• Class Number 3609
  • Term Code 3430
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
    • Prof Gavin Huttley
    • Prof Gavin Huttley
    • Katherine Caley
  • Class Dates
  • Class Start Date 19/02/2024
  • Class End Date 24/05/2024
  • Census Date 05/04/2024
  • Last Date to Enrol 26/02/2024
SELT Survey Results

This course introduces the key methods and technologies of bioinformatics as pertinent to genomics. These are the fastest growing fields of biology and perhaps science. Bioinformatics is a rapidly growing scientific discipline at the interface of genomics, statistics and computer science. It has distinct but overlapping aspects: the development of computational infrastructure (e.g. algorithms, programs, databases) and their use to analyse a wide variety of biological data. Among these data, genes, transcripts, and epigenetic features play a central role. Making sense of the rapid and large-scale data acquisition of today's genomics, transcriptomics, proteomics, and other -omics projects poses the major challenge of modern biology. Analyses of these data relies on advances in bioinformatics and statistics.

As computer literacy is central to bioinformatics, it is also central to this course. Accordingly, the course includes a brief review of the Python programming language. Other topics covered include techniques for sequence comparison, population and comparative genomics.

Honours pathway option (HPO)

An Enrichment-type HPO is offered. Students in this option must attend 7 additional 1hr workshops, which will help develop skills necessary to undertake further research in this area. We cover a problem related to course material in considerable detail. We will review a published method and independently design and implement computational experiments to evaluate the method. This addresses LO's 3-5.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

  1. Describe and apply a variety of methods in bioinformatics, including computer programming. 
  2. Describe and logically evaluate current research procedures across a range of topics in bioinformatics.
  3. Demonstrate the ability to reason logically about problems to which bioinformatic analysis of DNA sequence data can be applied.
  4. Evaluate research methodology in the context of bioinformatic analysis of DNA sequence data. 
  5. Demonstrate the ability to obtain and interpret quantitative results from mathematical and statistical models through analytical and computational methods. 

Research-Led Teaching

The final topic tackles real a data set collected by other ANU students. The class will develop an approach to evaluate a method for estimating the species makeup of this sample using techniques developed covered in the course.

Required Resources

Students need their own computer.

Recommended student system requirements 

ANU courses commonly use a number of online resources and activities including:

  • video material, similar to YouTube, for lectures and other instruction
  • two-way video conferencing for interactive learning
  • email and other messaging tools for communication
  • interactive web apps for formative and collaborative activities
  • print and photo/scan for handwritten work
  • home-based assessment.

To fully participate in ANU learning, students need:

  • A computer or laptop. Mobile devices may work well but in some situations a computer/laptop may be more appropriate.
  • Webcam
  • Speakers and a microphone (e.g. headset)
  • Reliable, stable internet connection. Broadband recommended. If using a mobile network or wi-fi then check performance is adequate.
  • Suitable location with minimal interruptions and adequate privacy for classes and assessments.
  • Printing, and photo/scanning equipment

For more information please see https://www.anu.edu.au/students/systems/recommended-student-system-requirements

Staff Feedback

Students will be given feedback in the following forms in this course:
  • Written comments
  • Verbal comments
  • Feedback to the whole class, to groups, to individuals, focus groups

Student Feedback

ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). The feedback given in these surveys is anonymous and provides the Colleges, University Education Committee and Academic Board with opportunities to recognise excellent teaching, and opportunities for improvement. The Surveys and Evaluation website provides more information on student surveys at ANU and reports on the feedback provided on ANU courses.

Other Information

Part of the course will involve in class quizzes conducted during computer lab sessions. These quizzes will be computational in nature and based on computing materials and resources made available during the course.

Plagiarism will attract academic penalties in accordance with the ANU guidelines. A student in this course is expected to be able to explain and defend any submitted assessment item. The course convener can conduct or initiate an additional interview about any submitted assessment item for any student. If there is a significant discrepancy between the two forms of assessment, it will be automatically treated as a case of suspected academic misconduct.

Class Schedule

Week/Session Summary of Activities Assessment
1 Course Introduction, Critical thinking & Python Revision Logical Thinking Assignment, Python Assignment
2 Sequence Comparison In class quiz
3 Sequence Comparison In class quizzes
4 Sequence Comparison Topic assignment
5 Molecular Evolution In class quiz
6 Molecular Evolution In class quizzes
7 Molecular Evolution Topic assignment
8 Mini-research project on the microbiome
9 Mini-research project on the microbiome Topic assignment
10 Mini-research project on the microbiome
11 Mini-research project on the microbiome
12 Mini-research project on the microbiome Topic assignment

Assessment Summary

Assessment task Value Due Date Return of assessment Learning Outcomes
Python Revision 5 % 26/02/2024 29/02/2024 1
Logical thinking 5 % 29/02/2024 07/03/2024 3
Sequence comparison Quiz 1 1 % 29/02/2024 29/02/2024 1
Sequence comparison Quiz 2 1 % 04/03/2024 04/03/2024 1
Sequence comparison Quiz 3 1 % 07/03/2024 07/03/2024 1
Sequence comparison assignment 22 % 18/03/2024 02/04/2024 1,2,3,4,5
Molecular evolution Quiz 1 1 % 21/03/2024 21/03/2024 1
Molecular evolution Quiz 2 1 % 25/03/2024 25/03/2024 1
Molecular evolution Quiz 3 1 % 28/03/2024 28/03/2024 1
Molecular evolution assignment 22 % 22/04/2024 06/05/2024 1,2,3,4,5
Mini-research project on the microbiome assignment 1 10 % 02/05/2024 07/05/2024 2,3,4
Mini-research project on the microbiome assignment 2 30 % 23/05/2024 06/06/2024 1,2,3,4,5

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details


ANU has educational policies, procedures and guidelines, which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Misconduct Rule before the commencement of their course. Other key policies and guidelines include:

Assessment Requirements

The ANU is using Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit the ANU Online website Students may choose not to submit assessment items through Turnitin. In this instance you will be required to submit, alongside the assessment item itself, hard copies of all references included in the assessment item.

Moderation of Assessment

Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.


For many labs, there will be an in-class quiz (each is worth 1% of the course total).

Unless a medical certificate is provided, or a timetabling conflict, there will not be an opportunity to take the quizzes outside of the lab.

Assessment Task 1

Value: 5 %
Due Date: 26/02/2024
Return of Assessment: 29/02/2024
Learning Outcomes: 1

Python Revision

This will be a programming assignment worth 5% of the course.

Assessment Task 2

Value: 5 %
Due Date: 29/02/2024
Return of Assessment: 07/03/2024
Learning Outcomes: 3

Logical thinking

This will be a written assignment worth 5% of the entire course.

Assessment Task 3

Value: 1 %
Due Date: 29/02/2024
Return of Assessment: 29/02/2024
Learning Outcomes: 1

Sequence comparison Quiz 1

An in class programming quiz worth 1% of the entire course.

Assessment Task 4

Value: 1 %
Due Date: 04/03/2024
Return of Assessment: 04/03/2024
Learning Outcomes: 1

Sequence comparison Quiz 2

An in class programming quiz worth 1% of the entire course.

Assessment Task 5

Value: 1 %
Due Date: 07/03/2024
Return of Assessment: 07/03/2024
Learning Outcomes: 1

Sequence comparison Quiz 3

An in class programming quiz worth 1% of the entire course.

Assessment Task 6

Value: 22 %
Due Date: 18/03/2024
Return of Assessment: 02/04/2024
Learning Outcomes: 1,2,3,4,5

Sequence comparison assignment

A mix of programming and written answers. The assignment will be worth 22% of the entire course.

Assessment Task 7

Value: 1 %
Due Date: 21/03/2024
Return of Assessment: 21/03/2024
Learning Outcomes: 1

Molecular evolution Quiz 1

An in class programming quiz worth 1% of the entire course.

Assessment Task 8

Value: 1 %
Due Date: 25/03/2024
Return of Assessment: 25/03/2024
Learning Outcomes: 1

Molecular evolution Quiz 2

An in class programming quiz worth 1% of the entire course.

Assessment Task 9

Value: 1 %
Due Date: 28/03/2024
Return of Assessment: 28/03/2024
Learning Outcomes: 1

Molecular evolution Quiz 3

An in class programming quiz worth 1% of the entire course.

Assessment Task 10

Value: 22 %
Due Date: 22/04/2024
Return of Assessment: 06/05/2024
Learning Outcomes: 1,2,3,4,5

Molecular evolution assignment

A mix of programming and written answers. The assignment will be worth 22% of the entire course.

Assessment Task 11

Value: 10 %
Due Date: 02/05/2024
Return of Assessment: 07/05/2024
Learning Outcomes: 2,3,4

Mini-research project on the microbiome assignment 1

A written assignment worth 10% of the entire course.

Assessment Task 12

Value: 30 %
Due Date: 23/05/2024
Return of Assessment: 06/06/2024
Learning Outcomes: 1,2,3,4,5

Mini-research project on the microbiome assignment 2

A mix of programming and written answers. The assignment will be worth 30% of the entire course.

Academic Integrity

Academic integrity is a core part of our culture as a community of scholars. At its heart, academic integrity is about behaving ethically. This means that all members of the community commit to honest and responsible scholarly practice and to upholding these values with respect and fairness. The Australian National University commits to embedding the values of academic integrity in our teaching and learning. We ensure that all members of our community understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. The ANU expects staff and students to uphold high standards of academic integrity and act ethically and honestly, to ensure the quality and value of the qualification that you will graduate with. The University has policies and procedures in place to promote academic integrity and manage academic misconduct. Visit the following Academic honesty & plagiarism website for more information about academic integrity and what the ANU considers academic misconduct. The ANU offers a number of services to assist students with their assignments, examinations, and other learning activities. The Academic Skills and Learning Centre offers a number of workshops and seminars that you may find useful for your studies.

Online Submission

Due to the computational nature of the course, all assignment submission is done through the a dedicated system on the class server (nbgrader or an equivalent). The exceptions are strictly written assignments which will be submitted through TurnItIn on Wattle.

Hardcopy Submission

There will be no hard copy submissions.

Late Submission

Late submission of assessment tasks without an extension are penalised at the rate of 5% of the possible marks available per working day or part thereof. Late submission of assessment tasks is not accepted after 10 working days after the due date, or on or after the date specified in the course outline for the return of the assessment item. Late submission is not accepted for take-home examinations.

Referencing Requirements

Accepted academic practice for referencing sources that you use in presentations can be found via the links on the Wattle site, under the file named “ANU and College Policies, Program Information, Student Support Services and Assessment”. Alternatively, you can seek help through the Students Learning Development website.

Returning Assignments

Most assignments will be returned by the nbgrader (or an equivalent mechanism, instructions posted on the class chat). Other assignments will be returned via Wattle.

Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure The Course Convener may grant extensions for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.

Resubmission of Assignments

Resubmission is not allowed.

Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information. In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service — including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy. If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

Distribution of grades policy

Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes. Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.

Support for students

The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).
Prof Gavin Huttley

Research Interests

genetics, genomics, statistics, computing, bioinformatics

Prof Gavin Huttley

Prof Gavin Huttley

Research Interests

Prof Gavin Huttley

Katherine Caley

Research Interests

Katherine Caley


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