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Related Experiment Video

Updated: Nov 25, 2025

Isolation and Analysis of Microbial Communities in Soil, Rhizosphere, and Roots in Perennial Grass Experiments
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microeco: an R package for data mining in microbial community ecology.

Chi Liu1,2, Yaoming Cui3, Xiangzhen Li2

  • 1Engineering Research Center of Soil Remediation of Fujian Province University, College of Resources and Environment, Fujian Agriculture and Forestry University, 15 Shangxiadian Road, Fuzhou 350002, China.

FEMS Microbiology Ecology
|December 17, 2020
PubMed
Summary

The microeco R package simplifies microbial community analysis. It offers a comprehensive pipeline for high-throughput sequencing data, making complex statistics and data mining faster and more accessible for researchers.

Keywords:
co-occurrence networkdifferential abundance testdiversityenvironmental factorsfunctional profilemicrobial community

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Area of Science:

  • Microbial Ecology
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput sequencing generates vast microbial community data, particularly from amplicon sequencing.
  • Post-bioinformatic analysis of this data, including statistical and data mining tasks on operational taxonomic unit (OTU) and taxonomic assignment tables, is often complex and time-consuming.

Purpose of the Study:

  • To present 'microeco', an integrated R package designed as a user-friendly analysis pipeline for microbial community and environmental data.
  • To streamline and accelerate the statistical analysis and data mining of microbial ecology datasets.

Main Methods:

  • Development of the 'microeco' R package utilizing the R6 class system.
  • Integration of diverse analytical approaches including data preprocessing, diversity analyses (alpha and beta), differential abundance testing, network analysis, and functional analysis.

Main Results:

  • The 'microeco' package provides a modular and flexible framework for microbial community analysis.
  • It offers accessible tools for various ecological analyses, from basic plotting to advanced network and functional assessments.

Conclusions:

  • 'microeco' offers a fast, flexible, and modular solution for microbial community data analysis, enhancing researcher efficiency.
  • The package provides powerful and convenient tools, available via CRAN and GitHub, for the scientific community.