What Is Metagenomics Teaching Us, and What Is Missed?

2020 ◽  
Vol 74 (1) ◽  
pp. 117-135 ◽  
Author(s):  
Felicia N. New ◽  
Ilana L. Brito
Keyword(s):  
Environmental Sustainability ◽  
Metagenomic Data ◽  
Model Organisms ◽  
Metagenomic Sequencing ◽  
Shotgun Metagenomic Sequencing ◽  
Metagenome Assembly ◽  
Environmental Bacteria ◽  
And Function ◽  
Analytical Tools ◽  
Technological Platforms

Shotgun metagenomic sequencing has revolutionized our ability to detect and characterize the diversity and function of complex microbial communities. In this review, we highlight the benefits of using metagenomics as well as the breadth of conclusions that can be made using currently available analytical tools, such as greater resolution of species and strains across phyla and functional content, while highlighting challenges of metagenomic data analysis. Major challenges remain in annotating function, given the dearth of functional databases for environmental bacteria compared to model organisms, and the technical difficulties of metagenome assembly and phasing in heterogeneous environmental samples. In the future, improvements and innovation in technology and methodology will lead to lowered costs. Data integration using multiple technological platforms will lead to a better understanding of how to harness metagenomes. Subsequently, we will be able not only to characterize complex microbiomes but also to manipulate communities to achieve prosperous outcomes for health, agriculture, and environmental sustainability.

scholarly journals Evaluation of the CosmosID Bioinformatics Platform for Prosthetic Joint-Associated Sonicate Fluid Shotgun Metagenomic Data Analysis

2018 ◽  
Vol 57 (2) ◽  
Author(s):  
Qun Yan ◽  
Yu Mi Wi ◽  
Matthew J. Thoendel ◽  
Yash S. Raval ◽  
Kerryl E. Greenwood-Quaintance ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Metagenomic Data ◽  
Metagenomic Sequencing ◽  
Antibacterial Resistance ◽  
Sequencing Data ◽  
Bacterial Detection ◽  
Shotgun Metagenomic Sequencing ◽  
Prosthetic Joint ◽  
Validation Set ◽  
Fluid Culture

ABSTRACT We previously demonstrated that shotgun metagenomic sequencing can detect bacteria in sonicate fluid, providing a diagnosis of prosthetic joint infection (PJI). A limitation of the approach that we used is that data analysis was time-consuming and specialized bioinformatics expertise was required, both of which are barriers to routine clinical use. Fortunately, automated commercial analytic platforms that can interpret shotgun metagenomic data are emerging. In this study, we evaluated the CosmosID bioinformatics platform using shotgun metagenomic sequencing data derived from 408 sonicate fluid samples from our prior study with the goal of evaluating the platform vis-à-vis bacterial detection and antibiotic resistance gene detection for predicting staphylococcal antibacterial susceptibility. Samples were divided into a derivation set and a validation set, each consisting of 204 samples; results from the derivation set were used to establish cutoffs, which were then tested in the validation set for identifying pathogens and predicting staphylococcal antibacterial resistance. Metagenomic analysis detected bacteria in 94.8% (109/115) of sonicate fluid culture-positive PJIs and 37.8% (37/98) of sonicate fluid culture-negative PJIs. Metagenomic analysis showed sensitivities ranging from 65.7 to 85.0% for predicting staphylococcal antibacterial resistance. In conclusion, the CosmosID platform has the potential to provide fast, reliable bacterial detection and identification from metagenomic shotgun sequencing data derived from sonicate fluid for the diagnosis of PJI. Strategies for metagenomic detection of antibiotic resistance genes for predicting staphylococcal antibacterial resistance need further development.

scholarly journals BEEM-Static: Accurate inference of ecological interactions from cross-sectional metagenomic data

2020 ◽  
Author(s):  
Chenhao Li ◽  
Tamar V. Av-Shalom ◽  
Jun Wei Gerald Tan ◽  
Junmei Samantha Kwah ◽  
Kern Rei Chng ◽  
...  
Keyword(s):  
Ecological Model ◽  
Expectation Maximization Algorithm ◽  
Microbial Interactions ◽  
Metagenomic Data ◽  
Metagenomic Sequencing ◽  
Ecological Interactions ◽  
Cross Sectional ◽  
Key Species ◽  
Alternate Model ◽  
And Function

AbstractMotivationThe structure and function of diverse microbial communities is underpinned by ecological interactions that remain uncharacterized. With rapid adoption of metagenomic sequencing for studying microbiomes, data-driven inference of microbial interactions based on abundance correlations is widely used, but with the drawback that ecological interpretations may not be possible. Leveraging cross-sectional metagenomic datasets for unravelling ecological structure in a scalable manner thus remains an open problem.MethodsWe present an expectation-maximization algorithm (BEEM-Static) that can be applied to cross-sectional datasets to infer interaction networks based on an ecological model (generalized Lotka-Volterra). The method exhibits robustness to violations in model assumptions by using statistical filters to identify and remove corresponding samples.ResultsBenchmarking against 10 state-of-the-art correlation based methods showed that BEEM-Static can infer presence and directionality of ecological interactions even with relative abundance data (AUC-ROC>0.85), a task that other methods struggle with (AUC-ROC<0.63). In addition, BEEM-Static can tolerate a high fraction of samples (up to 40%) being not at steady state or coming from an alternate model. Applying BEEM-Static to a large public dataset of human gut microbiomes (n=4,617) identified multiple stable equilibria that better reflect ecological enterotypes with distinct carrying capacities and interactions for key species.ConclusionBEEM-Static provides new opportunities for mining ecologically interpretable interactions and systems insights from the growing corpus of metagenomic data.

scholarly journals Diversity and Function of Wolf Spider Gut Microbiota Revealed by Shotgun Metagenomics

2021 ◽  
Vol 12 ◽  
Author(s):  
Runbiao Wu ◽  
Luyu Wang ◽  
Jianping Xie ◽  
Zhisheng Zhang
Keyword(s):  
Gut Microbiota ◽  
Wolf Spider ◽  
Metagenomic Sequencing ◽  
Wolf Spiders ◽  
Shotgun Metagenomics ◽  
Microbial Genomes ◽  
Shotgun Metagenomic Sequencing ◽  
Crucial Component ◽  
Genes Encoding ◽  
And Function

Wolf spiders (Lycosidae) are crucial component of integrated pest management programs and the characteristics of their gut microbiota are known to play important roles in improving fitness and survival of the host. However, there are only few studies of the gut microbiota among closely related species of wolf spider. Whether wolf spiders gut microbiota vary with habitats remains unknown. Here, we used shotgun metagenomic sequencing to compare the gut microbiota of two wolf spider species, Pardosa agraria and P. laura from farmland and woodland ecosystems, respectively. The results show that the gut microbiota of Pardosa spiders is similar in richness and abundance. Approximately 27.3% of the gut microbiota of P. agraria comprises Proteobacteria, and approximately 34.5% of the gut microbiota of P. laura comprises Firmicutes. We assembled microbial genomes and found that the gut microbiota of P. laura are enriched in genes for carbohydrate metabolism. In contrast, those of P. agraria showed a higher proportion of genes encoding acetyltransferase, an enzyme involved in resistance to antibiotics. We reconstructed three high-quality and species-level microbial genomes: Vulcaniibacterium thermophilum, Anoxybacillus flavithermus and an unknown bacterium belonging to the family Simkaniaceae. Our results contribute to an understanding of the diversity and function of gut microbiota in closely related spiders.

scholarly journals From single nuclei to whole genome assemblies

2019 ◽  
Author(s):  
Merce Montoliu-Nerin ◽  
Marisol Sánchez-García ◽  
Claudia Bergin ◽  
Manfred Grabherr ◽  
Barbara Ellis ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Genomic Data ◽  
Life Cycles ◽  
Genomic Research ◽  
Metagenomic Data ◽  
Model Organisms ◽  
Genomic Study ◽  
And Function ◽  
Genome Assemblies

SummaryA large proportion of Earth's biodiversity constitutes organisms that cannot be cultured, have cryptic life-cycles and/or live submerged within their substrates1–4. Genomic data are key to unravel both their identity and function5. The development of metagenomic methods6,7 and the advent of single cell sequencing8–10 have revolutionized the study of life and function of cryptic organisms by upending the need for large and pure biological material, and allowing generation of genomic data from complex or limited environmental samples. Genome assemblies from metagenomic data have so far been restricted to organisms with small genomes, such as bacteria11, archaea12 and certain eukaryotes13. On the other hand, single cell technologies have allowed the targeting of unicellular organisms, attaining a better resolution than metagenomics8,9,14–16, moreover, it has allowed the genomic study of cells from complex organisms one cell at a time17,18. However, single cell genomics are not easily applied to multicellular organisms formed by consortia of diverse taxa, and the generation of specific workflows for sequencing and data analysis is needed to expand genomic research to the entire tree of life, including sponges19, lichens3,20, intracellular parasites21,22, and plant endophytes23,24. Among the most important plant endophytes are the obligate mutualistic symbionts, arbuscular mycorrhizal (AM) fungi, that pose an additional challenge with their multinucleate coenocytic mycelia25. Here, the development of a novel single nuclei sequencing and assembly workflow is reported. This workflow allows, for the first time, the generation of reference genome assemblies from large scale, unbiased sorted, and sequenced AM fungal nuclei circumventing tedious, and often impossible, culturing efforts. This method opens infinite possibilities for studies of evolution and adaptation in these important plant symbionts and demonstrates that reference genomes can be generated from complex non-model organisms by isolating only a handful of their nuclei.

scholarly journals Metagenomic Insights into The Diversity and Functions of Microbial Assemblages in Tasik Kenyir Ecosystem

2017 ◽  
Author(s):  
Mohd Ezhar Mohd Noor ◽  
Sharifah Noor Emilia Syed Jamil Fadaak ◽  
Mohd Noor Mat Isa ◽  
Mohd Faizal Abu Bakar ◽  
Muhd Danish-Daniel Abdullah
Keyword(s):  
Carbon Fixation ◽  
Dna Isolation ◽  
Metagenomic Data ◽  
Data Sets ◽  
Metagenomic Sequencing ◽  
Microbial Species ◽  
Disturbed Area ◽  
Shotgun Metagenomic Sequencing ◽  
Pristine Area ◽  
Disturbed Areas

AbstractTropical freshwater lake such as Tasik Kenyir are underrepresented among the growing number of environmental metagenomic data sets. In Tasik Kenyir, water from two different sites, pristine and disturbed areas were sampled. After the filtration process, genomic DNA from both sites were extracted using Meta-G-nome DNA isolation kit and shotgun metagenomic sequencing was carried out on Illumina HiSeq2500 Desktop Sequencer (Illumina, Inc.). Raw data were then trimmed and assembled using Metagenomic Assembler program, MetaVelvet. Data analysis was carried out using software Blast2GO (BioBam Bioinformatic S.L). The total number of sequence reads was 189,158 from TKS1.5m (disturbed area) and 246,577 from TKS2.5m (pristine area).The results indicate that sequence reads of microbial species were presence at disturbed area near the aquaculture zone was lower than the sequence reads of microbial species were presence at pristine area. When compared to archaea, both samples were dominated by bacteria (more than 90%) suggesting that bacteria are absolutely dominant in the prokaryotic communities in the freshwater samples. The lake appears to contain a mixture of autotrophs and heterotrophs capable of performing main biogeochemical cycles like nitrogen fixation byKlebsiellasp for TKS1.5m andPontibactersp. for TKS2.5m. and carbon fixation by heterotrophicAlcaligenessp. andShewanella decolorationiin TKS1.5m, and byPantoeasp. in TKS2.5m. Present study will advance our understanding of the importance of freshwater microbial communities for ecosystem and human health.

scholarly journals Prospective associations of the infant gut microbiome and microbial function with social behaviors related to autism at age 3 years

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hannah E. Laue ◽  
Susan A. Korrick ◽  
Emily R. Baker ◽  
Margaret R. Karagas ◽  
Juliette C. Madan
Keyword(s):  
Gut Microbiome ◽  
Social Behaviors ◽  
Autism Spectrum ◽  
Rrna Gene ◽  
Social Responsiveness ◽  
Metagenomic Sequencing ◽  
Shotgun Metagenomic Sequencing ◽  
Infant Gut Microbiome ◽  
One Year ◽  
And Function

Abstract The hypothesized link between gut bacteria and autism spectrum disorder (ASD) has been explored through animal models and human studies with microbiome assessment after ASD presentation. We aimed to prospectively characterize the association between the infant/toddler gut microbiome and ASD-related social behaviors at age 3 years. As part of an ongoing birth cohort gut bacterial diversity, structure, taxa, and function at 6 weeks (n = 166), 1 year (n = 158), 2 years (n = 129), and 3 years (n = 140) were quantified with 16S rRNA gene and shotgun metagenomic sequencing (n = 101 six weeks, n = 103 one year). ASD-related social behavior was assessed at age 3 years using Social Responsiveness Scale (SRS-2) T-scores. Covariate-adjusted linear and permutation-based models were implemented. Microbiome structure at 1 year was associated with SRS-2 total T-scores (p = 0.01). Several taxa at 1, 2, and 3 years were associated with SRS-2 performance, including many in the Lachnospiraceae family. Higher relative abundance of Adlercreutzia equolifaciens and Ruminococcus torques at 1 year related to poorer SRS-2 performance. Two functional pathways, l-ornithine and vitamin B6 biosynthesis, were associated with better social skills at 3 years. Our results support potential associations between early-childhood gut microbiome and social behaviors. Future mechanistic studies are warranted to pinpoint sensitive targets for intervention.

scholarly journals MetaLAFFA: a flexible, end-to-end, distributed computing-compatible metagenomic functional annotation pipeline

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Alexander Eng ◽  
Adrian J. Verster ◽  
Elhanan Borenstein
Keyword(s):  
Distributed Computing ◽  
Functional Annotation ◽  
Sequence Data ◽  
Metagenomic Data ◽  
Metagenomic Sequencing ◽  
Distributed Computing Systems ◽  
Annotation Pipeline ◽  
Shotgun Metagenomic Sequencing ◽  
End To End ◽  
Functional Profiles

Abstract Background Microbial communities have become an important subject of research across multiple disciplines in recent years. These communities are often examined via shotgun metagenomic sequencing, a technology which can offer unique insights into the genomic content of a microbial community. Functional annotation of shotgun metagenomic data has become an increasingly popular method for identifying the aggregate functional capacities encoded by the community’s constituent microbes. Currently available metagenomic functional annotation pipelines, however, suffer from several shortcomings, including limited pipeline customization options, lack of standard raw sequence data pre-processing, and insufficient capabilities for integration with distributed computing systems. Results Here we introduce MetaLAFFA, a functional annotation pipeline designed to take unfiltered shotgun metagenomic data as input and generate functional profiles. MetaLAFFA is implemented as a Snakemake pipeline, which enables convenient integration with distributed computing clusters, allowing users to take full advantage of available computing resources. Default pipeline settings allow new users to run MetaLAFFA according to common practices while a Python module-based configuration system provides advanced users with a flexible interface for pipeline customization. MetaLAFFA also generates summary statistics for each step in the pipeline so that users can better understand pre-processing and annotation quality. Conclusions MetaLAFFA is a new end-to-end metagenomic functional annotation pipeline with distributed computing compatibility and flexible customization options. MetaLAFFA source code is available at https://github.com/borenstein-lab/MetaLAFFA and can be installed via Conda as described in the accompanying documentation.

scholarly journals Evaluation of the effects of library preparation procedure and sample characteristics on the accuracy of metagenomic profiles

2021 ◽  
Author(s):  
Christopher Gaulke ◽  
Emily R Schmeltzer ◽  
Mark Dasenko ◽  
Brett M Tyler ◽  
Rebecca Vega Thurber ◽  
...  
Keyword(s):  
Microbial Community ◽  
Cost Effective ◽  
Low Complexity ◽  
Careful Consideration ◽  
Metagenomic Data ◽  
Metagenomic Sequencing ◽  
Library Preparation ◽  
Dna Amount ◽  
Preparation Methods ◽  
Shotgun Metagenomic Sequencing

Shotgun metagenomic sequencing has transformed our understanding of microbial community ecology. However, preparing metagenomic libraries for high-throughput DNA sequencing remains a costly, labor-intensive, and time-consuming procedure, which in turn limits the utility of metagenomes. Several library preparation procedures have recently been developed to offset these costs, but it is unclear how these newer procedures compare to current standards in the field. In particular, it is not clear if all such procedures perform equally well across different types of microbial communities, or if features of the biological samples being processed (e.g., DNA amount) impact the accuracy of the approach. To address these questions, we assessed how five different shotgun DNA sequence library preparation methods, including the commonly used Nextera® Flex kit, perform when applied to metagenomic DNA. We measured each method's ability to produce metagenomic data that accurately represents the underlying taxonomic and genetic diversity of the community. We performed these analyses across a range of microbial community types (e.g., soil, coral-associated, mouse-gut-associated) and input DNA amounts. We find that the type of community and amount of input DNA influence each method’s performance, indicating that careful consideration may be needed when selecting between methods, especially for low complexity communities. However, cost-effective preparation methods we assessed are generally comparable to the current gold standard Nextera® DNA Flex kit for high-complexity communities. Overall, the results from this analysis will help expand and even facilitate access to metagenomic approaches in future studies.

scholarly journals Metaviral SPAdes: assembly of viruses from metagenomic data

2020 ◽  
Vol 36 (14) ◽  
pp. 4126-4129 ◽  
Author(s):  
Dmitry Antipov ◽  
Mikhail Raiko ◽  
Alla Lapidus ◽  
Pavel A Pevzner
Keyword(s):  
Markov Models ◽  
State Of The Art ◽  
Supplementary Information ◽  
Metagenomic Data ◽  
Metagenomic Sequencing ◽  
Coverage Depth ◽  
Viral Genomes ◽  
Shotgun Metagenomic Sequencing ◽  
Bacterial Chromosomes ◽  
Metagenomic Assembly

Abstract Motivation Although the set of currently known viruses has been steadily expanding, only a tiny fraction of the Earth’s virome has been sequenced so far. Shotgun metagenomic sequencing provides an opportunity to reveal novel viruses but faces the computational challenge of identifying viral genomes that are often difficult to detect in metagenomic assemblies. Results We describe a MetaviralSPAdes tool for identifying viral genomes in metagenomic assembly graphs that is based on analyzing variations in the coverage depth between viruses and bacterial chromosomes. We benchmarked MetaviralSPAdes on diverse metagenomic datasets, verified our predictions using a set of virus-specific Hidden Markov Models and demonstrated that it improves on the state-of-the-art viral identification pipelines. Availability and implementation Metaviral SPAdes includes ViralAssembly, ViralVerify and ViralComplete modules that are available as standalone packages: https://github.com/ablab/spades/tree/metaviral_publication, https://github.com/ablab/viralVerify/ and https://github.com/ablab/viralComplete/. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Pre- and post-weaning diet alters the faecal metagenome in the cat with differences in vitamin and carbohydrate metabolism gene abundances

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Wayne Young ◽  
Christina D. Moon ◽  
David G. Thomas ◽  
Nick J. Cave ◽  
Emma N. Bermingham
Keyword(s):  
Bacterial Species ◽  
Companion Animals ◽  
Domestic Cat ◽  
Metagenomic Sequencing ◽  
Pet Food ◽  
Pet Owners ◽  
Shotgun Metagenomic Sequencing ◽  
And Function ◽  
The Impact ◽  
Metabolism Gene

Abstract Dietary format, and its role in pet nutrition, is of interest to pet food manufacturers and pet owners alike. The aim of the present study was to investigate the effects of pre- and post-weaning diets (kibbled or canned) on the composition and function of faecal microbiota in the domestic cat by shotgun metagenomic sequencing and gene taxonomic and functional assignment using MG-RAST. Post-weaning diet had a dramatic effect on community composition; 147 of the 195 bacterial species identified had significantly different mean relative abundances between kittens fed kibbled and canned diets. The kittens fed kibbled diets had relatively higher abundances of Lactobacillus (>100-fold), Bifidobacterium (>100-fold), and Collinsella (>9-fold) than kittens fed canned diets. There were relatively few differences in the predicted microbiome functions associated with the pre-weaning diet. Post-weaning diet affected the abundance of functional gene groups. Genes involved in vitamin biosynthesis, metabolism, and transport, were significantly enriched in the metagenomes of kittens fed the canned diet. The impact of post-weaning diet on the metagenome in terms of vitamin biosynthesis functions suggests that modulation of the microbiome function through diet may be an important avenue for improving the nutrition of companion animals.

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