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Episodic

A complete pipeline for fitting and testing Fixed Local Clock (FLC) molecular clock models for episodic evolution.

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About

Episodic is a tool for fitting and testing Fixed Local Clock (FLC) molecular clock models for episodic evolution. The package is built on top of SNK, and provides a complete pipeline for fitting and testing models of episodic evolution using BEAST.

Episodic implements the ideas of Tay et al. (2022 and 2023) and detects episodic evolution through Bayesian inference of molecular clock models.

Given a multiple sequence alignment and a list of groups to test for episodic evolution, episodic will: - Configure BEAST analyses for strict, relaxed (UCGD) and stem fixed local clock models. - Configure marginal likelihood analyses for each clock model. - Run all the BEAST and marginal likelihood analyses. - Plot and summarise the results. - Compute and plot Bayes factors for the marginal likelihood analyses. - Produce maximum clade credibility (MCC) trees for each clock model. - Compute bayes factor on effect size for the FLC models (foreground vs background). - Run rank and quantile tests on relaxed clock models. - Handel the execution of the pipeline on a HPC cluster via snakemake profiles. - Produce a report of the results (TBD).

Outputs

Episodic writes results to output.dir (optionally timestamped when output.dated: true).

For the complete output file reference (including optional branches and side-effect plots), see:

Guides

Reproducible walkthroughs for previously published studies are documented in:

Features

  • Complete pipeline - episodic provides a complete pipeline for fitting and testing FLC models of episodic evolution.
  • Flexible - episodic is built on top of SNK, and provides a flexible framework for fitting and testing FLC models of episodic evolution.
  • Easy to use - episodic is easy to use, and provides a simple interface for fitting and testing FLC models of episodic evolution.
  • robust - episodic is robust, and provides a robust framework for fitting and testing FLC models of episodic evolution.