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)
Distance Education - Callaghan
- Semester 2 - 2015
1. Explain the core dogma of molecular biology and the central ideas of population genetics
2. 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.
3. 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
4. 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.
This course replaces the following course(s): BIOS6110. Students who have successfully completed BIOS6110 are not eligible to enrol in BIOS6111.
Data Management and Statistical Computing (BIOS6010); Mathematical Background for Biostatistics (BIOS6040); Principles of Statistical Inference (BIOS6050); Linear Models (BIOS6070); Probability and Distribution Theory (BIOS6170).
Written Assignment: Essays / Written Assignments
Self-Directed 6 hour(s) per Week for Full Term
Not relevant for distance learning mode.