VBCG

VBCG identifies and uses a curated set of 20 validated bacterial core genes to generate high-fidelity phylogenomic reconstructions of bacterial species and strains.


Key Features:

  • High Phylogenetic Fidelity: Evaluates 148 previously used bacterial core genes across 30,522 complete genomes from 11,262 species by comparing each gene's phylogeny to the 16S rRNA gene tree to assess fidelity.
  • Comprehensive Gene Set: Selects 20 genes with the highest phylogenetic fidelity from the initial pool, minimizing missing data across species.
  • Enhanced Resolution: Delivers superior resolution at species and strain levels, demonstrated on Escherichia coli strains and outperforming the 16S rRNA gene tree alone.
  • Improved Analysis Speed: Reduces computational burden by focusing on a smaller, reliable gene set, accelerating phylogenomic analyses.

Scientific Applications:

  • Bacterial Evolution Studies: Facilitates detailed reconstruction and analysis of bacterial evolutionary relationships using high-fidelity core-gene phylogenies.
  • Strain Typing and Tracking: Enables typing and tracking of bacterial strains, including human pathogens, for epidemiological and public health investigations.
  • Research on Bacterial Diversity: Supports comparative analyses of bacterial species diversity and microbial ecology using the curated core-gene set.

Methodology:

The approach evaluated 148 candidate core genes across 30,522 complete genomes (11,262 species) and selected 20 genes based on presence, single-copy ratio, and phylogenetic fidelity measured by comparing individual gene trees to the 16S rRNA gene tree, with the workflow implemented as a Python pipeline.

Topics

Details

License:
GPL-2.0
Cost:
Free of charge
Tool Type:
desktop application, workflow
Operating Systems:
Mac, Linux, Windows
Programming Languages:
Python
Added:
2/23/2024
Last Updated:
11/24/2024

Operations

Publications

Tian R, Imanian B. VBCG: 20 validated bacterial core genes for phylogenomic analysis with high fidelity and resolution. Microbiome. 2023;11(1). doi:10.1186/s40168-023-01705-9. PMID:37936197. PMCID:PMC10631056.