Microbial ecology research is generating unprecedented amounts of data, and analyzing these datasets remains challenging. Researchers often need to combine multiple software tools, manually adjust numerous parameters, and reconcile different data formats before biological patterns can be effectively interpreted. These fragmented workflows increase technical complexity and make it difficult to achieve consistent and reproducible results across studies.
In a new study published in Microbiome and One Health, a research team has developed LorMe (Lightweight One-Line Resolving Microbial Ecology), an open-source R framework designed to provide a unified approach for standardized microbiome analysis. The researchers introduced an analytical platform that enables the transition from raw microbiome data processing to publication-ready visualization through a streamlined workflow.
LorMe is freely available through the Comprehensive R Archive Network (CRAN: https://cran.r-project.org/web/packages/LorMe) and GitHub (https://github.com/wangnq111/LorMe), allowing researchers from diverse fields, including agriculture, environmental science, and health-related microbiome studies, to adopt and further develop the framework.
Complex process
“Microbial community analysis has become increasingly complex because researchers need to combine many different computational methods and software packages,” says first author Ningqi Wang from Nanjing Agricultural University. “To that end, LorMe was designed to reduce this technical burden by providing a common framework where different analytical approaches can work together while maintaining flexibility and transparency.”
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Unlike conventional approaches that require researchers to work across separate analytical frameworks and data structures, LorMe allows seamless data exchange with widely used microbiome analysis packages, including phyloseq and microeco.
“This compatibility allows us to integrate diverse analytical approaches without repeatedly restructuring their datasets, providing a more consistent framework for microbiome data analysis,” says Wang. “The framework also introduces a one-command pipeline that performs major microbiome analyses, including microbial community profiling, identification of taxa associated with experimental conditions, co-occurrence network construction, and meta-network analysis integrating microbial associations with differential taxa.”
Completed within seconds
In a demonstration dataset containing approximately 22,000 microbial features, the complete workflow was completed within seconds on a standard laptop while generating figures, statistical results, and intermediate files required for reproducible research.
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The researchers further evaluated LorMe using a real rhizosphere microbiome dataset from eggplants with contrasting resistance to bacterial wilt. “The analysis successfully reproduced biologically meaningful microbial patterns, demonstrating that the framework can support practical ecological investigations while providing a standardized analytical workflow,” adds Wang.
“By lowering technical barriers and improving reproducibility, we hope LorMe can help researchers focus more on understanding microbial ecological mechanisms rather than managing complicated computational workflows.” says senior and co-corresponding author Zhong Wei.
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