mOTUlizer

mOTUlizer implements mOTUpan to estimate core genomes from sets of genomes with varying completeness for metagenomic and single-cell genomic analyses.

Key Features:

• Iterative Bayesian methodology: Employs an iterative Bayesian approach that computes the likelihood of each gene cluster being core or accessory by analysing presence/absence patterns across genomes.
• Scalability and efficiency: Scales to datasets comprising thousands of genomes, enabling analyses of large metagenomic collections.
• Comparative performance: Demonstrates comparable accuracy to Roary and PPanGGOLiN on high-quality genomes while extending core-genome estimation to datasets with lower genome completeness.
• Quality estimation via bootstrapping: Incorporates a bootstrapping procedure to assess the quality and robustness of core-genome predictions across varying genome completeness and dataset sizes.

Scientific Applications:

• Microbial ecology: Characterizes core genomic content across diverse microbial populations in metagenomic and environmental genomic studies.
• Evolutionary biology: Supports comparative and evolutionary analyses of genomes, including metagenome-assembled genomes and single-cell genomes from uncultured microbial clades.

Methodology:

Computational steps include estimating gene-cluster presence/absence patterns, applying iterative Bayesian inference to classify core versus accessory genes by computing likelihoods, and using bootstrapping for quality assessment.