GENE352 Genomic Analysis and Bioinformatics

Updated: 15 February 2019
Credit Points 6
Location Teaching Period Mode of Study
Armidale Trimester 1 Online
Armidale Trimester 1 On Campus
Intensive School(s) None
Supervised Exam There is no supervised examination.
Pre-requisites STAT100 or STAT210 or AMTH250
Co-requisites None
Restrictions BINF350 or BINF550 or GENE350 or GENE552

R and various bioinformatics software packages are used throughout the unit.; The School of Environmental and Rural Science considers all practical/laboratory/tutorial activities as essential to student learning. Attendance and participation in all practical/laboratory/tutorial classes (sessions) is mandatory - exemptions will not be granted without supporting evidence.

Combined Units GENE552 - Genomic Analysis and Bioinformatics
Coordinator(s) Julius Van Der Werf (
Unit Description

This applied genetics unit introduces the concepts and methods needed to work with and analyse data from modern genomic platforms. The topics covered include microarray analysis, genome wide association studies, reconstruction of phylogenies, measures of population diversity, DNA sequencing, gene searches, alignments and biological databases. Besides the conceptual background, students will receive practical experience in working with the different technologies using real datasets and will learn to build/use bioinformatics solutions. This unit is recommended for biomedical, life sciences and other students interested in working with the latest genomic technologies.

Materials Textbook information will be displayed approximately 8 weeks prior to the commencement of the teaching period. Please note that textbook requirements may vary from one teaching period to the next.
Disclaimer Unit information may be subject to change prior to commencement of the teaching period.
Assessment Assessment information will be published prior to commencement of the teaching period.
Learning Outcomes (LO) Upon completion of this unit, students will be able to:
  1. demonstrate a broad and coherent theoretical and technical knowledge of the subject by working with and analysing data from high throughput genetic projects;
  2. demonstrate autonomy and use well-developed judgement to analyse and evaluate information using analytical methods relevant to modern biology;
  3. use computational tools to interpret biological data and therefore demonstrate the ability to work autonomously, analyse information and evaluate information; and
  4. use bioinformatics methods to answer biological questions and thereby demonstrating the ability to work autonomously, use well-developed judgement and transmit this knowledge to others using written and verbal methods.