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Unit information: Genome Evolution in 2020/21

Unit name Genome Evolution
Unit code BIOLM0033
Credit points 10
Level of study M/7
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Professor. Pisani
Open unit status Not open

Scientific programming, Statistics and R.



School/department School of Biological Sciences
Faculty Faculty of Life Sciences


This unit will introduce students to the theoretical and practical aspects of the study of genome evolution. These include the design of analyses and use of tools such as phylogenetic trees and networks to identify pattern of genomic change (e.g. identification of gene duplications and of horizontally transferred genes) across the tree of life. The students will have the opportunity to plan, develop and implement their own evolutionary analyses to answer a wide variety of biological questions.

The aim of this unit will be to:

  1. Provide students with a detailed understanding of the concepts behind designing and performing evolutionary analyses.
  2. Provide students with the skills required to use and interact with the pieces of software required to perform such analyses (methods to reconstruct and compare phylogenetic trees and individual gene sequences to identify similarities and differences of functional and evolutionary relevance).

Intended learning outcomes

The Learning Outcomes (LOs) for this unit are:

A: Knowledge and Understanding:

  1. to understand the theoretical aspects and rationale behind genome evolution.
  2. to develop knowledge on the different data types and levels of analyses that can be performed to understand different processes of genomic change.
  3. to acquire the concepts behind the use of different software to analyse evolutionary datasets.

B: Intellectual Skills/Attributes:

  1. to devise optimal experimental design to test hypotheses of genome evolution.
  2. to design and implement pipelines and critically assess their suitability to different analysis types.
  3. to plan the best use of different computational methods to attack different evolutionary problems.

C: Other Skills /Attributes (Practical/Professional/Transferable):

  1. to acquire proficiency performing analyses of evolutionary data.
  2. to demonstrate competence with different software to perform evolutionary analyses.
  3. To gain strengths in integrating different pieces of software to produce holistic analyses of evolutionary data.

Teaching details

The unit will be delivered through a mixture of short lectures followed by individual exercises with computers. Blackboard will be used to engage students with the unit content.

Assessment Details

A summative computer assessment will consist of a final computer task and an essay integrating all the learning objectives.

Reading and References

Nacimento et al. (2017) A biologist guide to Bayesian phylogenetic analysis. Nat Ecol Evol. 1:1446–1454.

Yang Z. (2014) Molecular Evolution: a statistical approach. Oxford University Press.

Soucy et al. (2015) Horizontal gene transfer: building the web of life. Nature Reviews Genetics 16:472–482.