Related Experiment Video
Updated: Aug 5, 2026
14:58
High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
Published on: March 5, 2022
rbims: an R package for integrative functional profiling and pathway-level discrimination in metagenome-assembled
Karla P López-Martínez1, Stephanie Hereira-Pacheco2, Diana Hernández-Oaxaca3
1Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico City, Mexico.
Frontiers in Bioinformatics
|July 28, 2026
Summary
This study introduces rbims, an R package for analyzing microbial metabolic potential from metagenome-assembled genomes (MAGs). It helps identify key traits linked to environmental factors in complex microbial communities.
Area of Science:
- Microbial Ecology
- Bioinformatics
- Genomics
Background:
- Metagenomics allows the study of uncultured microbes through metagenome-assembled genomes (MAGs), crucial for understanding ecosystem functions.
- Analyzing large and complex MAGs datasets for functional traits and metabolic potential is challenging.
- Existing tools lack integrated approaches for comparative functional profiling and identifying ecologically relevant traits.
Purpose of the Study:
- To present rbims, a modular R package for the integrative functional profiling of MAGs and metagenomic data.
- To facilitate comparative analyses and visualizations of metabolic repertoires across different conditions.
- To enable the identification of key metabolic traits associated with environmental factors using a novel discriminant framework.
Main Methods:
- rbims supports multiple annotation databases (KEGG, dbCAN, InterProScan, MEROPS, PICRUSt2) for comprehensive functional profiling.
- It calculates gene presence/absence, abundance, and pathway coverage, enabling metadata-informed comparative analyses.
- A discriminant framework combining differential abundance analysis (ALDEx2) and feature ranking identifies environment-associated traits, including pathway-level directional bias testing.
Main Results:
- The rbims package was successfully applied to 42 MAGs from a hydrocarbon enrichment experiment.
- Widespread potential for hexadecane and phenanthrene degradation was identified.
- Enriched oxidoreductase protein families and significant pathway-level directional biases for hydrocarbon degradation in deep-water MAGs were revealed.
Conclusions:
- rbims provides a user-friendly, reproducible framework for functional interpretation in genome-resolved metagenomics.
- The package integrates annotation parsing, quantitative trait analysis, statistical discrimination, and visualization.
- rbims aids in uncovering ecologically meaningful metabolic traits and understanding microbial community functions in response to environmental gradients.