Outline

Overview

This unit aims to equip students with a robust understanding of contemporary applied genomics techniques, focusing on their application in real-world scenarios such as agriculture, conservation, and medicine. A key aspect of the unit is the development of proficiency in utilizing genomic tools within a Linux environment, enabling students to work efficiently with large-scale genomic datasets and advanced bioinformatics pipelines. By mastering these computational biology skills, students will not only gain an in-depth knowledge of genomic technologies but also become highly employable in an increasingly data-driven job market. The ability to analyse and interpret genomic data is essential for addressing some of the most pressing global challenges, from improving crop yields and conserving biodiversity to advancing medical treatments and personalized healthcare. As the demand for skilled professionals in computational biology continues to grow, students will be well-positioned for success in diverse sectors, including research, healthcare, environmental conservation, and biotechnology.

Unit details and rules

Academic unit	Life and Environmental Sciences Academic Operations
Credit points	6
Prerequisites ?	6 credit points of (GEGE2X01 or QBIO2XXX or DATA2X01 or GENE2XXX or MBLG2X72 or ENVX2001 or DATA2X02)
Corequisites ?	None
Prohibitions ?	ANSC3107
Assumed knowledge ?	Genetics at 2000 level, Biology at 1000 level, algebra
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Brandon Velie, brandon.velie@sydney.edu.au
Lecturer(s)	Brandon Velie, brandon.velie@sydney.edu.au
Lecturer(s)	Emily Remnant, emily.remnant@sydney.edu.au

Assessment

The census date for this unit availability is 1 September 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam ?	Final Exam MCQ and SAQ	40%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
In-person practical, skills, or performance task or test	Tutorial Quiz 1 Consist of a combination of MCQ and SAQ questions/practical exercises using the class servers.	5%	Week 03	20 minutes	AI prohibited
Outcomes assessed:
In-person practical, skills, or performance task or test	Test 1 Consist of a combination of MCQ and SAQ questions/practical exercises using the class servers.	30%	Week 08	90 minutes	AI prohibited
Outcomes assessed:
In-person practical, skills, or performance task or test	Tutorial Quiz 2 Consist of a combination of MCQ and SAQ questions/practical exercises using the class servers.	5%	Week 11	20 minutes	AI prohibited
Outcomes assessed:
Presentation	Group Presentation In class presentation	20%	Week 13	7 minutes	AI allowed
Outcomes assessed:
= group assignment ?

Assessment summary

All assessments must be written/presented in English. Failure to meet this criteria or submit an assessment by the deadline will automatically result in a mark of 0 for that assessment (or, if applicable, for that portion of the assessment). Exemptions to this policy are only possible when special consideration is approved as detailed in the Unit of Study Guide and on the GEGE3004 Canvas page.

Group Project: This assessments MUST be completed as a group. Using the People tab on the unit's Canvas page, students will form groups of 4-6 people (groups of less than 4 or more than 6 will not be allowed without UoSC approval). A detailed description of this assessment can be found on page 7 of the Unit of Study guide and on the GEGE3004 Canvas page.
Computational exercises: Students are expected to complete all practical exercises (unless noted as optional). Completed computational exercise results may be required for assessment tasks.
Quizes and Test: Assessments will consist of a combination of MCQ and SAQ questions/practical exercises using the class servers.

Detailed information for each assessment and assessment rubrics can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2021 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100	At HD level, a student demonstrates a flair for the subject as well as a detailed and comprehensive understanding of the unit material. A ‘High Distinction’ reflects exceptional achievement and is awarded to a student who demonstrates the ability to apply their subject knowledge and understanding to produce original solutions for novel or highly complex problems and/or comprehensive critical discussions of theoretical concepts.
Distinction	75 - 84	At DI level, a student demonstrates an aptitude for the subject and a well-developed understanding of the unit material. A ‘Distinction’ reflects excellent achievement and is awarded to a student who demonstrates an ability to apply their subject knowledge and understanding of the subject to produce good solutions for challenging problems and/or a understanding of the subject to produce good solutions for challenging problems and/or a reasonably well-developed critical analysis of theoretical concepts.
Credit	65 - 74	At CR level, a student demonstrates a good command and knowledge of the unit material. A ‘Credit’ reflects solid achievement and is awarded to a student who has a broad general understanding of the unit material and can solve routine problems and/or identify and superficially discuss theoretical concepts.
Pass	50 - 64	At PS level, a student demonstrates proficiency in the unit material. A ‘Pass’ reflects satisfactory achievement and is awarded to a student who has threshold knowledge.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Simple extensions and late submissions will NOT be accepted without an approved special consideration. Students who experience any form of illness, injury, or misadventure that prevents or affects the preparation or performance in an exam or assessment are encouraged to apply for special consideration. Details on how to apply for special consideration can be found at: sydney.edu.au/special-consideration-and-arrangements.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to Unit of Study & Assessments, WHS	Workshop (5 hr)
Week 02	Applied Genomics Week 2	Workshop (5 hr)
Week 03	Applied Genomics Week 3	Workshop (5 hr)
Week 04	Applied Genomics Week 4	Workshop (5 hr)
Week 05	Applied Genomics Week 5	Workshop (5 hr)
Week 06	Applied Genomics Week 6	Workshop (5 hr)
Week 07	Applied Genomics Week 7	Workshop (5 hr)
Week 08	Applied Genomics Week 8	Workshop (5 hr)
Week 09	Applied Genomics Week 9	Workshop (5 hr)
Week 10	Applied Genomics Week 10	Workshop (5 hr)
Week 11	Applied Genomics Week 11	Workshop (5 hr)
Week 12	Applied Genomics Week 12	Workshop (5 hr)
Week 13	Applied Genomics Week 13	Workshop (5 hr)

Attendance and class requirements

Attendance: Unless otherwise stated, students are expected to attend at least 80% of timetabled activities as defined in the unit of study outline. A student may fail this unit of study because of inadequate attendance.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

It cannot be emphasised too strongly that no one book is likely to give complete coverage of the subject. Textbooks differ in both factual content and the emphasis given to various aspects of computational genomics. We will provide access to some relevant reading materials via Canvas. Students interested in a career in computational genomics are encouraged to extend their knowledge through wider reading and engagement in coding forums.

Genome Assembly and Annotation:

Dominguez Del Angel V, Hjerde E, Sterck L, Capella-Gutierrez S, Notredame C, Vinnere Pettersson O, Amselem J, Bouri L, Bocs S, Klopp C, Gibrat JF, Vlasova A, Leskosek BL, Soler L, Binzer-Panchal M, Lantz H.Ten steps to get started in Genome Assembly and Annotation.F1000Res. 2018 Feb 5;7. pii: ELIXIR-148. doi: 10.12688/f1000research.13598.1. eCollection 2018.

Khan AR, Pervez MT, Babar ME, Naveed N, Shoaib M. A Comprehensive Study of De Novo Genome Assemblers: Current Challenges and Future Prospective. Evol Bioinform Online. 2018 Feb 20;14:1176934318758650. doi: 10.1177/1176934318758650. eCollection 2018.

Variant calling:

Li, H. & Durbin, R. 2009. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics, 25, 1754-60.

Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G. & Durbin, R. 2009. The Sequence Alignment/Map format and SAMtools. Bioinformatics, 25, 2078-9.

Lindblad-Toh, K., Wade, C.M. et al. 2005 Genome sequence, comparative analysis, and haplotype structure of the domestic dog Nature 2005 Dec 8;438(7069):803-19

Association mapping:

Beatrice Amyotte, Amy J. Bowen, Travis Banks, Istvan Rajcan, Daryl J. Somers (2017) Mapping the sensory perception of apple using descriptive sensory evaluation in a genome wide association study PLOS One https://doi.org/10.1371/journal.pone.0171710

Karlsson, E. K., Baranowska, I., Wade, C. M., Salmon Hillbertz, N. H., Zody, M. C., Anderson, N., Biagi, T. M., Patterson, N., Pielberg, G. R., Kulbokas, E. J., 3rd, Comstock, K. E., Keller, E. T., Mesirov, J. P., Von Euler, H., Kampe, O., Hedhammar, A., Lander, E. S., Andersson, G., Andersson, L. & Lindblad-Toh, K. 2007. Efficient mapping of mendelian traits in dogs through genome-wide association. Nat Genet, 39, 1321-8.

Little,C.C. 1979. The Inheritance of Coat Color in Dogs Howell Book House; 1st edition (June 1979)

http://bioinformatics.org.au/ws09/presentations/Day3_JStankovich.pdf

Evolutionary Genomics:

Mailund T, Munch K, Schierup MH (2014) Lineage sorting in apes. Annual Review of Genetics, 48: 519–535.

Liu L, Xi Z, Wu S, Davis CC, Edwards SV (2015) Estimating phylogenetic trees from genome-scale data. Annals of the New York Academy of Sciences, 1360: 36–53.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

LO1. Describe elements of genomic architecture and recognise the genomic mechanisms of inheritance and relationships among organisms
LO2. Develop proficiency in applying genomic applications on Linux-based systems
LO3. Apply bioinformatic approaches to assemble and align genomes from whole genome sequencing data
LO4. Analyse whole genome data to investigate mutations
LO5. Recognise the importance of contemporary applied genomics techniques to solving future challenges in conservation, agriculture, and medicine
LO6. Design and communicate a project proposal to answer a research question in computational genomics

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Science Threshold Standards -

Competency code	Taught, Practiced or Assessed	Competency standard
3.1		Synthesising and evaluating information from a range of sources, including traditional and emerging information technologies and methods
3.2		Formulating hypotheses, proposals and predictions and designing and undertaking experiments in a safe and responsible manner
3.3		Applying recognised methods and appropriate practical techniques and tools, and being able to adapt these techniques when necessary
3.4		Collecting, recording and interpreting data and incorporating qualitative and quantitative evidence into scientifically defensible arguments
4.1		Presenting information, articulating arguments and conclusions, in a variety of modes, to diverse audiences, and for a range of purposes
4.2		Appropriately documenting the essential details of procedures undertaken, key observations, results and conclusions
5.1		Demonstrating a capacity for self-directed learning

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

Additional information

A computer is required for all class activities, thus students who are able to should bring their personal laptop to class as there will only be a limited number of University computers available during class.

Additional costs

Genomics is a computationally demanding field and while some computers will be available for use during class, students will require their own computers to complete assignments outside of timetabled activities and are highly encouraged to bring and use their personal computers during class.

Site visit guidelines

N/A

Work, health and safety

We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non-compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Additional costs

Site visit guidelines

Work, health and safety

Disclaimer

On this page

Useful links

GEGE3004: Applied Genomics

Semester 2, 2025 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Additional costs

Site visit guidelines

Work, health and safety

Disclaimer

On this page

Useful links