4.5 Article

microeco: an R package for data mining in microbial community ecology

Journal

FEMS MICROBIOLOGY ECOLOGY
Volume 97, Issue 2, Pages -

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/femsec/fiaa255

Keywords

co-occurrence network; differential abundance test; diversity; environmental factors; functional profile; microbial community

Categories

Funding

  1. National Natural Science Foundation of China [31670503]
  2. 13th Five-year Informatization Plan of Chinese Academy of Sciences [XXH13503-03-106]
  3. China Biodiversity Observation Networks (Sino BON)

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The paper introduces an integrated R package "microeco" as an analysis pipeline for microbial community and environmental data, incorporating a variety of common and advanced methods to address data analysis issues in microbial community ecology research.
A large amount of sequencing data is produced in microbial community ecology studies using the high-throughput sequencing technique, especially amplicon-sequencing-based community data. After conducting the initial bioinformatic analysis of amplicon sequencing data, performing the subsequent statistics and data mining based on the operational taxonomic unit and taxonomic assignment tables is still complicated and time-consuming. To address this problem, we present an integrated R package-'microeco' as an analysis pipeline for treating microbial community and environmental data. This package was developed based on the R6 class system and combines a series of commonly used and advanced approaches in microbial community ecology research. The package includes classes for data preprocessing, taxa abundance plotting, venn diagram, alpha diversity analysis, beta diversity analysis, differential abundance test and indicator taxon analysis, environmental data analysis, null model analysis, network analysis and functional analysis. Each class is designed to provide a set of approaches that can be easily accessible to users. Compared with other R packages in the microbial ecology field, the microeco package is fast, flexible and modularized to use and provides powerful and convenient tools for researchers. The microeco package can be installed from CRAN (The Comprehensive R Archive Network) or github (https://github.com/ChiLiubio/microeco).

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