Course syllabus

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Welcome to BIOINF 702: Comparative Bioinformatics.

Lecture outline

Weeks 1-3 (Linz): Homology, pairwise and multiple sequence alignments, Markov chains, Hidden Markov models, introduction to phylogenetic trees.

Weeks 4-6 (Drummond): Recap on probability theory, models of sequence evolution, maximum likelihood phylogenetic inference, Bayesian phylogenetic inference, BEAST2, applications.

Weeks 7-12 (Matzke): Using model-based inference on phylogeny-linked datasets to test hypotheses about character and trait evolution, diversification (speciation, extinction, and their interaction with other processes), competition, macroecology, biogeography, and more. Many classic questions in evolutionary biology are being re-investigated within the model-based framework, such as contingency versus convergence, the causes of extinction and speciation, punctuated equilibrium and more.

 

Learning outcomes

  • Describe the application of computational methods to the inference of pairwise and multiple sequence alignments and, hence, of positional homology

  • Write scripts in Perl, Python or R that implement some of the principle algorithms used in comparative bioinformatics and phylogenetics.

  • Describe the design and operation of Markov models and understand how they may be applied in sequence alignment, and for modelling sequence evolution and phylogenetic relationships.

  • Critically evaluate the appropriate techniques and methods to perform comparative analysis of biological data

  • Explain the commonalities and differences between Maximum Likelihood versus Bayesian inference.

  • Apply various methods for maximizing the likelihood (analytic versus numerical), and their advantages/disadvantages.
  • Describe various methods for penalizing likelihood based on model complexity (LRT, AIC, AICc, BIC; or Bayes Factors in a Bayesian context), and their assumptions and advantages/disadvantages

 

Course contact hours/timetable

Lectures are scheduled for Monday, Tuesday, Wednesday 11am-12pm (Biology Building, Room 113).

Labs are scheduled for Tuesday 1-4pm (every second week), Science Chem, Room 331. Depending on the lecturer, this time may or may not be used.

 

Contacts

 

Assessment

  • 60% assignments (4 assignments, 15% each)
  • 40% final exam

 

Handling illness or absence

  • If you must leave for family emergencies etc., please talk to the lecturer, or somehow get a message to the department. Very few problems are so urgent that we cannot be told quite quickly.
  • For problems affecting assignments or tests, see the lecturer, as soon as reasonably possible.
  • For illness during exams (or other problems that affect exam performance) students must contact the University within one week of the last affected examination, to apply for an aegrotat pass (for illness) or compassionate pass (other problems).
    THE ONE WEEK LIMIT IS STRICTLY ENFORCED.

Refer to the University information about Aegrotat and Compassionate Considerations: https://www.auckland.ac.nz/en/for/current-students/cs-academic-information/cs-examination-information/cs-aegrotat-and-compassionate-consideration.html

 

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting his or her learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the world-wide web. A student's assessed work may be reviewed against electronic source material using computerised detection mechanisms. Upon reasonable request, students may be required to provide an electronic version of their work for computerised review.

Please refer to http://www.auckland.ac.nz/uoa/home/about/teaching-learning/honesty.

Harassment

Every member of the University has a right to dignity and respect. Please see the policy on harassment: https://www.auckland.ac.nz/en/for/current-students/cs-student-support-and-services/cs-personal-support/bullying-and-harassment.html. 

Help with Canvas

For help with Canvas see: https://www.auckland.ac.nz/en/about/learning-and-teaching/CanvasHomepage/canvas-help---support.html.

 

Course summary:

Date Details Due