Using the Metagenomics RAST Server (MG-RAST) for Analyzing Shotgun Metagenomes

Glass, Elizabeth M.; Wilkening, Jared; Wilke, Andreas; Antonopoulos, Dionysios A.; Meyer, Patrick

doi:10.1101/pdb.prot5368

Cited by 268 publications

(227 citation statements)

References 3 publications

Supporting

Mentioning

222

Contrasting

Unclassified

Order By: Relevance

“…DNA samples from the feces of nonmedicated and medicated pigs at days 0 and 14 were isolated, and samples of like treatment and sampling date were pooled for pyrosequencing. Metagenome sequences (1,202,058 total) were analyzed in MG-RAST for SEED subsystems (26), and in-house for clusters of orthologous groups (COGs). All metagenomes showed functional stability over time by both COG and subsystem analyses (Fig.…”

Section: Resultsmentioning

confidence: 99%

In-feed antibiotic effects on the swine intestinal microbiome

Looft

Johnson

Allen³

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

904

661

View full text Add to dashboard Cite

Antibiotics have been administered to agricultural animals for disease treatment, disease prevention, and growth promotion for over 50 y. The impact of such antibiotic use on the treatment of human diseases is hotly debated. We raised pigs in a highly controlled environment, with one portion of the littermates receiving a diet containing performance-enhancing antibiotics [chlortetracycline, sulfamethazine, and penicillin (known as ASP250)] and the other portion receiving the same diet but without the antibiotics. We used phylogenetic, metagenomic, and quantitative PCR-based approaches to address the impact of antibiotics on the swine gut microbiota. Bacterial phylotypes shifted after 14 d of antibiotic treatment, with the medicated pigs showing an increase in Proteobacteria (1-11%) compared with nonmedicated pigs at the same time point. This shift was driven by an increase in Escherichia coli populations. Analysis of the metagenomes showed that microbial functional genes relating to energy production and conversion were increased in the antibiotic-fed pigs. The results also indicate that antibiotic resistance genes increased in abundance and diversity in the medicated swine microbiome despite a high background of resistance genes in nonmedicated swine. Some enriched genes, such as aminoglycoside O-phosphotransferases, confer resistance to antibiotics that were not administered in this study, demonstrating the potential for indirect selection of resistance to classes of antibiotics not fed. The collateral effects of feeding subtherapeutic doses of antibiotics to agricultural animals are apparent and must be considered in cost-benefit analyses.intestinal microbiota | microbiome shifts | swine bacteria | BioTrove microarray | metagenomics

show abstract

Section: Resultsmentioning

confidence: 99%

In-feed antibiotic effects on the swine intestinal microbiome

Looft

Johnson

Allen³

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

904

661

View full text Add to dashboard Cite

show abstract

“…Comparison of SEED-based functional analyses of a metatranscriptome data set (Gilbert et al 2008) computed by MEGAN4 and by MG-RAST (Glass et al 2010). Comparison of the taxonomic analysis of a 16S rRNA data set (Morris et al 2010), computed using five different approaches: MEGAN4's BLASTN-based SILVA analysis, the RDP website's classifier (Cole et al 2009), MG-RAST's RDP-based approach (Glass et al 2010), the SILVA website's aligner (Pruesse et al 2007), and MG-RAST's SILVA-based approach targeting the SSU gene. In this figure, the bar charts on higher-rank nodes reflect the total number of reads assigned to the corresponding node or to any of the nodes in the subtree below the node.…”

Section: Functional Analysismentioning

confidence: 99%

“…There are several other tools that also employ a homologybased approach, such as MG-RAST (Glass et al 2010), WebCARMA (Gerlach et al 2009), IMG/M (Markowitz et al 2006), and CAMERA (Seshadri et al 2007). The Galaxy framework supports basic metagenomic analyses (Kosakovsky Pond et al 2009).…”

mentioning

confidence: 99%

Integrative analysis of environmental sequences using MEGAN4

Huson¹,

Mitra²,

Ruscheweyh³

et al. 2011

Genome Res.

1,296

1,090

View full text Add to dashboard Cite

A major challenge in the analysis of environmental sequences is data integration. The question is how to analyze different types of data in a unified approach, addressing both the taxonomic and functional aspects. To facilitate such analyses, we have substantially extended MEGAN, a widely used taxonomic analysis program. The new program, MEGAN4, provides an integrated approach to the taxonomic and functional analysis of metagenomic, metatranscriptomic, metaproteomic, and rRNA data. While taxonomic analysis is performed based on the NCBI taxonomy, functional analysis is performed using the SEED classification of subsystems and functional roles or the KEGG classification of pathways and enzymes. A number of examples illustrate how such analyses can be performed, and show that one can also import and compare classification results obtained using others' tools. MEGAN4 is freely available for academic purposes, and installers for all three major operating systems can be downloaded from www-ab.informatik.uni-tuebingen.de/software/megan.

show abstract

“…With this method, gene/function abundance can be directly derived from shotgun metagenomics sequencing by assigning reads to protein sequence or protein families that have functional annotation in KEGG, SEED and COG database. 42,43 This method can be very informative, as it could be not a single species that affects host phenotypes, but a bacterial function present across multiple species. In this case, the same function carried out by similar genes from different species is investigated as a single variable, providing potential molecular mechanisms regardless of taxonomy information.…”

Section: Microbiota Characterizationmentioning

confidence: 99%

Investigating a holobiont: Microbiota perturbations and transkingdom networks

et al. 2016

View full text Add to dashboard Cite

The scientific community has recently come to appreciate that, rather than existing as independent organisms, multicellular hosts and their microbiota comprise a complex evolving superorganism or metaorganism, termed a holobiont. This point of view leads to a re-evaluation of our understanding of different physiological processes and diseases. In this paper we focus on experimental and computational approaches which, when combined in one study, allowed us to dissect mechanisms (traditionally named host-microbiota interactions) regulating holobiont physiology. Specifically, we discuss several approaches for microbiota perturbation, such as use of antibiotics and germ-free animals, including advantages and potential caveats of their usage. We briefly review computational approaches to characterize the microbiota and, more importantly, methods to infer specific components of microbiota (such as microbes or their genes) affecting host functions. One such approach called transkingdom network analysis has been recently developed and applied in our study. 1 Finally, we also discuss common methods used to validate the computational predictions of host-microbiota interactions using in vitro and in vivo experimental systems.

show abstract

Using the Metagenomics RAST Server (MG-RAST) for Analyzing Shotgun Metagenomes

Cited by 268 publications

References 3 publications

In-feed antibiotic effects on the swine intestinal microbiome

In-feed antibiotic effects on the swine intestinal microbiome

Integrative analysis of environmental sequences using MEGAN4

Investigating a holobiont: Microbiota perturbations and transkingdom networks

Contact Info

Product

Resources

About