ClonalTREE

ClonalTREE reconstructs clonal evolution histories from time-course genomic sequencing data by inferring haplotypes and clone frequencies in long-term evolution experiments (LTEEs).

Key Features:

• Haplotype Reconstruction: Reconstructs bacterial clone haplotypes by analyzing variant allele frequency (VAF) data across multiple time points.
• Clonal Frequency Estimation: Estimates frequencies of clones within the population to track clonal composition dynamics over the experiment.
• Evolutionary History Reconstruction: Formulates a maximum likelihood framework assuming spontaneous mutations with mutation likelihood proportional to the clone's frequency at the time of mutation to infer clonal evolutionary history.
• Heuristic Algorithms: Implements heuristic algorithms to solve the maximum likelihood inference problem efficiently, prioritizing computational speed and near-optimal accuracy.
• Integration with Experimental Data: Incorporates clonal sequencing data when available to refine reconstruction results.
• Validation and Performance: Validated on simulated datasets and experimental LTEE data, including studies of Escherichia coli, demonstrating reconstruction of clonal histories from genome sequencing time-course data.

Scientific Applications:

• Bacterial Evolution: Dissects adaptive dynamics and evolutionary pressures on correlated mutations in bacterial populations during LTEEs.
• Microbial Ecology and Evolutionary Biology: Supports studies requiring precise clonal reconstruction to link genetic changes to population-level processes.
• Genomics of Time-Course Data: Enables analysis of time-course genomic sequencing to connect mutation trajectories with clonal composition and phenotypic outcomes.

Methodology:

Defines a maximum likelihood function under the assumption of spontaneous mutations with likelihood proportional to clone frequency at mutation time, and applies heuristic algorithms to solve the inference problem efficiently.