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Methods to Assess Microbial Populations01:30

Methods to Assess Microbial Populations

Assessing microbial populations is crucial for understanding microbial roles in health, ecology, and industry. Various complementary techniques—both culture-based and molecular—enable detailed analysis of microbial abundance, diversity, and function.Viable Plate CountThe viable plate count is a traditional culture-based method used to estimate the number of living microbes in a sample. After serial dilution, the sample is spread onto nutrient agar plates. Each viable cell forms a visible...
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Microbial communities, comprising bacteria, archaea, and eukaryotic microorganisms, inhabit diverse ecosystems and play crucial roles in environmental and biological processes. Their diversity is defined by three main parameters: species richness (the number of distinct species), species abundance (the relative quantity of each species), and species evenness (how uniformly individual species are distributed in various locations). These factors together shape the structure and ecological balance...
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The best practice for microbiome analysis using R.

Tao Wen1,2, Guoqing Niu2, Tong Chen3

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Summary

This study organizes 324 R packages for microbiome analysis, classifying them by function to aid researchers. It also reviews integrated tools and proposes an optimal analysis pipeline, with code available on GitHub.

Keywords:
R packageamplicondata analysismetagenomemicrobiomevisualization

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • Advancements in sequencing technology have led to numerous microbiome studies and analysis tools.
  • R language is a popular platform for microbiome data analysis, but the vast number of packages presents a challenge.
  • Researchers need guidance to select suitable, efficient, and user-friendly R packages for microbiome analysis.

Purpose of the Study:

  • To organize and classify 324 common R packages for microbiome analysis based on application categories.
  • To systematically review and compare integrated R packages (e.g., phyloseq, microbiome) for microbiome analysis.
  • To establish an optimal microbiome analysis pipeline using R and provide practical code examples.

Main Methods:

  • Categorization of 324 R packages based on analysis types: diversity, difference, biomarker, correlation/network, functional prediction, and others.
  • Systematic review and comparison of integrated R packages, detailing their advantages and limitations.
  • Development of a comprehensive microbiome analysis pipeline with extensive code examples.

Main Results:

  • A curated list of 324 R packages for microbiome analysis, classified by function, is presented.
  • Key integrated R packages are evaluated, offering insights into their strengths and weaknesses.
  • A recommended microbiome analysis pipeline is proposed, supported by practical code examples.

Conclusions:

  • This work provides a structured overview of R packages for microbiome analysis, simplifying tool selection for researchers.
  • The proposed pipeline and available code facilitate efficient and effective microbiome data analysis.
  • This resource serves as a theoretical basis and practical reference for future microbiome tool development.