TransPhylo

TransPhylo reconstructs transmission trees and infers transmission networks from pathogen genomic data using a Bayesian framework that accounts for within-host diversity and uncertainty.

Key Features:

• Bayesian inference: Uses a Bayesian framework to reconstruct densely sampled outbreaks and to integrate prior information and uncertainty in analyses.
• Time-labeled phylogeny reconstruction (BEAST): Employs BEAST to infer time-labeled phylogenies from genomic sequence data.
• Transmission network inference (MCMC): Uses a Monte Carlo Markov chain approach to infer transmission networks from reconstructed phylogenies.
• Within-host diversity consideration: Accounts for within-host genetic diversity, relevant to pathogens with long carriage periods or large intrahost populations.
• Uncertainty assessment: Quantifies uncertainty in transmission reconstructions and reports that substantial uncertainty can persist even with high substitution rates.

Scientific Applications:

• Genomic epidemiology investigations: Applied to investigate the genomic epidemiology of infectious disease outbreaks.
• Identification of transmission events and clusters: Supports identification of key transmission events and clusters by integrating genomic reconstructions with epidemiological interpretation.
• Tuberculosis outbreak analysis: Has been applied to real-world outbreaks such as tuberculosis to identify epidemiological links while acknowledging reconstruction uncertainty.

Methodology:

Pathogen genomic sequences are input; BEAST is used to infer time-labeled phylogenies; a Monte Carlo Markov chain method infers transmission networks from the phylogeny; the workflow assesses uncertainty in the reconstructions.