A new method for inferring timetrees from temporally sampled molecular sequences.

Area of Science:

• * Evolutionary biology
• * Bioinformatics
• * Computational virology

Background:

• * Pathogen timetrees visualize the temporal spread of pathogens using strain evolutionary history.
• * Inferring these trees relies on molecular sequences from temporally sampled strains to estimate divergence times.

Purpose of the Study:

• * To present a new method, RelTime with Dated Tips (RTDT), for estimating pathogen timetrees.
• * To evaluate RTDT's accuracy and computational efficiency compared to existing methods.

Main Methods:

• * RTDT utilizes a relative rate framework, distinct from current algebraic approaches.
• * The method requires fewer priors than Bayesian methods and has low computing needs.

Main Results:

• * RTDT demonstrated excellent accuracy in time estimates and confidence interval coverage on simulated datasets.
• * Empirical data analysis showed RTDT-generated dates consistent with existing literature.
• * Comparative benchmarking revealed no single method excels in all situations.

Conclusions:

• * RTDT provides a computationally efficient and accurate tool for pathogen timetree inference.
• * A guideline is offered for selecting appropriate methods for empirical data analysis.
• * RTDT is integrated into the MEGA X software for user accessibility.