Metagenomics Approach
Metagenomics Approach - Related Videos
16:05
Large-Scale Screens of Metagenomic Libraries
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Cited by 9 •
2007
Metagenomic libraries archive large fragments of contiguous genomic sequences from microorganisms without requiring prior cultivation. Generating a ...
10:21
A High Throughput Screen for Biomining Cellulase Activity from Metagenomic Libraries
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Cited by 24 •
2011
This protocol describes a high throughput screen for cellulolytic activity from a metagenomic library expressed in Escherichia coli. The screen is solution based and highly automated, and uses one-pot chemistry in 384 well microplates with the final readout as an absorbance measurement.
06:21
Depletion of Ribosomal RNA for Mosquito Gut Metagenomic RNA-seq
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Cited by 20 •
2013
A ribosomal RNA (rRNA) depletion protocol was developed to enrich messenger RNA (mRNA) for RNA-seq of the mosquito gut metatranscriptome. Sample specific rRNA probes, which were used to remove rRNA via subtraction, were created from the mosquito and its gut microbes. Performance of the protocol can result in the removal of approximately 90-99% of rRNA.
07:33
Nanopore DNA Sequencing for Metagenomic Soil Analysis
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Cited by 8 •
2017
Nanopore technology for sequencing biomolecules has wide applications in the life sciences, including identification of pathogens, food safety monitoring, genomic analysis, metagenomic environmental monitoring, and characterization of bacterial antibiotic resistance. In this article, the procedure for metagenomic soil DNA sequencing for species identification using the nanopore sequencing technology is demonstrated.
08:43
Metagenomic Analysis of Silage
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Cited by 19 •
2017
Metagenomics was used to investigate the microbiome of silage cattle feed. Analysis was performed by shotgun sequencing. This approach was used to characterize the composition of the microbial community within the cattle feed.
FAQs
The workflow links direct sequencing of community DNA to bioinformatics analysis. Computational methods assemble recovered sequences, associate them with organisms, and predict genes or metabolic pathways. This connection allows researchers to move from raw genetic material to evidence about who is present and what the community may be capable of doing, even when individual members have not been isolated.
Many environmental and biological communities contain organisms that are difficult or impossible to grow in the laboratory. A culture-independent strategy can therefore capture genetic information that cultivation alone might miss. This broader access helps reveal microbial diversity and functional potential in complex samples rather than restricting analysis to species that readily survive laboratory conditions.
Sequence assembly creates longer genetic representations from the directly obtained fragments, supporting more informative downstream analysis. Researchers can use these assembled sequences to identify organisms and predict genes or metabolic pathways. The resulting information helps characterize both community composition and possible biological functions, providing a functional perspective alongside a description of microbial diversity.
Community structure concerns the organisms detected in a sample and their diversity, whereas functional potential concerns the genes and metabolic pathways predicted from the recovered DNA. Examining both dimensions can show not only which microbes are present but also what biological activities the community may support. This distinction is valuable for interpreting ecological interactions and system behavior.
A typical analysis begins with collecting an environmental or biological sample and extracting its community DNA. Researchers then sequence the DNA directly, apply bioinformatics to assemble the resulting sequences, identify organisms, and predict genes or metabolic pathways. The final interpretation connects these outputs with microbial diversity, community structure, function, or ecological interactions in the sampled system.
The approach can be applied to diverse environmental and biological settings, including soil, oceans, and host-associated microbiomes. It is also relevant to other complex communities where many microorganisms coexist. Comparing such samples can reveal differences in microbial diversity, community structure, and predicted function across habitats, hosts, or other biological contexts without requiring each organism to be cultured.
In biology and environmental science, metagenomic data can clarify community structure and ecological interactions in settings such as soil and oceans. In health research, host-associated microbiomes provide a context for examining microbial communities. Biotechnology can draw on predicted genes and metabolic pathways. Across these fields, the approach strengthens analysis of microbial diversity and functional potential.