10 Units

Provides an introduction to the field of bioinformatics from a statistical point of view. Students will be taught how to apply appropriate statistical methods to the analysis of Bioinformatic data. The course is offered in conjunction with the Biostatistics Collaboration of Australia (BCA)

Faculty Faculty of Health and Medicine
School School of Medicine and Public Health
Availability Semester 2 - 2014 (Distance Education - Callaghan)

Previously offered in 2012


On completion students will: i) Explain the core dogma of molecular biology and the central ideas of population genetics ii) Access appropriate web based sources for data, and download the data in suitable format, when given a problem which requires genome or proteome data for its solution. iii) Understand and apply core bioinformatics techniques for the analysis of DNA and protein sequence data, such as global sequence alignment, BLAST, Hidden Markov Models, evolutionary models and phylogenetic tree fitting. iv) Process large quantities of data (such as the expression profiles of thousands of genes resulting from microarray experiments) using R, and communicate results in language suitable for presentation to both a bioinformatics journal and a lay audience.


The first component of the course is an introduction to various topics of elementary molecular biology and population genetics. Conducting database searches (of DNA, RNA, amino acids and proteins databases) is one of the most common tasks in bioinformatics, so a grounding in these methods is provided.
Students will also be given a grounding in the analysis of single and multiple DNA or protein sequences, Hidden Markov Models and their applications, Evolutionary models, Phylogenetic trees and Analysis of microarrays.

Replacing Course(s) This course will replace the existing BCA course BIOS6110 (Bioinformatics and Statistical Genetics)
Transition N/A
Industrial Experience 0
Assumed Knowledge Data Management and Statistical Computing (BIOS6010); Mathematical Background for Biostatistics (BIOS6040); Principles of Statistical Inference (BIOS6050); Linear Models (BIOS6070); Probability and Distribution Theory (BIOS6170).
Modes of Delivery Distance Learning : Paper Based
Teaching Methods Self Directed Learning
Assessment Items
  • Essays / Written Assignments - Three assignments each worth 20%, final take home exam worth 40%
Contact Hours
  • Self Directed Learning: for 6 hour(s) per Week for Full Term
Compulsory Components None listed
Course Materials None listed
Timetable 2014 Course Timetables for BIOS6111

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