BIO00056I

Phylogeny and the molecular clock workshop

Author

Daniel Jeffares

Published

February 24, 2025

Note

This web page is a work in progress.

1 Learning objectives

The aim of this practical is to learn to visualise and interpret the data using phylogenetic analyses. You will see that we can extract a lot of information about evolutionary processes and populations from a phylogeny.

By the end of this workshop, you should be able to:

Build phylogenetic trees using using MEGA software
Infer evolutionary relationships based on phylogeny
Correlate molecular evolutionary changes with time using R

These two articles will be useful fo those that want to extend their knowledge.

Baum (2005): The Tree-Thinking Challenge
Shao (2017): Evolution of Influenza A Virus by Mutation and Re-Assortment

Glossary

Technical definitions for this workshop.

molecular clock:
node (of a phylogenetic tree):
branch (of a phylogenetic tree):
mutation rate:
purifying selection:

2 Introduction

2.1 Phylogenetic trees and the molecular clock

Much of what we can infer from phylogenies uses the model of the ‘molecular clock’. This is the observation that there tends to be a uniform, ‘clock-like’ rate of genetic change per year both within and between species. The rate of change differs between very different species and between genes.

Rates of change between species differ because:

small organisms (like viruses and bacteria) have short generation times
small organisms have high mutation rates

Rates of change between genes within a genome differ because: * the amound of purifying selection

2.2 The molecular clock and the real world

Evolutionary thinking can also help us to understand disease. For example, the global pandemic of the coronavirus SARS-Cov2 (which causes COVID-19 disease) is an RNA virus. As with all viruses, SARS-Cov2 is constantly undergoing evolutionary change.

The initial outbreak of the SARS-Cov2 virus in Wuhan China, was dated with amazing accuracy (to the month) to August 2019 using a time tree: https://doi.org/10.3390/v13091790. Being able to tell when outbreaks occur, as well as where, can be a very powerful tool as it can supplement what we can see from epidemiology. Analysis of DNA sequences is the only way we can obtain this information.

3 Exercises

Note

We do not advise doing this workshop on a Mac. The Mega software does not work very well on the Mac OS. It is best to use the University PCs which have the software installed.

4 Summary: what we have learned

5 After the workshop: exam style questions

5.1 Question 1.

5.2 Question 2.

Acknowledegment

This workshop was designed and shared by François Balloux.

--- title: "BIO00056I" subtitle: "Phylogeny and the molecular clock workshop" author: Daniel Jeffares date: 2025-02-24 format: html: code-copy: true code-tools: true code-link: true toc: true number-sections: true toc-location: right toc-depth: 2 highlight-style: github editor: source --- :::callout-note This web page is a work in progress. ::: # Learning objectives The aim of this practical is to learn to visualise and interpret the data using phylogenetic analyses. You will see that we can extract a lot of information about evolutionary processes and populations from a phylogeny. By the end of this workshop, you should be able to: * Build phylogenetic trees using using MEGA software * Infer evolutionary relationships based on phylogeny * Correlate molecular evolutionary changes with time using R These two articles will be useful fo those that want to extend their knowledge. * Baum (2005): [The Tree-Thinking Challenge](https://www.science.org/doi/10.1126/science.1117727) * Shao (2017): [Evolution of Influenza A Virus by Mutation and Re-Assortment](https://www.mdpi.com/1422-0067/18/8/1650) :::callout-note # Glossary Technical definitions for this workshop. * *molecular clock*: * *node* (of a phylogenetic tree): * *branch* (of a phylogenetic tree): * *mutation rate*: * *purifying selection*: ::: # Introduction ## Phylogenetic trees and the molecular clock Much of what we can infer from phylogenies uses the model of the 'molecular clock'. This is the observation that there tends to be a uniform, 'clock-like' rate of genetic change per year both *within* and *between* species. The *rate* of change differs between very different species and between genes. Rates of change between species differ because: * small organisms (like viruses and bacteria) have short generation times * small organisms have high mutation rates Rates of change between genes within a genome differ because: * the amound of *purifying selection* ## The molecular clock and the real world Evolutionary thinking can also help us to understand disease. For example, the global pandemic of the coronavirus SARS-Cov2 (which causes COVID-19 disease) is an RNA virus. As with all viruses, SARS-Cov2 is constantly undergoing evolutionary change. The initial outbreak of the SARS-Cov2 virus in Wuhan China, was dated with amazing accuracy (to the month) to August 2019 using a time tree: https://doi.org/10.3390/v13091790. Being able to tell when outbreaks occur, as well as where, can be a very powerful tool as it can supplement what we can see from epidemiology. Analysis of DNA sequences is the only way we can obtain this information. # Exercises :::callout-note **We do not advise doing this workshop on a Mac.** The Mega software does not work very well on the Mac OS. It is best to use the University PCs which have the software installed. ::: # Summary: what we have learned # After the workshop: exam style questions ## Question 1. ## Question 2. :::callout-note # Acknowledegment This workshop was designed and shared by [François Balloux](https://en.wikipedia.org/wiki/Fran%C3%A7ois_Balloux). :::