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Metagenomics

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Cover of 'Metagenomics'

Table of Contents

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    Book Overview
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    Chapter 1 Construction of Small-Insert and Large-Insert Metagenomic Libraries.
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    Chapter 2 Extraction of Total DNA and RNA from Marine Filter Samples and Generation of a cDNA as Universal Template for Marker Gene Studies.
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    Chapter 3 Construction and Screening of Marine Metagenomic Large Insert Libraries.
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    Chapter 4 Constructing and Screening a Metagenomic Library of a Cold and Alkaline Extreme Environment.
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    Chapter 5 DNA-, RNA-, and Protein-Based Stable-Isotope Probing for High-Throughput Biomarker Analysis of Active Microorganisms.
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    Chapter 6 Assessing Bacterial and Fungal Diversity in the Plant Endosphere.
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    Chapter 7 Shotgun Metagenomic Sequencing Analysis of Soft-Rot Enterobacteriaceae in Polymicrobial Communities.
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    Chapter 8 Cloning and Expression of Metagenomic DNA in Streptomyces lividans and Subsequent Fermentation for Optimized Production.
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    Chapter 9 Degradation Network Reconstruction Guided by Metagenomic Data.
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    Chapter 10 Novel Tools for the Functional Expression of Metagenomic DNA.
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    Chapter 11 A Microtiter Plate-Based Assay to Screen for Active and Stereoselective Hydrolytic Enzymes in Enzyme Libraries.
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    Chapter 12 Screening for Cellulase Encoding Clones in Metagenomic Libraries.
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    Chapter 13 Liquid Phase Multiplex High-Throughput Screening of Metagenomic Libraries Using p-Nitrophenyl-Linked Substrates for Accessory Lignocellulosic Enzymes.
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    Chapter 14 Screening Glycosyltransferases for Polyphenol Modifications.
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    Chapter 15 Methods for the Isolation of Genes Encoding Novel PHA Metabolism Enzymes from Complex Microbial Communities.
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    Chapter 16 Function-Based Metagenomic Library Screening and Heterologous Expression Strategy for Genes Encoding Phosphatase Activity.
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    Chapter 17 Activity-Based Screening of Metagenomic Libraries for Hydrogenase Enzymes.
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    Chapter 18 Screening for N-AHSL-Based-Signaling Interfering Enzymes.
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    Chapter 19 Mining Microbial Signals for Enhanced Biodiscovery of Secondary Metabolites.
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    Chapter 20 Erratum to: Cloning and Expression of Metagenomic DNA in Streptomyces lividans and Subsequent Fermentation for Optimized Production
Attention for Chapter 5: DNA-, RNA-, and Protein-Based Stable-Isotope Probing for High-Throughput Biomarker Analysis of Active Microorganisms.
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Chapter title
DNA-, RNA-, and Protein-Based Stable-Isotope Probing for High-Throughput Biomarker Analysis of Active Microorganisms.
Chapter number 5
Book title
Metagenomics
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6691-2_5
Pubmed ID
Book ISBNs
978-1-4939-6689-9, 978-1-4939-6691-2, 978-1-4939-6689-9, 978-1-4939-6691-2
Authors

Eleanor Jameson, Martin Taubert, Sara Coyotzi, Yin Chen, Özge Eyice, Hendrik Schäfer, J. Colin Murrell, Josh D. Neufeld, Marc G. Dumont, Jameson, Eleanor, Taubert, Martin, Coyotzi, Sara, Chen, Yin, Eyice, Özge, Schäfer, Hendrik, Murrell, J. Colin, Neufeld, Josh D., Dumont, Marc G.

Editors

Wolfgang R. Streit, Rolf Daniel

Abstract

Stable-isotope probing (SIP) enables researchers to target active populations within complex microbial communities, which is achieved by providing growth substrates enriched in heavy isotopes, usually in the form of (13)C, (18)O, or (15)N. After growth on the substrate and subsequent extraction of microbial biomarkers, typically nucleic acids or proteins, the SIP technique is used for the recovery and analysis of isotope-labeled biomarkers from active microbial populations. In the years following the initial development of DNA- and RNA-based SIP, it was common practice to characterize labeled populations by targeted gene analysis. Such approaches usually involved fingerprint-based analyses or sequencing of clone libraries containing 16S rRNA genes or functional marker gene amplicons. Although molecular fingerprinting remains a valuable approach for rapid confirmation of isotope labeling, recent advances in sequencing technology mean that it is possible to obtain affordable and comprehensive amplicon profiles, metagenomes, or metatranscriptomes from SIP experiments. Not only can the abundance of microbial groups be inferred from metagenomes, but researchers can bin, assemble, and explore individual genomes to build hypotheses about the metabolic capabilities of labeled microorganisms. Analysis of labeled mRNA is a more recent advance that can provide independent metatranscriptome-based analysis of active microorganisms. The power of metatranscriptomics is that mRNA abundance often correlates closely with the corresponding activity of encoded enzymes, thus providing insight into microbial metabolism at the time of sampling. Together, these advances have improved the sensitivity of SIP methods and allow the use of labeled substrates at ecologically relevant concentrations. Particularly as methods improve and costs continue to drop, we expect that the integration of SIP with multiple omics-based methods will become prevalent components of microbial ecology studies, leading to further breakthroughs in our understanding of novel microbial populations and elucidation of the metabolic function of complex microbial communities. In this chapter we provide protocols for obtaining labeled DNA, RNA, and proteins that can be used for downstream omics-based analyses.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 5%
Unknown 40 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 21%
Student > Master 9 21%
Student > Ph. D. Student 4 10%
Student > Doctoral Student 4 10%
Student > Bachelor 2 5%
Other 7 17%
Unknown 7 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 21%
Agricultural and Biological Sciences 5 12%
Immunology and Microbiology 4 10%
Environmental Science 4 10%
Chemical Engineering 1 2%
Other 6 14%
Unknown 13 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 27 September 2022.
All research outputs
#14,356,775
of 24,127,822 outputs
Outputs from Methods in molecular biology
#3,877
of 13,611 outputs
Outputs of similar age
#220,121
of 428,278 outputs
Outputs of similar age from Methods in molecular biology
#329
of 1,075 outputs
Altmetric has tracked 24,127,822 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,611 research outputs from this source. They receive a mean Attention Score of 3.5. This one has gotten more attention than average, scoring higher than 69% 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 428,278 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,075 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.