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Updated: May 7, 2026
Metagenomic Analysis of Silage
Published on: January 13, 2017
A user's guide to quantitative and comparative analysis of metagenomic datasets
Chengwei Luo1, Luis M Rodriguez-R, Konstantinos T Konstantinidis
1Center for Bioinformatics and Computational Genomics, Georgia Institute of Technology, Atlanta, Georgia, USA; School of Biology, Georgia Institute of Technology, Atlanta, Georgia, USA; School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.
This guide offers bioinformatic pipelines for analyzing microbial metagenomics data, regardless of sequencing technology. It addresses assembly, coverage, taxonomic identification, and differential abundance analysis for microbial communities.
Area of Science:
- Microbiology
- Bioinformatics
- Genomics
Background:
- Metagenomics has advanced microbial studies, revealing community diversity and metabolic potential.
- Standardized metagenomic analysis tools are lacking due to evolving sequencing technologies (e.g., long vs. short reads).
Purpose of the Study:
- To provide a guide to bioinformatic pipelines for metagenomic data analysis.
- To present tools for assembly, coverage assessment, taxonomic identification, and differential abundance analysis.
- To offer practical guidelines for handling metagenomic data independent of sequencing platform.
Main Methods:
- Development and presentation of bioinformatic pipelines for metagenomic analysis.
- Focus on pipelines for assembly, sequence coverage determination, taxonomic classification, and differential gene/species abundance.
- Pipelines are adaptable to different sequencing data types and freely available.
Main Results:
- Established pipelines for key metagenomic analysis tasks.
- Demonstrated adaptability of pipelines to various sequencing data types.
- Provided practical guidelines for microbial community metagenomic analysis.
Conclusions:
- The developed pipelines offer a standardized approach to metagenomic data analysis.
- The guidelines facilitate handling diverse microbial community data, irrespective of sequencing platform.
- Further improvements in computational aspects of metagenomics are discussed.
