rbims Logo Rbims

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rbims (Reconstruction of Bin Metabolisms) is an R package designed to streamline the functional analysis and visualization of metagenome-assembled genomes (MAGs). It supports annotation integration from KEGG, InterProScan, dbCAN, MEROPS, and Picrust2 allowing researchers to quantify gene presence, abundance, and pathway coverage across microbial genomes.

  • rbims also integrates ALDEx2-derived effect sizes and random forest importance scores into a consensus table that prioritizes candidate discriminant KOs.This allows to distinguish enriched KO’s between groups.

  • Moreover, rbims enables a pathway-level directional bias analysis based on the discriminant output. This hierarchical framework allows rbims to distinguish between isolated gene-level differences and structurally coherent pathway-level enrichment across experimental gradients.

  • The package includes a curated database for hydrocarbon degradation pathways (aerobic and anaerobic) and provides tools to generate publication-ready visualizations such as heatmaps and bubble plots. It is designed to assist in exploratory trait analysis and early-stage hypothesis generation in genome-resolved metagenomics.

✨ Features

  • Conda environment creation tutorial (rbimsenv) to run consistent annotations.
  • Import functional annotations from KEGG, InterProScan, dbCAN, MEROPS, and PICRUSt2.
  • Calculate presence, abundance, and pathway coverage per MAG.
  • Subset data by gene, enzyme, pathway, or domain.
  • Performs a discriminant analysis and pathway directional bias to distinguish enrichment of pathways between environmental gradients.
  • Visualize functional traits with customizable bubble plots and heatmaps.
  • Integrate sample or genome metadata.
  • Export data frames and visualizations for publication.
  • Curated database for hydrocarbon degradation not covered in KEGG.
  • Performs a discriminant analysis and pathway directional bias test to distinguish between isolated gene-level differences and structurally coherent pathway-level enrichment across experimental gradients.
Workflow

Figure 1: Overview of the rbims workflow. A) Steps to create the rbims conda environment integrating external tools (from KEGG, dbCAN, MEROPS, InterProScan) and running annotations. B) Workflow in R to import annotations using read() functions (blue). Map profile tables using mapping_ko() (green) and extract traits via get_subset() and calc_pathway_directional_bias() functions (orange). Results are visualized with plot() functions (purple).

🚀 Quick Install 

install.packages("devtools")
library(devtools)
install_github("mirnavazquez/RbiMs")
library(rbims)

If you are on macOS, install XQuartz.
If using Ubuntu, install system dependency: libcairo2-dev.

🧬 Case Study: Oil-Enriched Marine MAGs

A complete example using MAGs from a hydrocarbon enrichment experiment is available in the folder /Hidrocarburos, including annotation files and code to reproduce the figures in our manuscript.

👩‍💻 Contributors

  • Mirna Vázquez-Rosas-Landa – lead developer
  • Karla P. López-Martínez – co-developer, documentation, manuscript
  • Stephanie Hereira-Pacheco – functions and documentation
  • Diana Hernández-Oaxaca – conda environment setup
  • Frida López-Ruiz – testing and documentation

📚 References

🌐 Website

Full documentation: https://mirnavazquez.github.io/RbiMs