Master of Science (Bioinformatics)
Course code: MC-SCIBIF
March
Commonwealth Supported Places (CSPs) available
Access Melbourne is available
March
AUD $60,992 (2026 indicative first year fee)
IELTS 6.5: with no band less than 6.0
Course structure
Overview
The Master of Science (Bioinformatics) is a 200-point course, made up of:
- Discipline subjects (137.5 points), including compulsory subjects and electives
- A professional skills subject – scientific communication (12.5 points)
- A research project (50 points).
In your first-year, your subjects will be tailored to you depending on your previous academic background (biology or biomedicine, computer science, mathematics or statistics).
In your second year, you'll take subjects that build your knowledge of advanced analysis techniques.
You'll also undertake a research project, over 12–18 months, working on a real-world bioinformatics research question. To support you and provide direction, you’ll be matched with one of our expert researchers and practitioners from across the Melbourne Biomedical Precinct.
Plus, you’ll take a subject on communication for research scientists, which ensures you’re able to speak and write about your research professionally and impactfully.
Your elective subjects are selected in consultation with the Course Coordinator.
Sample course plan
View some sample course plans to help you select subjects that will meet the requirements for this coursework.
Research project stream A
| Accordion | |
|---|---|
Year 1100 pts |
|
| Semester 1 · 50 pts | |
| Winter · 12.5 pts |
|
| Semester 2 · 37.5 pts | |
| Accordion | |
|---|---|
Year 2100 pts |
|
| Semester 1 · 50 pts | |
| Semester 2 · 50 pts | |
Research project stream B
| Accordion | |
|---|---|
Year 1100 pts |
|
| Semester 1 · 50 pts | |
| Winter · 12.5 pts |
|
| Semester 2 · 37.5 pts | |
| Accordion | |
|---|---|
Year 2100 pts |
|
| Semester 1 · 50 pts | |
| Semester 2 · 50 pts | |
Research project stream A
| Accordion | |
|---|---|
Year 1100 pts |
|
| Semester 1 · 50 pts | |
| Winter · 12.5 pts |
|
| Semester 2 · 37.5 pts | |
| Accordion | |
|---|---|
Year 2100 pts |
|
| Semester 1 · 50 pts | |
| Semester 2 · 50 pts | |
null
| Accordion | |
|---|---|
Year 1100 pts |
|
| Semester 1 · 50 pts | |
| Semester 2 · 50 pts | |
| Accordion | |
|---|---|
Year 250 pts |
|
| Semester 1 · 50 pts | |
Explore this course
Explore the subjects you could choose as part of this degree.
Select the stream appropriate to your academic background.
Biology/biomedicine background
Complete all the following subjects, plus two 12.5-point elective in consultation with the Course Coordinator. (Note: Completing COMP90059 meets the prerequisite for COMP90038)
| Accordion | |
|---|---|
| Elements of Bioinformatics · 12.5 pts |
Bioinformatics is a key research tool in modern agriculture, medicine, and the life sciences in general. It forms a bridge between complex experimental and clinical data and the elucidation of biological knowledge. This subject presents bioinformatics in the context of its role in science, using examples from a variety of fields to illustrate the history, current status, and future directions of bioinformatics research and practice. |
| Introduction to Programming · 12.5 pts |
AIMS This subject introduces the fundamental concepts of computing programming, and how to solve simple problems using high-level procedural language, with a specific emphasis on data manipulation, transformation, and visualisation of data. INDICATIVE CONTENT Fundamental programming constructs; fundamental data structures; abstraction; basic program structures; algorithmic problem solving; use of modules. The subject assumes no prior knowledge of computer programming and is not suitable for students with prior programming experience. |
| Algorithms and Complexity · 12.5 pts |
AIMS The aim of this subject is for students to develop familiarity and competence in assessing and designing computer programs for computational efficiency. Although computers manipulate data very quickly, to solve large-scale problems, we must design strategies so that the calculations combine effectively. Over the latter half of the 20th century, an elegant theory of computational efficiency developed. This subject introduces students to the fundamentals of this theory and to many of the classical algorithms and data structures that solve key computational questions. These questions include distance computations in networks, searching items in large collections, and sorting them in order. INDICATIVE CONTENT Topics covered include complexity classes and asymptotic notation; empirical analysis of algorithms; abstract data types including queues, trees, priority queues and graphs; algorithmic techniques including brute force, divide-and-conquer, dynamic programming and greedy approaches; space and time trade-offs; and the theoretical limits of algorithm power. |
| Probability · 12.5 pts |
This subject offers a thorough grounding in the basic concepts of mathematical probability and probabilistic modelling. Topics covered include random experiments and sample spaces, probability axioms and theorems, discrete and continuous random variables/distributions (including measures of location, spread and shape), expectations and generating functions, independence of random variables and measures of dependence (covariance and correlation), methods for deriving the distributions of transformations of random variables or approximations for them (including the central limit theorem). The probability distributions and models discussed in the subject arise frequently in real world applications. These include a number of widely used one- and two-dimensional (particularly the bivariate normal) distributions and also fundamental probability models such as Poisson processes and Markov chains. |
| Elements of Probability · 12.5 pts |
Randomness is inherent in biological data and the analysis of data arising in both Bioinformatics and Biostatistics requires knowledge of sophisticated probability models and statistical techniques. This subject develops the underlying probability theory that is necessary to understand these models and techniques. Computer packages are used for numerical calculations but no programming skills are required. Elements of Probability will be co-taught with MAST20006 Probability for Statistics. |
| Statistics · 12.5 pts |
This subject introduces the basic elements of statistical modelling, computation and data analysis. It is an entry point to further study of both mathematical and applied statistics, as well as broader data science. Students will develop the ability to fit statistical models to data, estimate parameters of interest and test hypotheses. Both classical and Bayesian approaches will be covered. The importance of the underlying mathematical theory of statistics and the use of modern statistical software will be emphasised. Concepts covered include: descriptive statistics, random sample, statistical inference, point estimation, interval estimation, properties of estimators, maximum likelihood, confidence intervals, hypothesis testing and Bayesian inference. Applications covered include: exploratory data analysis, inference for samples from univariate distributions, simple linear regression, correlation, goodness-of-fit tests and analysis of variance. |
| Elements of Statistics · 12.5 pts |
The analysis of data arising in Bioinformatics and Biostatistics requires the use of sophisticated statistical techniques and computing packages. This subject introduces the basic elements of statistical modelling, computation and data analysis. Students will develop the ability to fit statistical models to data, estimate parameters of interest and test hypotheses. Both classical and Bayesian approaches will be covered. The importance of the underlying mathematical theory of statistics and the use of modern statistical software will be emphasised. Concepts covered include: descriptive statistics, random sample, statistical inference, point estimation, interval estimation, properties of estimators, maximum likelihood, confidence intervals, hypothesis testing, Bayesian inference. Applications covered include: exploratory data analysis, inference for samples from univariate distributions, simple linear regression, correlation, goodness-of-fit tests, analysis of variance. The lectures in this subject are co-taught with MAST20005 Statistics; the practice classes are separate. |
Mathematics and statistics background
Complete all the following subjects, plus one 12.5-point elective in consultation with the Course Coordinator.
| Accordion | |
|---|---|
| Foundations of Genetics and Genomics · 12.5 pts |
This subject will describe the fundamental characteristics of a genome, its structure and how genetic information contained within the genome is expressed and transmitted. The subject will integrate the molecular basis of genetic variation with the principles of Mendelian, quantitative and population genetics to explain patterns of genetic variation. A core aspect of this subject will be the development of analytical skills associated with solving genetic-based problems and interpreting data from genetic experiments. |
| CEBD20003 · pts | |
| Elements of Bioinformatics · 12.5 pts |
Bioinformatics is a key research tool in modern agriculture, medicine, and the life sciences in general. It forms a bridge between complex experimental and clinical data and the elucidation of biological knowledge. This subject presents bioinformatics in the context of its role in science, using examples from a variety of fields to illustrate the history, current status, and future directions of bioinformatics research and practice. |
| Algorithms and Complexity · 12.5 pts |
AIMS The aim of this subject is for students to develop familiarity and competence in assessing and designing computer programs for computational efficiency. Although computers manipulate data very quickly, to solve large-scale problems, we must design strategies so that the calculations combine effectively. Over the latter half of the 20th century, an elegant theory of computational efficiency developed. This subject introduces students to the fundamentals of this theory and to many of the classical algorithms and data structures that solve key computational questions. These questions include distance computations in networks, searching items in large collections, and sorting them in order. INDICATIVE CONTENT Topics covered include complexity classes and asymptotic notation; empirical analysis of algorithms; abstract data types including queues, trees, priority queues and graphs; algorithmic techniques including brute force, divide-and-conquer, dynamic programming and greedy approaches; space and time trade-offs; and the theoretical limits of algorithm power. |
| Human Physiology · 12.5 pts |
Physiology is an integrative study of the control of normal body function. The specialised organ systems to be studied include the nervous, cardiovascular, muscular, respiratory, kidney and digestive systems. During this subject students will learn that physiology is an experimental science with many key concepts arising from qualitative and quantitative observation and analysis of living organisms. The lectures will incorporate active interaction between students and lecturers using live polling software to answer questions during lectures. |
| Introduction to Programming · 12.5 pts |
AIMS This subject introduces the fundamental concepts of computing programming, and how to solve simple problems using high-level procedural language, with a specific emphasis on data manipulation, transformation, and visualisation of data. INDICATIVE CONTENT Fundamental programming constructs; fundamental data structures; abstraction; basic program structures; algorithmic problem solving; use of modules. The subject assumes no prior knowledge of computer programming and is not suitable for students with prior programming experience. |
Computer science background
Complete all the following subjects, plus one 12.5-point elective in consultation with the Course Coordinator.
| Accordion | |
|---|---|
| Foundations of Genetics and Genomics · 12.5 pts |
This subject will describe the fundamental characteristics of a genome, its structure and how genetic information contained within the genome is expressed and transmitted. The subject will integrate the molecular basis of genetic variation with the principles of Mendelian, quantitative and population genetics to explain patterns of genetic variation. A core aspect of this subject will be the development of analytical skills associated with solving genetic-based problems and interpreting data from genetic experiments. |
| CEBD20003 · pts | |
| Elements of Bioinformatics · 12.5 pts |
Bioinformatics is a key research tool in modern agriculture, medicine, and the life sciences in general. It forms a bridge between complex experimental and clinical data and the elucidation of biological knowledge. This subject presents bioinformatics in the context of its role in science, using examples from a variety of fields to illustrate the history, current status, and future directions of bioinformatics research and practice. |
| Human Physiology · 12.5 pts |
Physiology is an integrative study of the control of normal body function. The specialised organ systems to be studied include the nervous, cardiovascular, muscular, respiratory, kidney and digestive systems. During this subject students will learn that physiology is an experimental science with many key concepts arising from qualitative and quantitative observation and analysis of living organisms. The lectures will incorporate active interaction between students and lecturers using live polling software to answer questions during lectures. |
| Probability · 12.5 pts |
This subject offers a thorough grounding in the basic concepts of mathematical probability and probabilistic modelling. Topics covered include random experiments and sample spaces, probability axioms and theorems, discrete and continuous random variables/distributions (including measures of location, spread and shape), expectations and generating functions, independence of random variables and measures of dependence (covariance and correlation), methods for deriving the distributions of transformations of random variables or approximations for them (including the central limit theorem). The probability distributions and models discussed in the subject arise frequently in real world applications. These include a number of widely used one- and two-dimensional (particularly the bivariate normal) distributions and also fundamental probability models such as Poisson processes and Markov chains. |
| Elements of Probability · 12.5 pts |
Randomness is inherent in biological data and the analysis of data arising in both Bioinformatics and Biostatistics requires knowledge of sophisticated probability models and statistical techniques. This subject develops the underlying probability theory that is necessary to understand these models and techniques. Computer packages are used for numerical calculations but no programming skills are required. Elements of Probability will be co-taught with MAST20006 Probability for Statistics. |
| Statistics · 12.5 pts |
This subject introduces the basic elements of statistical modelling, computation and data analysis. It is an entry point to further study of both mathematical and applied statistics, as well as broader data science. Students will develop the ability to fit statistical models to data, estimate parameters of interest and test hypotheses. Both classical and Bayesian approaches will be covered. The importance of the underlying mathematical theory of statistics and the use of modern statistical software will be emphasised. Concepts covered include: descriptive statistics, random sample, statistical inference, point estimation, interval estimation, properties of estimators, maximum likelihood, confidence intervals, hypothesis testing and Bayesian inference. Applications covered include: exploratory data analysis, inference for samples from univariate distributions, simple linear regression, correlation, goodness-of-fit tests and analysis of variance. |
| Elements of Statistics · 12.5 pts |
The analysis of data arising in Bioinformatics and Biostatistics requires the use of sophisticated statistical techniques and computing packages. This subject introduces the basic elements of statistical modelling, computation and data analysis. Students will develop the ability to fit statistical models to data, estimate parameters of interest and test hypotheses. Both classical and Bayesian approaches will be covered. The importance of the underlying mathematical theory of statistics and the use of modern statistical software will be emphasised. Concepts covered include: descriptive statistics, random sample, statistical inference, point estimation, interval estimation, properties of estimators, maximum likelihood, confidence intervals, hypothesis testing, Bayesian inference. Applications covered include: exploratory data analysis, inference for samples from univariate distributions, simple linear regression, correlation, goodness-of-fit tests, analysis of variance. The lectures in this subject are co-taught with MAST20005 Statistics; the practice classes are separate. |
Students from all backgrounds will take the following second year subjects.
Core
Complete all the following subjects:
| Accordion | |
|---|---|
| Statistics for Bioinformatics · 12.5 pts |
Bioinformatics involves the analysis of biological data and randomness is inherent in both the biological processes themselves and the sampling mechanisms by which they are observed. This subject first introduces stochastic processes and their applications in Bioinformatics, including evolutionary models. It then considers the application of classical statistical methods including estimation, hypothesis testing, model selection, multiple comparisons, and multivariate statistical techniques in Bioinformatics. |
| Bioinformatics Case Studies · 12.5 pts |
Bioinformatics is a diverse discipline that draws on a range of technical areas and is applied to a range of biological problems. In this subject a series of case studies is used to illustrate the application of bioinformatics to biological, agricultural, and medical problems. These case studies will be directly based on current practical research and taught by the researchers. |
| Algorithms for Bioinformatics · 12.5 pts |
Technological advances in DNA sequencing, RNA sequencing and proteomics have provided a wealth of data from which biological insight can be obtained. Refining this data is a non-trivial matter due to the increased input sizes seen in modern high-throughput bioinformatics. This subject provides algorithmic strategies and data structures capable of meeting the challenge. While focused on bioinformatic data, the concepts herein apply to big data analysis as a whole. This subject covers key algorithms and data structures used in bioinformatics and assumes you have experience in programming. Strategies which frequently appear in modern software are explored so that bioinformatics tools may be appropriately selected, executed, and interpreted. This exploration yields a toolkit from which new computational methods can be created. Indicative topics include sequence operations for comparison, alignment and indexing, graph data structures in the context of genome assembly, phylogenetics and network analysis, and both supervised and unsupervised machine learning within the fields of optimisation, dimensionality reduction, clustering and classification. |
| Communication for Research Scientists · 12.5 pts |
As a scientist, it is not only important to be able to experiment, research and discover, it is also vital that you can communicate your research effectively in a variety of ways. Even the most brilliant research is wasted if no one knows it has been done or if your target audience is unable to understand it. In this subject you will develop your written and oral communication skills to ensure that you communicate your science as effectively as possible. We will cover effective science writing and oral presentations across a number of formats: writing a thesis; preparing, submitting and publishing journal papers; searching for, evaluating and citing appropriate references; peer review, making the most of conferences; applying for grants and jobs; and using social media to publicise your research. You will have multiple opportunities to practice, receive feedback and improve both your oral and written communication skills. Please note: students must be undertaking their own research in order to enrol in this subject. |
Selective
Complete one of the following subjects:
| Accordion | |
|---|---|
| Computational Genomics · 12.5 pts |
AIM The study of genomics is on the forefront of biology. Current laboratory technologies generate huge amounts of data and computational analysis is necessary to make sense of these data. This subject covers a broad range of approaches to the computational analysis of genomic data. Students will learn the theory behind a variety of different approaches to genomic analysis, and be introduced to key tools in current use, preparing them to use existing methods appropriately as well as developing new ways to analyse genomic data. Students will also have opportunities to apply their skills in workshops and assignments using both existing computational genomics tools and writing custom Python functions. Computational Genomics can be taken as an elective subject. It can also be taken by undergraduate students, exchange students and PhD students, subject to the written approval of the subject coordinator. INDICATIVE CONTENT This subject covers the computational analysis of several important forms of genomic data. Topics include computational resource management, reproducible research principles, genomics workflows, sequence alignment, genome annotation, parallel computing, metagenomics and single-cell sequencing. The subject domain rapidly progresses, and subject content is regularly revised and updated. Practical work includes writing bioinformatics functions with Python code, accessing genomics data repositories and using popular command-line tools. Please view this video for further information: Computational Genomics |
| Genomics and Bioinformatics · 12.5 pts |
This subject describes how technologies enabling the sequencing of complete genomes have transformed biological research in the past decades. Bioinformatics provides the tools to analyse these massive data connecting nucleic acids to the structures and functions of life. The advanced topics will review current knowledge on genomics and transcriptomics and describe the databases used to gather this information. The course will provide to non-specialised life-scientists the core concepts in genomics and bioinformatics. It will describe how to utilise public databases to retrieve biological information and develop a critical understanding of the methods used to generate them. This subject will explore how genomes are sequenced and annotated, and how connections are drawn between the different levels of molecular organisation to build a systems understanding of complex biological processes. |
All students undertake a 50-point research project. Select one of the following streams in consultation with the Project Coordinator.
Stream A
| Accordion | |
|---|---|
| Bioinformatics Research Project Pt 1 · 12.5 pts |
This research subject provides students with an opportunity to design and execute an original bioinformatics research project, under supervision. The project must be a computer/computing/computational project only and will involve the development or application of bioinformatics tools to address a significant research problem or question. The subject also provides students with skills and knowledge for understanding original research and enhanced written and oral communication skills. The process of matching students with supervisors and research projects is coordinated by the subject coordinator and occurs midway through the first semester of the Master of Science (Bioinformatics) (MC-SCIBIF). Students are supplied with a list of potential projects/supervisors. However, students may also choose to identify an appropriate supervisor and project of their choosing and request approval from the subject coordinator. Research project subject organisation:
|
| Bioinformatics Research Project Pt 2 · 12.5 pts |
This research subject provides students with an opportunity to design and execute an original bioinformatics research project, under supervision. The project must be a computer/computing/computational project only and will involve the development or application of bioinformatics tools to address a significant research problem or question. The subject also provides students with skills and knowledge for understanding original research and enhanced written and oral communication skills. The process of matching students with supervisors and research projects is coordinated by the subject coordinator and occurs midway through the first semester of the Master of Science (Bioinformatics) (MC-SCIBIF). Students are supplied with a list of potential projects/supervisors. However, students may also choose to identify an appropriate supervisor and project of their choosing and request approval from the subject coordinator. Research project subject organisation:
|
| Bioinformatics Research Project Pt 3 · 25 pts |
This research subject provides students with an opportunity to design and execute an original bioinformatics research project, under supervision. The project must be a computer/computing/computational project only and will involve the development or application of bioinformatics tools to address a significant research problem or question. The subject also provides students with skills and knowledge for understanding original research and enhanced written and oral communication skills. The process of matching students with supervisors and research projects is coordinated by the subject coordinator and occurs midway through the first semester of the Master of Science (Bioinformatics) (MC-SCIBIF). Students are supplied with a list of potential projects/supervisors. However, students may also choose to identify an appropriate supervisor and project of their choosing and request approval from the subject coordinator. Research project subject organisation:
|
Stream B
| Accordion | |
|---|---|
| Bioinformatics Research Project Pt 1 · 12.5 pts |
This research subject provides students with an opportunity to design and execute an original bioinformatics research project, under supervision. The project must be a computer/computing/computational project only and will involve the development or application of bioinformatics tools to address a significant research problem or question. The subject also provides students with skills and knowledge for understanding original research and enhanced written and oral communication skills. The process of matching students with supervisors and research projects is coordinated by the subject coordinator and occurs midway through the first semester of the Master of Science (Bioinformatics) (MC-SCIBIF). Students are supplied with a list of potential projects/supervisors. However, students may also choose to identify an appropriate supervisor and project of their choosing and request approval from the subject coordinator. Research project subject organisation:
|
| Bioinformatics Research Project Pt 2 · 12.5 pts |
This research subject provides students with an opportunity to design and execute an original bioinformatics research project, under supervision. The project must be a computer/computing/computational project only and will involve the development or application of bioinformatics tools to address a significant research problem or question. The subject also provides students with skills and knowledge for understanding original research and enhanced written and oral communication skills. The process of matching students with supervisors and research projects is coordinated by the subject coordinator and occurs midway through the first semester of the Master of Science (Bioinformatics) (MC-SCIBIF). Students are supplied with a list of potential projects/supervisors. However, students may also choose to identify an appropriate supervisor and project of their choosing and request approval from the subject coordinator. Research project subject organisation:
|
| Bioinformatics Research Project Pt 3 · 12.5 pts |
This research subject provides students with an opportunity to design and execute an original bioinformatics research project, under supervision. The project must be a computer/computing/computational project only and will involve the development or application of bioinformatics tools to address a significant research problem or question. The subject also provides students with skills and knowledge for understanding original research and enhanced written and oral communication skills. The process of matching students with supervisors and research projects is coordinated by the subject coordinator and occurs midway through the first semester of the Master of Science (Bioinformatics) (MC-SCIBIF). Students are supplied with a list of potential projects/supervisors. However, students may also choose to identify an appropriate supervisor and project of their choosing and request approval from the subject coordinator. Research project subject organisation:
|
| Bioinformatics Research Project Pt 4 · 12.5 pts |
This research subject provides students with an opportunity to design and execute an original bioinformatics research project, under supervision. The project must be a computer/computing/computational project only and will involve the development or application of bioinformatics tools to address a significant research problem or question. The subject also provides students with skills and knowledge for understanding original research and enhanced written and oral communication skills. The process of matching students with supervisors and research projects is coordinated by the subject coordinator and occurs midway through the first semester of the Master of Science (Bioinformatics) (MC-SCIBIF). Students are supplied with a list of potential projects/supervisors. However, students may also choose to identify an appropriate supervisor and project of their choosing and request approval from the subject coordinator. Research project subject organisation:
|
Stream C
| Accordion | |
|---|---|
| Bioinformatics Research Project Pt 1 · 25 pts |
This research subject provides students with an opportunity to design and execute an original bioinformatics research project, under supervision. The project must be a computer/computing/computational project only and will involve the development or application of bioinformatics tools to address a significant research problem or question. The subject also provides students with skills and knowledge for understanding original research and enhanced written and oral communication skills. The process of matching students with supervisors and research projects is coordinated by the subject coordinator and occurs midway through the first semester of the Master of Science (Bioinformatics) (MC-SCIBIF). Students are supplied with a list of potential projects/supervisors. However, students may also choose to identify an appropriate supervisor and project of their choosing and request approval from the subject coordinator. Research project subject organisation:
|
| Bioinformatics Research Project Pt 2 · 25 pts |
This research subject provides students with an opportunity to design and execute an original bioinformatics research project, under supervision. The project must be a computer/computing/computational project only and will involve the development or application of bioinformatics tools to address a significant research problem or question. The subject also provides students with skills and knowledge for understanding original research and enhanced written and oral communication skills. The process of matching students with supervisors and research projects is coordinated by the subject coordinator and occurs midway through the first semester of the Master of Science (Bioinformatics) (MC-SCIBIF). Students are supplied with a list of potential projects/supervisors. However, students may also choose to identify an appropriate supervisor and project of their choosing and request approval from the subject coordinator. Research project subject organisation:
|