MG-RAST statistics

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MG-RAST specifications


Unique identifier OMICS_11133
Restrictions to use None
Data access File download, Browse, Application programming interface
User data submission Allowed
Maintained Yes


  • person_outline Folker Meyer

Publications for MG-RAST

MG-RAST citations


What Is the Role of Archaea in Plants? New Insights from the Vegetation of Alpine Bogs

PMCID: 5956146
PMID: 29743201
DOI: 10.1128/mSphere.00122-18
call_split See protocol

[…] tx analysis was conducted (). The taxonomic structure of the archaeal community was aligned and annotated with the RefSeq database as a reference (). The taxonomic structure was then exported via the MG-RAST API server () and further visualized using krona tool version 2.7 (). […]


Gut microbiomes of wild great apes fluctuate seasonally in response to diet

Nat Commun
PMCID: 5934369
PMID: 29725011
DOI: 10.1038/s41467-018-04204-w

[…] ool samples sequenced over the V1–V3 region of bacterial 16S rDNA were selected for analysis (175 individual human stool samples). Bacterial 16S sequences from the Mongolian humans were obtained from MG-RAST (project no. 8437), while bacterial 16S sequences from the Old World monkeys were obtained from personal correspondence with Suleyman Yildirim (red-tailed guenons, black-and-white colobi, and […]


Taxon Function Decoupling as an Adaptive Signature of Lake Microbial Metacommunities Under a Chronic Polymetallic Pollution Gradient

Front Microbiol
PMCID: 5943556
PMID: 29774016
DOI: 10.3389/fmicb.2018.00869

[…] ntigs). Firstly, de novo assemblies of raw reads were performed using the RAY Meta (Boisvert et al., ) assembler. Secondly, to explore contig features and gene contents, contigs were submitted to the MG-RAST webserver (Glass et al., ) and ORF prediction was conducted using the FragGeneScan tool (Rho et al., ). Afterwards, contigs were annotated with the BLAT tool implemented in MG-RAST against the […]


Flexible metagenome analysis using the MGX framework

PMCID: 5937802
PMID: 29690922
DOI: 10.1186/s40168-018-0460-1

[…] Currently, several applications are available to users adressing the task of computational metagenome analysis, most notably MG-RAST, IMG/M, and the EBI metagenomics portal. Also, web-based infrastructures such as CyVerse provide some basic capabilities to process metagenome data, and virtual machines like CloVR enable meta […]


Comparison of normalization methods for the analysis of metagenomic gene abundance data

BMC Genomics
PMCID: 5910605
PMID: 29678163
DOI: 10.1186/s12864-018-4637-6
call_split See protocol

[…] f healthy individuals in North and South America []. DNA was sequenced using massively parallel sequencing (454 sequencing) with an average of 1.6·105 reads per sample. The reads were downloaded from MG-RAST database [], and translated into all six reading frames, which were in turn mapped to eggNOG database v4.5 [] using HMMER []. Mapped reads with e-value of max 10−5 were kept. The Marine datase […]


MEGAN LR: new algorithms allow accurate binning and easy interactive exploration of metagenomic long reads and contigs

Biol Direct
PMCID: 5910613
PMID: 29678199
DOI: 10.1186/s13062-018-0208-7

[…] sually involves a best-hit strategy to assign reads to functional classes.Software or websites for analyzing microbiome shotgun sequencing samples usually provide some level of interactivity, such as MG-RAST []. The interactive microbiome analysis tool MEGAN, which was first used in 2006 [], is explicitly designed to enable users to interactively explore large numbers of microbiome samples contain […]


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MG-RAST institution(s)
Argonne National Laboratory, Mathematics and Computer Science Division, Argonne, IL, USA; University of Chicago, Chicago, IL, USA; Purdue University, School of Electrical & Computer Engineering, West Lafayette, IN, USA; Purdue University, Department of Computer Sciences, West Lafayette, IN, USA
MG-RAST funding source(s)
This work was supported in part by the NIH award U01HG006537 “OSDF: Support infrastructure for NextGen sequence storage, analysis, and management,” by the Gordon and Betty Moore Foundation with the grant “6-34881, METAZen-Going the Last Mile for Solving the Metadata Crisis),” and by the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research, under contract DE-AC02-06CH11357 as part of “Resource Aware Intelligent Network Services (RAINS).”

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