Nalbantoglu, O. Ufuk. "Information Theoretic Metagenome Assembly Allows the Discovery of Disease Biomarkers in Human Microbiome." Entropy 23, no. 2 (February 2, 2021): 187. http://dx.doi.org/10.3390/e23020187.
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Quantitative metagenomics is an important field that has delivered successful microbiome biomarkers associated with host phenotypes. The current convention mainly depends on unsupervised assembly of metagenomic contigs with a possibility of leaving interesting genetic material unassembled. Additionally, biomarkers are commonly defined on the differential relative abundance of compositional or functional units. Accumulating evidence supports that microbial genetic variations are as important as the differential abundance content, implying the need for novel methods accounting for the genetic variations in metagenomics studies. We propose an information theoretic metagenome assembly algorithm, discovering genomic fragments with maximal self-information, defined by the empirical distributions of nucleotides across the phenotypes and quantified with the help of statistical tests. Our algorithm infers fragments populating the most informative genetic variants in a single contig, named supervariant fragments. Experiments on simulated metagenomes, as well as on a colorectal cancer and an atherosclerotic cardiovascular disease dataset consistently discovered sequences strongly associated with the disease phenotypes. Moreover, the discriminatory power of these putative biomarkers was mainly attributed to the genetic variations rather than relative abundance. Our results support that a focus on metagenomics methods considering microbiome population genetics might be useful in discovering disease biomarkers with a great potential of translating to molecular diagnostics and biotherapeutics applications.
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Filipic, Brankica, Katarina Novovic, David J. Studholme, Milka Malesevic, Nemanja Mirkovic, Milan Kojic, and Branko Jovcic. "Shotgun metagenomics reveals differences in antibiotic resistance genes among bacterial communities in Western Balkans glacial lakes sediments." Journal of Water and Health 18, no. 3 (May 21, 2020): 383–97. http://dx.doi.org/10.2166/wh.2020.227.
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Abstract Long-term overuse of antibiotics has driven the propagation and spreading of antibiotic resistance genes (ARGs) such as efflux pumps in the environment, which can be transferred to clinically relevant pathogens. This study explored the abundance and diversity of ARGs and mobile genetic elements within bacterial communities from sediments of three Western Balkans glacial lakes: Plav Lake (high impact of human population), Black Lake (medium impact of human population) and Donje Bare Lake (remote lake, minimal impact of human population) via shotgun metagenomics. Assembled metagenomic sequences revealed that Resistance-Nodulation-Division (RND) efflux pumps genes were most abundant in metagenome from the Plav Lake. The Integron Finder bioinformatics tool detected 38 clusters of attC sites lacking integron-integrases (CALIN) elements: 20 from Plav Lake, four from Black Lake and 14 from Donje Bare Lake. A complete integron sequence was recovered only from the assembled metagenome from Plav Lake. Plasmid contents within the metagenomes were similar, with proportions of contigs being plasmid-related: 1.73% for Plav Lake, 1.59% for Black Lake and 1.64% for Donje Bare Lake. The investigation showed that RNDs and mobile genetic elements content correlated with human population impact.
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Bai, Geng-Hao, Sheng-Chieh Lin, Yi-Hsiang Hsu, and Shih-Yen Chen. "The Human Virome: Viral Metagenomics, Relations with Human Diseases, and Therapeutic Applications." Viruses 14, no. 2 (January 28, 2022): 278. http://dx.doi.org/10.3390/v14020278.
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The human body is colonized by a wide range of microorganisms. The field of viromics has expanded since the first reports on the detection of viruses via metagenomic sequencing in 2002. With the continued development of reference materials and databases, viral metagenomic approaches have been used to explore known components of the virome and discover new viruses from various types of samples. The virome has attracted substantial interest since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic. Increasing numbers of studies and review articles have documented the diverse virome in various sites in the human body, as well as interactions between the human host and the virome with regard to health and disease. However, there have been few studies of direct causal relationships. Viral metagenomic analyses often lack standard references and are potentially subject to bias. Moreover, most virome-related review articles have focused on the gut virome and did not investigate the roles of the virome in other sites of the body in human disease. This review presents an overview of viral metagenomics, with updates regarding the relations between alterations in the human virome and the pathogenesis of human diseases, recent findings related to COVID-19, and therapeutic applications related to the human virome.
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Simon, Carola, and Rolf Daniel. "Metagenomic Analyses: Past and Future Trends." Applied and Environmental Microbiology 77, no. 4 (December 17, 2010): 1153–61. http://dx.doi.org/10.1128/aem.02345-10.
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ABSTRACTMetagenomics has revolutionized microbiology by paving the way for a cultivation-independent assessment and exploitation of microbial communities present in complex ecosystems. Metagenomics comprising construction and screening of metagenomic DNA libraries has proven to be a powerful tool to isolate new enzymes and drugs of industrial importance. So far, the majority of the metagenomically exploited habitats comprised temperate environments, such as soil and marine environments. Recently, metagenomes of extreme environments have also been used as sources of novel biocatalysts. The employment of next-generation sequencing techniques for metagenomics resulted in the generation of large sequence data sets derived from various environments, such as soil, the human body, and ocean water. Analyses of these data sets opened a window into the enormous taxonomic and functional diversity of environmental microbial communities. To assess the functional dynamics of microbial communities, metatranscriptomics and metaproteomics have been developed. The combination of DNA-based, mRNA-based, and protein-based analyses of microbial communities present in different environments is a way to elucidate the compositions, functions, and interactions of microbial communities and to link these to environmental processes.
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Zorrilla, Francisco, Filip Buric, Kiran R. Patil, and Aleksej Zelezniak. "metaGEM: reconstruction of genome scale metabolic models directly from metagenomes." Nucleic Acids Research 49, no. 21 (October 6, 2021): e126-e126. http://dx.doi.org/10.1093/nar/gkab815.
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Abstract Metagenomic analyses of microbial communities have revealed a large degree of interspecies and intraspecies genetic diversity through the reconstruction of metagenome assembled genomes (MAGs). Yet, metabolic modeling efforts mainly rely on reference genomes as the starting point for reconstruction and simulation of genome scale metabolic models (GEMs), neglecting the immense intra- and inter-species diversity present in microbial communities. Here, we present metaGEM (https://github.com/franciscozorrilla/metaGEM), an end-to-end pipeline enabling metabolic modeling of multi-species communities directly from metagenomes. The pipeline automates all steps from the extraction of context-specific prokaryotic GEMs from MAGs to community level flux balance analysis (FBA) simulations. To demonstrate the capabilities of metaGEM, we analyzed 483 samples spanning lab culture, human gut, plant-associated, soil, and ocean metagenomes, reconstructing over 14,000 GEMs. We show that GEMs reconstructed from metagenomes have fully represented metabolism comparable to isolated genomes. We demonstrate that metagenomic GEMs capture intraspecies metabolic diversity and identify potential differences in the progression of type 2 diabetes at the level of gut bacterial metabolic exchanges. Overall, metaGEM enables FBA-ready metabolic model reconstruction directly from metagenomes, provides a resource of metabolic models, and showcases community-level modeling of microbiomes associated with disease conditions allowing generation of mechanistic hypotheses.
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Kasmanas, Jonas Coelho, Alexander Bartholomäus, Felipe Borim Corrêa, Tamara Tal, Nico Jehmlich, Gunda Herberth, Martin von Bergen, Peter F. Stadler, André Carlos Ponce de Leon Ferreira de Carvalho, and Ulisses Nunes da Rocha. "HumanMetagenomeDB: a public repository of curated and standardized metadata for human metagenomes." Nucleic Acids Research 49, no. D1 (November 22, 2020): D743—D750. http://dx.doi.org/10.1093/nar/gkaa1031.
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Abstract Metagenomics became a standard strategy to comprehend the functional potential of microbial communities, including the human microbiome. Currently, the number of metagenomes in public repositories is increasing exponentially. The Sequence Read Archive (SRA) and the MG-RAST are the two main repositories for metagenomic data. These databases allow scientists to reanalyze samples and explore new hypotheses. However, mining samples from them can be a limiting factor, since the metadata available in these repositories is often misannotated, misleading, and decentralized, creating an overly complex environment for sample reanalysis. The main goal of the HumanMetagenomeDB is to simplify the identification and use of public human metagenomes of interest. HumanMetagenomeDB version 1.0 contains metadata of 69 822 metagenomes. We standardized 203 attributes, based on standardized ontologies, describing host characteristics (e.g. sex, age and body mass index), diagnosis information (e.g. cancer, Crohn's disease and Parkinson), location (e.g. country, longitude and latitude), sampling site (e.g. gut, lung and skin) and sequencing attributes (e.g. sequencing platform, average length and sequence quality). Further, HumanMetagenomeDB version 1.0 metagenomes encompass 58 countries, 9 main sample sites (i.e. body parts), 58 diagnoses and multiple ages, ranging from just born to 91 years old. The HumanMetagenomeDB is publicly available at https://webapp.ufz.de/hmgdb/.
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Nayfach, Stephen, David Páez-Espino, Lee Call, Soo Jen Low, Hila Sberro, Natalia N. Ivanova, Amy D. Proal, et al. "Metagenomic compendium of 189,680 DNA viruses from the human gut microbiome." Nature Microbiology 6, no. 7 (June 24, 2021): 960–70. http://dx.doi.org/10.1038/s41564-021-00928-6.
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AbstractBacteriophages have important roles in the ecology of the human gut microbiome but are under-represented in reference databases. To address this problem, we assembled the Metagenomic Gut Virus catalogue that comprises 189,680 viral genomes from 11,810 publicly available human stool metagenomes. Over 75% of genomes represent double-stranded DNA phages that infect members of the Bacteroidia and Clostridia classes. Based on sequence clustering we identified 54,118 candidate viral species, 92% of which were not found in existing databases. The Metagenomic Gut Virus catalogue improves detection of viruses in stool metagenomes and accounts for nearly 40% of CRISPR spacers found in human gut Bacteria and Archaea. We also produced a catalogue of 459,375 viral protein clusters to explore the functional potential of the gut virome. This revealed tens of thousands of diversity-generating retroelements, which use error-prone reverse transcription to mutate target genes and may be involved in the molecular arms race between phages and their bacterial hosts.
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Rahman, Mohammad Arifur, and Huzefa Rangwala. "IDMIL: an alignment-free Interpretable Deep Multiple Instance Learning (MIL) for predicting disease from whole-metagenomic data." Bioinformatics 36, Supplement_1 (July 1, 2020): i39—i47. http://dx.doi.org/10.1093/bioinformatics/btaa477.
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Abstract Motivation The human body hosts more microbial organisms than human cells. Analysis of this microbial diversity provides key insight into the role played by these microorganisms on human health. Metagenomics is the collective DNA sequencing of coexisting microbial organisms in an environmental sample or a host. This has several applications in precision medicine, agriculture, environmental science and forensics. State-of-the-art predictive models for phenotype predictions from metagenomic data rely on alignments, assembly, extensive pruning, taxonomic profiling and reference sequence databases. These processes are time consuming and they do not consider novel microbial sequences when aligned with the reference genome, limiting the potential of whole metagenomics. We formulate the problem of predicting human disease from whole-metagenomic data using Multiple Instance Learning (MIL), a popular supervised learning paradigm. Our proposed alignment-free approach provides higher accuracy in prediction by harnessing the capability of deep convolutional neural network (CNN) within a MIL framework and provides interpretability via neural attention mechanism. Results The MIL formulation combined with the hierarchical feature extraction capability of deep-CNN provides significantly better predictive performance compared to popular existing approaches. The attention mechanism allows for the identification of groups of sequences that are likely to be correlated to diseases providing the much-needed interpretation. Our proposed approach does not rely on alignment, assembly and reference sequence databases; making it fast and scalable for large-scale metagenomic data. We evaluate our method on well-known large-scale metagenomic studies and show that our proposed approach outperforms comparative state-of-the-art methods for disease prediction. Availability and implementation https://github.com/mrahma23/IDMIL.
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Jaiani, Ekaterine, Ia Kusradze, Tamar Kokashvili, Natia Geliashvili, Nino Janelidze, Adam Kotorashvili, Nato Kotaria, Archil Guchmanidze, Marina Tediashvili, and David Prangishvili. "Microbial Diversity and Phage–Host Interactions in the Georgian Coastal Area of the Black Sea Revealed by Whole Genome Metagenomic Sequencing." Marine Drugs 18, no. 11 (November 14, 2020): 558. http://dx.doi.org/10.3390/md18110558.
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Viruses have the greatest abundance and highest genetic diversity in marine ecosystems. The interactions between viruses and their hosts is one of the hot spots of marine ecology. Besides their important role in various ecosystems, viruses, especially bacteriophages and their gene pool, are of enormous interest for the development of new gene products with high innovation value. Various studies have been conducted in diverse ecosystems to understand microbial diversity and phage–host interactions; however, the Black Sea, especially the Eastern coastal area, remains among the least studied ecosystems in this regard. This study was aimed at to fill this gap by analyzing microbial diversity and bacteriophage–host interactions in the waters of Eastern Black Sea using a metagenomic approach. To this end, prokaryotic and viral metagenomic DNA from two sampling sites, Poti and Gonio, were sequenced on the Illumina Miseq platform and taxonomic and functional profiles of the metagenomes were obtained using various bioinformatics tools. Our metagenomics analyses allowed us to identify the microbial communities, with Proteobacteria, Cyanobacteria, Actinibacteria, and Firmicutes found to be the most dominant bacterial phyla and Synechococcus and Candidatus Pelagibacter phages found to be the most dominant viral groups in the Black Sea. As minor groups, putative phages specific to human pathogens were identified in the metagenomes. We also characterized interactions between the phages and prokaryotic communities by determining clustered regularly interspaced short palindromic repeats (CRISPR), prophage-like sequences, and integrase/excisionase sequences in the metagenomes, along with identification of putative horizontally transferred genes in the viral contigs. In addition, in the viral contig sequences related to peptidoglycan lytic activity were identified as well. This is the first study on phage and prokaryote diversity and their interactions in the Eastern coastal area of the Black Sea using a metagenomic approach.
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Liu, Michael Y., Paul Worden, Leigh G. Monahan, Matthew Z. DeMaere, Catherine M. Burke, Steven P. Djordjevic, Ian G. Charles, and Aaron E. Darling. "Evaluation of ddRADseq for reduced representation metagenome sequencing." PeerJ 5 (September 19, 2017): e3837. http://dx.doi.org/10.7717/peerj.3837.
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BackgroundProfiling of microbial communities via metagenomic shotgun sequencing has enabled researches to gain unprecedented insight into microbial community structure and the functional roles of community members. This study describes a method and basic analysis for a metagenomic adaptation of the double digest restriction site associated DNA sequencing (ddRADseq) protocol for reduced representation metagenome profiling.MethodsThis technique takes advantage of the sequence specificity of restriction endonucleases to construct an Illumina-compatible sequencing library containing DNA fragments that are between a pair of restriction sites located within close proximity. This results in a reduced sequencing library with coverage breadth that can be tuned by size selection. We assessed the performance of the metagenomic ddRADseq approach by applying the full method to human stool samples and generating sequence data.ResultsThe ddRADseq data yields a similar estimate of community taxonomic profile as obtained from shotgun metagenome sequencing of the same human stool samples. No obvious bias with respect to genomic G + C content and the estimated relative species abundance was detected.DiscussionAlthough ddRADseq does introduce some bias in taxonomic representation, the bias is likely to be small relative to DNA extraction bias. ddRADseq appears feasible and could have value as a tool for metagenome-wide association studies.
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Zhang, Xinyan, and Nengjun Yi. "Fast zero-inflated negative binomial mixed modeling approach for analyzing longitudinal metagenomics data." Bioinformatics 36, no. 8 (January 6, 2020): 2345–51. http://dx.doi.org/10.1093/bioinformatics/btz973.
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Abstract Motivation Longitudinal metagenomics data, including both 16S rRNA and whole-metagenome shotgun sequencing data, enhanced our abilities to understand the dynamic associations between the human microbiome and various diseases. However, analytic tools have not been fully developed to simultaneously address the main challenges of longitudinal metagenomics data, i.e. high-dimensionality, dependence among samples and zero-inflation of observed counts. Results We propose a fast zero-inflated negative binomial mixed modeling (FZINBMM) approach to analyze high-dimensional longitudinal metagenomic count data. The FZINBMM approach is based on zero-inflated negative binomial mixed models (ZINBMMs) for modeling longitudinal metagenomic count data and a fast EM-IWLS algorithm for fitting ZINBMMs. FZINBMM takes advantage of a commonly used procedure for fitting linear mixed models, which allows us to include various types of fixed and random effects and within-subject correlation structures and quickly analyze many taxa. We found that FZINBMM remarkably outperformed in computational efficiency and was statistically comparable with two R packages, GLMMadaptive and glmmTMB, that use numerical integration to fit ZINBMMs. Extensive simulations and real data applications showed that FZINBMM outperformed other previous methods, including linear mixed models, negative binomial mixed models and zero-inflated Gaussian mixed models. Availability and implementation FZINBMM has been implemented in the R package NBZIMM, available in the public GitHub repository http://github.com//nyiuab//NBZIMM. Supplementary information Supplementary data are available at Bioinformatics online.
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McGhee, Jordan J., Nick Rawson, Barbara A. Bailey, Antonio Fernandez-Guerra, Laura Sisk-Hackworth, and Scott T. Kelley. "Meta-SourceTracker: application of Bayesian source tracking to shotgun metagenomics." PeerJ 8 (March 24, 2020): e8783. http://dx.doi.org/10.7717/peerj.8783.
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Background Microbial source tracking methods are used to determine the origin of contaminating bacteria and other microorganisms, particularly in contaminated water systems. The Bayesian SourceTracker approach uses deep-sequencing marker gene libraries (16S ribosomal RNA) to determine the proportional contributions of bacteria from many potential source environments to a given sink environment simultaneously. Since its development, SourceTracker has been applied to an extensive diversity of studies, from beach contamination to human behavior. Methods Here, we demonstrate a novel application of SourceTracker to work with metagenomic datasets and tested this approach using sink samples from a study of coastal marine environments. Source environment metagenomes were obtained from metagenomics studies of gut, freshwater, marine, sand and soil environments. As part of this effort, we implemented features for determining the stability of source proportion estimates, including precision visualizations for performance optimization, and performed domain-specific source-tracking analyses (i.e., Bacteria, Archaea, Eukaryota and viruses). We also applied SourceTracker to metagenomic libraries generated from samples collected from the International Space Station (ISS). Results SourceTracker proved highly effective at predicting the composition of known sources using shotgun metagenomic libraries. In addition, we showed that different taxonomic domains sometimes presented highly divergent pictures of environmental source origins for both the coastal marine and ISS samples. These findings indicated that applying SourceTracker to separate domains may provide a deeper understanding of the microbial origins of complex, mixed-source environments, and further suggested that certain domains may be preferable for tracking specific sources of contamination.
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Averina, Olga V., Alexey S. Kovtun, Svetlana I. Polyakova, Anastasia M. Savilova, Denis V. Rebrikov, and Valery N. Danilenko. "The bacterial neurometabolic signature of the gut microbiota of young children with autism spectrum disorders." Journal of Medical Microbiology 69, no. 4 (April 1, 2020): 558–71. http://dx.doi.org/10.1099/jmm.0.001178.
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Introduction. The human gut microbiota is currently seen as an important factor that can promote autism spectrum disorder (ASD) development in children. Aim. This study aimed to detect differences in the taxonomic composition and content of bacterial genes encoding key enzymes involved in the metabolism of neuroactive biomarker compounds in the metagenomes of gut microbiota of children with ASD and neurotypical children. Methodology. A whole metagenome sequencing approach was used to obtain metagenomic data on faecal specimens of 36 children with ASD and 21 healthy neurotypical children of 3–5 years old. Taxonomic analysis was conducted using MetaPhlAn2. The developed bioinformatics algorithm and created catalogue of the orthologues were applied to identify bacterial genes of neuroactive compounds in the metagenomes. For the identification of metagenomic signatures of children with ASD, Wilcoxon's test and adjustment for multiple comparisons were used. Results. Statistically significant differences with decreases in average abundance in the microbiota of ASD children were found for the genera Barnesiella and Parabacteroides and species Alistipes putredinis , B. caccae , Bacteroides intestinihominis, Eubacterium rectale , Parabacteroides distasonis and Ruminococcus lactaris . Average relative abundances of the detected genes and neurometabolic signature approach did not reveal many significant differences in the metagenomes of the groups that were compared. We noted decreases in the abundance of genes linked to production of GABA, melatonine and butyric acid in the ASD metagenomes. Conclusion. For the first time, the neurometabolic signature of the gut microbiota of young children with ASD is presented. The data can help to provide a comparative assessment of the transcriptional and metabolomic activity of the identified genes.
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Jonsson, Viktor, Tobias Österlund, Olle Nerman, and Erik Kristiansson. "Modelling of zero-inflation improves inference of metagenomic gene count data." Statistical Methods in Medical Research 28, no. 12 (November 25, 2018): 3712–28. http://dx.doi.org/10.1177/0962280218811354.
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Metagenomics enables the study of gene abundances in complex mixtures of microorganisms and has become a standard methodology for the analysis of the human microbiome. However, gene abundance data is inherently noisy and contains high levels of biological and technical variability as well as an excess of zeros due to non-detected genes. This makes the statistical analysis challenging. In this study, we present a new hierarchical Bayesian model for inference of metagenomic gene abundance data. The model uses a zero-inflated overdispersed Poisson distribution which is able to simultaneously capture the high gene-specific variability as well as zero observations in the data. By analysis of three comprehensive datasets, we show that zero-inflation is common in metagenomic data from the human gut and, if not correctly modelled, it can lead to substantial reductions in statistical power. We also show, by using resampled metagenomic data, that our model has, compared to other methods, a higher and more stable performance for detecting differentially abundant genes. We conclude that proper modelling of the gene-specific variability, including the excess of zeros, is necessary to accurately describe gene abundances in metagenomic data. The proposed model will thus pave the way for new biological insights into the structure of microbial communities.
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Alves, Luana de Fátima, Cauã Antunes Westmann, Gabriel Lencioni Lovate, Guilherme Marcelino Viana de Siqueira, Tiago Cabral Borelli, and María-Eugenia Guazzaroni. "Metagenomic Approaches for Understanding New Concepts in Microbial Science." International Journal of Genomics 2018 (August 23, 2018): 1–15. http://dx.doi.org/10.1155/2018/2312987.
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Over the past thirty years, since the dawn of metagenomic studies, a completely new (micro) universe was revealed, with the potential to have profound impacts on many aspects of the society. Remarkably, the study of human microbiome provided a new perspective on a myriad of human traits previously regarded as solely (epi-) genetically encoded, such as disease susceptibility, immunological response, and social and nutritional behaviors. In this context, metagenomics has established a powerful framework for understanding the intricate connections between human societies and microbial communities, ultimately allowing for the optimization of both human health and productivity. Thus, we have shifted from the old concept of microbes as harmful organisms to a broader panorama, in which the signal of the relationship between humans and microbes is flexible and directly dependent on our own decisions and practices. In parallel, metagenomics has also been playing a major role in the prospection of “hidden” genetic features and the development of biotechnological applications, through the discovery of novel genes, enzymes, pathways, and bioactive molecules with completely new or improved biochemical functions. Therefore, this review highlights the major milestones over the last three decades of metagenomics, providing insights into both its potentialities and current challenges.
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Raethong, Nachon, Massalin Nakphaichit, Narissara Suratannon, Witida Sathitkowitchai, Wanlapa Weerapakorn, Suttipun Keawsompong, and Wanwipa Vongsangnak. "Analysis of Human Gut Microbiome: Taxonomy and Metabolic Functions in Thai Adults." Genes 12, no. 3 (February 25, 2021): 331. http://dx.doi.org/10.3390/genes12030331.
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The gut microbiome plays a major role in the maintenance of human health. Characterizing the taxonomy and metabolic functions of the human gut microbiome is necessary for enhancing health. Here, we analyzed the metagenomic sequencing, assembly and construction of a meta-gene catalogue of the human gut microbiome with the overall aim of investigating the taxonomy and metabolic functions of the gut microbiome in Thai adults. As a result, the integrative analysis of 16S rRNA gene and whole metagenome shotgun (WMGS) sequencing data revealed that the dominant gut bacterial families were Lachnospiraceae and Ruminococcaceae of the Firmicutes phylum. Consistently, across 3.8 million (M) genes annotated from 163.5 gigabases (Gb) of WMGS sequencing data, a significant number of genes associated with carbohydrate metabolism of the dominant bacterial families were identified. Further identification of bacterial community-wide metabolic functions promisingly highlighted the importance of Roseburia and Faecalibacterium involvement in central carbon metabolism, sugar utilization and metabolism towards butyrate biosynthesis. This work presents an initial study of shotgun metagenomics in a Thai population-based cohort in a developing Southeast Asian country.
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Krause, Thomas, Jyotsna Talreja Wassan, Paul Mc Kevitt, Haiying Wang, Huiru Zheng, and Matthias Hemmje. "Analyzing Large Microbiome Datasets Using Machine Learning and Big Data." BioMedInformatics 1, no. 3 (November 8, 2021): 138–65. http://dx.doi.org/10.3390/biomedinformatics1030010.
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Metagenomics promises to provide new valuable insights into the role of microbiomes in eukaryotic hosts such as humans. Due to the decreasing costs for sequencing, public and private repositories for human metagenomic datasets are growing fast. Metagenomic datasets can contain terabytes of raw data, which is a challenge for data processing but also an opportunity for advanced machine learning methods like deep learning that require large datasets. However, in contrast to classical machine learning algorithms, the use of deep learning in metagenomics is still an exception. Regardless of the algorithms used, they are usually not applied to raw data but require several preprocessing steps. Performing this preprocessing and the actual analysis in an automated, reproducible, and scalable way is another challenge. This and other challenges can be addressed by adjusting known big data methods and architectures to the needs of microbiome analysis and DNA sequence processing. A conceptual architecture for the use of machine learning and big data on metagenomic data sets was recently presented and initially validated to analyze the rumen microbiome. The same architecture can be used for clinical purposes as is discussed in this paper.
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Biney-Assan, Tracy, and Michael Kron. "Molecular Microbiology in Clinical Practice: Current and Future Applications." AL-Kindy College Medical Journal 18, no. 3 (December 31, 2022): 167–72. http://dx.doi.org/10.47723/kcmj.v18i2.857.
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Technological advances have yielded new molecular biology-based methods for the diagnosis of infectious diseases. The newest and most powerful molecular diagnostic tests are available at regional and national reference laboratories, as well as at specialized centers that are certified to conduct metagenomic testing. Metagenomic assays utilize advances in DNA extraction technology, DNA sequence library construction, high throughput DNA sequencing and automated data analysis to identify millions of individual strands of DNA extracted from clinical samples. At present, metagenomic assays are only possible at a small number of special research, academic and commercial laboratories. Continued research in human and pathogen genomic organization and host-pathogen interactions, represent important future goals that will maximize the information obtained from metagenomic assays. To illustrate the power and limitations of metagenomics, we report on a previously healthy 27 year old woman with work related exposure to ill animals, and who developed a rapidly progressive, severe diffuse interstitial pneumonitis that ultimately ended in the need for a double lung transplant. Metagenomic testing on DNA extracted from pleural fluid and nasopharyngeal swabs demonstrated the presence of expected normal bacterial flora along with some unexpected herpesvirus and non-HIV retroviral elements integrated into the patients DNA. Although no specific pathogen was ultimately identified to explain this patient’s severe disease, the sample preparation and data analysis methods detailed herein illustrate the powerful benefits and limitations of metagenomic testing.
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Elmassry, Moamen M., Sunghwan Kim, and Ben Busby. "Predicting drug-metagenome interactions: Variation in the microbial β-glucuronidase level in the human gut metagenomes." PLOS ONE 16, no. 1 (January 7, 2021): e0244876. http://dx.doi.org/10.1371/journal.pone.0244876.
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Characterizing the gut microbiota in terms of their capacity to interfere with drug metabolism is necessary to achieve drug efficacy and safety. Although examples of drug-microbiome interactions are well-documented, little has been reported about a computational pipeline for systematically identifying and characterizing bacterial enzymes that process particular classes of drugs. The goal of our study is to develop a computational approach that compiles drugs whose metabolism may be influenced by a particular class of microbial enzymes and that quantifies the variability in the collective level of those enzymes among individuals. The present paper describes this approach, with microbial β-glucuronidases as an example, which break down drug-glucuronide conjugates and reactivate the drugs or their metabolites. We identified 100 medications that may be metabolized by β-glucuronidases from the gut microbiome. These medications included morphine, estrogen, ibuprofen, midazolam, and their structural analogues. The analysis of metagenomic data available through the Sequence Read Archive (SRA) showed that the level of β-glucuronidase in the gut metagenomes was higher in males than in females, which provides a potential explanation for the sex-based differences in efficacy and toxicity for several drugs, reported in previous studies. Our analysis also showed that infant gut metagenomes at birth and 12 months of age have higher levels of β-glucuronidase than the metagenomes of their mothers and the implication of this observed variability was discussed in the context of breastfeeding as well as infant hyperbilirubinemia. Overall, despite important limitations discussed in this paper, our analysis provided useful insights on the role of the human gut metagenome in the variability in drug response among individuals. Importantly, this approach exploits drug and metagenome data available in public databases as well as open-source cheminformatics and bioinformatics tools to predict drug-metagenome interactions.
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Miossec, Matthieu J., Sandro L. Valenzuela, Marcos Pérez-Losada, W. Evan Johnson, Keith A. Crandall, and Eduardo Castro-Nallar. "Evaluation of computational methods for human microbiome analysis using simulated data." PeerJ 8 (August 11, 2020): e9688. http://dx.doi.org/10.7717/peerj.9688.
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Background Our understanding of the composition, function, and health implications of human microbiota has been advanced by high-throughput sequencing and the development of new genomic analyses. However, trade-offs among alternative strategies for the acquisition and analysis of sequence data remain understudied. Methods We assessed eight popular taxonomic profiling pipelines; MetaPhlAn2, metaMix, PathoScope 2.0, Sigma, Kraken, ConStrains, Centrifuge and Taxator-tk, against a battery of metagenomic datasets simulated from real data. The metagenomic datasets were modeled on 426 complete or permanent draft genomes stored in the Human Oral Microbiome Database and were designed to simulate various experimental conditions, both in the design of a putative experiment; read length (75–1,000 bp reads), sequence depth (100K–10M), and in metagenomic composition; number of species present (10, 100, 426), species distribution. The sensitivity and specificity of each of the pipelines under various scenarios were measured. We also estimated the relative root mean square error and average relative error to assess the abundance estimates produced by different methods. Additional datasets were generated for five of the pipelines to simulate the presence within a metagenome of an unreferenced species, closely related to other referenced species. Additional datasets were also generated in order to measure computational time on datasets of ever-increasing sequencing depth (up to 6 × 107). Results Testing of eight pipelines against 144 simulated metagenomic datasets initially produced 1,104 discrete results. Pipelines using a marker gene strategy; MetaPhlAn2 and ConStrains, were overall less sensitive, than other pipelines; with the notable exception of Taxator-tk. This difference in sensitivity was largely made up in terms of runtime, significantly lower than more sensitive pipelines that rely on whole-genome alignments such as PathoScope2.0. However, pipelines that used strategies to speed-up alignment between genomic references and metagenomic reads, such as kmerization, were able to combine both high sensitivity and low run time, as is the case with Kraken and Centrifuge. Absent species genomes in the database mostly led to assignment of reads to the most closely related species available in all pipelines. Our results therefore suggest that taxonomic profilers that use kmerization have largely superseded those that use gene markers, coupling low run times with high sensitivity and specificity. Taxonomic profilers using more time-consuming read reassignment, such as PathoScope 2.0, provided the most sensitive profiles under common metagenomic sequencing scenarios. All the results described and discussed in this paper can be visualized using the dedicated R Shiny application (https://github.com/microgenomics/HumanMicrobiomeAnalysis). All of our datasets, pipelines and results are made available through the GitHub repository for future benchmarking.
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Benoit, Gaëtan, Pierre Peterlongo, Mahendra Mariadassou, Erwan Drezen, Sophie Schbath, Dominique Lavenier, and Claire Lemaitre. "Multiple comparative metagenomics using multisetk-mer counting." PeerJ Computer Science 2 (November 14, 2016): e94. http://dx.doi.org/10.7717/peerj-cs.94.
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BackgroundLarge scale metagenomic projects aim to extract biodiversity knowledge between different environmental conditions. Current methods for comparing microbial communities face important limitations. Those based on taxonomical or functional assignation rely on a small subset of the sequences that can be associated to known organisms. On the other hand,de novomethods, that compare the whole sets of sequences, either do not scale up on ambitious metagenomic projects or do not provide precise and exhaustive results.MethodsThese limitations motivated the development of a newde novometagenomic comparative method, called Simka. This method computes a large collection of standard ecological distances by replacing species counts byk-mer counts. Simka scales-up today’s metagenomic projects thanks to a new parallelk-mer counting strategy on multiple datasets.ResultsExperiments on public Human Microbiome Project datasets demonstrate that Simka captures the essential underlying biological structure. Simka was able to compute in a few hours both qualitative and quantitative ecological distances on hundreds of metagenomic samples (690 samples, 32 billions of reads). We also demonstrate that analyzing metagenomes at thek-mer level is highly correlated with extremely precisede novocomparison techniques which rely on all-versus-all sequences alignment strategy or which are based on taxonomic profiling.
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Nogueira, Teresa, Daniel G. Silva, Susana Lopes, and Ana Botelho. "Database of Metagenomes of Sediments from Estuarine Aquaculture Farms in Portugal—AquaRAM Project Collection." Data 7, no. 11 (November 20, 2022): 167. http://dx.doi.org/10.3390/data7110167.
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Aquaculture farms and estuarine environments close to human activities play a critical role in the interaction between aquatic and terrestrial surroundings and animal and human health. The AquaRAM project aimed to study estuarine aquaculture farms in Portugal as a reservoir of antibiotic resistance genes and the potential of its spread due to mobile genetic elements. We have assembled a collection of metagenomic data from 30 sediment samples from oysters, mussels, and gilt-head sea bream aquaculture farms. This collection includes samples of the estuarine environment of three rivers and one lagoon located from the north to the south of Portugal, namely, the Lima River in Viana do Castelo, Aveiro Lagoon in Aveiro, Tagus River in Alcochete, and Sado River in Setúbal. Statistical data from the raw metagenome files, as well as the file sizes of the assembled nucleotide and protein sequences, are also presented. The link to the statistics and the download page for all the metagenomes is also listed below.
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Saltykova, Assia, Florence E. Buytaers, Sarah Denayer, Bavo Verhaegen, Denis Piérard, Nancy H. C. Roosens, Kathleen Marchal, and Sigrid C. J. De Keersmaecker. "Strain-Level Metagenomic Data Analysis of Enriched In Vitro and In Silico Spiked Food Samples: Paving the Way towards a Culture-Free Foodborne Outbreak Investigation Using STEC as a Case Study." International Journal of Molecular Sciences 21, no. 16 (August 8, 2020): 5688. http://dx.doi.org/10.3390/ijms21165688.
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Culture-independent diagnostics, such as metagenomic shotgun sequencing of food samples, could not only reduce the turnaround time of samples in an outbreak investigation, but also allow the detection of multi-species and multi-strain outbreaks. For successful foodborne outbreak investigation using a metagenomic approach, it is, however, necessary to bioinformatically separate the genomes of individual strains, including strains belonging to the same species, present in a microbial community, which has up until now not been demonstrated for this application. The current work shows the feasibility of strain-level metagenomics of enriched food matrix samples making use of data analysis tools that classify reads against a sequence database. It includes a brief comparison of two database-based read classification tools, Sigma and Sparse, using a mock community obtained by in vitro spiking minced meat with a Shiga toxin-producing Escherichia coli (STEC) isolate originating from a described outbreak. The more optimal tool Sigma was further evaluated using in silico simulated metagenomic data to explore the possibilities and limitations of this data analysis approach. The performed analysis allowed us to link the pathogenic strains from food samples to human isolates previously collected during the same outbreak, demonstrating that the metagenomic approach could be applied for the rapid source tracking of foodborne outbreaks. To our knowledge, this is the first study demonstrating a data analysis approach for detailed characterization and phylogenetic placement of multiple bacterial strains of one species from shotgun metagenomic WGS data of an enriched food sample.
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Rodino, Kyle G., and Bobbi S. Pritt. "Novel Applications of Metagenomics for Detection of Tickborne Pathogens." Clinical Chemistry 68, no. 1 (December 30, 2021): 69–74. http://dx.doi.org/10.1093/clinchem/hvab228.
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Abstract Background Tick populations have expanded in many parts of the globe, bringing with them an enhanced appreciation and discovery of novel tickborne pathogens, as well an increased in reported human cases of tickborne disease. Targeted and unbiased (shotgun) clinical metagenomic sequencing tests are increasingly used for detection of known and emerging infectious agents and have recently been employed for detection of tickborne pathogens. Content This review describes the types of metagenomic sequencing assays used for detection of emerging tickborne pathogens and reviews the recent literature on this topic. Important diagnostic and interpretative challenges are also covered. Summary Metagenomic analysis has emerged as a powerful tool for detection, discovery, characterization, and classification of tickborne pathogens. Shotgun metagenomics is especially promising because it allows for detection of all tickborne bacteria, viruses, and parasites in a single specimen. Despite the potential advantages, there are several important challenges, including high cost, complexity of testing and interpretation, and slow turnaround time. No doubt, these challenges will diminish with increased use and advances in the field.
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Moore, Nicole E., Jing Wang, Joanne Hewitt, Dawn Croucher, Deborah A. Williamson, Shevaun Paine, Seiha Yen, Gail E. Greening, and Richard J. Hall. "Metagenomic Analysis of Viruses in Feces from Unsolved Outbreaks of Gastroenteritis in Humans." Journal of Clinical Microbiology 53, no. 1 (October 22, 2014): 15–21. http://dx.doi.org/10.1128/jcm.02029-14.
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The etiology of an outbreak of gastroenteritis in humans cannot always be determined, and ∼25% of outbreaks remain unsolved in New Zealand. It is hypothesized that novel viruses may account for a proportion of unsolved cases, and new unbiased high-throughput sequencing methods hold promise for their detection. Analysis of the fecal metagenome can reveal the presence of viruses, bacteria, and parasites which may have evaded routine diagnostic testing. Thirty-one fecal samples from 26 gastroenteritis outbreaks of unknown etiology occurring in New Zealand between 2011 and 2012 were selected forde novometagenomic analysis. A total data set of 193 million sequence reads of 150 bp in length was produced on an Illumina MiSeq. The metagenomic data set was searched for virus and parasite sequences, with no evidence of novel pathogens found. Eight viruses and one parasite were detected, each already known to be associated with gastroenteritis, including adenovirus, rotavirus, sapovirus, andDientamoeba fragilis. In addition, we also describe the first detection of human parechovirus 3 (HPeV3) in Australasia. Metagenomics may thus provide a useful audit tool when applied retrospectively to determine where routine diagnostic processes may have failed to detect a pathogen.
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Patumcharoenpol, Preecha, Massalin Nakphaichit, Gianni Panagiotou, Anchalee Senavonge, Narissara Suratannon, and Wanwipa Vongsangnak. "MetGEMs Toolbox: Metagenome-scale models as integrative toolbox for uncovering metabolic functions and routes of human gut microbiome." PLOS Computational Biology 17, no. 1 (January 6, 2021): e1008487. http://dx.doi.org/10.1371/journal.pcbi.1008487.
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Investigating metabolic functional capability of a human gut microbiome enables the quantification of microbiome changes, which can cause a phenotypic change of host physiology and disease. One possible way to estimate the functional capability of a microbial community is through inferring metagenomic content from 16S rRNA gene sequences. Genome-scale models (GEMs) can be used as scaffold for functional estimation analysis at a systematic level, however up to date, there is no integrative toolbox based on GEMs for uncovering metabolic functions. Here, we developed the MetGEMs (metagenome-scale models) toolbox, an open-source application for inferring metabolic functions from 16S rRNA gene sequences to facilitate the study of the human gut microbiome by the wider scientific community. The developed toolbox was validated using shotgun metagenomic data and shown to be superior in predicting functional composition in human clinical samples compared to existing state-of-the-art tools. Therefore, the MetGEMs toolbox was subsequently applied for annotating putative enzyme functions and metabolic routes related in human disease using atopic dermatitis as a case study.
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Shi, Yu, Guoping Wang, Harry Cheuk-Hay Lau, and Jun Yu. "Metagenomic Sequencing for Microbial DNA in Human Samples: Emerging Technological Advances." International Journal of Molecular Sciences 23, no. 4 (February 16, 2022): 2181. http://dx.doi.org/10.3390/ijms23042181.
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Whole genome metagenomic sequencing is a powerful platform enabling the simultaneous identification of all genes from entirely different kingdoms of organisms in a complex sample. This technology has revolutionised multiple areas from microbiome research to clinical diagnoses. However, one of the major challenges of a metagenomic study is the overwhelming non-microbial DNA present in most of the host-derived specimens, which can inundate the microbial signals and reduce the sensitivity of microorganism detection. Various host DNA depletion methods to facilitate metagenomic sequencing have been developed and have received considerable attention in this context. In this review, we present an overview of current host DNA depletion approaches along with explanations of their underlying principles, advantages and disadvantages. We also discuss their applications in laboratory microbiome research and clinical diagnoses and, finally, we envisage the direction of the further perfection of metagenomic sequencing in samples with overabundant host DNA.
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Petrosino, Joseph F., Sarah Highlander, Ruth Ann Luna, Richard A. Gibbs, and James Versalovic. "Metagenomic Pyrosequencing and Microbial Identification." Clinical Chemistry 55, no. 5 (May 1, 2009): 856–66. http://dx.doi.org/10.1373/clinchem.2008.107565.
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Abstract Background: The Human Microbiome Project has ushered in a new era for human metagenomics and high-throughput next-generation sequencing strategies. Content: This review describes evolving strategies in metagenomics, with a special emphasis on the core technology of DNA pyrosequencing. The challenges of microbial identification in the context of microbial populations are discussed. The development of next-generation pyrosequencing strategies and the technical hurdles confronting these methodologies are addressed. Bioinformatics-related topics include taxonomic systems, sequence databases, sequence-alignment tools, and classifiers. DNA sequencing based on 16S rRNA genes or entire genomes is summarized with respect to potential pyrosequencing applications. Summary: Both the approach of 16S rDNA amplicon sequencing and the whole-genome sequencing approach may be useful for human metagenomics, and numerous bioinformatics tools are being deployed to tackle such vast amounts of microbiological sequence diversity. Metagenomics, or genetic studies of microbial communities, may ultimately contribute to a more comprehensive understanding of human health, disease susceptibilities, and the pathophysiology of infectious and immune-mediated diseases.
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Shin, Jae Hong, and Mina Rho. "Human Resistome Study with Metagenomic Sequencing Data." Hanyang Medical Reviews 38, no. 2 (2018): 73. http://dx.doi.org/10.7599/hmr.2018.38.2.73.
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Dutton, Rachel J., and Peter J. Turnbaugh. "Taking a metagenomic view of human nutrition." Current Opinion in Clinical Nutrition and Metabolic Care 15, no. 5 (September 2012): 448–54. http://dx.doi.org/10.1097/mco.0b013e3283561133.
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YAMADA, Takuji. "Human Gut Metagenomic Analysis toward Clinical Studies." KAGAKU TO SEIBUTSU 51, no. 12 (2013): 802–8. http://dx.doi.org/10.1271/kagakutoseibutsu.51.802.
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Magiorkinis, Gkikas, Philippa C. Matthews, Susan E. Wallace, Katie Jeffery, Kevin Dunbar, Richard Tedder, Jean L. Mbisa, et al. "Potential for diagnosis of infectious disease from the 100,000 Genomes Project Metagenomic Dataset: Recommendations for reporting results." Wellcome Open Research 4 (October 14, 2019): 155. http://dx.doi.org/10.12688/wellcomeopenres.15499.1.
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The identification of microbiological infection is usually a diagnostic investigation, a complex process that is firstly initiated by clinical suspicion. With the emergence of high-throughput sequencing (HTS) technologies, metagenomic analysis has unveiled the power to identify microbial DNA/RNA from a diverse range of clinical samples (1). Metagenomic analysis of whole human genomes at the clinical/research interface bypasses the steps of clinical scrutiny and targeted testing and has the potential to generate unexpected findings relating to infectious and sometimes transmissible disease. There is no doubt that microbial findings that may have a significant impact on a patient’s treatment and their close contacts should be reported to those with clinical responsibility for the sample-donating patient. There are no clear recommendations on how such findings that are incidental, or outside the original investigation, should be handled. Here we aim to provide an informed protocol for the management of incidental microbial findings as part of the 100,000 Genomes Project which may have broader application in this emerging field. As with any other clinical information, we aim to prioritise the reporting of data that are most likely to be of benefit to the patient and their close contacts. We also set out to minimize risks, costs and potential anxiety associated with the reporting of results that are unlikely to be of clinical significance. Our recommendations aim to support the practice of microbial metagenomics by providing a simplified pathway that can be applied to reporting the identification of potential pathogens from metagenomic datasets. Given that the ambition for UK sequenced human genomes over the next 5 years has been set to reach 5 million and the field of metagenomics is rapidly evolving, the guidance will be regularly reviewed and will likely adapt over time as experience develops.
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Kostryukova, E. S., I. Y. Karpova, A. K. Larin, A. C. Popenko, A. V. Tyaht, and E. N. Ilina. "Variability in the relative quantity of human DNA resulted from metagenomic analysis of gut microbiota." Biomeditsinskaya Khimiya 60, no. 6 (2014): 695–701. http://dx.doi.org/10.18097/pbmc20146006695.
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We conducted the comparative study of seven different methods of total DNA extraction from human feces. All these methods are recommended in protocols for metagenomic analysis of human gut microbiota. We studied the relative quantity of human DNA calculated from shotgun sequencing on a SOLiD 4 genetic analyzer of metagenomic samples. It was shown that either initial amount of feces or a method applied for total DNA extraction do not affect on final relative human DNA abundance, which is less than 1% in healthy people. Invariance of this parameter allows to consider increased abundance of human DNA in metagenomic samples as a potential marker of inflammatory bowel diseases.
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Sereno, Denis, Franck Dorkeld, Mohammad Akhoundi, and Pascale Perrin. "Pathogen Species Identification from Metagenomes in Ancient Remains: The Challenge of Identifying Human Pathogenic Species of Trypanosomatidae via Bioinformatic Tools." Genes 9, no. 8 (August 20, 2018): 418. http://dx.doi.org/10.3390/genes9080418.
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Accurate species identification from ancient DNA samples is a difficult task that would shed light on the evolutionary history of pathogenic microorganisms. The field of palaeomicrobiology has undoubtedly benefited from the advent of untargeted metagenomic approaches that use next-generation sequencing methodologies. Nevertheless, assigning ancient DNA at the species level is a challenging process. Recently, the gut microbiome analysis of three pre-Columbian Andean mummies (Santiago-Rodriguez et al., 2016) has called into question the identification of Leishmania in South America. The accurate assignment would be important because it will provide some key elements that are linked to the evolutionary scenario for visceral leishmaniasis agents in South America. Here, we recovered the metagenomic data filed in the metagenomics RAST server (MG-RAST) to identify the different members of the Trypanosomatidae family that have infected these ancient remains. For this purpose, we used the ultrafast metagenomic sequence classifier, based on an exact alignment of k-mers (Kraken) and Bowtie2, an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. The analyses, which have been conducted on the most exhaustive genomic database possible on Trypanosomatidae, show that species assignments could be biased by a lack of some genomic sequences of Trypanosomatidae species (strains). Nevertheless, our work raises the issue of possible co-infections by multiple members of the Trypanosomatidae family in these three pre-Columbian mummies. In the three mummies, we show the presence of DNA that is reminiscent of a probable co-infection with Leptomonas seymouri, a parasite of insect’s gut, and Lotmaria.
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Buttler, Jeremy, and Devin M. Drown. "Accuracy and Completeness of Long Read Metagenomic Assemblies." Microorganisms 11, no. 1 (December 30, 2022): 96. http://dx.doi.org/10.3390/microorganisms11010096.
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Microbes influence the surrounding environment and contribute to human health. Metagenomics can be used as a tool to explore the interactions between microbes. Metagenomic assemblies built using long read nanopore data depend on the read level accuracy. The read level accuracy of nanopore sequencing has made dramatic improvements over the past several years. However, we do not know if the increased read level accuracy allows for faster assemblers to make as accurate metagenomic assemblies as slower assemblers. Here, we present the results of a benchmarking study comparing three commonly used long read assemblers, Flye, Raven, and Redbean. We used a prepared DNA standard of seven bacteria as our input community. We prepared a sequencing library using a VolTRAX V2 and sequenced using a MinION mk1b. We basecalled with Guppy v5.0.7 using the super-accuracy model. We found that increasing read depth benefited each of the assemblers, and nearly complete community member chromosomes were assembled with as little as 10× read depth. Polishing assemblies using Medaka had a predictable improvement in quality. We found Flye to be the most robust across taxa and was the most effective assembler for recovering plasmids. Based on Flye’s consistency for chromosomes and increased effectiveness at assembling plasmids, we would recommend using Flye in future metagenomic studies.
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Bengtsson-Palme, Johan, Martin Angelin, Mikael Huss, Sanela Kjellqvist, Erik Kristiansson, Helena Palmgren, D. G. Joakim Larsson, and Anders Johansson. "The Human Gut Microbiome as a Transporter of Antibiotic Resistance Genes between Continents." Antimicrobial Agents and Chemotherapy 59, no. 10 (August 10, 2015): 6551–60. http://dx.doi.org/10.1128/aac.00933-15.
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ABSTRACTPrevious studies of antibiotic resistance dissemination by travel have, by targeting only a select number of cultivable bacterial species, omitted most of the human microbiome. Here, we used explorative shotgun metagenomic sequencing to address the abundance of >300 antibiotic resistance genes in fecal specimens from 35 Swedish students taken before and after exchange programs on the Indian peninsula or in Central Africa. All specimens were additionally cultured for extended-spectrum beta-lactamase (ESBL)-producing enterobacteria, and the isolates obtained were genome sequenced. The overall taxonomic diversity and composition of the gut microbiome remained stable before and after travel, but there was an increasing abundance ofProteobacteriain 25/35 students. The relative abundance of antibiotic resistance genes increased, most prominently for genes encoding resistance to sulfonamide (2.6-fold increase), trimethoprim (7.7-fold), and beta-lactams (2.6-fold). Importantly, the increase observed occurred without any antibiotic intake. Of 18 students visiting the Indian peninsula, 12 acquired ESBL-producingEscherichia coli, while none returning from Africa were positive. Despite deep sequencing efforts, the sensitivity of metagenomics was not sufficient to detect acquisition of the low-abundant genes responsible for the observed ESBL phenotype. In conclusion, metagenomic sequencing of the intestinal microbiome of Swedish students returning from exchange programs in Central Africa or the Indian peninsula showed increased abundance of genes encoding resistance to widely used antibiotics.
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Ivy, Morgan, Matthew Thoendel, Patricio Jeraldo, Kerryl Greenwood-Quaintance, Arlen D. Hanssen, Matthew Abdel, Nicholas Chia, et al. "Direct Detection and Identification of Prosthetic Joint Pathogens in Synovial Fluid (SF) by Metagenomic Shotgun Sequencing." Open Forum Infectious Diseases 4, suppl_1 (2017): S32. http://dx.doi.org/10.1093/ofid/ofx162.078.
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Abstract Background Detection and identification of microorganism(s) involved in periprosthetic joint infection (PJI) can inform surgical management and directed antibiotic therapy. Metagenomic shotgun sequencing is a powerful tool with the potential to change how many PJIs are diagnosed as it allows direct detection and identification of pathogens in clinical specimens. In the largest series to date, we utilized a metagenomics-based approach applied to SF to define potential microbial etiologies of failed total knee arthroplasties (TKAs). Methods Synovial fluid was collected from 112 failed TKAs [74 PJI and 38 aseptic implant failure (AF)] via preoperative arthrocentesis. Cell count and differential, standardized culture and DNA-based metagenomic shotgun sequencing were performed. Human DNA was depleted using the MolYsis basic kit prior to DNA extraction, whole genome amplification, and sequencing. Taxonomic assignment of reads and pathogen identification was achieved using a pipeline incorporating k-mer- and marker gene-based classification software. A scheme for analysis and filtration of false-positives was created and applied, incorporating cut-offs for the number of reads, quality scores, and coverage across a reference genome. Patients were classified as having PJI using the IDSA criteria and expert review. Analyses were recorded as percent agreement, with 95% confidence intervals (CI), of metagenomics to SF culture. Results Metagenomic analysis identified the known pathogen in 54 (90%) (CI, 79.5%–96.2%) of the 60 culture-positive PJIs analyzed and one (2%) (CI, 0.0%–8.9%) potential polymicrobial infection not detected by culture. For the 14 culture-negative PJIs tested, metagenomics showed 79% (CI, 49.2%–95.3%) agreement for negative findings; potential pathogens were identified in three (21%) (CI, 4.7%–50.8%) culture-negative PJI cases, with one being polymicrobial. Of the 37 culture-negative AF cases, metagenomics showed 97% (CI, 85.8%–99.9%) agreement with negative culture and identified one (3%) (CI, 0.0%–14.2%) potential pathogen. For the one culture-positive AF case, metagenomic results were negative, suggesting possible culture contamination. Conclusion Metagenomic shotgun sequencing performed on SF can be used to diagnose PJI and may be particularly useful for culture-negative PJI. Disclosures R. Patel, ASM: Board Member, None; CD Diagnostics, BioFire, Curetis, Merck, Hutchison Biofilm Medical Solutions, Accelerate Diagnostics, Allergan, and The Medicines Company: Grant Investigator, Grant recipient; Curetis: Consultant, Monies paid to my employer; A patent on Bordetella pertussis/parapertussis PCR issued, a patent on a device/method for sonication with royalties paid by Samsung to Mayo Clinic, and a patent on an anti-biofilm substance issued: Patents, Patents, any money is paid to my employer; Actelion: DSMB, Money paid to my employer; ASM and IDSA: Editor’s stipends, Editor’s stipends; NBME, Up-to-Date and the Infectious Diseases Board Review Course: NBME, Up-to-Date and the Infectious Diseases Board Review Course, Honoraria; Roche, ASM, and IDSA: Travel reimbursement, Travel reimbursement
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Devaraj, Sridevi, Peera Hemarajata, and James Versalovic. "The Human Gut Microbiome and Body Metabolism: Implications for Obesity and Diabetes." Clinical Chemistry 59, no. 4 (April 1, 2013): 617–28. http://dx.doi.org/10.1373/clinchem.2012.187617.
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BACKGROUND Obesity, metabolic syndrome, and type 2 diabetes are major public health challenges. Recently, interest has surged regarding the possible role of the intestinal microbiota as potential novel contributors to the increased prevalence of these 3 disorders. CONTENT Recent advances in microbial DNA sequencing technologies have resulted in the widespread application of whole-genome sequencing technologies for metagenomic DNA analysis of complex ecosystems such as the human gut. Current evidence suggests that the gut microbiota affect nutrient acquisition, energy harvest, and a myriad of host metabolic pathways. CONCLUSION Advances in the Human Microbiome Project and human metagenomics research will lead the way toward a greater understanding of the importance and role of the gut microbiome in metabolic disorders such as obesity, metabolic syndrome, and diabetes.
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Dai, Die, Jiaying Zhu, Chuqing Sun, Min Li, Jinxin Liu, Sicheng Wu, Kang Ning, Li-jie He, Xing-Ming Zhao, and Wei-Hua Chen. "GMrepo v2: a curated human gut microbiome database with special focus on disease markers and cross-dataset comparison." Nucleic Acids Research 50, no. D1 (November 12, 2021): D777—D784. http://dx.doi.org/10.1093/nar/gkab1019.
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Abstract GMrepo (data repository for Gut Microbiota) is a database of curated and consistently annotated human gut metagenomes. Its main purposes are to increase the reusability and accessibility of human gut metagenomic data, and enable cross-project and phenotype comparisons. To achieve these goals, we performed manual curation on the meta-data and organized the datasets in a phenotype-centric manner. GMrepo v2 contains 353 projects and 71,642 runs/samples, which are significantly increased from the previous version. Among these runs/samples, 45,111 and 26,531 were obtained by 16S rRNA amplicon and whole-genome metagenomics sequencing, respectively. We also increased the number of phenotypes from 92 to 133. In addition, we introduced disease-marker identification and cross-project/phenotype comparison. We first identified disease markers between two phenotypes (e.g. health versus diseases) on a per-project basis for selected projects. We then compared the identified markers for each phenotype pair across datasets to facilitate the identification of consistent microbial markers across datasets. Finally, we provided a marker-centric view to allow users to check if a marker has different trends in different diseases. So far, GMrepo includes 592 marker taxa (350 species and 242 genera) for 47 phenotype pairs, identified from 83 selected projects. GMrepo v2 is freely available at: https://gmrepo.humangut.info.
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Huy, Pham Quang, Nguyen Kim Thoa, and Dang Thi Cam Ha. "Diversity of reductive dechlorinating bacteria and archaea in herbicide/dioxin-contaminated soils from Bien Hoa airbase using metagenomic approach." Vietnam Journal of Biotechnology 18, no. 4 (May 24, 2021): 773–84. http://dx.doi.org/10.15625/1811-4989/18/4/15799.
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Heavy herbicide/dioxin contamination of soil was derived a negative effect on the microbial biodiversity, soil quality, animal and human health in Central and South of Vietnam. This is the first time, the application metagenomic tools investigated soil microbial structural community of undetoxified (C - 21,605 ng TEQ/kg dry soil) and bioremediated (BHR - 13.2 ng TEQ/kg dry soil) which could not only help us to explore the potential risks associated with contaminated soils but also provide insights into possible soil bioremediation technology by stimulating indigenous microbes. Four methanogen genera, Methanosarcina (24 - 322 OTUs respectively C – BHR samples), Methanocella (13 - 63 OTUs), Methanosaeta (7 - 42 OTUs) and Methanococcus (6 - 69 OTUs) have been dominantly detected in both two metagenomes. Twenty genera of archaea belonging to the phylum Euryarchaeota were found. They could be clustered within 14 different families and nine archaeal genera including unclassified archaea (17 OTUs – C; 145 OTUs - BHR). In metagenome C and BHR, 12 genera of sulfate reducing bacteria (SRB) with different number (2 - 77; 61 - 904 OTUs) respectively were presented. Four SRB genera are dominated in C metagenome, it is linear also in BHR. The highest number is genus Desulfovibrio detected in both examined metagenomes. However, the relationship features of these bacterial groups need deeply investigation for understanding their role of reductive dechlorination, anaerobic degradation in herbicide/dioxin contaminated heavy soil and sediment. These results provide additional evidence to explain why heavy herbicide/dioxin contaminated soil was detoxified successfully at Bien Hoa airbase, Vietnam.
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Cao, Yang, Xiaofei Zheng, Fei Li, and Xiaochen Bo. "mmnet: An R Package for Metagenomics Systems Biology Analysis." BioMed Research International 2015 (2015): 1–5. http://dx.doi.org/10.1155/2015/167249.
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The human microbiome plays important roles in human health and disease. Previous microbiome studies focused mainly on single pure species function and overlooked the interactions in the complex communities on system-level. A metagenomic approach introduced recently integrates metagenomic data with community-level metabolic network modeling, but no comprehensive tool was available for such kind of approaches. To facilitate these kinds of studies, we developed an R package,mmnet, to implement community-level metabolic network reconstruction. The package also implements a set of functions for automatic analysis pipeline construction including functional annotation of metagenomic reads, abundance estimation of enzymatic genes, community-level metabolic network reconstruction, and integrated network analysis. The result can be represented in an intuitive way and sent to Cytoscape for further exploration. The package has substantial potentials in metagenomic studies that focus on identifying system-level variations of human microbiome associated with disease.
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Lugli, Gabriele Andrea, Sabrina Duranti, Christian Milani, Leonardo Mancabelli, Francesca Turroni, Douwe van Sinderen, and Marco Ventura. "Uncovering Bifidobacteria via Targeted Sequencing of the Mammalian Gut Microbiota." Microorganisms 7, no. 11 (November 6, 2019): 535. http://dx.doi.org/10.3390/microorganisms7110535.
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Bifidobacteria are among the most prevalent gut commensals in mammals, playing crucial functional roles that start from their early colonization of the infant gastrointestinal tract and last throughout the life span of their host. Metagenomic approaches have been employed to unveil the genetic features of bifidobacteria in order to understand how they participate in the correct development of a healthy microbiome. Nevertheless, their low relative abundance in many environmental samples may represent a major limitation for metagenomics approaches. To overcome this restriction, we applied an enrichment method that allows amplification of bifidobacterial DNA obtained from human or animal fecal samples for up to 26,500-fold, resulting in the metagenomic reconstruction of genomes belonging to bifidobacterial strains, present at very low abundance in collected samples. Functional predictions of the genes from these reconstructed genomes allows us to identify unique signatures among members of the same bifidobacterial species, highlighting genes correlated with the uptake of nutrients and adhesion to the intestinal mucosa.
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Fulci, Valerio, Laura Stronati, Salvatore Cucchiara, Ilaria Laudadio, and Claudia Carissimi. "Emerging Roles of Gut Virome in Pediatric Diseases." International Journal of Molecular Sciences 22, no. 8 (April 16, 2021): 4127. http://dx.doi.org/10.3390/ijms22084127.
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In the last decade, the widespread application of shotgun metagenomics provided extensive characterization of the bacterial “dark matter” of the gut microbiome, propelling the development of dedicated, standardized bioinformatic pipelines and the systematic collection of metagenomic data into comprehensive databases. The advent of next-generation sequencing also unravels a previously underestimated viral population (virome) present in the human gut. Despite extensive efforts to characterize the human gut virome, to date, little is known about the childhood gut virome. However, alterations of the gut virome in children have been linked to pathological conditions such as inflammatory bowel disease, type 1 diabetes, malnutrition, diarrhea and celiac disease.
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Lai, Yi Yu, Yanming Li, Jidong Lang, Xunliang Tong, Lina Zhang, Jianhuo Fang, Jingli Xing, et al. "Metagenomic Human Repiratory Air in a Hospital Environment." PLOS ONE 10, no. 10 (October 2, 2015): e0139044. http://dx.doi.org/10.1371/journal.pone.0139044.
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Gill, S. R., M. Pop, R. T. DeBoy, P. B. Eckburg, P. J. Turnbaugh, B. S. Samuel, J. I. Gordon, D. A. Relman, C. M. Fraser-Liggett, and K. E. Nelson. "Metagenomic Analysis of the Human Distal Gut Microbiome." Science 312, no. 5778 (June 2, 2006): 1355–59. http://dx.doi.org/10.1126/science.1124234.
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Zhang, Quan, Thomas G. Doak, and Yuzhen Ye. "Expanding the catalog of cas genes with metagenomes." Nucleic Acids Research 42, no. 4 (December 5, 2013): 2448–59. http://dx.doi.org/10.1093/nar/gkt1262.
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Abstract The CRISPR (clusters of regularly interspaced short palindromic repeats)–Cas adaptive immune system is an important defense system in bacteria, providing targeted defense against invasions of foreign nucleic acids. CRISPR–Cas systems consist of CRISPR loci and cas (CRISPR-associated) genes: sequence segments of invaders are incorporated into host genomes at CRISPR loci to generate specificity, while adjacent cas genes encode proteins that mediate the defense process. We pursued an integrated approach to identifying putative cas genes from genomes and metagenomes, combining similarity searches with genomic neighborhood analysis. Application of our approach to bacterial genomes and human microbiome datasets allowed us to significantly expand the collection of cas genes: the sequence space of the Cas9 family, the key player in the recently engineered RNA-guided platforms for genome editing in eukaryotes, is expanded by at least two-fold with metagenomic datasets. We found genes in cas loci encoding other functions, for example, toxins and antitoxins, confirming the recently discovered potential of coupling between adaptive immunity and the dormancy/suicide systems. We further identified 24 novel Cas families; one novel family contains 20 proteins, all identified from the human microbiome datasets, illustrating the importance of metagenomics projects in expanding the diversity of cas genes.
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Parras-Moltó, Marcos, and Daniel Aguirre de Cárcer. "A comprehensive human minimal gut metagenome extends the host’s metabolic potential." Microbial Genomics 6, no. 11 (November 1, 2020). http://dx.doi.org/10.1099/mgen.0.000466.
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Accumulating evidence suggests that humans could be considered as holobionts in which the gut microbiota play essential functions. Initial metagenomic studies reported a pattern of shared genes in the gut microbiome of different individuals, leading to the definition of the minimal gut metagenome as the set of microbial genes necessary for homeostasis and present in all healthy individuals. This study analyses the minimal gut metagenome of the most comprehensive dataset available, including individuals from agriculturalist and industrialist societies, also embodying highly diverse ethnic and geographical backgrounds. The outcome, based on metagenomic predictions for community composition data, resulted in a minimal metagenome comprising 3412 genes, mapping to 1856 reactions and 128 metabolic pathways predicted to occur across all individuals. These results were substantiated by the analysis of two additional datasets describing the microbial community compositions of larger Western cohorts, as well as a substantial shotgun metagenomics dataset. Subsequent analyses showed the plausible metabolic complementarity provided by the minimal gut metagenome to the human genome.
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Liu, Pu, Shuofeng Hu, Zhen He, Chao Feng, Guohua Dong, Sijing An, Runyan Liu, Fang Xu, Yaowen Chen, and Xiaomin Ying. "Towards Strain-Level Complexity: Sequencing Depth Required for Comprehensive Single-Nucleotide Polymorphism Analysis of the Human Gut Microbiome." Frontiers in Microbiology 13 (May 5, 2022). http://dx.doi.org/10.3389/fmicb.2022.828254.
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Intestinal bacteria strains play crucial roles in maintaining host health. Researchers have increasingly recognized the importance of strain-level analysis in metagenomic studies. Many analysis tools and several cutting-edge sequencing techniques like single cell sequencing have been proposed to decipher strains in metagenomes. However, strain-level complexity is far from being well characterized up to date. As the indicator of strain-level complexity, metagenomic single-nucleotide polymorphisms (SNPs) have been utilized to disentangle conspecific strains. Lots of SNP-based tools have been developed to identify strains in metagenomes. However, the sufficient sequencing depth for SNP and strain-level analysis remains unclear. We conducted ultra-deep sequencing of the human gut microbiome and constructed an unbiased framework to perform reliable SNP analysis. SNP profiles of the human gut metagenome by ultra-deep sequencing were obtained. SNPs identified from conventional and ultra-deep sequencing data were thoroughly compared and the relationship between SNP identification and sequencing depth were investigated. The results show that the commonly used shallow-depth sequencing is incapable to support a systematic metagenomic SNP discovery. In contrast, ultra-deep sequencing could detect more functionally important SNPs, which leads to reliable downstream analyses and novel discoveries. We also constructed a machine learning model to provide guidance for researchers to determine the optimal sequencing depth for their projects (SNPsnp, https://github.com/labomics/SNPsnp). To conclude, the SNP profiles based on ultra-deep sequencing data extend current knowledge on metagenomics and highlights the importance of evaluating sequencing depth before starting SNP analysis. This study provides new ideas and references for future strain-level investigations.
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Soverini, Matteo, Simone Rampelli, Silvia Turroni, Patrizia Brigidi, Elena Biagi, and Marco Candela. "Do the human gut metagenomic species possess the minimal set of core functionalities necessary for life?" BMC Genomics 21, no. 1 (September 30, 2020). http://dx.doi.org/10.1186/s12864-020-07087-8.
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Abstract Background Advances in bioinformatics recently allowed for the recovery of ‘metagenomes assembled genomes’ from human microbiome studies carried on with shotgun sequencing techniques. Such approach is used as a mean to discover new unclassified metagenomic species, putative biological entities having distinct metabolic traits. Results In the present analysis we compare 400 genomes from isolates available on NCBI database and 10,000 human gut metagenomic species, screening all of them for the presence of a minimal set of core functionalities necessary, but not sufficient, for life. As a result, the metagenome-assembled genomes resulted systematically depleted in genes encoding for essential functions apparently needed to support autonomous bacterial life. Conclusions The relevant degree of lacking core functionalities that we observed in metagenome-assembled genomes raises some concerns about the effective completeness of metagenome-assembled genomes, suggesting caution in extrapolating biological information about their metabolic propensity and ecology in a complex environment like the human gastrointestinal tract.
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Song, Kai. "Reads Binning Improves the Assembly of Viral Genome Sequences From Metagenomic Samples." Frontiers in Microbiology 12 (May 21, 2021). http://dx.doi.org/10.3389/fmicb.2021.664560.
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Metagenomes can be considered as mixtures of viral, bacterial, and other eukaryotic DNA sequences. Mining viral sequences from metagenomes could shed insight into virus–host relationships and expand viral databases. Current alignment-based methods are unsuitable for identifying viral sequences from metagenome sequences because most assembled metagenomic contigs are short and possess few or no predicted genes, and most metagenomic viral genes are dissimilar to known viral genes. In this study, I developed a Markov model-based method, VirMC, to identify viral sequences from metagenomic data. VirMC uses Markov chains to model sequence signatures and construct a scoring model using a likelihood test to distinguish viral and bacterial sequences. Compared with the other two state-of-the-art viral sequence-prediction methods, VirFinder and PPR-Meta, my proposed method outperformed VirFinder and had similar performance with PPR-Meta for short contigs with length less than 400 bp. VirMC outperformed VirFinder and PPR-Meta for identifying viral sequences in contaminated metagenomic samples with eukaryotic sequences. VirMC showed better performance in assembling viral-genome sequences from metagenomic data (based on filtering potential bacterial reads). Applying VirMC to human gut metagenomes from healthy subjects and patients with type-2 diabetes (T2D) revealed that viral contigs could help classify healthy and diseased statuses. This alignment-free method complements gene-based alignment approaches and will significantly improve the precision of viral sequence identification.