Outline

Overview

The average mammalian genome is 3 billion nucleotides long and some other organisms have genomes that are even larger. Working with DNA at the nucleotide level on an organismal scale is impossible without the assistance of high performance computing. This unit will investigate strategies to manipulate genomic data on a whole organism scale. You will learn how scientists use high performance computing and web-based resources to compare and assemble genomes, map genes that cause specific phenotypes, and uncover mutations that cause phenotypic changes in organisms that influence health, external characteristics, production and disease. By doing this unit you will develop skills in the analysis of big data, you will gain familiarity with high performance computing worktop environments and learn to use bioinformatics tools that are commonly applied in research.

Unit details and rules

Unit code	GEGE3004
Academic unit	Life and Environmental Sciences Academic Operations
Credit points	6
Prohibitions ?	ANSC3107
Prerequisites ?	6cp of (GEGE2X01 or QBIO2XXX or DATA2X01 or GENE2XXX or MBLG2X72 or ENVX2001 or DATA2X02)
Corequisites ?	None
Assumed knowledge ?	Genetics at 2000 level, Biology at 1000 level, algebra
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Claire Wade, claire.wade@sydney.edu.au
Lecturer(s)	Claire Wade, claire.wade@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Record+)	GEGE3004 final exam Four long-answer questions	40%	Formal exam period	2 hours
Outcomes assessed:
Small test	Evolutionary Genomics Assessment On-line assessed multiple-choice questions and short answer questions.	10%	Week -04 Due date: 25 Aug 2022 at 17:00 Closing date: 26 Aug 2022	One hour
Outcomes assessed:
Online task	Intra-semester test Short answer/MCQ (5%) Practical exercise (15%)	20%	Week 09 Due date: 07 Oct 2022 at 17:32 Closing date: 07 Oct 2022	1 hour 30 min
Outcomes assessed:
Assignment	Group processes Peer review of group contributions	5%	Week 11 Due date: 21 Oct 2022 at 17:00 Closing date: 24 Oct 2022	Assess 3 unique project proposals
Outcomes assessed:
Presentation	Grant proposal Recorded presentation followed by upload of final proposal from template.	25%	Week 13 Due date: 03 Nov 2022 at 17:00 Closing date: 15 Nov 2022	5-7 min [Wk 11], 6 page proposal [Wk 13]
Outcomes assessed:
= group assignment ? = Type B final exam ?

Assessment summary

Grant proposal: Students will work in groups of 3-4 people (self-sign up) to design and pitch a research proposal addressing one of the problems provided. Multiple groups may attempt the same scenario. The group will create a proposal presentation that will be reviewed by their peers. Groups will have the opportunity to revise their project based on peer review before final submission for grading by staff.
Group processes: Students must review the proposals suggested by other groups and rate the contributions of group members.
Computational exercises: Students are expected to complete all practical exercises (unless noted as optional). Completed results may be required for assessment tasks in the mid-semester test.
Intra-Semester Class Test An in-class or take-home assessment will consist of a combination of MCQ, short answer, long answer or practical exercises using the class servers. Mark adjustments are NOT available.
Final exam: The exam will consist of four long-answer questions randomly chosen from a list of potential questions provided to students before the end of semester. Class computing servers will not be used in the Final Examination. Mark adjustments are NOT available. If a second replacement exam is required, this exam may be delivered via an alternative assessment method, such as a viva voce (oral exam). The alternative assessment will meet the same learning outcomes as the original exam. The format of the alternative assessment will be determined by the unit coordinator.

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 2014 (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
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

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:

Late assignments will be penalised at 5% per day or part thereof. Late peer reviews will NOT be accepted as this will impede other assessment activities. Assessments more than 10 days will receive zero marks.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	1. Introduction to unit and group project 2. Introduction to Linux and Virtual Machine 3. Genomics on the world-wide web	Workshop (4 hr)
Week 02	1. Genome sequencing; 2. Small organism genome assembly	Workshop (4 hr)
Week 03	Organism assembly next steps: order, orientation, annotation	Workshop (4 hr)
Week 04	Evolutionary Genomics	Workshop (4 hr)
Week 05	DNA Genotyping - from single markers to arrays	Workshop (4 hr)
Week 06	Diversity, Relationship, inbreeding and parentage.	Workshop (4 hr)
Week 07	Selective sweep and homozygosity	Workshop (4 hr)
Week 08	Map a Mendelian trait with complete penetrance.	Workshop (4 hr)
Week 09	Time allocated for Intra-semester examination (20%) [Multiple choice from question pool (5%) - 10 min, Practical exercise using the class servers (15%) - 60 min]	Workshop (4 hr)
Week 10	Complex trait and stratified data mapping and GWAS result visualisation	Workshop (4 hr)
Week 11	RNA Seq	Workshop (4 hr)
Week 12	Mutation detection	Workshop (4 hr)
Week 13	Time to revise Group project and unit review by zoom	Workshop (4 hr)

Attendance and class requirements

Attendance: While expected, attendance is not required at the workshop classes. However, attendance may be randomly recorded throughout semester. Students should seek consideration for non-attendance via Sydney Student. Completion of workshop exercises IS required and completion will be assessed via the intra-semester assessments 1 (20%) and 2 (10%).

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
LO2. understand the genomic mechanisms of inheritance and relationships among organisms
LO3. apply bioinformatic approaches to assemble a genome from whole genome sequencing data
LO4. analyse genomic alignments to detect functional mutations in protein coding sequences
LO5. analyse whole genome genotyping data to discover a locus responsible for a trait with Mendelian inheritance
LO6. write a project proposal to answer a research question in computational genomics
LO7. describe the basis of phylogeny
LO8. analyse genotyping data to generate a phylogenomic cladogram.

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

National Standard of Competency for Architects -

Competency code	Taught, Practiced or Assessed	Competency standard
5.1		Application of creative imagination and aesthetic judgement in producing a resolved project design in regard to site planning, physical composition and spatial planning as appropriate to the project brief.
5.3		Evaluation and integration of regulatory requirements.

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.

At student request lecture content is increased.

Additional information

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.

General laboratory safety rules

No eating or drinking is allowed in any laboratory under any circumstances
A laboratory coat and closed-toe shoes are mandatory
Follow safety instructions in your manual and posted in laboratories
In case of fire, follow instructions posted outside the laboratory door
First aid kits, eye wash and fire extinguishers are located in or immediately outside each laboratory
As a precautionary measure, it is recommended that you have a current tetanus immunisation. This can be obtained from University Health Service: unihealth.usyd.edu.au/

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

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

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

GEGE3004: Applied Genomics

Semester 2, 2022 [Normal day] - Remote

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

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

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect