BIOSCI 700 : Advanced Phylogenetics


2022 Semester One (1223) (15 POINTS)

Course Prescription

Students will learn advanced computational methods for inferring phylogenetic trees and studying macroevolutionary processes, including phylogenetic dating, coalescence, epidemic phylogeography, and estimation of ancestral traits and biogeography. Relevant skills in computation (BEAST, command-line programs, R) and statistics (Bayesian methods, model-based inference) will also be taught.

Course Overview

Why students would want to take this course and how it may help with future study/career opportunities: this course will be especially useful for student looking to continue in research involving evolution, phylogenies, and/or computational biology, as it will give you the knowledge and skills needed to begin research in these areas. More generally, you will gain skills in running open source software, command-line interfaces, dealing with sequence data, and visualising, analysing and processing biological data in R. These are job skills in the 21st century.

BIOSCI700: Advanced Phylogenetics will give students a thorough tour of modern phylogenetics: the models and methods behind sequence alignment and phylogenetic inference. The point of this course is go "under the hood" so that students learn the fundamentals of what is going on inside the "black box" of different computer programs. The course will also focus on "what we learn with phylogenies," i.e. using phylogenies to learn about the history of life, especially focusing on "macroevolution" -- evolution studied across clades rather than in individual populations. Modern macroevolution research is heavily based on model-based inference, so methods of inference (Maximum Likelihood, Bayesian), will be discussed along with the macroevolutionary models that are used. A key skill will be critical thinking about the assumptions of various models, because all models are relatively simple approximations of the fantastically complex and heterogeneous process of evolution, and we regularly discover that bad assumptions in our models can cause mistaken inference.

The practical work for the course will revolve around computer labs. The goal will be to have students develop the confidence to figure out how to get programs and analyses to run on their own computers, rather than be "hand held" with step-by-step instructions that will not be useful in future work with new or revised programs in the future. The macroevolutionary portion of the course will make use of R packages commonly used in the scientific literature.

This course replaces BIOINF 702: Comparative Bioinformatics, and covers similar topics (focusing on evolution & phylogenetics rather than e.g. protein structure or sequencing methods).

Course Requirements

Restriction: BIOINF 702

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement
Capability 5: Independence and Integrity

Learning Outcomes

By the end of this course, students will be able to:
  1. Describe the application of computational methods to the inference of pairwise and multiple sequence alignments and, hence, of positional homology (Capability 1)
  2. Write scripts in R that implement some of the principle algorithms used in comparative bioinformatics and phylogenetics. (Capability 3)
  3. 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. (Capability 1)
  4. Criticially evaluate the models and methods to perform comparative analysis of biological data (Capability 2)
  5. Explain the commonalities and differences between Maximum Likelihood versus Bayesian inference. (Capability 1)
  6. Apply various methods for maximising the likelihood (analytic versus numerical), and their advantages/disadvantages. (Capability 1)
  7. 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 (Capability 1 and 2)
  8. Design, implement, and explain an analysis of macroevolutionary data using R (Capability 3, 4 and 5)


Assessment Type Percentage Classification
Laboratories 60% Individual Coursework
Final Exam 40% Individual Coursework
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6 7 8
Final Exam

Key Topics

Homology, pairwise and multiple sequence alignments, Markov chains, Hidden Markov models, introduction to phylogenetic trees.

Recap on probability theory, models of sequence evolution, maximum likelihood phylogenetic inference, Bayesian phylogenetic inference, BEAST2, applications.

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.

Special Requirements

No special requirements.

Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course, you can expect 3 hours of lectures/discussions per week, a 3 hour tutorial every 2 weeks, 3 hours of reading and thinking about the content and 3 hours of work on assignments and/or test preparation.

Delivery Mode

Campus Experience

This course is designated a "campus experience," meaning:

  • Attendance is expected at scheduled activities including labs/tutorials to complete components of thecourse.
  • Lectures will be available as recordings. Other learning activities including labs will not be available as recordings, unless the entire campus is forced online due another COVID Alert.
  • The course will not include live online events, unless the entire campus is forced online due another COVID Alert.
  • Attendance on campus is required for the exam (except for remote/otherwise exempt students).
  • The activities for the course are scheduled as a standard weekly timetable.

Learning Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

We will work mostly from key publications that are referenced each week. For the Phylogenetic Comparative Methods portion of the course, we will rely heavily on:

Harmon, Luke (2019). Phylogenetic Comparative Methods: learning from trees. 

Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.

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 their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.


The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Learning Continuity

In the event of an unexpected disruption, we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, and if disruption occurs you should refer to the university website for information about how to proceed.

The delivery mode may change depending on COVID restrictions. Any changes will be communicated through Canvas.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter


Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course students may be asked to submit coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.

Published on 10/11/2021 12:36 p.m.