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Statistical Genomics (MAST30033)
Undergraduate level 3Points: 12.5On Campus (Parkville)
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Semester 2
Email: kimanh.lecao@unimelb.edu.au
Overview
Availability | Semester 2 |
---|---|
Fees | Look up fees |
This subject introduces the biology and technology underlying modern genomics data, features of the resulting data types including the frequency and patterns of error and missingness, and the statistical methods used to analyse them. It will include hands-on data analysis using R software. The material covered will evolve as genomics technology and practice change, and will span the following four areas: introduction to genomics technology and the resulting data (approx 25% of course), population genetics (approx 20% of course) including stochastic models and statistical inference, association analysis (approx 40% of course) including tests of association and major sources of confounding, and heritability and prediction (approx 15% of course) both in human genetics and for animal and plant breeding.
Intended learning outcomes
On completion of this subject, students should have:
- Ability to explain the key genomics assays, their purpose and the strengths and limitations of the data generated.
- An understanding of the role of population genetics theory in interpreting genomics data.
- Ability to perform a range of association analyses using SNP and sequence data.
- Awareness of the major problems in association analyses that can lead to false inferences.
- An understanding of the strengths and weaknesses of SNP-based heritability relative to traditional measures of heritability.
- Ability to explain the use of statistical models in predicting phenotype from genomic data, and the uses and limitations of genomic prediction.
Generic skills
In addition to learning specific skills that will assist students in their future careers in science, they will have the opportunity to develop generic skills that will assist them in any future career path. In particular
- computer-based data handling and statistical analysis of large data sets using the R software (students are expected to have some skills at entry but the subject will take them to a higher level)
- ability to read, understand, modify and use short computer programs
- time-management: completing assignments according to deadlines while making judgments about time required for different pars of the assignment.
Last updated: 3 November 2022
Eligibility and requirements
Prerequisites
Students must complete
Code | Name | Teaching period | Credit Points |
---|---|---|---|
GENE20001 | Principles of Genetics | Semester 1 (On Campus - Parkville) |
12.5 |
Or equivalent knowledge of genetics
And one of the following subject sets (A, or B):
A.)
One of:
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST30032 | Biological Modelling and Simulation | Semester 1 (On Campus - Parkville) |
12.5 |
MAST20005 | Statistics | Semester 2 (On Campus - Parkville) |
12.5 |
B.)
Both of:
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST20031 | Analysis of Biological Data | Semester 1 (On Campus - Parkville) |
12.5 |
MAST10006 | Calculus 2 |
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
|
12.5 |
Corequisites
None
Non-allowed subjects
None
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 3 November 2022
Assessment
Additional details
Four computer-based assignments handed out and discussed during computer lab classes, completed in students' own time, and due the following week. Submissions will include computer code, results generated (numerical and graphical) plus sections of text interpreting the results (total 10 pages per assignment) due in weeks, 2, 5, 8 and 11 (12.5% per assignment). A 2-hour written exam due during the examination period (50%).
Last updated: 3 November 2022
Dates & times
- Semester 2
Principal coordinator Kim-Anh Le Cao Mode of delivery On Campus (Parkville) Contact hours 48 hours: 24 x one-hour lectures (2 lectures per week), 12 x two-hour practice classes (1 per week). Teaching period 23 July 2018 to 21 October 2018 Last self-enrol date 3 August 2018 Census date 31 August 2018 Last date to withdraw without fail 21 September 2018 Assessment period ends 16 November 2018 Semester 2 contact information
Email: kimanh.lecao@unimelb.edu.au
Last updated: 3 November 2022
Further information
- Texts
Prescribed texts
There are no specifically prescribed or recommended texts for this subject.
- Related Handbook entries
This subject contributes to the following:
Type Name Major Computational Biology Informal specialisation Science-credited subjects - new generation B-SCI and B-ENG. - Available through the Community Access Program
About the Community Access Program (CAP)
This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.
Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.
Last updated: 3 November 2022