# Difference between revisions of "Divergence Times"

From MolEvol

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== Optional Tutorial: Phylodynamics of Infectious Diseases == | == Optional Tutorial: Phylodynamics of Infectious Diseases == | ||

− | Many students are interested in Bayesian methods for understanding epidemiology. There are some great tools for applying these methods: | + | Many students are interested in Bayesian methods for understanding epidemiology. There are some great tools for applying these methods, including BEAST2: |

− | * Trevor Bedford's tutorial on: [https://github.com/trvrb/influenza-dynamics-practical Inferring spatiotemporal dynamics of the H1N1 influenza pandemic from sequence data] | + | * Alexei Drummand & Remco Bouckaert's BEAST2 tutorial on: [http://beast2.googlecode.com/files/MEPsv2.0.2.pdf Measurably evolving populations, estimating rates, and Bayesian skyline plots] |

+ | * Trevor Bedford's tutorial on (using BEAST1): [https://github.com/trvrb/influenza-dynamics-practical Inferring spatiotemporal dynamics of the H1N1 influenza pandemic from sequence data] |

## Revision as of 14:48, 14 July 2013

Course materials by Tracy Heath on Bayesian Divergence Time Estimation (2013).

## Contents

## Lecture

Bayesian methods for dating species divergences (slides)

## Lab Practical: Divergence time estimation using BEAST

### Tutorial

The detailed tutorial and data files can be found here: http://treethinkers.org/divergence-time-estimation-using-beast/

### Software

- BEAST - software package includes:
- BEAST
- BEAUti
- LogCombiner
- TreeAnnotator

- FigTree
- Tracer
- DendroPy (*optional)
- starttree (*optional)

## Optional Tutorial: Phylodynamics of Infectious Diseases

Many students are interested in Bayesian methods for understanding epidemiology. There are some great tools for applying these methods, including BEAST2:

- Alexei Drummand & Remco Bouckaert's BEAST2 tutorial on: Measurably evolving populations, estimating rates, and Bayesian skyline plots
- Trevor Bedford's tutorial on (using BEAST1): Inferring spatiotemporal dynamics of the H1N1 influenza pandemic from sequence data