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Updated: Jun 27, 2026
08:43
Metagenomic Analysis of Silage
Published on: January 13, 2017
A bioinformatician's guide to metagenomics
Victor Kunin1, Alex Copeland, Alla Lapidus
1Microbial Ecology Program, DOE Joint Genome Institute, 2800 Mitchell Drive, Walnut Creek, CA, USA.
Microbiology and Molecular Biology Reviews : MMBR
|December 5, 2008
Summary
This review outlines best practices for random shotgun metagenomic projects, guiding bioinformaticians and biologists through data generation, analysis, and management for microbial community studies.
Area of Science:
- Microbiology
- Bioinformatics
- Genomics
Background:
- Random shotgun metagenomics is increasingly dominant for sequence data.
- Standardized best practices for execution and analysis are crucial.
Purpose of the Study:
- To provide a step-by-step guide to metagenomic project bioinformatic analysis.
- To assist researchers in making informed decisions throughout the project lifecycle.
Main Methods:
- Pre-sequencing considerations (community composition, data type).
- Recommendations for sampling and data generation (metadata, library construction, sequencing).
- Application of sequence processing (read preprocessing, assembly, gene prediction/annotation) to metagenomes.
- Metagenome-specific analyses (binning, dominant population, gene-centric).
- Data management strategies.
Main Results:
- A comprehensive workflow for metagenomic projects is presented.
- Key decision points and analytical strategies are detailed.
- Differences between metagenomic and whole-genome project analyses are highlighted.
Conclusions:
- Adherence to best practices enhances the quality and interpretability of metagenomic data.
- This guide supports bioinformaticians and biologists in navigating complex metagenomic projects.
- Effective data management is essential for successful metagenomic research.
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