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Microbial Environmental Genomics (MEG)

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Cover of 'Microbial Environmental Genomics (MEG)'

Table of Contents

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    Book Overview
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    Chapter 1 "Deciphering Archaeal Communities" Omics Tools in the Study of Archaeal Communities.
  3. Altmetric Badge
    Chapter 2 Investigating the Endobacteria Which Thrive in Arbuscular Mycorrhizal Fungi
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    Chapter 3 GenoSol Platform: A Logistic and Technical Platform for Conserving and Exploring Soil Microbial Diversity.
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    Chapter 4 Sample Preparation for Fungal Community Analysis by High-Throughput Sequencing of Barcode Amplicons
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    Chapter 5 Fungal Communities in Soils: Soil Organic Matter Degradation.
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    Chapter 6 DNA-Based Characterization and Identification of Arbuscular Mycorrhizal Fungi Species.
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    Chapter 7 Molecular Identification of Soil Eukaryotes and Focused Approaches Targeting Protist and Faunal Groups Using High-Throughput Metabarcoding.
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    Chapter 8 Identification and In Situ Distribution of a Fungal Gene Marker: The Mating Type Genes of the Black Truffle
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    Chapter 9 Stable-Isotope Probing RNA to Study Plant/Fungus Interactions
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    Chapter 10 Targeted Gene Capture by Hybridization to Illuminate Ecosystem Functioning.
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    Chapter 11 Hybridization of Environmental Microbial Community Nucleic Acids by GeoChip.
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    Chapter 12 Reconstruction of Transformation Processes Catalyzed by the Soil Microbiome Using Metagenomic Approaches.
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    Chapter 13 MG-RAST, a Metagenomics Service for Analysis of Microbial Community Structure and Function.
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    Chapter 14 Analysis of Active Methylotrophic Communities: When DNA-SIP Meets High-Throughput Technologies.
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    Chapter 15 Functional Metagenomics: Construction and High-Throughput Screening of Fosmid Libraries for Discovery of Novel Carbohydrate-Active Enzymes.
  17. Altmetric Badge
    Chapter 16 Metatranscriptomics of Soil Eukaryotic Communities
  18. Altmetric Badge
    Chapter 17 Analysis of Ancient DNA in Microbial Ecology.
Attention for Chapter 13: MG-RAST, a Metagenomics Service for Analysis of Microbial Community Structure and Function.
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Chapter title
MG-RAST, a Metagenomics Service for Analysis of Microbial Community Structure and Function.
Chapter number 13
Book title
Microbial Environmental Genomics (MEG)
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3369-3_13
Pubmed ID
Book ISBNs
978-1-4939-3367-9, 978-1-4939-3369-3
Authors

Keegan, Kevin P, Glass, Elizabeth M, Meyer, Folker, Kevin P. Keegan, Elizabeth M. Glass, Folker Meyer, Keegan, Kevin P., Glass, Elizabeth M.

Editors

Francis Martin, Stephane Uroz

Abstract

Approaches in molecular biology, particularly those that deal with high-throughput sequencing of entire microbial communities (the field of metagenomics), are rapidly advancing our understanding of the composition and functional content of microbial communities involved in climate change, environmental pollution, human health, biotechnology, etc. Metagenomics provides researchers with the most complete picture of the taxonomic (i.e., what organisms are there) and functional (i.e., what are those organisms doing) composition of natively sampled microbial communities, making it possible to perform investigations that include organisms that were previously intractable to laboratory-controlled culturing; currently, these constitute the vast majority of all microbes on the planet. All organisms contained in environmental samples are sequenced in a culture-independent manner, most often with 16S ribosomal amplicon methods to investigate the taxonomic or whole-genome shotgun-based methods to investigate the functional content of sampled communities. Metagenomics allows researchers to characterize the community composition and functional content of microbial communities, but it cannot show which functional processes are active; however, near parallel developments in transcriptomics promise a dramatic increase in our knowledge in this area as well. Since 2008, MG-RAST (Meyer et al., BMC Bioinformatics 9:386, 2008) has served as a public resource for annotation and analysis of metagenomic sequence data, providing a repository that currently houses more than 150,000 data sets (containing 60+ tera-base-pairs) with more than 23,000 publically available. MG-RAST, or the metagenomics RAST (rapid annotation using subsystems technology) server makes it possible for users to upload raw metagenomic sequence data in (preferably) fastq or fasta format. Assessments of sequence quality, annotation with respect to multiple reference databases, are performed automatically with minimal input from the user (see Subheading 4 at the end of this chapter for more details). Post-annotation analysis and visualization are also possible, directly through the web interface, or with tools like matR (metagenomic analysis tools for R, covered later in this chapter) that utilize the MG-RAST API ( http://api.metagenomics.anl.gov/api.html ) to easily download data from any stage in the MG-RAST processing pipeline. Over the years, MG-RAST has undergone substantial revisions to keep pace with the dramatic growth in the number, size, and types of sequence data that accompany constantly evolving developments in metagenomics and related -omic sciences (e.g., metatranscriptomics).

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 520 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 <1%
Canada 2 <1%
India 1 <1%
France 1 <1%
Estonia 1 <1%
United States 1 <1%
Unknown 512 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 105 20%
Student > Master 81 16%
Researcher 63 12%
Student > Bachelor 51 10%
Student > Doctoral Student 31 6%
Other 59 11%
Unknown 130 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 135 26%
Agricultural and Biological Sciences 115 22%
Immunology and Microbiology 29 6%
Environmental Science 20 4%
Computer Science 17 3%
Other 57 11%
Unknown 147 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 February 2016.
All research outputs
#8,577,479
of 25,837,817 outputs
Outputs from Methods in molecular biology
#2,641
of 14,425 outputs
Outputs of similar age
#126,504
of 405,194 outputs
Outputs of similar age from Methods in molecular biology
#268
of 1,468 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. This one has received more attention than most of these and is in the 66th percentile.
So far Altmetric has tracked 14,425 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done well, scoring higher than 80% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 405,194 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 1,468 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.