Metagenomic technologies have revolutionized our understanding of microbes in different spheres of life, revealing the massive diversity and complex functionalities of microbial communities across various environments. Shotgun metagenomics, which involves sequencing the DNA of all the organisms in a sample, is emerging as a powerful tool in assessing the microbial content. Unlike the traditional culturing approach, the shotgun metagenomic technology provides a comprehensive view of the entire microbial community, including potential functions that the organisms could be performing. In this chapter, we describe a typical bioinformatics workflow to generate the taxonomic profiles from metagenomic sequencing data and demonstrate a few basic statistical analyses that can be performed from this data to generate insights. In addition, we discuss the experimental and analytical considerations that must be taken into account while generating and making inferences from metagenomic data. Lastly, we provide insights on automating the workflow for consistent and reproducible large-scale analyses.

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Unlocking the Metagenome: Pipeline for Microbiome Data Analysis

  • Aarthi Ravikrishnan

摘要

Metagenomic technologies have revolutionized our understanding of microbes in different spheres of life, revealing the massive diversity and complex functionalities of microbial communities across various environments. Shotgun metagenomics, which involves sequencing the DNA of all the organisms in a sample, is emerging as a powerful tool in assessing the microbial content. Unlike the traditional culturing approach, the shotgun metagenomic technology provides a comprehensive view of the entire microbial community, including potential functions that the organisms could be performing. In this chapter, we describe a typical bioinformatics workflow to generate the taxonomic profiles from metagenomic sequencing data and demonstrate a few basic statistical analyses that can be performed from this data to generate insights. In addition, we discuss the experimental and analytical considerations that must be taken into account while generating and making inferences from metagenomic data. Lastly, we provide insights on automating the workflow for consistent and reproducible large-scale analyses.