Exodus

Exodus quantifies the relative abundance of highly similar genomes in mixed microbial samples from next-generation sequencing (NGS) data.

Key Features:

• Reference-based Python algorithm: Uses a reference-based approach implemented in Python to assign reads and quantify genomes in complex mixtures.
• Low error rates: Empirical and in silico testing on mixed genome datasets reported median error rates of 0%–0.21% depending on sample complexity.
• No false negatives: Demonstrated detection of all present genomes without false negatives, including low-abundance genomes among closely related sequences.

Scientific Applications:

• Microbial ecology and metagenomics: Quantification and discrimination of closely related microorganisms within environmental and microbiome samples using NGS.
• Synthetic microbial consortia analysis: Monitoring relative abundances of constituent strains in engineered communities composed of genetically related organisms.

Methodology:

Performance was evaluated using empirical and simulated (in silico) NGS datasets generated from mixed genomes; the reference-based Python algorithm was applied to these datasets, and the implementation is managed within a Snakemake workflow.