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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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MicrobiotaProcess: A comprehensive R package for deep mining microbiome.

Shuangbin Xu1,2, Li Zhan2, Wenli Tang2

  • 1Division of Laboratory Medicine, Microbiome Medicine Center, Zhujiang Hospital, Southern Medical University, Guangzhou 510515, China.

Innovation (Cambridge (Mass.))
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Summary

Microbiome research generates vast data. The MicrobiotaProcess package offers a new data structure (MPSE) and flexible analysis tools for efficient data mining and personalized microbiome data exploration.

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Area of Science:

  • Microbiome research
  • Bioinformatics
  • Computational ecology

Background:

  • Microbiome research is generating data at an unprecedented rate.
  • Current data structures and analysis methods lack efficiency and flexibility.
  • Challenges exist in integrating primary and intermediate microbiome data for downstream analysis.

Purpose of the Study:

  • To address the limitations in microbiome data management and analysis.
  • To introduce the MicrobiotaProcess R package and its MPSE data structure.
  • To provide a flexible and composable framework for microbiome data exploration and analysis.

Main Methods:

  • Development of the MPSE (Microbiome Profile and Sample Experiment) data structure for integrated data management.
  • Design of a tidy framework with composable functions for deconstructed analysis tasks.
  • Implementation of interoperability with existing R packages for expanded analytical capabilities.

Main Results:

  • The MPSE data structure facilitates better integration and exploration of microbiome data.
  • Composable functions enable personalized analysis workflows and complex task execution.
  • MicrobiotaProcess enhances data visualization for improved interpretation of results.

Conclusions:

  • MicrobiotaProcess offers a robust solution for managing and analyzing large-scale microbiome data.
  • The package empowers researchers with flexible tools for data exploration and custom analysis.
  • It serves as a valuable resource for both microbiome and ecological data analysis.