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Metagenomics - a guide from sampling to data analysis.

Thomas T, Gilbert J, Meyer F - Microb Inform Exp (2012)

Bottom Line: The field of metagenomics has been responsible for substantial advances in microbial ecology, evolution, and diversity over the past 5 to 10 years, and many research laboratories are actively engaged in it now.This review summarizes the current opinions in metagenomics, and provides practical guidance and advice on sample processing, sequencing technology, assembly, binning, annotation, experimental design, statistical analysis, data storage, and data sharing.As more metagenomic datasets are generated, the availability of standardized procedures and shared data storage and analysis becomes increasingly important to ensure that output of individual projects can be assessed and compared.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Biotechnology and Biomolecular Sciences & Centre for Marine Bio-Innovation, The University of New South Wales, Sydney, NSW 2052, Australia. t.thomas@unsw.edu.au.

ABSTRACT
Metagenomics applies a suite of genomic technologies and bioinformatics tools to directly access the genetic content of entire communities of organisms. The field of metagenomics has been responsible for substantial advances in microbial ecology, evolution, and diversity over the past 5 to 10 years, and many research laboratories are actively engaged in it now. With the growing numbers of activities also comes a plethora of methodological knowledge and expertise that should guide future developments in the field. This review summarizes the current opinions in metagenomics, and provides practical guidance and advice on sample processing, sequencing technology, assembly, binning, annotation, experimental design, statistical analysis, data storage, and data sharing. As more metagenomic datasets are generated, the availability of standardized procedures and shared data storage and analysis becomes increasingly important to ensure that output of individual projects can be assessed and compared.

No MeSH data available.


Flow diagram of a typical metagenome projects. Dashed arrows indicate steps that can be omitted.
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Figure 1: Flow diagram of a typical metagenome projects. Dashed arrows indicate steps that can be omitted.

Mentions: This review gives an overview of the field of metagenomics, with particular emphasis on the steps involved in a typical sequence-based metagenome project (Figure 1). We describe and discuss sample processing, sequencing technology, assembly, binning, annotation, experimental design, statistical analysis, and data storage and sharing. Clearly, any kind of metagenomic dataset will benefit from the rich information available from other metagenome projects, and it is hoped that common, yet flexible, standards and interactions among scientists in the field will facilitate this sharing of information. This review article summarizes the current thinking in the field and introduces current practices and key issues that those scientists new to the field need to consider for a successful metagenome project.


Metagenomics - a guide from sampling to data analysis.

Thomas T, Gilbert J, Meyer F - Microb Inform Exp (2012)

Flow diagram of a typical metagenome projects. Dashed arrows indicate steps that can be omitted.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3351745&req=5

Figure 1: Flow diagram of a typical metagenome projects. Dashed arrows indicate steps that can be omitted.
Mentions: This review gives an overview of the field of metagenomics, with particular emphasis on the steps involved in a typical sequence-based metagenome project (Figure 1). We describe and discuss sample processing, sequencing technology, assembly, binning, annotation, experimental design, statistical analysis, and data storage and sharing. Clearly, any kind of metagenomic dataset will benefit from the rich information available from other metagenome projects, and it is hoped that common, yet flexible, standards and interactions among scientists in the field will facilitate this sharing of information. This review article summarizes the current thinking in the field and introduces current practices and key issues that those scientists new to the field need to consider for a successful metagenome project.

Bottom Line: The field of metagenomics has been responsible for substantial advances in microbial ecology, evolution, and diversity over the past 5 to 10 years, and many research laboratories are actively engaged in it now.This review summarizes the current opinions in metagenomics, and provides practical guidance and advice on sample processing, sequencing technology, assembly, binning, annotation, experimental design, statistical analysis, data storage, and data sharing.As more metagenomic datasets are generated, the availability of standardized procedures and shared data storage and analysis becomes increasingly important to ensure that output of individual projects can be assessed and compared.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Biotechnology and Biomolecular Sciences & Centre for Marine Bio-Innovation, The University of New South Wales, Sydney, NSW 2052, Australia. t.thomas@unsw.edu.au.

ABSTRACT
Metagenomics applies a suite of genomic technologies and bioinformatics tools to directly access the genetic content of entire communities of organisms. The field of metagenomics has been responsible for substantial advances in microbial ecology, evolution, and diversity over the past 5 to 10 years, and many research laboratories are actively engaged in it now. With the growing numbers of activities also comes a plethora of methodological knowledge and expertise that should guide future developments in the field. This review summarizes the current opinions in metagenomics, and provides practical guidance and advice on sample processing, sequencing technology, assembly, binning, annotation, experimental design, statistical analysis, data storage, and data sharing. As more metagenomic datasets are generated, the availability of standardized procedures and shared data storage and analysis becomes increasingly important to ensure that output of individual projects can be assessed and compared.

No MeSH data available.