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Metagenomics is a rapidly growing field of research that aims at studying uncultured organisms to understand the true diversity of microbes, their functions, cooperation and evolution, in environments such as soil, water, ancient remains of animals, or the digestive system of animals and humans. The recent development of ultra-high throughput sequencing technologies, which do not require cloning or PCR amplification, and can produce huge numbers of DNA reads at an affordable cost, has boosted the number and scope of metagenomic sequencing projects. Increasingly, there is a need for new ways of comparing multiple metagenomics datasets, and for fast and user-friendly implementations of such approaches.
JCVI Metagenomics Reports (METAREP) is a Web 2.0 application designed to help scientists analyze and compare annotated metagenomics datasets. It utilizes Solr/Lucene, a high-performance scalable search engine, to quickly query large data collections. Furthermore, users can use its SQL-like query syntax to filter and refine datasets. METAREP provides graphical summaries for top taxonomic and functional classifications as well as a GO, NCBI Taxonomy and KEGG Pathway Browser. Users can compare absolute and relative counts of multiple datasets at various functional and taxonomic levels. Advanced comparative features comprise statistical tests as well as multidimensional scaling, heatmap and hierarchical clustering plots. Summaries can be exported as tab-delimited files, publication quality plots in PDF format. A data management layer allows collaborative data analysis and result sharing.
The term metagenomics refers to the use of sequencing methods to simultaneously identify genomic material from all organisms present in a sample, with the advantage of greater taxonomic resolution than culture or other methods. Applications include pathogen detection and discovery, species characterisation, antimicrobial resistance detection, virulence profiling, and study of the microbiome and microecological factors affecting health. However, metagenomics involves complex and multistep processes and there are important technical and methodological challenges that require careful consideration to support valid inference. We co-ordinated a multidisciplinary, international expert group to establish reporting guidelines that address specimen processing, nucleic acid extraction, sequencing platforms, bioinformatics considerations, quality assurance, limits of detection, power and sample size, confirmatory testing, causality criteria, cost, and ethical issues. The guidance recognises that metagenomics research requires pragmatism and caution in interpretation, and that this field is rapidly evolving.
It is widely recognized that most microbes in the biosphere remain uncultured and unknown. In the recent few years, whole genome shotgun (WGS) sequencing of environmental DNA (metagenomics) has revolutionized the field of environmental microbiology by allowing one to tap into the genomic content of microbial communities in specific ecological niches, deducing information on their biochemical potentials. However, ascribing specific functions to specific organisms remains very difficult in most cases, due to low sequence coverage and the lack of sequence assembly that result from metagenomics of complex microbial communities. Therefore, methods that link specific biogeochemical processes to specific members of such complex natural communities are urgently needed. We have developed and implemented a functional metagenomics approach that allows such a connection via substrate-specific stable isotope labeling, followed by WGS sequencing of the labeled DNA to describe bacterial populations involved in metabolism of single-carbon compounds in a freshwater lake. We also developed a pipeline for community transcript analysis based on ultrashort read high-throughput sequencing of messenger RNA, matching these to a specific scaffold. The methodologies described in this chapter can be applied in a wide variety of ecosystems for targeting methylotrophs as well as other functional guilds of microbes.
Metagenomics is a discipline that enables the genomic study of uncultured microorganisms. Faster, cheaper sequencing technologies and the ability to sequence uncultured microbes sampled directly from their habitats are expanding and transforming our view of the microbial world. Distilling meaningful information from the millions of new genomic sequences presents a serious challenge to bioinformaticians. In cultured microbes, the genomic data come from a single clone, making sequence assembly and annotation tractable. In metagenomics, the data come from heterogeneous microbial communities, sometimes containing more than 10,000 species, with the sequence data being noisy and partial. From sampling, to assembly, to gene calling and function prediction, bioinformatics faces new demands in interpreting voluminous, noisy, and often partial sequence data. Although metagenomics is a relative newcomer to science, the past few years have seen an explosion in computational methods applied to metagenomic-based research. It is therefore not within the scope of this article to provide an exhaustive review. Rather, we provide here a concise yet comprehensive introduction to the current computational requirements presented by metagenomics, and review the recent progress made. We also note whether there is software that implements any of the methods presented here, and briefly review its utility. Nevertheless, it would be useful if readers of this article would avail themselves of the comment section provided by this journal, and relate their own experiences. Finally, the last section of this article provides a few representative studies illustrating different facets of recent scientific discoveries made using metagenomics.
The microbiome is a complex community of Bacteria, Archaea, Eukarya, and viruses that infect humans and live in our tissues. It contributes the majority of genetic information to our metagenome and, consequently, influences our resistance and susceptibility to diseases, especially common inflammatory diseases, such as type 1 diabetes, ulcerative colitis, and Crohn's disease. Here we discuss how host-gene-microbial interactions are major determinants for the development of these multifactorial chronic disorders and, thus, for the relationship between genotype and phenotype. We also explore how genome-wide association studies (GWAS) on autoimmune and inflammatory diseases are uncovering mechanism-based subtypes for these disorders. Applying these emerging concepts will permit a more complete understanding of the etiologies of complex diseases and underpin the development of both next-generation animal models and new therapeutic strategies for targeting personalized disease phenotypes.
Metagenomics analysis was carried out on extracted DNA of Rhizospheric soil samples from Bambara groundnut. This dataset presented reports on the bacterial communities at the different growth stages of Bambara groundnut and the bulk soil. Paired-end Illumina-Miseq sequencing of 16S rRNA genes was carried on the soil samples of the bacterial community with the phyla dominated by Actinobacteria (30.1%), Proteobacteria (22%), Acidobacteria (20.9%), Bacteroides (8.4%), Chloroflex (4.5%) and Firmicutes (4.4%) in all the soil samples. Samples from the bulk soil had the least average percent phyla, while samples at seed maturity stage had the highest average percent phyla. The alpha diversity at p = 0.05 was highest at this stage compared to the others and the control. Rubrobacter was the most predominant genera, after which is Acidobacterium and Skermanella. The biodiversity profile generated from the metagenomics analysis is useful in increasing knowledge of the drought-tolerance ability of Bambara groundnut. The data generated can be used to compare bacterial diversity at different growth stages of plants.
Understanding the role of the microbiome in human health and how it can be modulated is becoming increasingly relevant for preventive medicine and for the medical management of chronic diseases. The development of high-throughput sequencing technologies has boosted microbiome research through the study of microbial genomes and allowing a more precise quantification of microbiome abundances and function. Microbiome data analysis is challenging because it involves high-dimensional structured multivariate sparse data and because of its compositional nature. In this review we outline some of the procedures that are most commonly used for microbiome analysis and that are implemented in R packages. We place particular emphasis on the compositional structure of microbiome data. We describe the principles of compositional data analysis and distinguish between standard methods and those that fit into compositional data analysis.
When a bacterial genome is compared to the metagenome of an environment it inhabits, most genes recruit at high sequence identity. In free-living bacteria (for instance marine bacteria compared against the ocean metagenome) certain genomic regions are totally absent in recruitment plots, representing therefore genes unique to individual bacterial isolates. We show that these Metagenomic Islands (MIs) are also visible in bacteria living in human hosts when their genomes are compared to sequences from the human microbiome, despite the compartmentalized structure of human-related environments such as the gut. From an applied point of view, MIs of human pathogens (e.g. those identified in enterohaemorragic Escherichia coli against the gut metagenome or in pathogenic Neisseria meningitidis against the oral metagenome) include virulence genes that appear to be absent in related strains or species present in the microbiome of healthy individuals. We propose that this strategy (i.e. recruitment analysis of pathogenic bacteria against the metagenome of healthy subjects) can be used to detect pathogenicity regions in species where the genes involved in virulence are poorly characterized. Using this approach, we detect well-known pathogenicity islands and identify new potential virulence genes in several human pathogens.
The development of next-generation sequencing (NGS) platforms spawned an enormous volume of data. This explosion in data has unearthed new scalability challenges for existing bioinformatics tools. The analysis of metagenomic sequences using bioinformatics pipelines is complicated by the substantial complexity of these data. In this article, we review several commonly-used online tools for metagenomics data analysis with respect to their quality and detail of analysis using simulated metagenomics data. There are at least a dozen such software tools presently available in the public domain. Among them, MGRAST, IMG/M, and METAVIR are the most well-known tools according to the number of citations by peer-reviewed scientific media up to mid-2015. Here, we describe 12 online tools with respect to their web link, annotation pipelines, clustering methods, online user support, and availability of data storage. We have also done the rating for each tool to screen more potential and preferential tools and evaluated five best tools using synthetic metagenome. The article comprehensively deals with the contemporary problems and the prospects of metagenomics from a bioinformatics viewpoint.
Identifying the causative pathogen in central nervous system (CNS) infections is crucial for patient management and prognosis. Many viruses can cause CNS infections, yet screening for each individually is costly and time-consuming. Most metagenomic assays can theoretically detect all pathogens, but often fail to detect viruses because of their small genome and low viral load. Viral metagenomics overcomes this by enrichment of the viral genomic content in a sample. VIDISCA-NGS is one of the available workflows for viral metagenomics, which requires only a small input volume and allows multiplexing of multiple samples per run. The performance of VIDISCA-NGS was tested on 45 cerebrospinal fluid (CSF) samples from patients with suspected CNS infections in which a virus was identified and quantified by polymerase chain reaction. Eighteen were positive for an RNA virus, and 34 for a herpesvirus. VIDISCA-NGS detected all RNA viruses with a viral load >2 × 104 RNA copies/mL (n = 6) and 8 of 12 of the remaining low load samples. Only one herpesvirus was identified by VIDISCA-NGS, however, when withholding a DNase treatment, 11 of 18 samples with a herpesvirus load >104 DNA copies/mL were detected. Our results indicate that VIDISCA-NGS has the capacity to detect low load RNA viruses in CSF. Herpesvirus DNA in clinical samples is probably non-encapsidated and therefore difficult to detect by VIDISCA-NGS.
The metagenomic data obtained from marine environments is significantly useful for understanding marine microbial communities. In comparison with the conventional amplicon-based approach of metagenomics, the recent shotgun sequencing-based approach has become a powerful tool that provides an efficient way of grasping a diversity of the entire microbial community at a sampling point in the sea. However, this approach accelerates accumulation of the metagenome data as well as increase of data complexity. Moreover, when metagenomic approach is used for monitoring a time change of marine environments at multiple locations of the seawater, accumulation of metagenomics data will become tremendous with an enormous speed. Because this kind of situation has started becoming of reality at many marine research institutions and stations all over the world, it looks obvious that the data management and analysis will be confronted by the so-called Big Data issues such as how the database can be constructed in an efficient way and how useful knowledge should be extracted from a vast amount of the data. In this review, we summarize the outline of all the major databases of marine metagenome that are currently publically available, noting that database exclusively on marine metagenome is none but the number of metagenome databases including marine metagenome data are six, unexpectedly still small. We also extend our explanation to the databases, as reference database we call, that will be useful for constructing a marine metagenome database as well as complementing important information with the database. Then, we would point out a number of challenges to be conquered in constructing the marine metagenome database.
This review describes the recent advances in the study of food microbial ecology, with a focus on food fermentations. High-throughput sequencing (HTS) technologies have been widely applied to the study of food microbial consortia and the different applications of HTS technologies were exploited in order to monitor microbial dynamics in food fermentative processes. Phylobiomics was the most explored application in the past decade. Metagenomics and metatranscriptomics, although still underexploited, promise to uncover the functionality of complex microbial consortia. The new knowledge acquired will help to understand how to make a profitable use of microbial genetic resources and modulate key activities of beneficial microbes in order to ensure process efficiency, product quality and safety.
Some of the current software tools for comparative metagenomics provide ecologists with the ability to investigate and explore bacterial communities using α- & β-diversity. Feature subset selection--a sub-field of machine learning--can also provide a unique insight into the differences between metagenomic or 16S phenotypes. In particular, feature subset selection methods can obtain the operational taxonomic units (OTUs), or functional features, that have a high-level of influence on the condition being studied. For example, in a previous study we have used information-theoretic feature selection to understand the differences between protein family abundances that best discriminate between age groups in the human gut microbiome.
The human skin microbiome could provide another example, after the gut, of the strong positive or negative impact that human colonizing bacteria can have on health. Deciphering functional diversity and dynamics within human skin microbial communities is critical for understanding their involvement and for developing the appropriate substances for improving or correcting their action. We present a direct PCR-free high throughput sequencing approach to unravel the human skin microbiota specificities through metagenomic dataset analysis and inter-environmental comparison. The approach provided access to the functions carried out by dominant skin colonizing taxa, including Corynebacterium, Staphylococcus and Propionibacterium, revealing their specific capabilities to interact with and exploit compounds from the human skin. These functions, which clearly illustrate the unique life style of the skin microbial communities, stand as invaluable investigation targets for understanding and potentially modifying bacterial interactions with the human host with the objective of increasing health and well being.
Cellulases are a heterogeneous group of enzymes that synergistically catalyze the hydrolysis of cellulose, the major component of plant biomass. Such reaction has biotechnological applications in a broad spectrum of industries, where they can provide a more sustainable model of production. As a prerequisite for their implementation, these enzymes need to be able to operate in the conditions the industrial process requires. Thus, cellulases retrieved from extremophiles, and more specifically those of thermophiles, are likely to be more appropriate for industrial needs in which high temperatures are involved. Metagenomics, the study of genes and gene products from the whole community genomic DNA present in an environmental sample, is a powerful tool for bioprospecting in search of novel enzymes. In this review, we describe the cellulolytic systems, we summarize their biotechnological applications, and we discuss the strategies adopted in the field of metagenomics for the discovery of new cellulases, focusing on those of thermophilic microorganisms.
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