Scientists produce large amounts of sequencing data in microbial community ecology studies using high-throughput sequencing techniques, especially amplicon-sequencing-based community data. However, performing statistics and data mining is still complicated and time-consuming. A new paper published in FEMS Microbiology Ecology presents an integrated R package (‘microeco’) as an analysis pipeline for treating microbial community and environmental data. The microeco package is a modularized, powerful and user-friendly data mining tool for researchers, as co-author Minjie Yao presents in the #FEMSmicroBlog. #FascinatingMicrobes
A new tool to facilitate the work of microbial ecologists
It is time-consuming to find out if a tool is appropriate for the required data analysis. Switching from one tool to another and grasping a new tool are laboursome tasks for many researchers given that different data analysis tools are developed based on various languages, goals and visualization strategies.
Thus, we created the microeco tool which we described in the paper “microeco: An R package for data mining in microbial community ecology”. We aimed to help researchers with the difficulty of learning new analysis tools and the demand for fast data mining with the combination of many approaches. With this, we especially wanted to reach novice programmers. Thus, we employed abundant functions which were necessary for this goal.
To define several properties and functions, we implemented ten classes in microeco. They include the basic class microtable for data pre-processing and extending classes for 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. We encapsulated frequently-used and important functions for each class.
A complete tutorial for new users
The microeco package has four strong features:
- High modularity: each class covers a set of approaches, and the structure and functions of the package are easy to understand, remember, search and use
- High flexibility: diverse algorithms or approaches are implemented in each class, thus, the analysis is flexible
- Fast acting: algorithms have been optimized
- Power: important approaches are implemented or developed, such as the LEfSe, network analysis, null models and functional redundancy calculation
We provided a detailed tutorial online for users to understand the framework, class, function, algorithm, plot and data type. This tutorial is a complete guide to the important methods with example data in the package. In the tutorial, we did not focus too much on interpreting the results.
In addition, the help documents in the package are modularized. Hence, you can see all function descriptions of a class by performing only one search (e.g. ?microtable). We are looking forward to any suggestions and questions about the package. We will update the package to include more classes and functions in the future.
microeco is a fast, user friendly tool with high modularity, clear documentation, continuous development and technical support.”
— The authors of the paper
- Read the paper “microeco: An R package for data mining in microbial community ecology” by Liu et al. (2020) in FEMS Microbiology Ecology
Minjie Yao is an Associate Professor at the College of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou (China). She received her Ph.D. from the Institute of Geochemistry, Chinese Academy of Sciences, Guiyang (China) and worked as a post-doc in the Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu (China). Her research area focuses on soil microbiology and ecology, environmental metagenome and bioinformatics, especially on soil-microbe-plant relationships in grassland and farmland systems.
About this blog section
The section #FascinatingMicrobes for the #FEMSmicroBlog explains the science behind a paper and highlights the significance and broader context of a recent finding. One of the main goals is to share the fascinating spectrum of microbes across all fields of microbiology.
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