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Overview

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: 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).

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.

Course Contacts

Course Director: Alexei Drummond (alexei@cs.auckland.ac.nz)

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 2 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.

Course Prerequisites, Corequisites and Restrictions

Restriction

Locations and Semesters Offered

LocationSemester
City

Teaching and Learning

Campus Experience

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

  • Attendance is expected at scheduled activities including labs/tutorials to complete components of the course.
  • Learning activities including lectures and labs will not be available as recordings.
  • The course will not include live online events.
  • The activities for the course are scheduled as a standard weekly timetable.

Learning Resources

Taught courses use a learning and collaboration tool called Canvas to provide students with learning materials including 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.

Additional Information on Learning Resources

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. https://lukejharmon.github.io/pcm/ 

Copyright

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.

Health and Safety

The labs are computer labs that can be done online in the event of a University-mandated lockdown. No special safety issues with this course.

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.

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 for potential plagiarism or other forms of academic misconduct, using computerised detection mechanisms.

Similarly, research students must meet the University’s expectations of good research practice. This requires:

  • Honesty - in all aspects of research work
  • Accountability - in the conduct of research
  • Professional courtesy and fairness – in working with others
  • Good stewardship – on behalf of others
  • Transparency – of research process and presentation of results
  • Clarity - communication to be understandable, explainable and accessible

For more information on the University’s expectations of academic integrity, please see the Academic Conduct section of the University policy hub.

Disclaimer

Elements of this outline may be subject to change. The latest information about taught courses is made available to enrolled students in Canvas.

Students may be asked to submit assessments digitally. The University reserves the right to conduct scheduled tests and examinations 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.


Assessment and Learning Outcomes

Special Requirements

No special requirements.

Course Learning Outcomes

CLO #OutcomeProgramme Capability Link
1
2
3
4
5
6
7
8

Assessments

Assessment TypeAssessment PercentageAssessment Classification

Assessment to CLO Mapping

Assessment Type12345678

Student Feedback, Support and Charter

Student Feedback

Feedback on taught courses is gathered from students at the end of each semester through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback and respond with summaries and actions. Your feedback helps teachers to improve the course and its delivery for future students. In addition, class Representatives in each class can take feedback to the department and faculty staff-student consultative committees.

Additional Information on Student Feedback

Students are encouraged to submit SET reviews to provide feedback on the course.

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.

Tuākana

Tuākana is a multi-faceted programme for Māori and Pacific students providing topic specific tutorials, one-on-one sessions, test and exam preparation and more. Explore your options at Tuakana Learning Communities.

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.

Wellbeing

We all go through tough times during the semester, or see our friends struggling. There is lots of help out there - please see the Support Services page for information on support services in the University and the wider community.

Special Circumstances

If your ability to complete assessed work 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.

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.

Student Academic Complaints and Disputes

Students with concerns about teaching including how a course is delivered, the resources provided, or supervision arrangements, have the right to express their concerns and seek resolution. The university encourages informal resolution where possible, as this is quicker and less stressful. For information on the informal and formal complaints processes, please refer to the Student Academic Complaints Statute in the Student Policies and Guidelines section of the Policy Hub.