With the gradual maturity of sequencing technology, many microbiome studies have published, driving the emergence and advance of related analysis tools. R language is the widely used platform for microbiome data analysis for powerful functions. However, tens of thousands of R packages and numerous similar analysis tools have brought major challenges for many researchers to explore microbiome data. How to choose suitable, efficient, convenient, and easy-to-learn tools from the numerous R packages has become a problem for many microbiome researchers. We have organized 324 common R packages for microbiome analysis and classified them according to application categories (diversity, difference, biomarker, correlation and network, functional prediction, and others), which could help researchers quickly find relevant R packages for microbiome analysis. Furthermore, we systematically sorted the integrated R packages (phyloseq, microbiome, MicrobiomeAnalystR, Animalcules, microeco, and amplicon) for microbiome analysis, and summarized the advantages and limitations, which will help researchers choose the appropriate tools. Finally, we thoroughly reviewed the R packages for microbiome analysis, summarized most of the common analysis content in the microbiome, and formed the most suitable pipeline for microbiome analysis. This paper is accompanied by hundreds of examples with 10,000 lines codes in GitHub, which can help beginners to learn, also help analysts compare and test different tools. This paper systematically sorts the application of R in microbiome, providing an important theoretical basis and practical reference for the development of better microbiome tools in the future. All the code is available at GitHub github.com/taowenmicro/EasyMicrobiomeR.

Expansion of photovoltaic infrastructure in arid regions raises concerns about soil microhabitat degradation. Very few studies have systematically compared these recovery alternatives in reshaping the soil fungal communities during early recovery. This study investigated short-term effects (less that two-year recovery) of PV infrastructure and restoration (natural/artificial) on soil fungal diversity and enzymatic activities in Ningxia desert steppe. A total of 243 soil samples were analyzed to assess fungal diversity, composition, enzyme activities, and co-occurrence networks. The restoration method significantly affected soil fungal α-diversity and β-diversity in the experimental solar park. Specifically, at each recovery site, soil depth showed significant effect on fungal α-diversity. However, on a fine scale, artificial restoration significantly increased fungal species richness across soil depths. Ascomycota dominated across different sites, followed by Basidiomycota and Mucoromycota. Shared core genera Fusarium, Mortierella, and Geminibasidium were determined in both recovery sites. Sucrase/phenol oxidase (natural) and catalase/sucrase (artificial) were identified as key fungal drivers according to Random Forest models. Co-occurrence analysis suggested neither artificial restoration nor natural restoration has attained the level of natural habitats. Networks of artificial subsoil and natural topsoil were closest to natural habitat. These results emphasize the impact of restoration and PV shading on fungal communities via spatial heterogeneity and enzyme dynamics during initial recovery stage, providing insights for semi-arid ecosystem management under PV development.